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arxiv-664601 | 2410.01696 | CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs | <|reference_start|>CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs: Large language models (LLMs) have demonstrated limitations in handling combinatorial optimization problems involving long-range reasoning, partially due to causal hallucinations and huge search space. As for causal hallucinations, i.e., the inconsistency between reasoning and corresponding state transition, this paper introduces the Causal Relationship Enhancement (CRE) mechanism combining cause-effect interventions and the Individual Treatment Effect (ITE) to guarantee the solid causal rightness between each step of reasoning and state transition. As for the long causal range and huge search space limiting the performances of existing models featuring single-direction search, a Dual-End Searching (DES) approach is proposed to seek solutions by simultaneously starting from both the initial and goal states on the causal probability tree. By integrating CRE and DES (CreDes), our model has realized simultaneous multi-step reasoning, circumventing the inefficiencies from cascading multiple one-step reasoning like the Chain-of-Thought (CoT). Experiments demonstrate that CreDes significantly outperforms existing State-Of-The-Art (SOTA) solutions in long-range reasoning tasks in terms of both accuracy and time efficiency.<|reference_end|> | arxiv | @article{wang2024credes:,
title={CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving
Long-Range Reasoning Problems using LLMs},
author={Kangsheng Wang, Xiao Zhang, Hao Liu, Songde Han, Huimin Ma, Tianyu Hu},
journal={arXiv preprint arXiv:2410.01696},
year={2024},
archivePrefix={arXiv},
eprint={2410.01696},
primaryClass={cs.AI cs.CL}
} | wang2024credes: |
arxiv-664602 | 2410.01697 | MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning | <|reference_start|>MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning: Extensive research has shown that deep neural networks (DNNs) are vulnerable to slight adversarial perturbations$-$small changes to the input data that appear insignificant but cause the model to produce drastically different outputs. In addition to augmenting training data with adversarial examples generated from a specific attack method, most of the current defense strategies necessitate modifying the original model architecture components to improve robustness or performing test-time data purification to handle adversarial attacks. In this work, we demonstrate that strong feature representation learning during training can significantly enhance the original model's robustness. We propose MOREL, a multi-objective feature representation learning approach, encouraging classification models to produce similar features for inputs within the same class, despite perturbations. Our training method involves an embedding space where cosine similarity loss and multi-positive contrastive loss are used to align natural and adversarial features from the model encoder and ensure tight clustering. Concurrently, the classifier is motivated to achieve accurate predictions. Through extensive experiments, we demonstrate that our approach significantly enhances the robustness of DNNs against white-box and black-box adversarial attacks, outperforming other methods that similarly require no architectural changes or test-time data purification. Our code is available at https://github.com/salomonhotegni/MOREL<|reference_end|> | arxiv | @article{hotegni2024morel:,
title={MOREL: Enhancing Adversarial Robustness through Multi-Objective
Representation Learning},
author={Sedjro Salomon Hotegni, Sebastian Peitz},
journal={arXiv preprint arXiv:2410.01697},
year={2024},
archivePrefix={arXiv},
eprint={2410.01697},
primaryClass={cs.LG cs.CV}
} | hotegni2024morel: |
arxiv-664603 | 2410.01698 | COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation | <|reference_start|>COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation: With the rapidly increasing number of satellites in space and their enhanced capabilities, the amount of earth observation images collected by satellites is exceeding the transmission limits of satellite-to-ground links. Although existing learned image compression solutions achieve remarkable performance by using a sophisticated encoder to extract fruitful features as compression and using a decoder to reconstruct, it is still hard to directly deploy those complex encoders on current satellites' embedded GPUs with limited computing capability and power supply to compress images in orbit. In this paper, we propose COSMIC, a simple yet effective learned compression solution to transmit satellite images. We first design a lightweight encoder (i.e. reducing FLOPs by $2.6\sim 5\times $) on satellite to achieve a high image compression ratio to save satellite-to-ground links. Then, for reconstructions on the ground, to deal with the feature extraction ability degradation due to simplifying encoders, we propose a diffusion-based model to compensate image details when decoding. Our insight is that satellite's earth observation photos are not just images but indeed multi-modal data with a nature of Text-to-Image pairing since they are collected with rich sensor data (e.g. coordinates, timestamp, etc.) that can be used as the condition for diffusion generation. Extensive experiments show that COSMIC outperforms state-of-the-art baselines on both perceptual and distortion metrics.<|reference_end|> | arxiv | @article{zhang2024cosmic:,
title={COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation},
author={Ziyuan Zhang, Han Qiu, Maosen Zhang, Jun Liu, Bin Chen, Tianwei Zhang,
Hewu Li},
journal={arXiv preprint arXiv:2410.01698},
year={2024},
archivePrefix={arXiv},
eprint={2410.01698},
primaryClass={eess.IV cs.CV}
} | zhang2024cosmic: |
arxiv-664604 | 2410.01699 | Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding | <|reference_start|>Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding: The current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction during inference, resulting in substantial time consumption. In existing studies, Jacobi decoding, an iterative parallel decoding algorithm, has been used to accelerate the auto-regressive generation and can be executed without training. However, the Jacobi decoding relies on a deterministic criterion to determine the convergence of iterations. Thus, it works for greedy decoding but is incompatible with sampling-based decoding which is crucial for visual quality and diversity in the current auto-regressive text-to-image generation. In this paper, we propose a training-free probabilistic parallel decoding algorithm, Speculative Jacobi Decoding (SJD), to accelerate auto-regressive text-to-image generation. By introducing a probabilistic convergence criterion, our SJD accelerates the inference of auto-regressive text-to-image generation while maintaining the randomness in sampling-based token decoding and allowing the model to generate diverse images. Specifically, SJD facilitates the model to predict multiple tokens at each step and accepts tokens based on the probabilistic criterion, enabling the model to generate images with fewer steps than the conventional next-token-prediction paradigm. We also investigate the token initialization strategies that leverage the spatial locality of visual data to further improve the acceleration ratio under specific scenarios. We conduct experiments for our proposed SJD on multiple auto-regressive text-to-image generation models, showing the effectiveness of model acceleration without sacrificing the visual quality.<|reference_end|> | arxiv | @article{teng2024accelerating,
title={Accelerating Auto-regressive Text-to-Image Generation with Training-free
Speculative Jacobi Decoding},
author={Yao Teng, Han Shi, Xian Liu, Xuefei Ning, Guohao Dai, Yu Wang, Zhenguo
Li, Xihui Liu},
journal={arXiv preprint arXiv:2410.01699},
year={2024},
archivePrefix={arXiv},
eprint={2410.01699},
primaryClass={cs.CV}
} | teng2024accelerating |
arxiv-664605 | 2410.01702 | D(R, O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping | <|reference_start|>D(R, O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping: Dexterous grasping is a fundamental yet challenging skill in robotic manipulation, requiring precise interaction between robotic hands and objects. In this paper, we present D(R,O) Grasp, a novel framework that models the interaction between the robotic hand in its grasping pose and the object, enabling broad generalization across various robot hands and object geometries. Our model takes the robot hand's description and object point cloud as inputs and efficiently predicts kinematically valid and stable grasps, demonstrating strong adaptability to diverse robot embodiments and object geometries. Extensive experiments conducted in both simulated and real-world environments validate the effectiveness of our approach, with significant improvements in success rate, grasp diversity, and inference speed across multiple robotic hands. Our method achieves an average success rate of 87.53% in simulation in less than one second, tested across three different dexterous robotic hands. In real-world experiments using the LeapHand, the method also demonstrates an average success rate of 89%. D(R,O) Grasp provides a robust solution for dexterous grasping in complex and varied environments. The code, appendix, and videos are available on our project website at https://nus-lins-lab.github.io/drograspweb/.<|reference_end|> | arxiv | @article{wei2024d(r,,
title={D(R, O) Grasp: A Unified Representation of Robot and Object Interaction
for Cross-Embodiment Dexterous Grasping},
author={Zhenyu Wei, Zhixuan Xu, Jingxiang Guo, Yiwen Hou, Chongkai Gao, Zhehao
Cai, Jiayu Luo, Lin Shao},
journal={arXiv preprint arXiv:2410.01702},
year={2024},
archivePrefix={arXiv},
eprint={2410.01702},
primaryClass={cs.RO}
} | wei2024d(r, |
arxiv-664606 | 2410.01704 | An Exploration of Self-Supervised Mutual Information Alignment for Multi-Task Settings | <|reference_start|>An Exploration of Self-Supervised Mutual Information Alignment for Multi-Task Settings: There is a growing need for pluralistic alignment methods that can steer language models towards individual attributes and preferences. One such method, Self-Supervised Alignment with Mutual Information (SAMI), uses conditional mutual information to encourage the connection between behavioral preferences and model responses. We conduct two experiments exploring SAMI in multi-task settings. First, we compare SAMI to Direct Preference Optimization (DPO) on a multi-task benchmark (MT-Bench), using a stronger model to generate training data for a weaker one across diverse categories (humanities, STEM, extraction, coding, math, reasoning, and roleplay). Our results indicate that one iteration of SAMI has a 57% win rate against DPO, with significant variation in performance between task categories. Second, we examine SAMI's impact on mathematical accuracy (GSM-8K) relative to supervised fine-tuning (SFT). While SAMI increases zero-shot performance by 1.1%, SFT is more effective with a 3.2% boost. However, SAMI shows interesting scaling trends. When given 10 attempts, SAMI improves accuracy by 3.9%, while SFT achieves a 10.1% increase. Combining SAMI with SFT yields an additional improvement of 1.3% in multi-attempt settings, though single-attempt accuracy remains unchanged.<|reference_end|> | arxiv | @article{govande2024an,
title={An Exploration of Self-Supervised Mutual Information Alignment for
Multi-Task Settings},
author={Soham Govande},
journal={arXiv preprint arXiv:2410.01704},
year={2024},
archivePrefix={arXiv},
eprint={2410.01704},
primaryClass={cs.CL}
} | govande2024an |
arxiv-664607 | 2410.01706 | Performant, Memory Efficient and Scalable Multi-Agent Reinforcement Learning | <|reference_start|>Performant, Memory Efficient and Scalable Multi-Agent Reinforcement Learning: As the field of multi-agent reinforcement learning (MARL) progresses towards larger and more complex environments, achieving strong performance while maintaining memory efficiency and scalability to many agents becomes increasingly important. Although recent research has led to several advanced algorithms, to date, none fully address all of these key properties simultaneously. In this work, we introduce Sable, a novel and theoretically sound algorithm that adapts the retention mechanism from Retentive Networks to MARL. Sable's retention-based sequence modelling architecture allows for computationally efficient scaling to a large number of agents, as well as maintaining a long temporal context, making it well-suited for large-scale partially observable environments. Through extensive evaluations across six diverse environments, we demonstrate how Sable is able to significantly outperform existing state-of-the-art methods in the majority of tasks (34 out of 45, roughly 75\%). Furthermore, Sable demonstrates stable performance as we scale the number of agents, handling environments with more than a thousand agents while exhibiting a linear increase in memory usage. Finally, we conduct ablation studies to isolate the source of Sable's performance gains and confirm its efficient computational memory usage. Our results highlight Sable's performance and efficiency, positioning it as a leading approach to MARL at scale.<|reference_end|> | arxiv | @article{mahjoub2024performant,,
title={Performant, Memory Efficient and Scalable Multi-Agent Reinforcement
Learning},
author={Omayma Mahjoub, Sasha Abramowitz, Ruan de Kock, Wiem Khlifi, Simon du
Toit, Jemma Daniel, Louay Ben Nessir, Louise Beyers, Claude Formanek, Liam
Clark, Arnu Pretorius},
journal={arXiv preprint arXiv:2410.01706},
year={2024},
archivePrefix={arXiv},
eprint={2410.01706},
primaryClass={cs.LG cs.AI cs.MA}
} | mahjoub2024performant, |
arxiv-664608 | 2410.01707 | Interpretable Contrastive Monte Carlo Tree Search Reasoning | <|reference_start|>Interpretable Contrastive Monte Carlo Tree Search Reasoning: We propose SC-MCTS*: a novel Monte Carlo Tree Search (MCTS) reasoning algorithm for Large Language Models (LLMs), significantly improves both reasoning accuracy and speed. Our motivation comes from: 1. Previous MCTS LLM reasoning works often overlooked its biggest drawback--slower speed compared to CoT; 2. Previous research mainly used MCTS as a tool for LLM reasoning on various tasks with limited quantitative analysis or ablation studies of its components from reasoning interpretability perspective. 3. The reward model is the most crucial component in MCTS, however previous work has rarely conducted in-depth study or improvement of MCTS's reward models. Thus, we conducted extensive ablation studies and quantitative analysis on components of MCTS, revealing the impact of each component on the MCTS reasoning performance of LLMs. Building on this, (i) we designed a highly interpretable reward model based on the principle of contrastive decoding and (ii) achieved an average speed improvement of 51.9% per node using speculative decoding. Additionally, (iii) we improved UCT node selection strategy and backpropagation used in previous works, resulting in significant performance improvement. We outperformed o1-mini by an average of 17.4% on the Blocksworld multi-step reasoning dataset using Llama-3.1-70B with SC-MCTS*.<|reference_end|> | arxiv | @article{gao2024interpretable,
title={Interpretable Contrastive Monte Carlo Tree Search Reasoning},
author={Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei
Liu, Xuming Hu, Lijie Wen},
journal={arXiv preprint arXiv:2410.01707},
year={2024},
archivePrefix={arXiv},
eprint={2410.01707},
primaryClass={cs.CL cs.AI}
} | gao2024interpretable |
arxiv-664609 | 2410.01708 | Examining the Role of Relationship Alignment in Large Language Models | <|reference_start|>Examining the Role of Relationship Alignment in Large Language Models: The rapid development and deployment of Generative AI in social settings raise important questions about how to optimally personalize them for users while maintaining accuracy and realism. Based on a Facebook public post-comment dataset, this study evaluates the ability of Llama 3.0 (70B) to predict the semantic tones across different combinations of a commenter's and poster's gender, age, and friendship closeness and to replicate these differences in LLM-generated comments. The study consists of two parts: Part I assesses differences in semantic tones across social relationship categories, and Part II examines the similarity between comments generated by Llama 3.0 (70B) and human comments from Part I given public Facebook posts as input. Part I results show that including social relationship information improves the ability of a model to predict the semantic tone of human comments. However, Part II results show that even without including social context information in the prompt, LLM-generated comments and human comments are equally sensitive to social context, suggesting that LLMs can comprehend semantics from the original post alone. When we include all social relationship information in the prompt, the similarity between human comments and LLM-generated comments decreases. This inconsistency may occur because LLMs did not include social context information as part of their training data. Together these results demonstrate the ability of LLMs to comprehend semantics from the original post and respond similarly to human comments, but also highlights their limitations in generalizing personalized comments through prompting alone.<|reference_end|> | arxiv | @article{altenburger2024examining,
title={Examining the Role of Relationship Alignment in Large Language Models},
author={Kristen M. Altenburger, Hongda Jiang, Robert E. Kraut, Yi-Chia Wang,
and Jane Dwivedi-Yu},
journal={arXiv preprint arXiv:2410.01708},
year={2024},
archivePrefix={arXiv},
eprint={2410.01708},
primaryClass={cs.CL cs.SI}
} | altenburger2024examining |
arxiv-664610 | 2410.01709 | Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training | <|reference_start|>Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training: Test-time domain adaptation is a challenging task that aims to adapt a pre-trained model to limited, unlabeled target data during inference. Current methods that rely on self-supervision and entropy minimization underperform when the self-supervised learning (SSL) task does not align well with the primary objective. Additionally, minimizing entropy can lead to suboptimal solutions when there is limited diversity within minibatches. This paper introduces a meta-learning minimax framework for test-time training on batch normalization (BN) layers, ensuring that the SSL task aligns with the primary task while addressing minibatch overfitting. We adopt a mixed-BN approach that interpolates current test batch statistics with the statistics from source domains and propose a stochastic domain synthesizing method to improve model generalization and robustness to domain shifts. Extensive experiments demonstrate that our method surpasses state-of-the-art techniques across various domain adaptation and generalization benchmarks, significantly enhancing the pre-trained model's robustness on unseen domains.<|reference_end|> | arxiv | @article{tao2024meta-ttt:,
title={Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training},
author={Chen Tao, Li Shen, Soumik Mondal},
journal={arXiv preprint arXiv:2410.01709},
year={2024},
archivePrefix={arXiv},
eprint={2410.01709},
primaryClass={cs.LG}
} | tao2024meta-ttt: |
arxiv-664611 | 2410.01712 | Scaffolding Research Projects in Theory of Computing Courses | <|reference_start|>Scaffolding Research Projects in Theory of Computing Courses: Theory of Computing (ToC) is an important course in CS curricula because of its connections to other CS courses as a foundation for them. Traditional ToC course grading schemes are mostly exam-based, and sometimes a small weight for traditional proof-type assignments. Recent work experimented with a new type of assignment, namely a ``mock conference'' project wherein students approach and present ToC problems as if they were submitting to a ``real'' CS conference. In this paper we massively scaffold this existing project and provide our experiences in running such a conference in our own ToC course.<|reference_end|> | arxiv | @article{dougherty2024scaffolding,
title={Scaffolding Research Projects in Theory of Computing Courses},
author={Ryan E. Dougherty},
journal={arXiv preprint arXiv:2410.01712},
year={2024},
archivePrefix={arXiv},
eprint={2410.01712},
primaryClass={cs.CY}
} | dougherty2024scaffolding |
arxiv-664612 | 2410.01718 | COMUNI: Decomposing Common and Unique Video Signals for Diffusion-based Video Generation | <|reference_start|>COMUNI: Decomposing Common and Unique Video Signals for Diffusion-based Video Generation: Since videos record objects moving coherently, adjacent video frames have commonness (similar object appearances) and uniqueness (slightly changed postures). To prevent redundant modeling of common video signals, we propose a novel diffusion-based framework, named COMUNI, which decomposes the COMmon and UNIque video signals to enable efficient video generation. Our approach separates the decomposition of video signals from the task of video generation, thus reducing the computation complexity of generative models. In particular, we introduce CU-VAE to decompose video signals and encode them into latent features. To train CU-VAE in a self-supervised manner, we employ a cascading merge module to reconstitute video signals and a time-agnostic video decoder to reconstruct video frames. Then we propose CU-LDM to model latent features for video generation, which adopts two specific diffusion streams to simultaneously model the common and unique latent features. We further utilize additional joint modules for cross modeling of the common and unique latent features, and a novel position embedding method to ensure the content consistency and motion coherence of generated videos. The position embedding method incorporates spatial and temporal absolute position information into the joint modules. Extensive experiments demonstrate the necessity of decomposing common and unique video signals for video generation and the effectiveness and efficiency of our proposed method.<|reference_end|> | arxiv | @article{sun2024comuni:,
title={COMUNI: Decomposing Common and Unique Video Signals for Diffusion-based
Video Generation},
author={Mingzhen Sun, Weining Wang, Xinxin Zhu, Jing Liu},
journal={arXiv preprint arXiv:2410.01718},
year={2024},
archivePrefix={arXiv},
eprint={2410.01718},
primaryClass={cs.CV}
} | sun2024comuni: |
arxiv-664613 | 2410.01719 | OmniSR: Shadow Removal under Direct and Indirect Lighting | <|reference_start|>OmniSR: Shadow Removal under Direct and Indirect Lighting: Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms. The experiments show that our method outperforms state-of-the-art shadow removal techniques and can effectively generalize to indoor and outdoor scenes under various lighting conditions, enhancing the overall effectiveness and applicability of shadow removal methods.<|reference_end|> | arxiv | @article{xu2024omnisr:,
title={OmniSR: Shadow Removal under Direct and Indirect Lighting},
author={Jiamin Xu, Zelong Li, Yuxin Zheng, Chenyu Huang, Renshu Gu, Weiwei Xu,
Gang Xu},
journal={arXiv preprint arXiv:2410.01719},
year={2024},
archivePrefix={arXiv},
eprint={2410.01719},
primaryClass={cs.CV}
} | xu2024omnisr: |
arxiv-664614 | 2410.01720 | Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective | <|reference_start|>Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective: Synthetic data has become a pivotal resource in post-training tasks for large language models (LLMs) due to the scarcity of high-quality, specific data. While various methods have been developed to generate synthetic data, there remains a discernible gap between the practical effects of synthetic data and our theoretical comprehension. To address this challenge, we commence by presenting a detailed modeling of the prevalent synthetic data generation process. Building upon this modeling, we demonstrate that the generalization capability of the post-trained model is critically determined by the information gain derived from the generative model, as analyzed from a novel reverse-bottleneck perspective. Moreover, we introduce the concept of Generalization Gain via Mutual Information (GGMI) and elucidate the relationship between generalization gain and information gain. This analysis serves as a theoretical foundation for synthetic data generation and further highlights its connection with the generalization capability of post-trained models, offering an understanding about the design of synthetic data generation techniques and the optimization of the post-training process. We open source our code through an anonymous GitHub repository at https://anonymous.4open.science/r/Understanding-Synthetic.<|reference_end|> | arxiv | @article{gan2024towards,
title={Towards a Theoretical Understanding of Synthetic Data in LLM
Post-Training: A Reverse-Bottleneck Perspective},
author={Zeyu Gan, Yong Liu},
journal={arXiv preprint arXiv:2410.01720},
year={2024},
archivePrefix={arXiv},
eprint={2410.01720},
primaryClass={cs.AI cs.CL cs.LG}
} | gan2024towards |
arxiv-664615 | 2410.01723 | HarmoniCa: Harmonizing Training and Inference for Better Feature Cache in Diffusion Transformer Acceleration | <|reference_start|>HarmoniCa: Harmonizing Training and Inference for Better Feature Cache in Diffusion Transformer Acceleration: Diffusion Transformers (DiTs) have gained prominence for outstanding scalability and extraordinary performance in generative tasks. However, their considerable inference costs impede practical deployment. The feature cache mechanism, which involves storing and retrieving redundant computations across timesteps, holds promise for reducing per-step inference time in diffusion models. Most existing caching methods for DiT are manually designed. Although the learning-based approach attempts to optimize strategies adaptively, it suffers from discrepancies between training and inference, which hampers both the performance and acceleration ratio. Upon detailed analysis, we pinpoint that these discrepancies primarily stem from two aspects: (1) Prior Timestep Disregard, where training ignores the effect of cache usage at earlier timesteps, and (2) Objective Mismatch, where the training target (align predicted noise in each timestep) deviates from the goal of inference (generate the high-quality image). To alleviate these discrepancies, we propose HarmoniCa, a novel method that Harmonizes training and inference with a novel learning-based Caching framework built upon Step-Wise Denoising Training (SDT) and Image Error Proxy-Guided Objective (IEPO). Compared to the traditional training paradigm, the newly proposed SDT maintains the continuity of the denoising process, enabling the model to leverage information from prior timesteps during training, similar to the way it operates during inference. Furthermore, we design IEPO, which integrates an efficient proxy mechanism to approximate the final image error caused by reusing the cached feature. Therefore, IEPO helps balance final image quality and cache utilization, resolving the issue of training that only considers the impact of cache usage on the predicted output at each timestep.<|reference_end|> | arxiv | @article{huang2024harmonica:,
title={HarmoniCa: Harmonizing Training and Inference for Better Feature Cache
in Diffusion Transformer Acceleration},
author={Yushi Huang, Zining Wang, Ruihao Gong, Jing Liu, Xinjie Zhang, Jun
Zhang},
journal={arXiv preprint arXiv:2410.01723},
year={2024},
archivePrefix={arXiv},
eprint={2410.01723},
primaryClass={cs.CV}
} | huang2024harmonica: |
arxiv-664616 | 2410.01724 | Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting | <|reference_start|>Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting: Batch prompting is a common technique in large language models (LLMs) used to process multiple inputs simultaneously, aiming to improve computational efficiency. However, as batch sizes increase, performance degradation often occurs due to the model's difficulty in handling lengthy context inputs. Existing methods that attempt to mitigate these issues rely solely on batch data arrangement and majority voting rather than improving the design of the batch prompt itself. In this paper, we address these limitations by proposing "Auto-Demo Prompting," a novel approach that leverages the question-output pairs from earlier questions within a batch as demonstrations for subsequent answer inference. We provide a formal theoretical analysis of how Auto-Demo Prompting functions within the autoregressive generation process of LLMs, illustrating how it utilizes prior outputs to optimize the model's internal representations. Our method effectively bridges the gap between batch prompting and few-shot prompting, enhancing performance with only a slight compromise in token usage. Experimental results across five NLP tasks demonstrate its effectiveness in mitigating performance degradation and occasionally outperforming single prompts. Furthermore, it opens new avenues for applying few-shot learning techniques, such as demonstration selection, within batch prompting, making it a robust solution for real-world applications.<|reference_end|> | arxiv | @article{feng2024auto-demo,
title={Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for
Enhanced Batch Prompting},
author={Longyu Feng, Mengze Hong, Chen Jason Zhang},
journal={arXiv preprint arXiv:2410.01724},
year={2024},
archivePrefix={arXiv},
eprint={2410.01724},
primaryClass={cs.CL cs.AI}
} | feng2024auto-demo |
arxiv-664617 | 2410.01727 | Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing | <|reference_start|>Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing: Knowledge tracing (KT) is a popular approach for modeling students' learning progress over time, which can enable more personalized and adaptive learning. However, existing KT approaches face two major limitations: (1) they rely heavily on expert-defined knowledge concepts (KCs) in questions, which is time-consuming and prone to errors; and (2) KT methods tend to overlook the semantics of both questions and the given KCs. In this work, we address these challenges and present KCQRL, a framework for automated knowledge concept annotation and question representation learning that can improve the effectiveness of any existing KT model. First, we propose an automated KC annotation process using large language models (LLMs), which generates question solutions and then annotates KCs in each solution step of the questions. Second, we introduce a contrastive learning approach to generate semantically rich embeddings for questions and solution steps, aligning them with their associated KCs via a tailored false negative elimination approach. These embeddings can be readily integrated into existing KT models, replacing their randomly initialized embeddings. We demonstrate the effectiveness of KCQRL across 15 KT algorithms on two large real-world Math learning datasets, where we achieve consistent performance improvements.<|reference_end|> | arxiv | @article{ozyurt2024automated,
title={Automated Knowledge Concept Annotation and Question Representation
Learning for Knowledge Tracing},
author={Yilmazcan Ozyurt, Stefan Feuerriegel, Mrinmaya Sachan},
journal={arXiv preprint arXiv:2410.01727},
year={2024},
archivePrefix={arXiv},
eprint={2410.01727},
primaryClass={cs.LG cs.CL}
} | ozyurt2024automated |
arxiv-664618 | 2410.01728 | Multi-Robot Trajectory Generation via Consensus ADMM: Convex vs Non-Convex | <|reference_start|>Multi-Robot Trajectory Generation via Consensus ADMM: Convex vs Non-Convex: C-ADMM is a well-known distributed optimization framework due to its guaranteed convergence in convex optimization problems. Recently, C-ADMM has been studied in robotics applications such as multi-vehicle target tracking and collaborative manipulation tasks. However, few works have investigated the performance of C-ADMM applied to non-convex problems in robotics applications due to a lack of theoretical guarantees. For this project, we aim to quantitatively explore and examine the convergence behavior of non-convex C-ADMM through the scope of distributed multi-robot trajectory planning. We propose a convex trajectory planning problem by leveraging C-ADMM and Buffered Voronoi Cells (BVCs) to get around the non-convex collision avoidance constraint and compare this convex C-ADMM algorithm to a non-convex C-ADMM baseline with non-convex collision avoidance constraints. We show that the convex C-ADMM algorithm requires 1000 fewer iterations to achieve convergence in a multi-robot waypoint navigation scenario. We also confirm that the non-convex C-ADMM baseline leads to sub-optimal solutions and violation of safety constraints in trajectory generation.<|reference_end|> | arxiv | @article{chen2024multi-robot,
title={Multi-Robot Trajectory Generation via Consensus ADMM: Convex vs.
Non-Convex},
author={Jushan Chen},
journal={arXiv preprint arXiv:2410.01728},
year={2024},
archivePrefix={arXiv},
eprint={2410.01728},
primaryClass={cs.RO cs.SY eess.SY}
} | chen2024multi-robot |
arxiv-664619 | 2410.01729 | Evaluating Robustness of Reward Models for Mathematical Reasoning | <|reference_start|>Evaluating Robustness of Reward Models for Mathematical Reasoning: Reward models are key in reinforcement learning from human feedback (RLHF) systems, aligning the model behavior with human preferences. Particularly in the math domain, there have been plenty of studies using reward models to align policies for improving reasoning capabilities. Recently, as the importance of reward models has been emphasized, RewardBench is proposed to understand their behavior. However, we figure out that the math subset of RewardBench has different representations between chosen and rejected completions, and relies on a single comparison, which may lead to unreliable results as it only see an isolated case. Therefore, it fails to accurately present the robustness of reward models, leading to a misunderstanding of its performance and potentially resulting in reward hacking. In this work, we introduce a new design for reliable evaluation of reward models, and to validate this, we construct RewardMATH, a benchmark that effectively represents the robustness of reward models in mathematical reasoning tasks. We demonstrate that the scores on RewardMATH strongly correlate with the results of optimized policy and effectively estimate reward overoptimization, whereas the existing benchmark shows almost no correlation. The results underscore the potential of our design to enhance the reliability of evaluation, and represent the robustness of reward model. We make our code and data publicly available.<|reference_end|> | arxiv | @article{kim2024evaluating,
title={Evaluating Robustness of Reward Models for Mathematical Reasoning},
author={Sunghwan Kim, Dongjin Kang, Taeyoon Kwon, Hyungjoo Chae, Jungsoo Won,
Dongha Lee, Jinyoung Yeo},
journal={arXiv preprint arXiv:2410.01729},
year={2024},
archivePrefix={arXiv},
eprint={2410.01729},
primaryClass={cs.LG cs.AI cs.CL}
} | kim2024evaluating |
arxiv-664620 | 2410.01731 | ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation | <|reference_start|>ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation: The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of available components, their complex inter-dependence, and their dependence on the generation prompt. Here, we introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows. Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows. Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.<|reference_end|> | arxiv | @article{gal2024comfygen:,
title={ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation},
author={Rinon Gal, Adi Haviv, Yuval Alaluf, Amit H. Bermano, Daniel Cohen-Or,
Gal Chechik},
journal={arXiv preprint arXiv:2410.01731},
year={2024},
archivePrefix={arXiv},
eprint={2410.01731},
primaryClass={cs.CV cs.CL cs.GR}
} | gal2024comfygen: |
arxiv-664621 | 2410.01733 | Visual Perception in Text Strings | <|reference_start|>Visual Perception in Text Strings: Understanding visual semantics embedded in consecutive characters is a crucial capability for both large language models (LLMs) and multi-modal large language models (MLLMs). This type of artifact possesses the unique characteristic that identical information can be readily formulated in both texts and images, making them a significant proxy for analyzing modern LLMs' and MLLMs' capabilities in modality-agnostic vision understanding. In this work, we select ASCII art as a representative artifact, where the lines and brightness used to depict each concept are rendered by characters, and we frame the problem as an ASCII art recognition task. We benchmark model performance on this task by constructing an evaluation dataset with an elaborate categorization tree and also collect a training set to elicit the models' visual perception ability. Through a comprehensive analysis of dozens of models, results reveal that although humans can achieve nearly 100% accuracy, the state-of-the-art LLMs and MLLMs lag far behind. Models are capable of recognizing concepts depicted in the ASCII arts given only text inputs indicated by over 60% accuracy for some concepts, but most of them achieves merely around 30% accuracy when averaged across all categories. When provided with images as inputs, GPT-4o gets 82.68%, outperforming the strongest open-source MLLM by 21.95%. Although models favor different kinds of ASCII art depending on the modality provided, none of the MLLMs successfully benefit when both modalities are supplied simultaneously. Moreover, supervised fine-tuning helps improve models' accuracy especially when provided with the image modality, but also highlights the need for better training techniques to enhance the information fusion among modalities.<|reference_end|> | arxiv | @article{jia2024visual,
title={Visual Perception in Text Strings},
author={Qi Jia, Xiang Yue, Shanshan Huang, Ziheng Qin, Yizhu Liu, Bill Yuchen
Lin, Yang You},
journal={arXiv preprint arXiv:2410.01733},
year={2024},
archivePrefix={arXiv},
eprint={2410.01733},
primaryClass={cs.CL}
} | jia2024visual |
arxiv-664622 | 2410.01735 | LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits | <|reference_start|>LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits: Reward Models (RMs) play a crucial role in aligning LLMs with human preferences, enhancing their performance by ranking outputs during inference or iterative training. However, the degree to which an RM generalizes to new tasks is often not known a priori (e.g. some RMs may excel at scoring creative writing vs. math reasoning). Therefore, using only one fixed RM while training LLMs can be suboptimal. Moreover, optimizing LLMs with multiple RMs simultaneously can be prohibitively computationally-intensive and challenging due to conflicting signals from different RMs, potentially degrading performance. To address these challenges, we introduce LASeR (Learning to Adaptively Select Rewards), which iteratively trains LLMs using multiple RMs, selecting and utilizing the most well-suited RM for each instance to rank outputs and generate preference data, framed as a multi-armed bandit problem. Our results on commonsense and math reasoning tasks demonstrate that LASeR can boost iterative LLM optimization by optimizing for multiple RMs, improving the absolute average accuracy of Llama-3-8B over three datasets by 2.67% over training with ensemble RM scores while also showing superior training efficiency (e.g., a 2x speedup). Moreover, on WildChat, a benchmark of instruction-following prompts, we find that using Llama-3-8B LASeR leads to a 71.45% AlpacaEval win rate over sequentially optimizing multiple RMs. Extending to long-context generation tasks, we find that on Llama-3-8B, LASeR achieves an average improvement of 2.64 F1 and 2.42 F1 on single- and multi-document QA over random RM selection when used with best-of-n sampling. LASeR is robust to noisy rewards and generalizes to multiple settings. Finally, LASeR's RM selection changes depending on the underlying task or instance and we verify the presence of conflicting preferences from multiple RMs that can be mitigated using LASeR.<|reference_end|> | arxiv | @article{nguyen2024laser:,
title={LASeR: Learning to Adaptively Select Reward Models with Multi-Armed
Bandits},
author={Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal},
journal={arXiv preprint arXiv:2410.01735},
year={2024},
archivePrefix={arXiv},
eprint={2410.01735},
primaryClass={cs.CL cs.LG}
} | nguyen2024laser: |
arxiv-664623 | 2410.01736 | Recursive Abstractive Processing for Retrieval in Dynamic Datasets | <|reference_start|>Recursive Abstractive Processing for Retrieval in Dynamic Datasets: Recent retrieval-augmented models enhance basic methods by building a hierarchical structure over retrieved text chunks through recursive embedding, clustering, and summarization. The most relevant information is then retrieved from both the original text and generated summaries. However, such approaches face limitations with dynamic datasets, where adding or removing documents over time complicates the updating of hierarchical representations formed through clustering. We propose a new algorithm to efficiently maintain the recursive-abstractive tree structure in dynamic datasets, without compromising performance. Additionally, we introduce a novel post-retrieval method that applies query-focused recursive abstractive processing to substantially improve context quality. Our method overcomes the limitations of other approaches by functioning as a black-box post-retrieval layer compatible with any retrieval algorithm. Both algorithms are validated through extensive experiments on real-world datasets, demonstrating their effectiveness in handling dynamic data and improving retrieval performance.<|reference_end|> | arxiv | @article{chucri2024recursive,
title={Recursive Abstractive Processing for Retrieval in Dynamic Datasets},
author={Charbel Chucri, Rami Azouz, Joachim Ott},
journal={arXiv preprint arXiv:2410.01736},
year={2024},
archivePrefix={arXiv},
eprint={2410.01736},
primaryClass={cs.CL cs.LG}
} | chucri2024recursive |
arxiv-664624 | 2410.01737 | RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection | <|reference_start|>RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection: Multimodal Industrial Anomaly Detection (MIAD), utilizing 3D point clouds and 2D RGB images to identify the abnormal region of products, plays a crucial role in industrial quality inspection. However, the conventional MIAD setting presupposes that all 2D and 3D modalities are paired, overlooking the fact that multimodal data collected from the real world is often imperfect due to missing modalities. Consequently, MIAD models that demonstrate robustness against modal-incomplete data are highly desirable in practice. To address this practical challenge, we introduce a first-of-its-kind study that comprehensively investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD), to consider the imperfect learning environment in which the multimodal information may be incomplete. Not surprisingly, we discovered that most existing MIAD approaches are inadequate for addressing MIIAD challenges, leading to significant performance degradation on the MIIAD benchmark we developed. In this paper, we propose a novel two-stage Robust modAlity-imcomplete fusing and Detecting frAmewoRk, abbreviated as RADAR. Our bootstrapping philosophy is to enhance two stages in MIIAD, improving the robustness of the Multimodal Transformer: i) In feature fusion, we first explore learning modality-incomplete instruction, guiding the pre-trained Multimodal Transformer to robustly adapt to various modality-incomplete scenarios, and implement adaptive parameter learning based on a HyperNetwork; ii) In anomaly detection, we construct a real-pseudo hybrid module to highlight the distinctiveness of modality combinations, further enhancing the robustness of the MIIAD model. Our experimental results demonstrate that the proposed RADAR significantly surpasses conventional MIAD methods in terms of effectiveness and robustness on our newly created MIIAD dataset, underscoring its practical application value.<|reference_end|> | arxiv | @article{miao2024radar:,
title={RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection},
author={Bingchen Miao, Wenqiao Zhang, Juncheng Li, Siliang Tang, Zhaocheng Li,
Haochen Shi, Jun Xiao, Yueting Zhuang},
journal={arXiv preprint arXiv:2410.01737},
year={2024},
archivePrefix={arXiv},
eprint={2410.01737},
primaryClass={cs.CV cs.MM}
} | miao2024radar: |
arxiv-664625 | 2410.01738 | VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models | <|reference_start|>VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models: Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods directly design the overall geometry and texture of input character, making it challenging to ensure both creativity and legibility. In this paper, we introduce a dual-branch and training-free method, namely VitaGlyph, enabling flexible artistic typography along with controllable geometry change to maintain the readability. The key insight of VitaGlyph is to treat input character as a scene composed of Subject and Surrounding, followed by rendering them under varying degrees of geometry transformation. The subject flexibly expresses the essential concept of input character, while the surrounding enriches relevant background without altering the shape. Specifically, we implement VitaGlyph through a three-phase framework: (i) Knowledge Acquisition leverages large language models to design text descriptions of subject and surrounding. (ii) Regional decomposition detects the part that most matches the subject description and divides input glyph image into subject and surrounding regions. (iii) Typography Stylization firstly refines the structure of subject region via Semantic Typography, and then separately renders the textures of Subject and Surrounding regions through Controllable Compositional Generation. Experimental results demonstrate that VitaGlyph not only achieves better artistry and readability, but also manages to depict multiple customize concepts, facilitating more creative and pleasing artistic typography generation. Our code will be made publicly at https://github.com/Carlofkl/VitaGlyph.<|reference_end|> | arxiv | @article{feng2024vitaglyph:,
title={VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch
Diffusion Models},
author={Kailai Feng, Yabo Zhang, Haodong Yu, Zhilong Ji, Jinfeng Bai, Hongzhi
Zhang, Wangmeng Zuo},
journal={arXiv preprint arXiv:2410.01738},
year={2024},
archivePrefix={arXiv},
eprint={2410.01738},
primaryClass={cs.CV cs.AI}
} | feng2024vitaglyph: |
arxiv-664626 | 2410.01739 | Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning | <|reference_start|>Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning: Reinforcement learning encounters challenges in various environments related to robustness and explainability. Traditional Q-learning algorithms cannot effectively make decisions and utilize the historical learning experience. To overcome these limitations, we propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework, enhancing decision-making accuracy by endowing agents with human-like learning and reasoning capabilities. Drawing inspiration from cognitive science, our method maintains a subjective belief distribution over the expectation of actions, leveraging a cluster-based subjective belief model that enables agents to reason about the potential probability associated with each decision. CBDQ effectively mitigates overestimated phenomena and optimizes decision-making policies by integrating historical experiences with current contextual information, mimicking the dynamics of human decision-making. We evaluate the proposed method on discrete control benchmark tasks in various complicate environments. The results demonstrate that CBDQ exhibits stronger adaptability, robustness, and human-like characteristics in handling these environments, outperforming other baselines. We hope this work will give researchers a fresh perspective on understanding and explaining Q-learning.<|reference_end|> | arxiv | @article{gu2024mimicking,
title={Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning},
author={Xingrui Gu, Guanren Qiao, Chuyi Jiang, Tianqing Xia, Hangyu Mao},
journal={arXiv preprint arXiv:2410.01739},
year={2024},
archivePrefix={arXiv},
eprint={2410.01739},
primaryClass={cs.AI cs.LG}
} | gu2024mimicking |
arxiv-664627 | 2410.01744 | Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks | <|reference_start|>Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks: Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned documents, and webpage snapshots. Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs. Despite the importance of these scenarios, current multimodal large language models (MLLMs) struggle to handle such tasks due to two key challenges: (1) the scarcity of high-quality instruction tuning datasets for text-rich multi-image scenarios, and (2) the difficulty in balancing image resolution with visual feature sequence length. To address these challenges, we propose Leopard, a MLLM designed specifically for handling vision-language tasks involving multiple text-rich images. First, we curated about one million high-quality multimodal instruction-tuning data, tailored to text-rich, multi-image scenarios. Second, we developed an adaptive high-resolution multi-image encoding module to dynamically optimize the allocation of visual sequence length based on the original aspect ratios and resolutions of the input images. Experiments across a wide range of benchmarks demonstrate our model's superior capabilities in text-rich, multi-image evaluations and competitive performance in general domain evaluations.<|reference_end|> | arxiv | @article{jia2024leopard:,
title={Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks},
author={Mengzhao Jia, Wenhao Yu, Kaixin Ma, Tianqing Fang, Zhihan Zhang, Siru
Ouyang, Hongming Zhang, Meng Jiang, Dong Yu},
journal={arXiv preprint arXiv:2410.01744},
year={2024},
archivePrefix={arXiv},
eprint={2410.01744},
primaryClass={cs.CV cs.CL}
} | jia2024leopard: |
arxiv-664628 | 2410.01745 | PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation | <|reference_start|>PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation: Intrinsic motivation, inspired by the psychology of developmental learning in infants, stimulates exploration in agents without relying solely on sparse external rewards. Existing methods in reinforcement learning like Random Network Distillation (RND) face significant limitations, including (1) relying on raw visual inputs, leading to a lack of meaningful representations, (2) the inability to build a robust latent space, (3) poor target network initialization and (4) rapid degradation of intrinsic rewards. In this paper, we introduce Pre-trained Network Distillation (PreND), a novel approach to enhance intrinsic motivation in reinforcement learning (RL) by improving upon the widely used prediction-based method, RND. PreND addresses these challenges by incorporating pre-trained representation models into both the target and predictor networks, resulting in more meaningful and stable intrinsic rewards, while enhancing the representation learned by the model. We also tried simple but effective variants of the predictor network optimization by controlling the learning rate. Through experiments on the Atari domain, we demonstrate that PreND significantly outperforms RND, offering a more robust intrinsic motivation signal that leads to better exploration, improving overall performance and sample efficiency. This research highlights the importance of target and predictor networks representation in prediction-based intrinsic motivation, setting a new direction for improving RL agents' learning efficiency in sparse reward environments.<|reference_end|> | arxiv | @article{davoodabadi2024prend:,
title={PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through
Pre-trained Network Distillation},
author={Mohammadamin Davoodabadi, Negin Hashemi Dijujin, Mahdieh Soleymani
Baghshah},
journal={arXiv preprint arXiv:2410.01745},
year={2024},
archivePrefix={arXiv},
eprint={2410.01745},
primaryClass={cs.LG}
} | davoodabadi2024prend: |
arxiv-664629 | 2410.01746 | Leray-Schauder Mappings for Operator Learning | <|reference_start|>Leray-Schauder Mappings for Operator Learning: We present an algorithm for learning operators between Banach spaces, based on the use of Leray-Schauder mappings to learn a finite-dimensional approximation of compact subspaces. We show that the resulting method is a universal approximator of (possibly nonlinear) operators. We demonstrate the efficiency of the approach on two benchmark datasets showing it achieves results comparable to state of the art models.<|reference_end|> | arxiv | @article{zappala2024leray-schauder,
title={Leray-Schauder Mappings for Operator Learning},
author={Emanuele Zappala},
journal={arXiv preprint arXiv:2410.01746},
year={2024},
archivePrefix={arXiv},
eprint={2410.01746},
primaryClass={cs.LG cs.NA math.NA}
} | zappala2024leray-schauder |
arxiv-664630 | 2410.01748 | Not All LLM Reasoners Are Created Equal | <|reference_start|>Not All LLM Reasoners Are Created Equal: We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To this end, we evaluate their performance on pairs of existing math word problems together so that the answer to the second problem depends on correctly answering the first problem. Our findings reveal a significant reasoning gap in most LLMs, that is performance difference between solving the compositional pairs and solving each question independently. This gap is more pronounced in smaller, more cost-efficient, and math-specialized models. Moreover, instruction-tuning recipes and code generation have varying effects across LLM sizes, while finetuning on GSM can lead to task overfitting. Our analysis indicates that large reasoning gaps are not because of test-set leakage, but due to distraction from additional context and poor second-hop reasoning. Overall, LLMs exhibit systematic differences in their reasoning abilities, despite what their performance on standard benchmarks indicates.<|reference_end|> | arxiv | @article{hosseini2024not,
title={Not All LLM Reasoners Are Created Equal},
author={Arian Hosseini, Alessandro Sordoni, Daniel Toyama, Aaron Courville,
Rishabh Agarwal},
journal={arXiv preprint arXiv:2410.01748},
year={2024},
archivePrefix={arXiv},
eprint={2410.01748},
primaryClass={cs.LG}
} | hosseini2024not |
arxiv-664631 | 2410.01750 | AssessITS: Integrating procedural guidelines and practical evaluation metrics for organizational IT and Cybersecurity risk assessment | <|reference_start|>AssessITS: Integrating procedural guidelines and practical evaluation metrics for organizational IT and Cybersecurity risk assessment: In today's digitally driven landscape, robust Information Technology (IT) risk assessment practices are essential for safeguarding systems, digital communication, and data. This paper introduces 'AssessITS', an actionable method designed to provide organizations with comprehensive guidelines for conducting IT and cybersecurity risk assessments. Drawing extensively from NIST 800-30 Rev 1, COBIT 5, and ISO 31000, 'AssessITS' bridges the gap between high-level theoretical standards and practical implementation challenges. The paper outlines a step-by-step methodology that organizations can simply adopt to systematically identify, analyze, and mitigate IT risks. By simplifying complex principles into actionable procedures, this framework equips practitioners with the tools needed to perform risk assessments independently, without too much reliance on external vendors. The guidelines are developed to be straightforward, integrating practical evaluation metrics that allow for the precise quantification of asset values, threat levels, vulnerabilities, and impacts on confidentiality, integrity, and availability. This approach ensures that the risk assessment process is not only comprehensive but also accessible, enabling decision-makers to implement effective risk mitigation strategies customized to their unique operational contexts. 'AssessITS' aims to enable organizations to enhance their IT security strength through practical, actionable guidance based on internationally recognized standards.<|reference_end|> | arxiv | @article{rahman2024assessits:,
title={AssessITS: Integrating procedural guidelines and practical evaluation
metrics for organizational IT and Cybersecurity risk assessment},
author={Mir Mehedi Rahman, Naresh Kshetri, Sayed Abu Sayeed, Md Masud Rana},
journal={arXiv preprint arXiv:2410.01750},
year={2024},
archivePrefix={arXiv},
eprint={2410.01750},
primaryClass={cs.CR}
} | rahman2024assessits: |
arxiv-664632 | 2410.01752 | TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery | <|reference_start|>TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery: Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.<|reference_end|> | arxiv | @article{muthyala2024torchsisso:,
title={TorchSISSO: A PyTorch-Based Implementation of the Sure Independence
Screening and Sparsifying Operator for Efficient and Interpretable Model
Discovery},
author={Madhav Muthyala and Farshud Sorourifar and Joel A. Paulson},
journal={arXiv preprint arXiv:2410.01752},
year={2024},
archivePrefix={arXiv},
eprint={2410.01752},
primaryClass={cs.LG}
} | muthyala2024torchsisso: |
arxiv-664633 | 2410.01754 | Constant pH Simulation with FMM Electrostatics in GROMACS (B) GPU Accelerated Hamiltonian Interpolation | <|reference_start|>Constant pH Simulation with FMM Electrostatics in GROMACS (B) GPU Accelerated Hamiltonian Interpolation: The structural dynamics of biological macromolecules, such as proteins, DNA/RNA, or their complexes, are strongly influenced by protonation changes of their typically many titratable groups, which explains their pH sensitivity. In turn, conformational and environmental changes in the biomolecule affect the protonation state of these groups. With a few exceptions, conventional force field-based molecular dynamics (MD) simulations do not account for these effects, nor do they allow for coupling to a pH buffer. The $\lambda$-dynamics method implements this coupling and thus allows for MD simulations at constant pH. It uses separate Hamiltonians for the protonated and deprotonated states of each titratable group, with a $\lambda$ variable that continuously interpolates between them. However, rigorous implementations of Hamiltonian Interpolation (HI) $\lambda$-dynamics are prohibitively slow when used with Particle Mesh Ewald (PME). To circumvent this problem, it has been proposed to interpolate the charges instead of the Hamiltonians (QI). Here, we propose a rigorous yet efficient Multipole-Accelerated Hamiltonian Interpolation (MAHI) method to perform $\lambda$-dynamics in GROMACS. Starting from a charge-scaled Hamiltonian, precomputed with the Fast Multipole Method (FMM) or with PME, the correct HI forces are calculated with negligible computational overhead. We compare HI with QI and show that HI leads to more frequent transitions between protonation states, resulting in better sampling and accuracy. Our performance benchmarks show that introducing, e.g., 512 titratable sites to a one million atom MD system increases runtime by less than 20% compared to a regular FMM-based simulation. We have integrated the scheme into our GPU-FMM code for the simulation software GROMACS, allowing an easy and effortless transition from standard force field simulations to constant pH simulations.<|reference_end|> | arxiv | @article{kohnke2024constant,
title={Constant pH Simulation with FMM Electrostatics in GROMACS. (B) GPU
Accelerated Hamiltonian Interpolation},
author={Bartosz Kohnke, Eliane Briand, Carsten Kutzner and Helmut Grubm"uller},
journal={arXiv preprint arXiv:2410.01754},
year={2024},
archivePrefix={arXiv},
eprint={2410.01754},
primaryClass={cs.DC physics.comp-ph}
} | kohnke2024constant |
arxiv-664634 | 2410.01755 | Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes | <|reference_start|>Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes: Breast cancer's complexity and variability pose significant challenges in understanding its progression and guiding effective treatment. This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes and predict clinical outcomes. Using ProtGPT2, a language model designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins sequence. These embeddings were integrated with protein expression level to form enriched biological representations, which were analyzed using machine learning methods like ensemble K-means for clustering and XGBoost for classification. Our approach enabled successful clustering of patients into biologically distinct groups and accurately predicted clinical outcomes such as survival and biomarkers status, achieving high performance metrics, notably an F1 score of 0.88 for survival and 0.87 for biomarkers status prediction. Analysis of feature importance highlighted key proteins like KMT2C, GCN1, and CLASP2, linked to hormone receptor and Human Epidermal Growth Factor Receptor 2 (HER2) expression, which play a role in tumor progression and patient outcomes, respectively. Furthermore, protein-protein interaction networks and correlation analyses revealed the interdependence of proteins that may influence breast cancer subtype behaviors. These findings suggest that integrating protein sequence and expression data provides valuable insights into tumor biology and has significant potential to enhance personalized treatment strategies in breast cancer care.<|reference_end|> | arxiv | @article{sholehrasa2024integrating,
title={Integrating Protein Sequence and Expression Level to Analysis Molecular
Characterization of Breast Cancer Subtypes},
author={Hossein Sholehrasa},
journal={arXiv preprint arXiv:2410.01755},
year={2024},
archivePrefix={arXiv},
eprint={2410.01755},
primaryClass={q-bio.BM cs.LG}
} | sholehrasa2024integrating |
arxiv-664635 | 2410.01756 | ImageFolder: Autoregressive Image Generation with Folded Tokens | <|reference_start|>ImageFolder: Autoregressive Image Generation with Folded Tokens: Image tokenizers are crucial for visual generative models, e.g., diffusion models (DMs) and autoregressive (AR) models, as they construct the latent representation for modeling. Increasing token length is a common approach to improve the image reconstruction quality. However, tokenizers with longer token lengths are not guaranteed to achieve better generation quality. There exists a trade-off between reconstruction and generation quality regarding token length. In this paper, we investigate the impact of token length on both image reconstruction and generation and provide a flexible solution to the tradeoff. We propose ImageFolder, a semantic tokenizer that provides spatially aligned image tokens that can be folded during autoregressive modeling to improve both generation efficiency and quality. To enhance the representative capability without increasing token length, we leverage dual-branch product quantization to capture different contexts of images. Specifically, semantic regularization is introduced in one branch to encourage compacted semantic information while another branch is designed to capture the remaining pixel-level details. Extensive experiments demonstrate the superior quality of image generation and shorter token length with ImageFolder tokenizer.<|reference_end|> | arxiv | @article{li2024imagefolder:,
title={ImageFolder: Autoregressive Image Generation with Folded Tokens},
author={Xiang Li, Kai Qiu, Hao Chen, Jason Kuen, Jiuxiang Gu, Bhiksha Raj, Zhe
Lin},
journal={arXiv preprint arXiv:2410.01756},
year={2024},
archivePrefix={arXiv},
eprint={2410.01756},
primaryClass={cs.CV}
} | li2024imagefolder: |
arxiv-664636 | 2410.01760 | Competitive Ratio of Online Caching with Predictions: Lower and Upper Bounds | <|reference_start|>Competitive Ratio of Online Caching with Predictions: Lower and Upper Bounds: We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improve currently known bounds on the competitive ratio of the BlindOracle algorithm, which evicts a page predicted to be requested last. We also prove a lower bound on the competitive ratio of any randomized algorithm and show that a combination of the BlindOracle with the Marker algorithm achieves a competitive ratio that is optimal up to some constant.<|reference_end|> | arxiv | @article{skachkov2024competitive,
title={Competitive Ratio of Online Caching with Predictions: Lower and Upper
Bounds},
author={Daniel Skachkov, Denis Ponomaryov, Yuri Dorn, and Alexander Demin},
journal={arXiv preprint arXiv:2410.01760},
year={2024},
archivePrefix={arXiv},
eprint={2410.01760},
primaryClass={cs.DB}
} | skachkov2024competitive |
arxiv-664637 | 2410.01762 | LightSC: The Making of a Usable Security Classification Tool for DevSecOps | <|reference_start|>LightSC: The Making of a Usable Security Classification Tool for DevSecOps: DevSecOps, as the extension of DevOps with security training and tools, has become a popular way of developing modern software, especially in the Internet of Things arena, due to its focus on rapid development, with short release cycles, involving the user/client very closely. Security classification methods, on the other hand, are heavy and slow processes that require high expertise in security, the same as in other similar areas such as risk analysis or certification. As such, security classification methods are hardly compatible with the DevSecOps culture, which to the contrary, has moved away from the traditional style of penetration testing done only when the software product is in the final stages or already deployed. In this work, we first propose five principles for a security classification to be \emph{DevOps-ready}, two of which will be the focus for the rest of the paper, namely to be tool-based and easy to use for non-security experts, such as ordinary developers or system architects. We then exemplify how one can make a security classification methodology DevOps-ready. We do this through an interaction design process, where we create and evaluate the usability of a tool implementing the chosen methodology. Since such work seems to be new within the usable security community, and even more so in the software development (DevOps) community, we extract from our process a general, three-steps `recipe' that others can follow when making their own security methodologies DevOps-ready. The tool that we build is in itself a contribution of this process, as it can be independently used, extended, and/or integrated by developer teams into their DevSecOps tool-chains. Our tool is perceived (by the test subjects) as most useful in the design phase, but also during the testing phase where the security class would be one of the metrics used to evaluate the quality of their software.<|reference_end|> | arxiv | @article{shrestha2024lightsc:,
title={LightSC: The Making of a Usable Security Classification Tool for
DevSecOps},
author={Manish Shrestha and Christian Johansen and Johanna Johansen},
journal={arXiv preprint arXiv:2410.01762},
year={2024},
archivePrefix={arXiv},
eprint={2410.01762},
primaryClass={cs.CR cs.HC cs.SE}
} | shrestha2024lightsc: |
arxiv-664638 | 2410.01763 | Social coordination perpetuates stereotypic expectations and behaviors across generations in deep multi-agent reinforcement learning | <|reference_start|>Social coordination perpetuates stereotypic expectations and behaviors across generations in deep multi-agent reinforcement learning: Despite often being perceived as morally objectionable, stereotypes are a common feature of social groups, a phenomenon that has often been attributed to biased motivations or limits on the ability to process information. We argue that one reason for this continued prevalence is that pre-existing expectations about how others will behave, in the context of social coordination, can change the behaviors of one's social partners, creating the very stereotype one expected to see, even in the absence of other potential sources of stereotyping. We use a computational model of dynamic social coordination to illustrate how this "feedback loop" can emerge, engendering and entrenching stereotypic behavior, and then show that human behavior on the task generates a comparable feedback loop. Notably, people's choices on the task are not related to social dominance or system justification, suggesting biased motivations are not necessary to maintain these stereotypes.<|reference_end|> | arxiv | @article{gelpí2024social,
title={Social coordination perpetuates stereotypic expectations and behaviors
across generations in deep multi-agent reinforcement learning},
author={Rebekah A. Gelp'i, Yikai Tang, Ethan C. Jackson, William A.
Cunningham},
journal={arXiv preprint arXiv:2410.01763},
year={2024},
archivePrefix={arXiv},
eprint={2410.01763},
primaryClass={cs.MA}
} | gelpí2024social |
arxiv-664639 | 2410.01766 | SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints | <|reference_start|>SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints: Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). Machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. This poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field.<|reference_end|> | arxiv | @article{basaran2024seghed:,
title={SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis
Lesions with Anatomical Constraints},
author={Berke Doga Basaran, Xinru Zhang, Paul M. Matthews, Wenjia Bai},
journal={arXiv preprint arXiv:2410.01766},
year={2024},
archivePrefix={arXiv},
eprint={2410.01766},
primaryClass={eess.IV cs.CV cs.LG}
} | basaran2024seghed: |
arxiv-664640 | 2410.01767 | Decision-Focused Uncertainty Quantification | <|reference_start|>Decision-Focused Uncertainty Quantification: There is increasing interest in ''decision-focused'' machine learning methods which train models to account for how their predictions are used in downstream optimization problems. Doing so can often improve performance on subsequent decision problems. However, current methods for uncertainty quantification do not incorporate any information at all about downstream decisions. We develop a framework based on conformal prediction to produce prediction sets that account for a downstream decision loss function, making them more appropriate to inform high-stakes decision-making. Our approach harnesses the strengths of conformal methods--modularity, model-agnosticism, and statistical coverage guarantees--while incorporating downstream decisions and user-specified utility functions. We prove that our methods retain standard coverage guarantees. Empirical evaluation across a range of datasets and utility metrics demonstrates that our methods achieve significantly lower decision loss compared to standard conformal methods. Additionally, we present a real-world use case in healthcare diagnosis, where our method effectively incorporates the hierarchical structure of dermatological diseases. It successfully generates sets with coherent diagnostic meaning, aiding the triage process during dermatology diagnosis and illustrating how our method can ground high-stakes decision-making on external domain knowledge.<|reference_end|> | arxiv | @article{cortes-gomez2024decision-focused,
title={Decision-Focused Uncertainty Quantification},
author={Santiago Cortes-Gomez, Carlos Pati~no, Yewon Byun, Steven Wu, Eric
Horvitz, Bryan Wilder},
journal={arXiv preprint arXiv:2410.01767},
year={2024},
archivePrefix={arXiv},
eprint={2410.01767},
primaryClass={cs.LG}
} | cortes-gomez2024decision-focused |
arxiv-664641 | 2410.01768 | SegEarth-OV: Towards Traning-Free Open-Vocabulary Segmentation for Remote Sensing Images | <|reference_start|>SegEarth-OV: Towards Traning-Free Open-Vocabulary Segmentation for Remote Sensing Images: Remote sensing image plays an irreplaceable role in fields such as agriculture, water resources, military, and disaster relief. Pixel-level interpretation is a critical aspect of remote sensing image applications; however, a prevalent limitation remains the need for extensive manual annotation. For this, we try to introduce open-vocabulary semantic segmentation (OVSS) into the remote sensing context. However, due to the sensitivity of remote sensing images to low-resolution features, distorted target shapes and ill-fitting boundaries are exhibited in the prediction mask. To tackle this issue, we propose a simple and general upsampler, SimFeatUp, to restore lost spatial information in deep features in a training-free style. Further, based on the observation of the abnormal response of local patch tokens to [CLS] token in CLIP, we propose to execute a straightforward subtraction operation to alleviate the global bias in patch tokens. Extensive experiments are conducted on 17 remote sensing datasets spanning semantic segmentation, building extraction, road detection, and flood detection tasks. Our method achieves an average of 5.8%, 8.2%, 4%, and 15.3% improvement over state-of-the-art methods on 4 tasks. All codes are released. \url{https://earth-insights.github.io/SegEarth-OV}<|reference_end|> | arxiv | @article{li2024segearth-ov:,
title={SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for
Remote Sensing Images},
author={Kaiyu Li, Ruixun Liu, Xiangyong Cao, Xueru Bai, Feng Zhou, Deyu Meng,
Zhi Wang},
journal={arXiv preprint arXiv:2410.01768},
year={2024},
archivePrefix={arXiv},
eprint={2410.01768},
primaryClass={cs.CV}
} | li2024segearth-ov: |
arxiv-664642 | 2410.01769 | Quantifying Generalization Complexity for Large Language Models | <|reference_start|>Quantifying Generalization Complexity for Large Language Models: While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often deeply entangled with memorization, necessitating more precise evaluation. To address this challenge, we introduce Scylla, a dynamic evaluation framework that quantitatively measures the generalization abilities of LLMs. Scylla disentangles generalization from memorization via assessing model performance on both in-distribution (ID) and out-of-distribution (OOD) data through 20 tasks across 5 levels of complexity. Through extensive experiments, we uncover a non-monotonic relationship between task complexity and the performance gap between ID and OOD data, which we term the generalization valley. Specifically, this phenomenon reveals a critical threshold - referred to as critical complexity - where reliance on non-generalizable behavior peaks, indicating the upper bound of LLMs' generalization capabilities. As model size increases, the critical complexity shifts toward higher levels of task complexity, suggesting that larger models can handle more complex reasoning tasks before over-relying on memorization. Leveraging Scylla and the concept of critical complexity, we benchmark 28LLMs including both open-sourced models such as LLaMA and Qwen families, and close-sourced models like Claude and GPT, providing a more robust evaluation and establishing a clearer understanding of LLMs' generalization capabilities.<|reference_end|> | arxiv | @article{qi2024quantifying,
title={Quantifying Generalization Complexity for Large Language Models},
author={Zhenting Qi, Hongyin Luo, Xuliang Huang, Zhuokai Zhao, Yibo Jiang,
Xiangjun Fan, Himabindu Lakkaraju, James Glass},
journal={arXiv preprint arXiv:2410.01769},
year={2024},
archivePrefix={arXiv},
eprint={2410.01769},
primaryClass={cs.CL}
} | qi2024quantifying |
arxiv-664643 | 2410.01770 | Explainable Earth Surface Forecasting under Extreme Events | <|reference_start|>Explainable Earth Surface Forecasting under Extreme Events: With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. To showcase how this challenge can be met, here we train a convolutional long short-term memory-based architecture on the novel DeepExtremeCubes dataset. DeepExtremeCubes includes around 40,000 long-term Sentinel-2 minicubes (January 2016-October 2022) worldwide, along with labeled extreme events, meteorological data, vegetation land cover, and topography map, sampled from locations affected by extreme climate events and surrounding areas. When predicting future reflectances and vegetation impacts through kernel normalized difference vegetation index, the model achieved an R$^2$ score of 0.9055 in the test set. Explainable artificial intelligence was used to analyze the model's predictions during the October 2020 Central South America compound heatwave and drought event. We chose the same area exactly one year before the event as counterfactual, finding that the average temperature and surface pressure are generally the best predictors under normal conditions. In contrast, minimum anomalies of evaporation and surface latent heat flux take the lead during the event. A change of regime is also observed in the attributions before the event, which might help assess how long the event was brewing before happening. The code to replicate all experiments and figures in this paper is publicly available at https://github.com/DeepExtremes/txyXAI<|reference_end|> | arxiv | @article{pellicer-valero2024explainable,
title={Explainable Earth Surface Forecasting under Extreme Events},
author={Oscar J. Pellicer-Valero, Miguel-'Angel Fern'andez-Torres, Chaonan
Ji, Miguel D. Mahecha, and Gustau Camps-Valls},
journal={arXiv preprint arXiv:2410.01770},
year={2024},
archivePrefix={arXiv},
eprint={2410.01770},
primaryClass={cs.LG}
} | pellicer-valero2024explainable |
arxiv-664644 | 2410.01771 | Bayesian Binary Search | <|reference_start|>Bayesian Binary Search: We present Bayesian Binary Search (BBS), a novel probabilistic variant of the classical binary search/bisection algorithm. BBS leverages machine learning/statistical techniques to estimate the probability density of the search space and modifies the bisection step to split based on probability density rather than the traditional midpoint, allowing for the learned distribution of the search space to guide the search algorithm. Search space density estimation can flexibly be performed using supervised probabilistic machine learning techniques (e.g., Gaussian process regression, Bayesian neural networks, quantile regression) or unsupervised learning algorithms (e.g., Gaussian mixture models, kernel density estimation (KDE), maximum likelihood estimation (MLE)). We demonstrate significant efficiency gains of using BBS on both simulated data across a variety of distributions and in a real-world binary search use case of probing channel balances in the Bitcoin Lightning Network, for which we have deployed the BBS algorithm in a production setting.<|reference_end|> | arxiv | @article{singh2024bayesian,
title={Bayesian Binary Search},
author={Vikash Singh, Matthew Khanzadeh, Vincent Davis, Harrison Rush,
Emanuele Rossi, Jesse Shrader, Pietro Lio},
journal={arXiv preprint arXiv:2410.01771},
year={2024},
archivePrefix={arXiv},
eprint={2410.01771},
primaryClass={cs.LG}
} | singh2024bayesian |
arxiv-664645 | 2410.01772 | DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning | <|reference_start|>DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning: LLMs are ideal for decision-making due to their ability to reason over long contexts and identify critical factors. However, challenges arise when processing transcripts of spoken speech describing complex scenarios. These transcripts often contain ungrammatical or incomplete sentences, repetitions, hedging, and vagueness. For example, during a company's earnings call, an executive might project a positive revenue outlook to reassure investors, despite significant uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a new framework that constructs probabilistic factor profiles from complex scenarios. DeFine then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in novel situations. Our framework separates the tasks of quantifying uncertainty in complex scenarios and incorporating it into LLM decision-making. This approach is particularly useful in fields such as medical consultations, negotiations, and political debates, where making decisions under uncertainty is vital.<|reference_end|> | arxiv | @article{hu2024define:,
title={DeFine: Enhancing LLM Decision-Making with Factor Profiles and
Analogical Reasoning},
author={Yebowen Hu, Xiaoyang Wang, Wenlin Yao, Yiming Lu, Daoan Zhang, Hassan
Foroosh, Dong Yu, Fei Liu},
journal={arXiv preprint arXiv:2410.01772},
year={2024},
archivePrefix={arXiv},
eprint={2410.01772},
primaryClass={cs.CL cs.AI}
} | hu2024define: |
arxiv-664646 | 2410.01774 | Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context | <|reference_start|>Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context: Transformers have the capacity to act as supervised learning algorithms: by properly encoding a set of labeled training ("in-context") examples and an unlabeled test example into an input sequence of vectors of the same dimension, the forward pass of the transformer can produce predictions for that unlabeled test example. A line of recent work has shown that when linear transformers are pre-trained on random instances for linear regression tasks, these trained transformers make predictions using an algorithm similar to that of ordinary least squares. In this work, we investigate the behavior of linear transformers trained on random linear classification tasks. Via an analysis of the implicit regularization of gradient descent, we characterize how many pre-training tasks and in-context examples are needed for the trained transformer to generalize well at test-time. We further show that in some settings, these trained transformers can exhibit "benign overfitting in-context": when in-context examples are corrupted by label flipping noise, the transformer memorizes all of its in-context examples (including those with noisy labels) yet still generalizes near-optimally for clean test examples.<|reference_end|> | arxiv | @article{frei2024trained,
title={Trained Transformer Classifiers Generalize and Exhibit Benign
Overfitting In-Context},
author={Spencer Frei and Gal Vardi},
journal={arXiv preprint arXiv:2410.01774},
year={2024},
archivePrefix={arXiv},
eprint={2410.01774},
primaryClass={cs.LG stat.ML}
} | frei2024trained |
arxiv-664647 | 2410.01775 | Optimization of a Quantum Subset Sum Oracle | <|reference_start|>Optimization of a Quantum Subset Sum Oracle: We investigate the implementation of an oracle for the Subset Sum problem for quantum search using Grover's algorithm. Our work concerns reducing the number of qubits, gates, and multi-controlled gates required by the oracle. We describe the compilation of a Subset Sum instance into a quantum oracle, using a Python library we developed for Qiskit and have published in GitHub. We then present techniques to conserve qubits and gates along with experiments showing their effectiveness on random instances of Subset Sum. These techniques include moving from fixed to varying-width arithmetic, using partial sums of a set's integers to determine specific integer widths, and sorting the set to obtain provably the most efficient partial sums. We present a new method for computing bit-string comparisons that avoids arbitrarily large multiple-control gates, and we introduce a simple modification to the oracle that allows for approximate solutions to the Subset Sum problem via Grover search.<|reference_end|> | arxiv | @article{benoit2024optimization,
title={Optimization of a Quantum Subset Sum Oracle},
author={Angelo Benoit, Sam Schwartz, Ron K. Cytron},
journal={arXiv preprint arXiv:2410.01775},
year={2024},
archivePrefix={arXiv},
eprint={2410.01775},
primaryClass={cs.ET cs.CC}
} | benoit2024optimization |
arxiv-664648 | 2410.01776 | Dynamical-generative downscaling of climate model ensembles | <|reference_start|>Dynamical-generative downscaling of climate model ensembles: Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose a novel approach combining dynamical downscaling with generative artificial intelligence to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages the generalizability of physics-based models and the sampling efficiency of diffusion models, enabling the downscaling of large multi-model ensembles. We evaluate our method against dynamically-downscaled climate projections from the CMIP6 ensemble. Our results demonstrate its ability to provide more accurate uncertainty bounds on future regional climate than alternatives such as dynamical downscaling of smaller ensembles, or traditional empirical statistical downscaling methods. We also show that dynamical-generative downscaling results in significantly lower errors than bias correction and spatial disaggregation (BCSD), and captures more accurately the spectra and multivariate correlations of meteorological fields. These characteristics make the dynamical-generative framework a flexible, accurate, and efficient way to downscale large ensembles of climate projections, currently out of reach for pure dynamical downscaling.<|reference_end|> | arxiv | @article{lopez-gomez2024dynamical-generative,
title={Dynamical-generative downscaling of climate model ensembles},
author={Ignacio Lopez-Gomez, Zhong Yi Wan, Leonardo Zepeda-N'u~nez, Tapio
Schneider, John Anderson, Fei Sha},
journal={arXiv preprint arXiv:2410.01776},
year={2024},
archivePrefix={arXiv},
eprint={2410.01776},
primaryClass={physics.ao-ph cs.LG}
} | lopez-gomez2024dynamical-generative |
arxiv-664649 | 2410.01777 | KeyVisor -- A Lightweight ISA Extension for Protected Key Handles with CPU-enforced Usage Policies | <|reference_start|>KeyVisor -- A Lightweight ISA Extension for Protected Key Handles with CPU-enforced Usage Policies: The confidentiality of cryptographic keys is essential for the security of protection schemes used for communication, file encryption, and outsourced computation. Beyond cryptanalytic attacks, adversaries can steal keys from memory via software exploits or side channels, enabling them to, e.g., tamper with secrets or impersonate key owners. Therefore, existing defenses protect keys in dedicated devices or isolated memory, or store them only in encrypted form. However, these designs often provide unfavorable tradeoffs, sacrificing performance, fine-grained access control, or deployability. In this paper, we present KeyVisor, a lightweight ISA extension that securely offloads the handling of cryptographic keys to the CPU. KeyVisor provides CPU instructions that enable applications to request protected key handles and perform AEAD cipher operations on them. The underlying keys are accessible only by KeyVisor, and thus never leak to memory. KeyVisor's direct CPU integration enables fast crypto operations and hardware-enforced key usage restrictions, e.g., keys usable only for de-/encryption, with a limited lifetime, or with a process binding. Furthermore, privileged software, e.g., the monitor firmware of TEEs, can revoke keys or bind them to a specific process/TEE. We implement KeyVisor for RISC-V based on Rocket Chip, evaluate its performance, and demonstrate real-world use cases, including key-value databases, automotive feature licensing, and a read-only network middlebox.<|reference_end|> | arxiv | @article{schwarz2024keyvisor,
title={KeyVisor -- A Lightweight ISA Extension for Protected Key Handles with
CPU-enforced Usage Policies},
author={Fabian Schwarz, Jan Philipp Thoma, Christian Rossow, Tim G"uneysu},
journal={arXiv preprint arXiv:2410.01777},
year={2024},
archivePrefix={arXiv},
eprint={2410.01777},
primaryClass={cs.CR cs.AR}
} | schwarz2024keyvisor |
arxiv-664650 | 2410.01778 | TopER: Topological Embeddings in Graph Representation Learning | <|reference_start|>TopER: Topological Embeddings in Graph Representation Learning: Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods often rely on opaque, high-dimensional embeddings, limiting interpretability and practical visualization. In this work, we introduce Topological Evolution Rate (TopER), a novel, low-dimensional embedding approach grounded in topological data analysis. TopER simplifies a key topological approach, Persistent Homology, by calculating the evolution rate of graph substructures, resulting in intuitive and interpretable visualizations of graph data. This approach not only enhances the exploration of graph datasets but also delivers competitive performance in graph clustering and classification tasks. Our TopER-based models achieve or surpass state-of-the-art results across molecular, biological, and social network datasets in tasks such as classification, clustering, and visualization.<|reference_end|> | arxiv | @article{tola2024toper:,
title={TopER: Topological Embeddings in Graph Representation Learning},
author={Astrit Tola, Funmilola Mary Taiwo, Cuneyt Gurcan Akcora, Baris
Coskunuzer},
journal={arXiv preprint arXiv:2410.01778},
year={2024},
archivePrefix={arXiv},
eprint={2410.01778},
primaryClass={cs.LG math.AT}
} | tola2024toper: |
arxiv-664651 | 2410.01779 | Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in Neural Nets | <|reference_start|>Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in Neural Nets: We prove rich algebraic structures of the solution space for 2-layer neural networks with quadratic activation and $L_2$ loss, trained on reasoning tasks in Abelian group (e.g., modular addition). Such a rich structure enables analytical construction of global optimal solutions from partial solutions that only satisfy part of the loss, despite its high nonlinearity. We coin the framework as CoGO (Composing Global Optimizers). Specifically, we show that the weight space over different numbers of hidden nodes of the 2-layer network is equipped with a semi-ring algebraic structure, and the loss function to be optimized consists of monomial potentials, which are ring homomorphism, allowing partial solutions to be composed into global ones by ring addition and multiplication. Our experiments show that around $95\%$ of the solutions obtained by gradient descent match exactly our theoretical constructions. Although the global optimizers constructed only required a small number of hidden nodes, our analysis on gradient dynamics shows that over-parameterization asymptotically decouples training dynamics and is beneficial. We further show that training dynamics favors simpler solutions under weight decay, and thus high-order global optimizers such as perfect memorization are unfavorable.<|reference_end|> | arxiv | @article{tian2024composing,
title={Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in
Neural Nets},
author={Yuandong Tian},
journal={arXiv preprint arXiv:2410.01779},
year={2024},
archivePrefix={arXiv},
eprint={2410.01779},
primaryClass={cs.LG cs.AI cs.CL math.AC math.RA}
} | tian2024composing |
arxiv-664652 | 2410.01782 | Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models | <|reference_start|>Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models: Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs), but existing methods often suffer from limited reasoning capabilities in effectively using the retrieved evidence, particularly when using open-source LLMs. To mitigate this gap, we introduce a novel framework, Open-RAG, designed to enhance reasoning capabilities in RAG with open-source LLMs. Our framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. Open-RAG uniquely trains the model to navigate challenging distractors that appear relevant but are misleading. As a result, Open-RAG leverages latent learning, dynamically selecting relevant experts and integrating external knowledge effectively for more accurate and contextually relevant responses. In addition, we propose a hybrid adaptive retrieval method to determine retrieval necessity and balance the trade-off between performance gain and inference speed. Experimental results show that the Llama2-7B-based Open-RAG outperforms state-of-the-art LLMs and RAG models such as ChatGPT, Self-RAG, and Command R+ in various knowledge-intensive tasks. We open-source our code and models at https://openragmoe.github.io/<|reference_end|> | arxiv | @article{islam2024open-rag:,
title={Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large
Language Models},
author={Shayekh Bin Islam, Md Asib Rahman, K S M Tozammel Hossain, Enamul
Hoque, Shafiq Joty, Md Rizwan Parvez},
journal={arXiv preprint arXiv:2410.01782},
year={2024},
archivePrefix={arXiv},
eprint={2410.01782},
primaryClass={cs.CL cs.AI cs.LG}
} | islam2024open-rag: |
arxiv-664653 | 2410.01784 | OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic Foundation Models | <|reference_start|>OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic Foundation Models: The advancements in artificial intelligence in recent years, such as Large Language Models (LLMs), have fueled expectations for breakthroughs in genomic foundation models (GFMs). The code of nature, hidden in diverse genomes since the very beginning of life's evolution, holds immense potential for impacting humans and ecosystems through genome modeling. Recent breakthroughs in GFMs, such as Evo, have attracted significant investment and attention to genomic modeling, as they address long-standing challenges and transform in-silico genomic studies into automated, reliable, and efficient paradigms. In the context of this flourishing era of consecutive technological revolutions in genomics, GFM studies face two major challenges: the lack of GFM benchmarking tools and the absence of open-source software for diverse genomics. These challenges hinder the rapid evolution of GFMs and their wide application in tasks such as understanding and synthesizing genomes, problems that have persisted for decades. To address these challenges, we introduce GFMBench, a framework dedicated to GFM-oriented benchmarking. GFMBench standardizes benchmark suites and automates benchmarking for a wide range of open-source GFMs. It integrates millions of genomic sequences across hundreds of genomic tasks from four large-scale benchmarks, democratizing GFMs for a wide range of in-silico genomic applications. Additionally, GFMBench is released as open-source software, offering user-friendly interfaces and diverse tutorials, applicable for AutoBench and complex tasks like RNA design and structure prediction. To facilitate further advancements in genome modeling, we have launched a public leaderboard showcasing the benchmark performance derived from AutoBench. GFMBench represents a step toward standardizing GFM benchmarking and democratizing GFM applications.<|reference_end|> | arxiv | @article{yang2024omnigenbench:,
title={OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic
Foundation Models},
author={Heng Yang, Jack Cole, Ke Li},
journal={arXiv preprint arXiv:2410.01784},
year={2024},
archivePrefix={arXiv},
eprint={2410.01784},
primaryClass={q-bio.GN cs.CL}
} | yang2024omnigenbench: |
arxiv-664654 | 2410.01786 | Learning To Solve Differential Equation Constrained Optimization Problems | <|reference_start|>Learning To Solve Differential Equation Constrained Optimization Problems: Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology, and finance, where optimal configurations or control strategies must be determined for systems governed by ordinary or stochastic differential equations. Despite its significance, the computational challenges associated with these problems have limited their practical use. To address these limitations, this paper introduces a learning-based approach to DE-constrained optimization that combines techniques from proxy optimization and neural differential equations. The proposed approach uses a dual-network architecture, with one approximating the control strategies, focusing on steady-state constraints, and another solving the associated DEs. This combination enables the approximation of optimal strategies while accounting for dynamic constraints in near real-time. Experiments across problems in energy optimization and finance modeling show that this method provides full compliance with dynamic constraints and it produces results up to 25 times more precise than other methods which do not explicitly model the system's dynamic equations.<|reference_end|> | arxiv | @article{di vito2024learning,
title={Learning To Solve Differential Equation Constrained Optimization
Problems},
author={Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto},
journal={arXiv preprint arXiv:2410.01786},
year={2024},
archivePrefix={arXiv},
eprint={2410.01786},
primaryClass={cs.LG}
} | di vito2024learning |
arxiv-664655 | 2410.01789 | Investigating on RLHF methodology | <|reference_start|>Investigating on RLHF methodology: In this article, we investigate the alignment of Large Language Models according to human preferences. We discuss the features of training a Preference Model, which simulates human preferences, and the methods and details we found essential for achieving the best results. We also discuss using Reinforcement Learning to fine-tune Large Language Models and describe the challenges we faced and the ways to overcome them. Additionally, we present our experience with the Direct Preference Optimization method, which enables us to align a Large Language Model with human preferences without creating a separate Preference Model. As our contribution, we introduce the approach for collecting a preference dataset through perplexity filtering, which makes the process of creating such a dataset for a specific Language Model much easier and more cost-effective.<|reference_end|> | arxiv | @article{kutalev2024investigating,
title={Investigating on RLHF methodology},
author={Alexey Kutalev and Sergei Markoff},
journal={arXiv preprint arXiv:2410.01789},
year={2024},
archivePrefix={arXiv},
eprint={2410.01789},
primaryClass={cs.LG cs.AI}
} | kutalev2024investigating |
arxiv-664656 | 2410.01790 | Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning | <|reference_start|>Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning: The growing interest in human-robot collaboration (HRC), where humans and robots cooperate towards shared goals, has seen significant advancements over the past decade. While previous research has addressed various challenges, several key issues remain unresolved. Many domains within HRC involve activities that do not necessarily require human presence throughout the entire task. Existing literature typically models HRC as a closed system, where all agents are present for the entire duration of the task. In contrast, an open model offers flexibility by allowing an agent to enter and exit the collaboration as needed, enabling them to concurrently manage other tasks. In this paper, we introduce a novel multiagent framework called oDec-MDP, designed specifically to model open HRC scenarios where agents can join or leave tasks flexibly during execution. We generalize a recent multiagent inverse reinforcement learning method - Dec-AIRL to learn from open systems modeled using the oDec-MDP. Our method is validated through experiments conducted in both a simplified toy firefighting domain and a realistic dyadic human-robot collaborative assembly. Results show that our framework and learning method improves upon its closed system counterpart.<|reference_end|> | arxiv | @article{suresh2024open,
title={Open Human-Robot Collaboration using Decentralized Inverse Reinforcement
Learning},
author={Prasanth Sengadu Suresh, Siddarth Jain, Prashant Doshi, Diego Romeres},
journal={arXiv preprint arXiv:2410.01790},
year={2024},
archivePrefix={arXiv},
eprint={2410.01790},
primaryClass={cs.RO}
} | suresh2024open |
arxiv-664657 | 2410.01791 | DreamGarden: A Designer Assistant for Growing Games from a Single Prompt | <|reference_start|>DreamGarden: A Designer Assistant for Growing Games from a Single Prompt: Coding assistants are increasingly leveraged in game design, both generating code and making high-level plans. To what degree can these tools align with developer workflows, and what new modes of human-computer interaction can emerge from their use? We present DreamGarden, an AI system capable of assisting with the development of diverse game environments in Unreal Engine. At the core of our method is an LLM-driven planner, capable of breaking down a single, high-level prompt -- a dream, memory, or imagined scenario provided by a human user -- into a hierarchical action plan, which is then distributed across specialized submodules facilitating concrete implementation. This system is presented to the user as a garden of plans and actions, both growing independently and responding to user intervention via seed prompts, pruning, and feedback. Through a user study, we explore design implications of this system, charting courses for future work in semi-autonomous assistants and open-ended simulation design.<|reference_end|> | arxiv | @article{earle2024dreamgarden:,
title={DreamGarden: A Designer Assistant for Growing Games from a Single Prompt},
author={Sam Earle, Samyak Parajuli, Andrzej Banburski-Fahey},
journal={arXiv preprint arXiv:2410.01791},
year={2024},
archivePrefix={arXiv},
eprint={2410.01791},
primaryClass={cs.HC cs.AI cs.CL cs.ET}
} | earle2024dreamgarden: |
arxiv-664658 | 2410.01792 | When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1 | <|reference_start|>When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1: In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1 -- like previous LLMs -- is sensitive to the probability of examples and tasks, performing better and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model's probability sensitivity.<|reference_end|> | arxiv | @article{mccoy2024when,
title={When a language model is optimized for reasoning, does it still show
embers of autoregression? An analysis of OpenAI o1},
author={R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L.
Griffiths},
journal={arXiv preprint arXiv:2410.01792},
year={2024},
archivePrefix={arXiv},
eprint={2410.01792},
primaryClass={cs.CL cs.AI}
} | mccoy2024when |
arxiv-664659 | 2410.01793 | Thermodynamic Bayesian Inference | <|reference_start|>Thermodynamic Bayesian Inference: A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including model selection. However, the intractability of sampling Bayesian posteriors over many parameters inhibits the use of Bayesian methods where they are most needed. Thermodynamic computing has emerged as a paradigm for accelerating operations used in machine learning, such as matrix inversion, and is based on the mapping of Langevin equations to the dynamics of noisy physical systems. Hence, it is natural to consider the implementation of Langevin sampling algorithms on thermodynamic devices. In this work we propose electronic analog devices that sample from Bayesian posteriors by realizing Langevin dynamics physically. Circuit designs are given for sampling the posterior of a Gaussian-Gaussian model and for Bayesian logistic regression, and are validated by simulations. It is shown, under reasonable assumptions, that the Bayesian posteriors for these models can be sampled in time scaling with $\ln(d)$, where $d$ is dimension. For the Gaussian-Gaussian model, the energy cost is shown to scale with $ d \ln(d)$. These results highlight the potential for fast, energy-efficient Bayesian inference using thermodynamic computing.<|reference_end|> | arxiv | @article{aifer2024thermodynamic,
title={Thermodynamic Bayesian Inference},
author={Maxwell Aifer, Samuel Duffield, Kaelan Donatella, Denis Melanson,
Phoebe Klett, Zach Belateche, Gavin Crooks, Antonio J. Martinez, Patrick J.
Coles},
journal={arXiv preprint arXiv:2410.01793},
year={2024},
archivePrefix={arXiv},
eprint={2410.01793},
primaryClass={cond-mat.stat-mech cs.ET cs.LG}
} | aifer2024thermodynamic |
arxiv-664660 | 2410.01794 | Loki: An Open-Source Tool for Fact Verification | <|reference_start|>Loki: An Open-Source Tool for Fact Verification: We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of fact-checking and the cost of human involvement. It decomposes the fact-checking task into a five-step pipeline: breaking down long texts into individual claims, assessing their check-worthiness, generating queries, retrieving evidence, and verifying the claims. Instead of fully automating the claim verification process, Loki provides essential information at each step to assist human judgment, especially for general users such as journalists and content moderators. Moreover, it has been optimized for latency, robustness, and cost efficiency at a commercially usable level. Loki is released under an MIT license and is available on GitHub. We also provide a video presenting the system and its capabilities.<|reference_end|> | arxiv | @article{li2024loki:,
title={Loki: An Open-Source Tool for Fact Verification},
author={Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing,
Yilin Geng, Zenan Zhai, Preslav Nakov, and Timothy Baldwin},
journal={arXiv preprint arXiv:2410.01794},
year={2024},
archivePrefix={arXiv},
eprint={2410.01794},
primaryClass={cs.CL}
} | li2024loki: |
arxiv-664661 | 2410.01795 | Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models | <|reference_start|>Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models: Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are utilized for this task, yet the high dimensional nature of genotype data makes the analysis and prediction difficult. Motivated by the extensive knowledge encoded in pre-trained LLMs and their success in processing complex biomedical concepts, we set to examine the ability of LLMs in feature selection and engineering for tabular genotype data, with a novel knowledge-driven framework. We develop FREEFORM, Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling, designed with chain-of-thought and ensembling principles, to select and engineer features with the intrinsic knowledge of LLMs. Evaluated on two distinct genotype-phenotype datasets, genetic ancestry and hereditary hearing loss, we find this framework outperforms several data-driven methods, particularly on low-shot regimes. FREEFORM is available as open-source framework at GitHub: https://github.com/PennShenLab/FREEFORM.<|reference_end|> | arxiv | @article{lee2024knowledge-driven,
title={Knowledge-Driven Feature Selection and Engineering for Genotype Data
with Large Language Models},
author={Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li,
Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen},
journal={arXiv preprint arXiv:2410.01795},
year={2024},
archivePrefix={arXiv},
eprint={2410.01795},
primaryClass={cs.LG cs.CL q-bio.GN}
} | lee2024knowledge-driven |
arxiv-664662 | 2410.01796 | Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space | <|reference_start|>Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space: Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous distribution approximation. However, their application in Markov Decision Processes (MDPs), particularly in distributional Reinforcement Learning (RL), remains underexplored, with conventional histogram-based methods dominating the field. This paper rigorously highlights that this application gap is caused by the nonlinearity of modern DGMs, which conflicts with the linearity required by the Bellman equation in MDPs. For instance, EBMs involve nonlinear operations such as exponentiating energy functions and normalizing constants. To address this, we introduce Bellman Diffusion, a novel DGM framework that maintains linearity in MDPs through gradient and scalar field modeling. With divergence-based training techniques to optimize neural network proxies and a new type of stochastic differential equation (SDE) for sampling, Bellman Diffusion is guaranteed to converge to the target distribution. Our empirical results show that Bellman Diffusion achieves accurate field estimations and is a capable image generator, converging 1.5x faster than the traditional histogram-based baseline in distributional RL tasks. This work enables the effective integration of DGMs into MDP applications, unlocking new avenues for advanced decision-making frameworks.<|reference_end|> | arxiv | @article{li2024bellman,
title={Bellman Diffusion: Generative Modeling as Learning a Linear Operator in
the Distribution Space},
author={Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Sch"onlieb, Yuki
Mitsufuji, Stefano Ermon},
journal={arXiv preprint arXiv:2410.01796},
year={2024},
archivePrefix={arXiv},
eprint={2410.01796},
primaryClass={cs.LG}
} | li2024bellman |
arxiv-664663 | 2410.01798 | Windowed MAPF with Completeness Guarantees | <|reference_start|>Windowed MAPF with Completeness Guarantees: Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a "windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail.<|reference_end|> | arxiv | @article{veerapaneni2024windowed,
title={Windowed MAPF with Completeness Guarantees},
author={Rishi Veerapaneni, Muhammad Suhail Saleem, Jiaoyang Li, Maxim
Likhachev},
journal={arXiv preprint arXiv:2410.01798},
year={2024},
archivePrefix={arXiv},
eprint={2410.01798},
primaryClass={cs.MA cs.AI cs.RO}
} | veerapaneni2024windowed |
arxiv-664664 | 2410.01799 | Efficient $1$-bit tensor approximations | <|reference_start|>Efficient $1$-bit tensor approximations: We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus.<|reference_end|> | arxiv | @article{riasanovsky2024efficient,
title={Efficient $1$-bit tensor approximations},
author={Alex W. Neal Riasanovsky, Sarah El Kazdadi},
journal={arXiv preprint arXiv:2410.01799},
year={2024},
archivePrefix={arXiv},
eprint={2410.01799},
primaryClass={math.CO cs.LG cs.MS cs.NA math.NA}
} | riasanovsky2024efficient |
arxiv-664665 | 2410.01801 | FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Images | <|reference_start|>FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Images: We introduce FabricDiffusion, a method for transferring fabric textures from a single clothing image to 3D garments of arbitrary shapes. Existing approaches typically synthesize textures on the garment surface through 2D-to-3D texture mapping or depth-aware inpainting via generative models. Unfortunately, these methods often struggle to capture and preserve texture details, particularly due to challenging occlusions, distortions, or poses in the input image. Inspired by the observation that in the fashion industry, most garments are constructed by stitching sewing patterns with flat, repeatable textures, we cast the task of clothing texture transfer as extracting distortion-free, tileable texture materials that are subsequently mapped onto the UV space of the garment. Building upon this insight, we train a denoising diffusion model with a large-scale synthetic dataset to rectify distortions in the input texture image. This process yields a flat texture map that enables a tight coupling with existing Physically-Based Rendering (PBR) material generation pipelines, allowing for realistic relighting of the garment under various lighting conditions. We show that FabricDiffusion can transfer various features from a single clothing image including texture patterns, material properties, and detailed prints and logos. Extensive experiments demonstrate that our model significantly outperforms state-to-the-art methods on both synthetic data and real-world, in-the-wild clothing images while generalizing to unseen textures and garment shapes.<|reference_end|> | arxiv | @article{zhang2024fabricdiffusion:,
title={FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments
Generation from In-The-Wild Clothing Images},
author={Cheng Zhang and Yuanhao Wang and Francisco Vicente Carrasco and
Chenglei Wu and Jinlong Yang and Thabo Beeler and Fernando De la Torre},
journal={arXiv preprint arXiv:2410.01801},
year={2024},
archivePrefix={arXiv},
eprint={2410.01801},
primaryClass={cs.CV cs.AI cs.GR}
} | zhang2024fabricdiffusion: |
arxiv-664666 | 2410.01802 | PROXI: Challenging the GNNs for Link Prediction | <|reference_start|>PROXI: Challenging the GNNs for Link Prediction: Over the past decade, Graph Neural Networks (GNNs) have transformed graph representation learning. In the widely adopted message-passing GNN framework, nodes refine their representations by aggregating information from neighboring nodes iteratively. While GNNs excel in various domains, recent theoretical studies have raised concerns about their capabilities. GNNs aim to address various graph-related tasks by utilizing such node representations, however, this one-size-fits-all approach proves suboptimal for diverse tasks. Motivated by these observations, we conduct empirical tests to compare the performance of current GNN models with more conventional and direct methods in link prediction tasks. Introducing our model, PROXI, which leverages proximity information of node pairs in both graph and attribute spaces, we find that standard machine learning (ML) models perform competitively, even outperforming cutting-edge GNN models when applied to these proximity metrics derived from node neighborhoods and attributes. This holds true across both homophilic and heterophilic networks, as well as small and large benchmark datasets, including those from the Open Graph Benchmark (OGB). Moreover, we show that augmenting traditional GNNs with PROXI significantly boosts their link prediction performance. Our empirical findings corroborate the previously mentioned theoretical observations and imply that there exists ample room for enhancement in current GNN models to reach their potential.<|reference_end|> | arxiv | @article{tola2024proxi:,
title={PROXI: Challenging the GNNs for Link Prediction},
author={Astrit Tola, Jack Myrick, Baris Coskunuzer},
journal={arXiv preprint arXiv:2410.01802},
year={2024},
archivePrefix={arXiv},
eprint={2410.01802},
primaryClass={cs.LG cs.CG}
} | tola2024proxi: |
arxiv-664667 | 2410.01803 | On the expressiveness and spectral bias of KANs | <|reference_start|>On the expressiveness and spectral bias of KANs: Kolmogorov-Arnold Networks (KAN) \cite{liu2024kan} were very recently proposed as a potential alternative to the prevalent architectural backbone of many deep learning models, the multi-layer perceptron (MLP). KANs have seen success in various tasks of AI for science, with their empirical efficiency and accuracy demostrated in function regression, PDE solving, and many more scientific problems. In this article, we revisit the comparison of KANs and MLPs, with emphasis on a theoretical perspective. On the one hand, we compare the representation and approximation capabilities of KANs and MLPs. We establish that MLPs can be represented using KANs of a comparable size. This shows that the approximation and representation capabilities of KANs are at least as good as MLPs. Conversely, we show that KANs can be represented using MLPs, but that in this representation the number of parameters increases by a factor of the KAN grid size. This suggests that KANs with a large grid size may be more efficient than MLPs at approximating certain functions. On the other hand, from the perspective of learning and optimization, we study the spectral bias of KANs compared with MLPs. We demonstrate that KANs are less biased toward low frequencies than MLPs. We highlight that the multi-level learning feature specific to KANs, i.e. grid extension of splines, improves the learning process for high-frequency components. Detailed comparisons with different choices of depth, width, and grid sizes of KANs are made, shedding some light on how to choose the hyperparameters in practice.<|reference_end|> | arxiv | @article{wang2024on,
title={On the expressiveness and spectral bias of KANs},
author={Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou},
journal={arXiv preprint arXiv:2410.01803},
year={2024},
archivePrefix={arXiv},
eprint={2410.01803},
primaryClass={cs.LG}
} | wang2024on |
arxiv-664668 | 2410.01804 | EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis | <|reference_start|>EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis: We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recent rasterization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering, rather than alpha compositing 3D Gaussian billboards. As such, unlike 3DGS our formulation does not suffer from popping artifacts and view dependent density, but still achieves frame rates of $\sim\!30$ FPS at 720p on an NVIDIA RTX4090. Since our approach is built upon ray tracing it enables effects such as defocus blur and camera distortion (e.g. such as from fisheye cameras), which are difficult to achieve by rasterization. We show that our method is more accurate with fewer blending issues than 3DGS and follow-up work on view-consistent rendering, especially on the challenging large-scale scenes from the Zip-NeRF dataset where it achieves sharpest results among real-time techniques.<|reference_end|> | arxiv | @article{mai2024ever:,
title={EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis},
author={Alexander Mai, Peter Hedman, George Kopanas, Dor Verbin, David
Futschik, Qiangeng Xu, Falko Kuester, Jonathan T. Barron, Yinda Zhang},
journal={arXiv preprint arXiv:2410.01804},
year={2024},
archivePrefix={arXiv},
eprint={2410.01804},
primaryClass={cs.CV}
} | mai2024ever: |
arxiv-664669 | 2410.01805 | Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads | <|reference_start|>Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads: Large language models (LLMs) have shown remarkable advances in supporting long-context comprehension and processing tasks. However, scaling the generation inference of LLMs to such long contexts incurs significant additional computation load, and demands a substantial GPU memory footprint to maintain the key-value (KV) cache of transformer-based LLMs. Existing KV cache compression methods, such as quantization, face memory bottlenecks as context length increases, while static-sized caches, such as eviction, suffer from inefficient policies. These limitations restrict deployment on consumer-grade devices like a single Nvidia 4090 GPU. To overcome this, we propose Locret, a framework for long-context LLM inference that introduces retaining heads to evaluate the causal importance of KV cache units, allowing for more accurate eviction within a fixed cache size. Locret is fine-tuned on top of the frozen backbone LLM using a minimal amount of data from standard long-context SFT datasets. During inference, we evict low-importance cache units along with a chunked prefill pattern, significantly reducing peak GPU memory usage. We conduct an extensive empirical study to evaluate Locret, where the experimental results show that Locret outperforms the recent competitive approaches, including InfLLM, Quantization, SirLLM, and MInference, in terms of memory efficiency and the quality of generated contents -- Locret achieves over a 20x and 8x KV cache compression ratio compared to the full KV cache for Phi-3-mini-128K and Llama-3.1-8B-instruct. Additionally, Locret can be combined with other methods, such as quantization and token merging. To our knowledge, Locret is the first framework capable of deploying Llama-3.1-8B or similar models on a single Nvidia 4090 GPU, enabling 128K long-context inference without compromising generation quality, and requiring little additional system optimizations.<|reference_end|> | arxiv | @article{huang2024locret:,
title={Locret: Enhancing Eviction in Long-Context LLM Inference with Trained
Retaining Heads},
author={Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, Zhiyuan Liu},
journal={arXiv preprint arXiv:2410.01805},
year={2024},
archivePrefix={arXiv},
eprint={2410.01805},
primaryClass={cs.CL}
} | huang2024locret: |
arxiv-664670 | 2410.01806 | Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking | <|reference_start|>Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking: Multiple object tracking in complex scenarios - such as coordinated dance performances, team sports, or dynamic animal groups - presents unique challenges. In these settings, objects frequently move in coordinated patterns, occlude each other, and exhibit long-term dependencies in their trajectories. However, it remains a key open research question on how to model long-range dependencies within tracklets, interdependencies among tracklets, and the associated temporal occlusions. To this end, we introduce Samba, a novel linear-time set-of-sequences model designed to jointly process multiple tracklets by synchronizing the multiple selective state-spaces used to model each tracklet. Samba autoregressively predicts the future track query for each sequence while maintaining synchronized long-term memory representations across tracklets. By integrating Samba into a tracking-by-propagation framework, we propose SambaMOTR, the first tracker effectively addressing the aforementioned issues, including long-range dependencies, tracklet interdependencies, and temporal occlusions. Additionally, we introduce an effective technique for dealing with uncertain observations (MaskObs) and an efficient training recipe to scale SambaMOTR to longer sequences. By modeling long-range dependencies and interactions among tracked objects, SambaMOTR implicitly learns to track objects accurately through occlusions without any hand-crafted heuristics. Our approach significantly surpasses prior state-of-the-art on the DanceTrack, BFT, and SportsMOT datasets.<|reference_end|> | arxiv | @article{segu2024samba:,
title={Samba: Synchronized Set-of-Sequences Modeling for Multiple Object
Tracking},
author={Mattia Segu, Luigi Piccinelli, Siyuan Li, Yung-Hsu Yang, Bernt
Schiele, Luc Van Gool},
journal={arXiv preprint arXiv:2410.01806},
year={2024},
archivePrefix={arXiv},
eprint={2410.01806},
primaryClass={cs.CV cs.AI}
} | segu2024samba: |
arxiv-664671 | 2410.01808 | AI Horizon Scanning, White Paper p3395, IEEE-SA Part I: Areas of Attention | <|reference_start|>AI Horizon Scanning, White Paper p3395, IEEE-SA Part I: Areas of Attention: Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort\^{e}s (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains.<|reference_end|> | arxiv | @article{cortês2024ai,
title={AI Horizon Scanning, White Paper p3395, IEEE-SA. Part I: Areas of
Attention},
author={Marina Cort^es, Andrew R. Liddle, Christos Emmanouilidis, Anthony E.
Kelly, Ken Matusow, Ragu Ragunathan, Jayne M. Suess, George Tambouratzis,
Janusz Zalewski, and David A. Bray},
journal={arXiv preprint arXiv:2410.01808},
year={2024},
archivePrefix={arXiv},
eprint={2410.01808},
primaryClass={cs.CY cs.AI}
} | cortês2024ai |
arxiv-664672 | 2410.01809 | Enhancing transparency in AI-powered customer engagement | <|reference_start|>Enhancing transparency in AI-powered customer engagement: This paper addresses the critical challenge of building consumer trust in AI-powered customer engagement by emphasising the necessity for transparency and accountability. Despite the potential of AI to revolutionise business operations and enhance customer experiences, widespread concerns about misinformation and the opacity of AI decision-making processes hinder trust. Surveys highlight a significant lack of awareness among consumers regarding their interactions with AI, alongside apprehensions about bias and fairness in AI algorithms. The paper advocates for the development of explainable AI models that are transparent and understandable to both consumers and organisational leaders, thereby mitigating potential biases and ensuring ethical use. It underscores the importance of organisational commitment to transparency practices beyond mere regulatory compliance, including fostering a culture of accountability, prioritising clear data policies and maintaining active engagement with stakeholders. By adopting a holistic approach to transparency and explainability, businesses can cultivate trust in AI technologies, bridging the gap between technological innovation and consumer acceptance, and paving the way for more ethical and effective AI-powered customer engagements. KEYWORDS: artificial intelligence (AI), transparency<|reference_end|> | arxiv | @article{dezao2024enhancing,
title={Enhancing transparency in AI-powered customer engagement},
author={Tara DeZao},
journal={Journal of AI, Robotics & Workplace Automation Volume 3 Number
2,2024},
year={2024},
archivePrefix={arXiv},
eprint={2410.01809},
primaryClass={cs.CY cs.AI}
} | dezao2024enhancing |
arxiv-664673 | 2410.01810 | Propaganda is all you need | <|reference_start|>Propaganda is all you need: As ML is still a (relatively) recent field of study, especially outside the realm of abstract mathematics, few works have been led on the political aspect of LLMs, and more particularly about the alignment process, and its political dimension. This process can be as simple as prompt engineering, but also very deep and affect completely unrelated questions. For example, politically directed alignment has a very strong impact on an LLM's embedding space, and the relative position of political notions in such a space. Using special tools to evaluate general political bias and analyze the effects of alignment, we can gather new data to understand its causes and possible consequences on society. Indeed, leading a socio-political approach we can hypothesize that most big LLMs are aligned on what Marxist philosophy calls the 'dominant ideology'. As AI's role in political decision-making, at the citizen's scale but also in government agencies, such biases can have huge effects on societal change, either by creating a new and insidious pathway for societal uniformization or by allowing disguised extremist views to gain traction on the people.<|reference_end|> | arxiv | @article{kronlund-drouault2024propaganda,
title={Propaganda is all you need},
author={Paul Kronlund-Drouault},
journal={arXiv preprint arXiv:2410.01810},
year={2024},
archivePrefix={arXiv},
eprint={2410.01810},
primaryClass={cs.CY cs.AI}
} | kronlund-drouault2024propaganda |
arxiv-664674 | 2410.01811 | Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English | <|reference_start|>Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English: Although LLMs have been extremely effective in a large number of complex tasks, their understanding and functionality for regional languages and cultures are not well studied. In this paper, we explore the ability of various LLMs to comprehend the cultural aspects of two regional languages: Malayalam (state of Kerala, India) and Yoruba (West Africa). Using Hofstede's six cultural dimensions: Power Distance (PDI), Individualism (IDV), Motivation towards Achievement and Success (MAS), Uncertainty Avoidance (UAV), Long Term Orientation (LTO), and Indulgence (IVR), we quantify the cultural awareness of LLM-based responses. We demonstrate that although LLMs show a high cultural similarity for English, they fail to capture the cultural nuances across these 6 metrics for Malayalam and Yoruba. We also highlight the need for large-scale regional language LLM training with culturally enriched datasets. This will have huge implications for enhancing the user experience of chat-based LLMs and also improving the validity of large-scale LLM agent-based market research.<|reference_end|> | arxiv | @article{dawson2024evaluating,
title={Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English},
author={Fiifi Dawson, Zainab Mosunmola, Sahil Pocker, Raj Abhijit Dandekar,
Rajat Dandekar, Sreedath Panat},
journal={arXiv preprint arXiv:2410.01811},
year={2024},
archivePrefix={arXiv},
eprint={2410.01811},
primaryClass={cs.CY cs.AI cs.CL}
} | dawson2024evaluating |
arxiv-664675 | 2410.01812 | From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice | <|reference_start|>From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice: Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms, significantly impacting various sectors including healthcare. This comprehensive review explores the progression of LLMs to Multimodal Large Language Models (MLLMs) and their growing influence in medical practice. We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research. The review highlights the unique capabilities of MLLMs in integrating diverse data types, such as text, images, and audio, to provide more comprehensive insights into patient health. We also address the challenges facing MLLM implementation, including data limitations, technical hurdles, and ethical considerations. By identifying key research gaps, this paper aims to guide future investigations in areas such as dataset development, modality alignment methods, and the establishment of ethical guidelines. As MLLMs continue to shape the future of healthcare, understanding their potential and limitations is crucial for their responsible and effective integration into medical practice.<|reference_end|> | arxiv | @article{niu2024from,
title={From Text to Multimodality: Exploring the Evolution and Impact of Large
Language Models in Medical Practice},
author={Qian Niu, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Lawrence KQ Yan,
Yichao Zhang, Caitlyn Heqi Yin, Cheng Fei, Junyu Liu, Benji Peng},
journal={arXiv preprint arXiv:2410.01812},
year={2024},
archivePrefix={arXiv},
eprint={2410.01812},
primaryClass={cs.CY cs.AI cs.CL}
} | niu2024from |
arxiv-664676 | 2410.01813 | Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare | <|reference_start|>Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare: The disparity in healthcare personnel expertise and medical resources across different regions of the world is a pressing social issue. Artificial intelligence technology offers new opportunities to alleviate this issue. Segment Anything Model (SAM), which excels in intelligent image segmentation, has demonstrated exceptional performance in medical monitoring and assisted diagnosis. Unfortunately, the huge computational and storage overhead of SAM poses significant challenges for deployment on resource-limited edge devices. Quantization is an effective solution for model compression; however, traditional methods rely heavily on original data for calibration, which raises widespread concerns about medical data privacy and security. In this paper, we propose a data-free quantization framework for SAM, called DFQ-SAM, which learns and calibrates quantization parameters without any original data, thus effectively preserving data privacy during model compression. Specifically, we propose pseudo-positive label evolution for segmentation, combined with patch similarity, to fully leverage the semantic and distribution priors in pre-trained models, which facilitates high-quality data synthesis as a substitute for real data. Furthermore, we introduce scale reparameterization to ensure the accuracy of low-bit quantization. We perform extensive segmentation experiments on various datasets, and DFQ-SAM consistently provides significant performance on low-bit quantization. DFQ-SAM eliminates the need for data transfer in cloud-edge collaboration, thereby protecting sensitive data from potential attacks. It enables secure, fast, and personalized healthcare services at the edge, which enhances system efficiency and optimizes resource allocation, and thus facilitating the pervasive application of artificial intelligence in worldwide healthcare.<|reference_end|> | arxiv | @article{li2024privacy-preserving,
title={Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in
Healthcare},
author={Zhikai Li, Jing Zhang, and Qingyi Gu},
journal={arXiv preprint arXiv:2410.01813},
year={2024},
archivePrefix={arXiv},
eprint={2410.01813},
primaryClass={cs.CV cs.AI}
} | li2024privacy-preserving |
arxiv-664677 | 2410.01814 | A Graph Theoretic Approach to Analyze the Developing Metaverse | <|reference_start|>A Graph Theoretic Approach to Analyze the Developing Metaverse: Despite staggering growth over the past couple of decades, the concept of the metaverse is still in its early stages. Eventually, it is expected to become a common medium connecting every individual. Considering the complexity of this plausible scenario at hand, there's a need to define an advanced metaverse -- a metaverse in which, at every point in space and time, two distinct paradigms exist: that of the user in the physical world and that of its real-time digital replica in the virtual one, that can engage seamlessly with each other. The developing metaverse can be thus defined as the transitional period from the current state to, possibly, the advanced metaverse. This paper seeks to model, from a graphical standpoint, some of the structures in the current metaverse and ones that might be key to the developing and advanced metaverses under one umbrella, unlike existing approaches that treat different aspects of the metaverse in isolation. This integration allows for the accurate representation of cross-domain interactions, leading to optimized resource allocation, enhanced user engagement, and improved content distribution. This work demonstrates the usefulness of such an approach in capturing these correlations, providing a powerful tool for the analysis and future development of the metaverse.<|reference_end|> | arxiv | @article{dash2024a,
title={A Graph Theoretic Approach to Analyze the Developing Metaverse},
author={Anirudh Dash},
journal={arXiv preprint arXiv:2410.01814},
year={2024},
archivePrefix={arXiv},
eprint={2410.01814},
primaryClass={cs.CY}
} | dash2024a |
arxiv-664678 | 2410.01815 | AI in Food Marketing from Personalized Recommendations to Predictive Analytics: Comparing Traditional Advertising Techniques with AI-Driven Strategies | <|reference_start|>AI in Food Marketing from Personalized Recommendations to Predictive Analytics: Comparing Traditional Advertising Techniques with AI-Driven Strategies: Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.<|reference_end|> | arxiv | @article{khamoushi2024ai,
title={AI in Food Marketing from Personalized Recommendations to Predictive
Analytics: Comparing Traditional Advertising Techniques with AI-Driven
Strategies},
author={Elham Khamoushi},
journal={arXiv preprint arXiv:2410.01815},
year={2024},
archivePrefix={arXiv},
eprint={2410.01815},
primaryClass={cs.CY cs.AI}
} | khamoushi2024ai |
arxiv-664679 | 2410.01816 | Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects | <|reference_start|>Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects: Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, UI/layout design, graph-based methods, and interactive scene generation. Evaluation metrics such as Frechet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation.<|reference_end|> | arxiv | @article{fime2024automatic,
title={Automatic Scene Generation: State-of-the-Art Techniques, Models,
Datasets, Challenges, and Future Prospects},
author={Awal Ahmed Fime, Saifuddin Mahmud, Arpita Das, Md. Sunzidul Islam and
Hong-Hoon Kim},
journal={arXiv preprint arXiv:2410.01816},
year={2024},
archivePrefix={arXiv},
eprint={2410.01816},
primaryClass={cs.CV cs.AI}
} | fime2024automatic |
arxiv-664680 | 2410.01817 | From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis | <|reference_start|>From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis: This paper examines the governance of multimodal large language models (MM-LLMs) through individual and collective deliberation, focusing on analyses of politically sensitive videos. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings show that while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity of the situation, and emotional neutrality. Additionally, we explored the impact of different governance mechanisms: quadratic vs. weighted ranking voting and equal vs. 20-80 power distributions on users decision-making on how AI should behave. Specifically, quadratic voting enhanced perceptions of liberal democracy and political equality, and participants who were more optimistic about AI perceived the voting process to have a higher level of participatory democracy. Our results suggest the potential of applying DAO mechanisms to help democratize AI governance.<|reference_end|> | arxiv | @article{sharma2024from,
title={From Experts to the Public: Governing Multimodal Language Models in
Politically Sensitive Video Analysis},
author={Tanusree Sharma, Yujin Potter, Zachary Kilhoffer, Yun Huang, Dawn
Song, Yang Wang},
journal={arXiv preprint arXiv:2410.01817},
year={2024},
archivePrefix={arXiv},
eprint={2410.01817},
primaryClass={cs.CV cs.AI cs.CY}
} | sharma2024from |
arxiv-664681 | 2410.01818 | Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector | <|reference_start|>Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector: This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes increasingly central to banking operations, its energy-intensive processes contribute significantly to carbon emissions, posing environmental, regulatory, and reputational risks. Regulatory frameworks such as the EU AI Act, Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the Prudential Regulation Authority's SS1/23 are driving banks to incorporate environmental considerations into their AI model governance. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework and the Agentic RAG framework, offer more efficient and dynamic AI models, reducing their carbon footprint without compromising performance. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint within their RMFs, including adopting energy-efficient models, utilising green cloud computing, and implementing lifecycle management.<|reference_end|> | arxiv | @article{tkachenko2024integrating,
title={Integrating AI's Carbon Footprint into Risk Management Frameworks:
Strategies and Tools for Sustainable Compliance in Banking Sector},
author={Nataliya Tkachenko},
journal={arXiv preprint arXiv:2410.01818},
year={2024},
archivePrefix={arXiv},
eprint={2410.01818},
primaryClass={cs.CY cs.AI}
} | tkachenko2024integrating |
arxiv-664682 | 2410.01819 | Strategic AI Governance: Insights from Leading Nations | <|reference_start|>Strategic AI Governance: Insights from Leading Nations: Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.<|reference_end|> | arxiv | @article{tjondronegoro2024strategic,
title={Strategic AI Governance: Insights from Leading Nations},
author={Dian W. Tjondronegoro},
journal={arXiv preprint arXiv:2410.01819},
year={2024},
archivePrefix={arXiv},
eprint={2410.01819},
primaryClass={cs.CY cs.AI}
} | tjondronegoro2024strategic |
arxiv-664683 | 2410.01820 | PixelBytes: Catching Unified Representation for Multimodal Generation | <|reference_start|>PixelBytes: Catching Unified Representation for Multimodal Generation: This report introduces PixelBytes, a novel approach for unified multimodal representation learning. Inspired by existing sequence models such as Image Transformers, PixelCNN, and Mamba-Bytes, our method aims to capture diverse inputs in a cohesive representation, exploring the integration of different data types, particularly text, audio, and pixelated images (sprites). We conducted experiments on a specialized PixelBytes Pok{\'e}mon dataset. Initially, we investigated various model architectures, including Recurrent Neural Networks (RNNs), State Space Models (SSMs), and Attention-based models, focusing on bidirectional processing and our convolutional PxBy embedding technique. Subsequently, we evaluated models based on data reduction strategies and the effectiveness of autoregressive learning. We specifically examined Long Short-Term Memory (LSTM) networks in both predictive and autoregressive modes for our main experiments. Our findings suggest that autoregressive models outperform predictive models in this context. By adopting a flexible approach to multimodal modeling, PixelBytes contributes to the ongoing development of foundation models capable of understanding and generating multimodal data. The complete PixelBytes project, including code, models, and datasets, is available online.<|reference_end|> | arxiv | @article{furfaro2024pixelbytes:,
title={PixelBytes: Catching Unified Representation for Multimodal Generation},
author={Fabien Furfaro},
journal={arXiv preprint arXiv:2410.01820},
year={2024},
archivePrefix={arXiv},
eprint={2410.01820},
primaryClass={cs.CV cs.AI}
} | furfaro2024pixelbytes: |
arxiv-664684 | 2410.01821 | NFDIcore 20: A BFO-Compliant Ontology for Multi-Domain Research Infrastructures | <|reference_start|>NFDIcore 20: A BFO-Compliant Ontology for Multi-Domain Research Infrastructures: This paper presents NFDIcore 2.0, an ontology compliant with the Basic Formal Ontology (BFO) designed to represent the diverse research communities of the National Research Data Infrastructure (NFDI) in Germany. NFDIcore ensures the interoperability across various research disciplines, thereby facilitating cross-domain research. Each domain's individual requirements are addressed through specific ontology modules. This paper discusses lessons learned during the ontology development and mapping process, supported by practical validation through use cases in diverse research domains. The originality of NFDIcore lies in its adherence to BFO, the use of SWRL rules for efficient knowledge discovery, and its modular, extensible design tailored to meet the needs of heterogeneous research domains.<|reference_end|> | arxiv | @article{bruns2024nfdicore,
title={NFDIcore 2.0: A BFO-Compliant Ontology for Multi-Domain Research
Infrastructures},
author={Oleksandra Bruns (1,2), Tabea Tietz (1,2), Joerg Waitelonis (1,2),
Etienne Posthumus (2) and Harald Sack (1,2)},
journal={arXiv preprint arXiv:2410.01821},
year={2024},
archivePrefix={arXiv},
eprint={2410.01821},
primaryClass={cs.DL cs.AI}
} | bruns2024nfdicore |
arxiv-664685 | 2410.01822 | The Importance of Causality in Decision Making: A Perspective on Recommender Systems | <|reference_start|>The Importance of Causality in Decision Making: A Perspective on Recommender Systems: Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS literature has repeatedly highlighted that, in real-world scenarios, recommendation algorithms suffer many types of biases since assumptions ensuring unbiasedness are likely not met. In this discussion paper, we formulate the RS problem in terms of causality, using potential outcomes and structural causal models, by giving formal definitions of the causal quantities to be estimated and a general causal graph to serve as a reference to foster future research and development.<|reference_end|> | arxiv | @article{cavenaghi2024the,
title={The Importance of Causality in Decision Making: A Perspective on
Recommender Systems},
author={Emanuele Cavenaghi, Alessio Zanga, Fabio Stella, Markus Zanker},
journal={ACM Trans. Recomm. Syst. 2, 2, Article 17 (June 2024), 34 pages},
year={2024},
doi={10.1145/3629169},
archivePrefix={arXiv},
eprint={2410.01822},
primaryClass={cs.IR cs.AI}
} | cavenaghi2024the |
arxiv-664686 | 2410.01824 | AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers | <|reference_start|>AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers: Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to express unanticipated thoughts in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to be interviewed by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. Based on our experiences, we present specific recommendations for effective implementation.<|reference_end|> | arxiv | @article{wuttke2024ai,
title={AI Conversational Interviewing: Transforming Surveys with LLMs as
Adaptive Interviewers},
author={Alexander Wuttke, Matthias A{ss}enmacher, Christopher Klamm, Max M.
Lang, Quirin W"urschinger, Frauke Kreuter},
journal={arXiv preprint arXiv:2410.01824},
year={2024},
archivePrefix={arXiv},
eprint={2410.01824},
primaryClass={cs.HC cs.AI cs.CL}
} | wuttke2024ai |
arxiv-664687 | 2410.01825 | Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing | <|reference_start|>Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing: WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning, the scarcity of labelled data, and the incomprehensible channel state information (CSI) pose significant challenges. These issues affect deep learning models' performance and generalization across different environments. Consequently, self-supervised learning (SSL) is emerging as a promising strategy to extract meaningful data representations with minimal reliance on labelled samples. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC), which effectively learns from unlabelled data and adapts to diverse environments. CAPC integrates elements of Contrastive Predictive Coding (CPC) and the augmentation-based SSL method, Barlow Twins, promoting temporal and contextual consistency in data representations. This hybrid approach captures essential temporal information in CSI, crucial for tasks like human activity recognition (HAR), and ensures robustness against data distortions. Additionally, we propose a unique augmentation, employing both uplink and downlink CSI to isolate free space propagation effects and minimize the impact of electronic distortions of the transceiver. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities. Specifically, CAPC requires fewer labelled samples while significantly outperforming supervised learning and surpassing SSL baselines. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8 percent over other SSL baselines and 24.7 percent over supervised learning, emphasizing its exceptional cross-domain adaptability.<|reference_end|> | arxiv | @article{barahimi2024context-aware,
title={Context-Aware Predictive Coding: A Representation Learning Framework for
WiFi Sensing},
author={B. Barahimi, H. Tabassum, M. Omer, and O. Waqar},
journal={arXiv preprint arXiv:2410.01825},
year={2024},
archivePrefix={arXiv},
eprint={2410.01825},
primaryClass={eess.SP cs.LG}
} | barahimi2024context-aware |
arxiv-664688 | 2410.01827 | Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers | <|reference_start|>Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers: This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, and MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101, only an accuracy of 54% could be achieved, along with only 33% on efficientNetB0. A MobileNetV2-trained model was successfully deployed on a TKinter GUI application to make predictions using image or real-time video capture.<|reference_end|> | arxiv | @article{paneru2024analysis,
title={Analysis of Convolutional Neural Network-based Image Classifications: A
Multi-Featured Application for Rice Leaf Disease Prediction and
Recommendations for Farmers},
author={Biplov Paneru, Bishwash Paneru, Krishna Bikram Shah},
journal={arXiv preprint arXiv:2410.01827},
year={2024},
archivePrefix={arXiv},
eprint={2410.01827},
primaryClass={cs.CV cs.AI cs.LG eess.IV}
} | paneru2024analysis |
arxiv-664689 | 2410.01828 | Image-to-Image Translation Based on Deep Generative Modeling for Radiotherapy Synthetic Dataset Creation | <|reference_start|>Image-to-Image Translation Based on Deep Generative Modeling for Radiotherapy Synthetic Dataset Creation: Objective: Radiotherapy uses precise doses of radiation to treat cancer, requiring accurate verification, e.g. using the Electronic Portal Imaging Device (EPID), to guide treatment. To develop an effective artificial intelligence (AI) model for error detection and treatment verification, a large and well-annotated dataset of EPID images is needed, however, acquiring such high quality real data is difficult. While synthetic EPID data could be a viable alternative, it is critical to ensure that this data is as realistic as possible to effectively train an accurate and reliable AI model. The measurement uncertainty that is not modeled in EPID predictions but is present on real measured EPID images can hinder downstream tasks such as error detection and classification. Our research aims to improve synthetic EPID data through image-to-image (I2I) translation based on deep generative modeling. Approach: A dataset of 989 predicted EPID images and corresponding measured EPID images was used. We evaluate both paired and unpaired generative modeling approaches for this task. For the former, we introduce a novel modification of Variational Autoencoder (VAE) to I2I, a method that, to the best of our knowledge, has not been previously explored for this task. For the latter, we use UNsupervised Image-to-Image Translation Networks (UNIT). Results: Our results show that both models achieved some degree of I2I translation, with our novel modification of the VAE model outperforming the UNIT model in improving key metrics (mean absolute error: 4.1 cGy vs 6.4 cGy; relative dose difference in-field: 2.5% vs 5.5%; absolute dose difference in-field: 5.3 cGy vs 10.8 cGy). Significance: This enhanced synthetic data is expected to improve downstream tasks such as training neural networks for automated error detection and error classification in radiotherapy.<|reference_end|> | arxiv | @article{glazunova2024image-to-image,
title={Image-to-Image Translation Based on Deep Generative Modeling for
Radiotherapy Synthetic Dataset Creation},
author={Olga Glazunova, Cecile J.A. Wolfs and Frank Verhaegen},
journal={arXiv preprint arXiv:2410.01828},
year={2024},
archivePrefix={arXiv},
eprint={2410.01828},
primaryClass={eess.IV cs.CV}
} | glazunova2024image-to-image |
arxiv-664690 | 2410.01829 | Secure Backscatter Communications Through RIS: Modeling and Performance | <|reference_start|>Secure Backscatter Communications Through RIS: Modeling and Performance: Backscatter communication (BC) has emerged as a pivotal wireless communication paradigm owing to its low-power and cost-effective characteristics. However, BC faces various challenges from its low signal detection rate to its security vulnerabilities. Recently, reconfigurable intelligent surfaces (RIS) have surfaced as a transformative technology addressing power and communication performance issues in BC. However, the potential of RIS in addressing the security challenges of BC remains uncharted. This paper investigates the secrecy performance of RIS-aided BC, where all channels are distributed according to the Fisher-Snedecor $\mathcal{F}$ distribution. Specifically, we consider a RIS with $N$ reflecting elements to help a backscatter device (BD) establish a smart environment and enhance the secrecy performance in BC. Due to the nature of BC systems, our analysis considers two possible scenarios (i) in the absence of direct links and (ii) in the presence of direct links. In both cases, we first derive compact analytical expressions of the probability density function (PDF) and cumulative distribution function (CDF) for the received signal-to-noise ratio (SNR) at both a legitimate receiver and an eavesdropper. Then, to analyze the secrecy performance, we further derive analytical expressions of the average secrecy capacity (ASC) and secrecy outage probability (SOP) for both mentioned scenarios. In addition, regarding the importance of system behavior in a high SNR regime, we provide an asymptotic analysis of the SOP and ASC. Eventually, the Monte-Carlo simulation is used to validate the analytical results, revealing that utilizing RIS can greatly improve the secrecy performance of the BC system relative to traditional BC setups that do not incorporate RIS.<|reference_end|> | arxiv | @article{kaveh2024secure,
title={Secure Backscatter Communications Through RIS: Modeling and Performance},
author={Masoud Kaveh, Farshad Rostami Ghadi, Zhao Li, Zheng Yan, and Riku
Jantti},
journal={arXiv preprint arXiv:2410.01829},
year={2024},
archivePrefix={arXiv},
eprint={2410.01829},
primaryClass={cs.IT eess.SP math.IT}
} | kaveh2024secure |
arxiv-664691 | 2410.01833 | Ethical software requirements from user reviews: A systematic literature review | <|reference_start|>Ethical software requirements from user reviews: A systematic literature review: Context: The growing focus on ethics within SE, primarily due to the significant reliance of individuals' lives on software and the consequential social and ethical considerations that impact both people and society has brought focus on ethical software requirements identification and elicitation. User safety, privacy, and security concerns are of prime importance while developing software due to the widespread use of software across healthcare, education, and business domains. Thus, identifying and elicitating ethical software requirements from app user reviews, focusing on various aspects such as privacy, security, accountability, accessibility, transparency, fairness, safety, and social solidarity, are essential for developing trustworthy software solutions. Objective: This SLR aims to identify and analyze existing ethical requirements identification and elicitation techniques in the context of the formulated research questions. Method: We conducted an SLR based on Kitchenham et al's methodology. We identified and selected 47 primary articles for this study based on a predefined search protocol. Result: Ethical requirements gathering has recently driven drastic interest in the research community due to the rise of ML and AI-based approaches in decision-making within software applications. This SLR provides an overview of ethical requirements identification techniques and the implications of extracting and addressing them. This study also reports the data sources used for analyzing user reviews. Conclusion: This SLR provides an understanding of the ethical software requirements and underscores the importance of user reviews in developing trustworthy software. The findings can also help inform future research and guide software engineers or researchers in addressing software ethical requirements.<|reference_end|> | arxiv | @article{sorathiya2024ethical,
title={Ethical software requirements from user reviews: A systematic literature
review},
author={Aakash Sorathiya and Gouri Ginde},
journal={arXiv preprint arXiv:2410.01833},
year={2024},
archivePrefix={arXiv},
eprint={2410.01833},
primaryClass={cs.SE cs.HC}
} | sorathiya2024ethical |
arxiv-664692 | 2410.01835 | EgoAvatar: Egocentric View-Driven and Photorealistic Full-body Avatars | <|reference_start|>EgoAvatar: Egocentric View-Driven and Photorealistic Full-body Avatars: Immersive VR telepresence ideally means being able to interact and communicate with digital avatars that are indistinguishable from and precisely reflect the behaviour of their real counterparts. The core technical challenge is two fold: Creating a digital double that faithfully reflects the real human and tracking the real human solely from egocentric sensing devices that are lightweight and have a low energy consumption, e.g. a single RGB camera. Up to date, no unified solution to this problem exists as recent works solely focus on egocentric motion capture, only model the head, or build avatars from multi-view captures. In this work, we, for the first time in literature, propose a person-specific egocentric telepresence approach, which jointly models the photoreal digital avatar while also driving it from a single egocentric video. We first present a character model that is animatible, i.e. can be solely driven by skeletal motion, while being capable of modeling geometry and appearance. Then, we introduce a personalized egocentric motion capture component, which recovers full-body motion from an egocentric video. Finally, we apply the recovered pose to our character model and perform a test-time mesh refinement such that the geometry faithfully projects onto the egocentric view. To validate our design choices, we propose a new and challenging benchmark, which provides paired egocentric and dense multi-view videos of real humans performing various motions. Our experiments demonstrate a clear step towards egocentric and photoreal telepresence as our method outperforms baselines as well as competing methods. For more details, code, and data, we refer to our project page.<|reference_end|> | arxiv | @article{chen2024egoavatar:,
title={EgoAvatar: Egocentric View-Driven and Photorealistic Full-body Avatars},
author={Jianchun Chen, Jian Wang, Yinda Zhang, Rohit Pandey, Thabo Beeler,
Marc Habermann, Christian Theobalt},
journal={arXiv preprint arXiv:2410.01835},
year={2024},
archivePrefix={arXiv},
eprint={2410.01835},
primaryClass={cs.CV cs.GR}
} | chen2024egoavatar: |
arxiv-664693 | 2410.01836 | Temporal Graph Memory Networks For Knowledge Tracing | <|reference_start|>Temporal Graph Memory Networks For Knowledge Tracing: Tracing a student's knowledge growth given the past exercise answering is a vital objective in automatic tutoring systems to customize the learning experience. Yet, achieving this objective is a non-trivial task as it involves modeling the knowledge state across multiple knowledge components (KCs) while considering their temporal and relational dynamics during the learning process. Knowledge tracing methods have tackled this task by either modeling KCs' temporal dynamics using recurrent models or relational dynamics across KCs and questions using graph models. Albeit, there is a lack of methods that could learn joint embedding between relational and temporal dynamics of the task. Moreover, many methods that count for the impact of a student's forgetting behavior during the learning process use hand-crafted features, limiting their generalization on different scenarios. In this paper, we propose a novel method that jointly models the relational and temporal dynamics of the knowledge state using a deep temporal graph memory network. In addition, we propose a generic technique for representing a student's forgetting behavior using temporal decay constraints on the graph memory module. We demonstrate the effectiveness of our proposed method using multiple knowledge tracing benchmarks while comparing it to state-of-the-art methods.<|reference_end|> | arxiv | @article{gad2024temporal,
title={Temporal Graph Memory Networks For Knowledge Tracing},
author={Seif Gad, Sherif Abdelfattah, Ghodai Abdelrahman},
journal={arXiv preprint arXiv:2410.01836},
year={2024},
archivePrefix={arXiv},
eprint={2410.01836},
primaryClass={cs.CY cs.AI cs.LG}
} | gad2024temporal |
arxiv-664694 | 2410.01837 | Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale Codebases | <|reference_start|>Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale Codebases: Modern software systems like the Linux kernel are among the world's largest and most intricate codebases, continually evolving with new features and increasing complexity. Understanding these systems poses significant challenges due to their scale and the unstructured nature of development artifacts such as commits and mailing list discussions. We introduce Code-Survey, the first LLM-driven methodology designed to systematically explore and analyze large-scale codebases. The central principle behind Code-Survey is to treat LLMs as human participants, acknowledging that software development is also a social activity and thereby enabling the application of established social science techniques. By carefully designing surveys, Code-Survey transforms unstructured data, such as commits, emails, into organized, structured, and analyzable datasets. This enables quantitative analysis of complex software evolution and uncovers valuable insights related to design, implementation, maintenance, reliability, and security. To demonstrate the effectiveness of Code-Survey, we apply it to the Linux kernel's eBPF subsystem. We construct the Linux-bpf dataset, comprising over 670 features and 16,000 commits from the Linux community. Our quantitative analysis uncovers important insights into the evolution of eBPF, such as development patterns, feature interdependencies, and areas requiring attention for reliability and security. The insights have been initially validated by eBPF experts. Furthermore, Code-Survey can be directly applied to other subsystems within Linux and to other large-scale software projects. By providing a versatile tool for systematic analysis, Code-Survey facilitates a deeper understanding of complex software systems, enabling improvements across a variety of domains and supporting a wide range of empirical studies. The code and dataset is open-sourced.<|reference_end|> | arxiv | @article{zheng2024code-survey:,
title={Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale
Codebases},
author={Yusheng Zheng, Yiwei Yang, Haoqin Tu, Yuxi Huang},
journal={arXiv preprint arXiv:2410.01837},
year={2024},
archivePrefix={arXiv},
eprint={2410.01837},
primaryClass={cs.SE}
} | zheng2024code-survey: |
arxiv-664695 | 2410.01838 | What we should learn from pandemic publishing | <|reference_start|>What we should learn from pandemic publishing: Authors of COVID-19 papers produced during the pandemic were overwhelmingly not subject matter experts. Such a massive inflow of scholars from different expertise areas is both an asset and a potential problem. Domain-informed scientific collaboration is the key to preparing for future crises.<|reference_end|> | arxiv | @article{sikdar2024what,
title={What we should learn from pandemic publishing},
author={Satyaki Sikdar, Sara Venturini, Marie-Laure Charpignon, Sagar Kumar,
Francesco Rinaldi, Francesco Tudisco, Santo Fortunato, Maimuna S. Majumder},
journal={Nat. Hum. Behav. 8 (2024) 1631-1634},
year={2024},
doi={10.1038/s41562-024-01969-7},
archivePrefix={arXiv},
eprint={2410.01838},
primaryClass={physics.soc-ph cs.DL q-bio.OT}
} | sikdar2024what |
arxiv-664696 | 2410.01840 | Target Pose Guided Whole-body Grasping Motion Generation for Digital Humans | <|reference_start|>Target Pose Guided Whole-body Grasping Motion Generation for Digital Humans: Grasping manipulation is a fundamental mode for human interaction with daily life objects. The synthesis of grasping motion is also greatly demanded in many applications such as animation and robotics. In objects grasping research field, most works focus on generating the last static grasping pose with a parallel gripper or dexterous hand. Grasping motion generation for the full arm especially for the full humanlike intelligent agent is still under-explored. In this work, we propose a grasping motion generation framework for digital human which is an anthropomorphic intelligent agent with high degrees of freedom in virtual world. Given an object known initial pose in 3D space, we first generate a target pose for whole-body digital human based on off-the-shelf target grasping pose generation methods. With an initial pose and this generated target pose, a transformer-based neural network is used to generate the whole grasping trajectory, which connects initial pose and target pose smoothly and naturally. Additionally, two post optimization components are designed to mitigates foot-skating issue and hand-object interpenetration separately. Experiments are conducted on GRAB dataset to demonstrate effectiveness of this proposed method for whole-body grasping motion generation with randomly placed unknown objects.<|reference_end|> | arxiv | @article{shao2024target,
title={Target Pose Guided Whole-body Grasping Motion Generation for Digital
Humans},
author={Quanquan Shao, Yi Fang},
journal={arXiv preprint arXiv:2410.01840},
year={2024},
archivePrefix={arXiv},
eprint={2410.01840},
primaryClass={cs.RO cs.AI cs.GR}
} | shao2024target |
arxiv-664697 | 2410.01841 | A GEN AI Framework for Medical Note Generation | <|reference_start|>A GEN AI Framework for Medical Note Generation: The increasing administrative burden of medical documentation, particularly through Electronic Health Records (EHR), significantly reduces the time available for direct patient care and contributes to physician burnout. To address this issue, we propose MediNotes, an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio, generating structured and contextually accurate medical notes. The framework also incorporates advanced techniques like Quantized Low-Rank Adaptation (QLoRA) and Parameter-Efficient Fine-Tuning (PEFT) for efficient model fine-tuning in resource-constrained environments. Additionally, MediNotes offers a query-based retrieval system, allowing healthcare providers and patients to access relevant medical information quickly and accurately. Evaluations using the ACI-BENCH dataset demonstrate that MediNotes significantly improves the accuracy, efficiency, and usability of automated medical documentation, offering a robust solution to reduce the administrative burden on healthcare professionals while improving the quality of clinical workflows.<|reference_end|> | arxiv | @article{leong2024a,
title={A GEN AI Framework for Medical Note Generation},
author={Hui Yi Leong, Yi Fan Gao, Shuai Ji, Bora Kalaycioglu, Uktu Pamuksuz},
journal={arXiv preprint arXiv:2410.01841},
year={2024},
archivePrefix={arXiv},
eprint={2410.01841},
primaryClass={eess.AS cs.AI cs.CL cs.IR cs.SD}
} | leong2024a |
arxiv-664698 | 2410.01842 | Public interest in science or bots? Selective amplification of scientific articles on Twitter | <|reference_start|>Public interest in science or bots? Selective amplification of scientific articles on Twitter: With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world, this topic warrants critical study and attention. We used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article. Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to "Health and Human Science" are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.<|reference_end|> | arxiv | @article{rahman2024public,
title={Public interest in science or bots? Selective amplification of
scientific articles on Twitter},
author={Ashiqur Rahman, Ehsan Mohammadi, Hamed Alhoori},
journal={arXiv preprint arXiv:2410.01842},
year={2024},
doi={10.1108/AJIM-01-2024-0050},
archivePrefix={arXiv},
eprint={2410.01842},
primaryClass={cs.SI cs.CY cs.DL cs.LG}
} | rahman2024public |
arxiv-664699 | 2410.01844 | Urban Anomalies: A Simulated Human Mobility Dataset with Injected Anomalies | <|reference_start|>Urban Anomalies: A Simulated Human Mobility Dataset with Injected Anomalies: Human mobility anomaly detection based on location is essential in areas such as public health, safety, welfare, and urban planning. Developing models and approaches for location-based anomaly detection requires a comprehensive dataset. However, privacy concerns and the absence of ground truth hinder the availability of publicly available datasets. With this paper, we provide extensive simulated human mobility datasets featuring various anomaly types created using an existing Urban Patterns of Life Simulation. To create these datasets, we inject changes in the logic of individual agents to change their behavior. Specifically, we create four of anomalous agent behavior by (1) changing the agents' appetite (causing agents to have meals more frequently), (2) changing their group of interest (causing agents to interact with different agents from another group). (3) changing their social place selection (causing agents to visit different recreational places) and (4) changing their work schedule (causing agents to skip work), For each type of anomaly, we use three degrees of behavioral change to tune the difficulty of detecting the anomalous agents. To select agents to inject anomalous behavior into, we employ three methods: (1) Random selection using a centralized manipulation mechanism, (2) Spread based selection using an infectious disease model, and (3) through exposure of agents to a specific location. All datasets are split into normal and anomalous phases. The normal phase, which can be used for training models of normalcy, exhibits no anomalous behavior. The anomalous phase, which can be used for testing for anomalous detection algorithm, includes ground truth labels that indicate, for each five-minute simulation step, which agents are anomalous at that time. Datasets are generated using the maps (roads and buildings) for Atlanta and Berlin, having 1k agents in each simulation.<|reference_end|> | arxiv | @article{amiri2024urban,
title={Urban Anomalies: A Simulated Human Mobility Dataset with Injected
Anomalies},
author={Hossein Amiri, Ruochen Kong, Andreas Zufle},
journal={arXiv preprint arXiv:2410.01844},
year={2024},
archivePrefix={arXiv},
eprint={2410.01844},
primaryClass={cs.SI}
} | amiri2024urban |
arxiv-664700 | 2410.01847 | Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation | <|reference_start|>Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation: Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantification in predictions. Among these models, the CATSI (Context-Aware Time Series Imputation) stands out for its effectiveness by incorporating a context vector into the imputation process, capturing the global dependencies of each patient. In this paper, we propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework which leverages uncertainty quantification offered by variational inference. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG). Variational Inference assumes the shape of the posterior distribution and through minimization of the Kullback-Leibler(KL) divergence it finds variational densities that are closest to the true posterior distribution. Thus , we integrate the variational Bayesian deep learning layers into the CATSI model. Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model. Specifically, an instance of Bayes-CATSI outperforms CATSI by 9.57 %. We provide an open-source code implementation for applying Bayes-CATSI to other medical data imputation problems.<|reference_end|> | arxiv | @article{kulkarni2024bayes-catsi:,
title={Bayes-CATSI: A variational Bayesian deep learning framework for medical
time series data imputation},
author={Omkar Kulkarni and Rohitash Chandra},
journal={arXiv preprint arXiv:2410.01847},
year={2024},
archivePrefix={arXiv},
eprint={2410.01847},
primaryClass={cs.LG cs.AI cs.NE stat.ML}
} | kulkarni2024bayes-catsi: |
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