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arxiv-665401 | 2410.03064 | Geometric Collaborative Filtering with Convergence | <|reference_start|>Geometric Collaborative Filtering with Convergence: Latent variable collaborative filtering methods have been a standard approach to modelling user-click interactions due to their simplicity and effectiveness. However, there is limited work on analyzing the mathematical properties of these methods in particular on preventing the overfitting towards the identity, and such methods typically utilize loss functions that overlook the geometry between items. In this work, we introduce a notion of generalization gap in collaborative filtering and analyze this with respect to latent collaborative filtering models. We present a geometric upper bound that gives rise to loss functions, and a way to meaningfully utilize the geometry of item-metadata to improve recommendations. We show how these losses can be minimized and gives the recipe to a new latent collaborative filtering algorithm, which we refer to as GeoCF, due to the geometric nature of our results. We then show experimentally that our proposed GeoCF algorithm can outperform other all existing methods on the Movielens20M and Netflix datasets, as well as two large-scale internal datasets. In summary, our work proposes a theoretically sound method which paves a way to better understand generalization of collaborative filtering at large.<|reference_end|> | arxiv | @article{husain2024geometric,
title={Geometric Collaborative Filtering with Convergence},
author={Hisham Husain, Julien Monteil},
journal={arXiv preprint arXiv:2410.03064},
year={2024},
archivePrefix={arXiv},
eprint={2410.03064},
primaryClass={cs.IR cs.LG cs.SY eess.SY}
} | husain2024geometric |
arxiv-665402 | 2410.03065 | Compute Or Load KV Cache? Why Not Both? | <|reference_start|>Compute Or Load KV Cache? Why Not Both?: Recent advancements in Large Language Models (LLMs) have significantly increased context window sizes, enabling sophisticated applications but also introducing substantial computational overheads, particularly computing key-value (KV) cache in the prefill stage. Prefix caching has emerged to save GPU power in this scenario, which saves KV cache at disks and reuse them across multiple queries. However, traditional prefix caching mechanisms often suffer from substantial latency because the speed of loading KV cache from disks to GPU memory is bottlenecked by the throughput of I/O devices. To optimize the latency of long-context prefill, we propose Cake, a novel KV cache loader, which employs a bidirectional parallelized KV cache generation strategy. Upon receiving a prefill task, Cake simultaneously and dynamically loads saved KV cache from prefix cache locations and computes KV cache on local GPUs, maximizing the utilization of available computation and I/O bandwidth resources. Additionally, Cake automatically adapts to diverse system statuses without manual parameter. tuning. In experiments on various prompt datasets, GPUs, and I/O devices, Cake offers up to 68.1% Time To First Token (TTFT) reduction compare with compute-only method and 94.6% TTFT reduction compare with I/O-only method.<|reference_end|> | arxiv | @article{jin2024compute,
title={Compute Or Load KV Cache? Why Not Both?},
author={Shuowei Jin, Xueshen Liu, Qingzhao Zhang, Z. Morley Mao},
journal={arXiv preprint arXiv:2410.03065},
year={2024},
archivePrefix={arXiv},
eprint={2410.03065},
primaryClass={cs.LG}
} | jin2024compute |
arxiv-665403 | 2410.03066 | Hybrid Classical/RL Local Planner for Ground Robot Navigation | <|reference_start|>Hybrid Classical/RL Local Planner for Ground Robot Navigation: Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how the optimization criteria and constraints are defined, some planners may be better than others in specific situations. We consider two conceptually different planners. The first planner explores the velocity space in real-time and has superior path-tracking and motion smoothness performance. The second planner was trained using reinforcement learning methods to produce the best velocity based on its training $"$experience$"$. It is better at avoiding dynamic obstacles but at the expense of motion smoothness. We propose a simple yet effective meta-reasoning approach that takes advantage of both approaches by switching between planners based on the surroundings. We demonstrate the superiority of our hybrid planner, both qualitatively and quantitatively, over the individual planners on a live robot in different scenarios, achieving an improvement of 26% in the navigation time.<|reference_end|> | arxiv | @article{sharma2024hybrid,
title={Hybrid Classical/RL Local Planner for Ground Robot Navigation},
author={Vishnu D. Sharma, Jeongran Lee, Matthew Andrews, Ilija Hadv{z}i'c},
journal={arXiv preprint arXiv:2410.03066},
year={2024},
archivePrefix={arXiv},
eprint={2410.03066},
primaryClass={cs.RO}
} | sharma2024hybrid |
arxiv-665404 | 2410.03067 | FedCert: Federated Accuracy Certification | <|reference_start|>FedCert: Federated Accuracy Certification: Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data perturbations on clients remains a significant challenge. Previous studies have assessed the effectiveness of models in centralized training based on certified accuracy, which guarantees that a certain percentage of the model's predictions will remain correct even if the input data is perturbed. However, the challenge of extending these evaluations to FL remains unresolved due to the unknown client's local data. To tackle this challenge, this study proposed a method named FedCert to take the first step toward evaluating the robustness of FL systems. The proposed method is designed to approximate the certified accuracy of a global model based on the certified accuracy and class distribution of each client. Additionally, considering the Non-Independent and Identically Distributed (Non-IID) nature of data in real-world scenarios, we introduce the client grouping algorithm to ensure reliable certified accuracy during the aggregation step of the approximation algorithm. Through theoretical analysis, we demonstrate the effectiveness of FedCert in assessing the robustness and reliability of FL systems. Moreover, experimental results on the CIFAR-10 and CIFAR-100 datasets under various scenarios show that FedCert consistently reduces the estimation error compared to baseline methods. This study offers a solution for evaluating the robustness of FL systems and lays the groundwork for future research to enhance the dependability of decentralized learning. The source code is available at https://github.com/thanhhff/FedCert/.<|reference_end|> | arxiv | @article{nguyen2024fedcert:,
title={FedCert: Federated Accuracy Certification},
author={Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong
Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen},
journal={arXiv preprint arXiv:2410.03067},
year={2024},
archivePrefix={arXiv},
eprint={2410.03067},
primaryClass={cs.LG cs.CR cs.DC}
} | nguyen2024fedcert: |
arxiv-665405 | 2410.03069 | Interactive GDPR-Compliant Privacy Policy Generation for Software Applications | <|reference_start|>Interactive GDPR-Compliant Privacy Policy Generation for Software Applications: Software applications are designed to assist users in conducting a wide range of tasks or interactions. They have become prevalent and play an integral part in people's lives in this digital era. To use those software applications, users are sometimes requested to provide their personal information. As privacy has become a significant concern and many data protection regulations exist worldwide, software applications must provide users with a privacy policy detailing how their personal information is collected and processed. We propose an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications. To support this, we first built a library of privacy clauses based on existing privacy policy analysis. We then developed an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy for a given software application. We evaluated privacy policies generated by our approach in terms of readability, completeness and coverage and compared them to privacy policies generated by three existing privacy policy generators and a Generative AI-based tool. Our evaluation results show that the privacy policy generated by our approach is the most complete and comprehensive.<|reference_end|> | arxiv | @article{sangaroonsilp2024interactive,
title={Interactive GDPR-Compliant Privacy Policy Generation for Software
Applications},
author={Pattaraporn Sangaroonsilp, Hoa Khanh Dam, Omar Haggag, John Grundy},
journal={arXiv preprint arXiv:2410.03069},
year={2024},
archivePrefix={arXiv},
eprint={2410.03069},
primaryClass={cs.SE}
} | sangaroonsilp2024interactive |
arxiv-665406 | 2410.03070 | FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization | <|reference_start|>FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization: Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challenge arises when dealing with missing modalities in clients' datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. While previous studies have addressed the issue of complete-modality missing, they fail to tackle partial-modality missing on account of severe heterogeneity among clients at an instance level, where the pattern of missing data can vary significantly from one sample to another. To tackle this challenge, this study proposes a novel framework named FedMAC, designed to address multi-modality missing under conditions of partial-modality missing in FL. Additionally, to avoid trivial aggregation of multi-modal features, we introduce contrastive-based regularization to impose additional constraints on the latent representation space. The experimental results demonstrate the effectiveness of FedMAC across various client configurations with statistical heterogeneity, outperforming baseline methods by up to 26% in severe missing scenarios, highlighting its potential as a solution for the challenge of partially missing modalities in federated systems.<|reference_end|> | arxiv | @article{nguyen2024fedmac:,
title={FedMAC: Tackling Partial-Modality Missing in Federated Learning with
Cross-Modal Aggregation and Contrastive Regularization},
author={Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia
Hoang, Phi Le Nguyen, Thanh Trung Huynh},
journal={arXiv preprint arXiv:2410.03070},
year={2024},
archivePrefix={arXiv},
eprint={2410.03070},
primaryClass={cs.LG cs.MM}
} | nguyen2024fedmac: |
arxiv-665407 | 2410.03071 | Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs | <|reference_start|>Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs: Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, existing approaches, whether probabilistic or neural, frequently struggle to extract meaningful patterns from such data, resulting in incoherent topics. To address this challenge, we propose a novel approach that leverages large language models (LLMs) to extend short texts into more detailed sequences before applying topic modeling. To further improve the efficiency and solve the problem of semantic inconsistency from LLM-generated texts, we propose to use prefix tuning to train a smaller language model coupled with a variational autoencoder for short-text topic modeling. Our method significantly improves short-text topic modeling performance, as demonstrated by extensive experiments on real-world datasets with extreme data sparsity, outperforming current state-of-the-art topic models.<|reference_end|> | arxiv | @article{akash2024enhancing,
title={Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion
and Prefix-Tuned VAEs},
author={Pritom Saha Akash and Kevin Chen-Chuan Chang},
journal={arXiv preprint arXiv:2410.03071},
year={2024},
archivePrefix={arXiv},
eprint={2410.03071},
primaryClass={cs.CL cs.IR}
} | akash2024enhancing |
arxiv-665408 | 2410.03072 | Multi-Robot Motion Planning with Diffusion Models | <|reference_start|>Multi-Robot Motion Planning with Diffusion Models: Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distributions while using only single-robot data. Our algorithm, Multi-robot Multi-model planning Diffusion (MMD), does so by combining learned diffusion models with classical search-based techniques -- generating data-driven motions under collision constraints. Scaling further, we show how to compose multiple diffusion models to plan in large environments where a single diffusion model fails to generalize well. We demonstrate the effectiveness of our approach in planning for dozens of robots in a variety of simulated scenarios motivated by logistics environments. View video demonstrations in our supplementary material, and our code at: https://github.com/yoraish/mmd.<|reference_end|> | arxiv | @article{shaoul2024multi-robot,
title={Multi-Robot Motion Planning with Diffusion Models},
author={Yorai Shaoul, Itamar Mishani, Shivam Vats, Jiaoyang Li and Maxim
Likhachev},
journal={arXiv preprint arXiv:2410.03072},
year={2024},
archivePrefix={arXiv},
eprint={2410.03072},
primaryClass={cs.RO cs.AI cs.MA}
} | shaoul2024multi-robot |
arxiv-665409 | 2410.03073 | LEGO: QEC Decoding System Architecture for Dynamic Circuits | <|reference_start|>LEGO: QEC Decoding System Architecture for Dynamic Circuits: Quantum error correction (QEC) is a critical component of FTQC; the QEC decoder is an important part of Classical Computing for Quantum or C4Q. Recent years have seen fast development in real-time QEC decoders. Existing efforts to build real-time decoders have yet to achieve a critical milestone: decoding dynamic logical circuits with error-corrected readout and feed forward. Achieving this requires significant engineering effort to adapt and reconfigure the decoders during runtime, depending on the branching of the logical circuit. We present a QEC decoder architecture called LEGO, with the ambitious goal of supporting dynamic logical operations. LEGO employs a novel abstraction called the decoding block to describe the decoding problem of a dynamic logical circuit. Moreover, decoding blocks can be combined with three other ideas to improve the efficiency, accuracy and latency of the decoder. First, they provide data and task parallelisms when combined with fusion-based decoding. Second, they can exploit the pipeline parallelism inside multi-stage decoders. Finally, they serve as basic units of work for computational resource management. Using decoding blocks, LEGO can be easily reconfigured to support all QEC settings and to easily accommodate innovations in three interdependent fields: code, logical operations and qubit hardware. In contrast, existing decoders are highly specialized to a specific QEC setting, which leads to redundant research and engineering efforts, slows down innovation, and further fragments the nascent quantum computing industry.<|reference_end|> | arxiv | @article{wu2024lego:,
title={LEGO: QEC Decoding System Architecture for Dynamic Circuits},
author={Yue Wu, Namitha Liyanage, Lin Zhong},
journal={arXiv preprint arXiv:2410.03073},
year={2024},
archivePrefix={arXiv},
eprint={2410.03073},
primaryClass={quant-ph cs.SY eess.SY}
} | wu2024lego: |
arxiv-665410 | 2410.03074 | MetaOOD: Automatic Selection of OOD Detection Models | <|reference_start|>MetaOOD: Automatic Selection of OOD Detection Models: How can we automatically select an out-of-distribution (OOD) detection model for various underlying tasks? This is crucial for maintaining the reliability of open-world applications by identifying data distribution shifts, particularly in critical domains such as online transactions, autonomous driving, and real-time patient diagnosis. Despite the availability of numerous OOD detection methods, the challenge of selecting an optimal model for diverse tasks remains largely underexplored, especially in scenarios lacking ground truth labels. In this work, we introduce MetaOOD, the first zero-shot, unsupervised framework that utilizes meta-learning to automatically select an OOD detection model. As a meta-learning approach, MetaOOD leverages historical performance data of existing methods across various benchmark OOD datasets, enabling the effective selection of a suitable model for new datasets without the need for labeled data at the test time. To quantify task similarities more accurately, we introduce language model-based embeddings that capture the distinctive OOD characteristics of both datasets and detection models. Through extensive experimentation with 24 unique test dataset pairs to choose from among 11 OOD detection models, we demonstrate that MetaOOD significantly outperforms existing methods and only brings marginal time overhead. Our results, validated by Wilcoxon statistical tests, show that MetaOOD surpasses a diverse group of 11 baselines, including established OOD detectors and advanced unsupervised selection methods.<|reference_end|> | arxiv | @article{qin2024metaood:,
title={MetaOOD: Automatic Selection of OOD Detection Models},
author={Yuehan Qin, Yichi Zhang, Yi Nian, Xueying Ding, Yue Zhao},
journal={arXiv preprint arXiv:2410.03074},
year={2024},
archivePrefix={arXiv},
eprint={2410.03074},
primaryClass={cs.LG}
} | qin2024metaood: |
arxiv-665411 | 2410.03075 | Multilingual Topic Classification in X: Dataset and Analysis | <|reference_start|>Multilingual Topic Classification in X: Dataset and Analysis: In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models.<|reference_end|> | arxiv | @article{antypas2024multilingual,
title={Multilingual Topic Classification in X: Dataset and Analysis},
author={Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Jose
Camacho-Collados},
journal={arXiv preprint arXiv:2410.03075},
year={2024},
archivePrefix={arXiv},
eprint={2410.03075},
primaryClass={cs.CL}
} | antypas2024multilingual |
arxiv-665412 | 2410.03076 | Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation | <|reference_start|>Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation: First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, significantly improving sample efficiency in robot control compared to standard model-free reinforcement learning. However, FoPG algorithms can exhibit poor learning dynamics in contact-rich tasks like locomotion. Previous approaches address this issue by alleviating contact dynamics via algorithmic or simulation innovations. In contrast, we propose guiding the policy search by learning a residual over a simple baseline policy. For quadruped locomotion, we find that the role of residual policy learning in FoPG-based training (FoPG RPL) is primarily to improve asymptotic rewards, compared to improving sample efficiency for model-free RL. Additionally, we provide insights on applying FoPG's to pixel-based local navigation, training a point-mass robot to convergence within seconds. Finally, we showcase the versatility of FoPG RPL by using it to train locomotion and perceptive navigation end-to-end on a quadruped in minutes.<|reference_end|> | arxiv | @article{luo2024residual,
title={Residual Policy Learning for Perceptive Quadruped Control Using
Differentiable Simulation},
author={Jing Yuan Luo, Yunlong Song, Victor Klemm, Fan Shi, Davide Scaramuzza,
Marco Hutter},
journal={arXiv preprint arXiv:2410.03076},
year={2024},
archivePrefix={arXiv},
eprint={2410.03076},
primaryClass={cs.RO}
} | luo2024residual |
arxiv-665413 | 2410.03077 | CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions | <|reference_start|>CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions: With instruction tuning, Large Language Models (LLMs) can enhance their ability to adhere to commands. Diverging from most works focusing on data mixing, our study concentrates on enhancing the model's capabilities from the perspective of data sampling during training. Drawing inspiration from the human learning process, where it is generally easier to master solutions to similar topics through focused practice on a single type of topic, we introduce a novel instruction tuning strategy termed CommonIT: Commonality-aware Instruction Tuning. Specifically, we cluster instruction datasets into distinct groups with three proposed metrics (Task, Embedding and Length). We ensure each training mini-batch, or "partition", consists solely of data from a single group, which brings about both data randomness across mini-batches and intra-batch data similarity. Rigorous testing on LLaMa models demonstrates CommonIT's effectiveness in enhancing the instruction-following capabilities of LLMs through IT datasets (FLAN, CoT, and Alpaca) and models (LLaMa2-7B, Qwen2-7B, LLaMa 13B, and BLOOM 7B). CommonIT consistently boosts an average improvement of 2.1\% on the general domain (i.e., the average score of Knowledge, Reasoning, Multilinguality and Coding) with the Length metric, and 5.2\% on the special domain (i.e., GSM, Openfunctions and Code) with the Task metric, and 3.8\% on the specific tasks (i.e., MMLU) with the Embedding metric. Code is available at \url{https://github.com/raojay7/CommonIT}.<|reference_end|> | arxiv | @article{rao2024commonit:,
title={CommonIT: Commonality-Aware Instruction Tuning for Large Language Models
via Data Partitions},
author={Jun Rao, Xuebo Liu, Lian Lian, Shengjun Cheng, Yunjie Liao, Min Zhang},
journal={arXiv preprint arXiv:2410.03077},
year={2024},
archivePrefix={arXiv},
eprint={2410.03077},
primaryClass={cs.CL cs.AI}
} | rao2024commonit: |
arxiv-665414 | 2410.03078 | Partial-to-Full Registration based on Gradient-SDF for Computer-Assisted Orthopedic Surgery | <|reference_start|>Partial-to-Full Registration based on Gradient-SDF for Computer-Assisted Orthopedic Surgery: In computer-assisted orthopedic surgery (CAOS), accurate pre-operative to intra-operative bone registration is an essential and critical requirement for providing navigational guidance. This registration process is challenging since the intra-operative 3D points are sparse, only partially overlapped with the pre-operative model, and disturbed by noise and outliers. The commonly used method in current state-of-the-art orthopedic robotic system is bony landmarks based registration, but it is very time-consuming for the surgeons. To address these issues, we propose a novel partial-to-full registration framework based on gradient-SDF for CAOS. The simulation experiments using bone models from publicly available datasets and the phantom experiments performed under both optical tracking and electromagnetic tracking systems demonstrate that the proposed method can provide more accurate results than standard benchmarks and be robust to 90% outliers. Importantly, our method achieves convergence in less than 1 second in real scenarios and mean target registration error values as low as 2.198 mm for the entire bone model. Finally, it only requires random acquisition of points for registration by moving a surgical probe over the bone surface without correspondence with any specific bony landmarks, thus showing significant potential clinical value.<|reference_end|> | arxiv | @article{li2024partial-to-full,
title={Partial-to-Full Registration based on Gradient-SDF for Computer-Assisted
Orthopedic Surgery},
author={Tiancheng Li, Peter Walker, Danial Hammoud, Liang Zhao, Shoudong Huang},
journal={arXiv preprint arXiv:2410.03078},
year={2024},
archivePrefix={arXiv},
eprint={2410.03078},
primaryClass={cs.RO}
} | li2024partial-to-full |
arxiv-665415 | 2410.03080 | Generative Edge Detection with Stable Diffusion | <|reference_start|>Generative Edge Detection with Stable Diffusion: Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. Recently, generative edge detection methods, especially diffusion model based solutions, are initialized in the edge detection task. Despite great potential, the retraining of task-specific designed modules and multi-step denoising inference limits their broader applications. Upon closer investigation, we speculate that part of the reason is the under-exploration of the rich discriminative information encoded in extensively pre-trained large models (\eg, stable diffusion models). Thus motivated, we propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. Our model can be trained and inferred efficiently without specific network design due to the rich high-level and low-level prior knowledge empowered by the pre-trained stable diffusion. Specifically, we propose to finetune the denoising U-Net and predict latent edge maps directly, by taking the latent image feature maps as input. Additionally, due to the subjectivity and ambiguity of the edges, we also incorporate the granularity of the edges into the denoising U-Net model as one of the conditions to achieve controllable and diverse predictions. Furthermore, we devise a granularity regularization to ensure the relative granularity relationship of the multiple predictions. We conduct extensive experiments on multiple datasets and achieve competitive performance (\eg, 0.870 and 0.880 in terms of ODS and OIS on the BSDS test dataset).<|reference_end|> | arxiv | @article{zhou2024generative,
title={Generative Edge Detection with Stable Diffusion},
author={Caixia Zhou and Yaping Huang and Mochu Xiang and Jiahui Ren and Haibin
Ling and Jing Zhang},
journal={arXiv preprint arXiv:2410.03080},
year={2024},
archivePrefix={arXiv},
eprint={2410.03080},
primaryClass={cs.CV}
} | zhou2024generative |
arxiv-665416 | 2410.03083 | Scaling Parameter-Constrained Language Models with Quality Data | <|reference_start|>Scaling Parameter-Constrained Language Models with Quality Data: Scaling laws in language modeling traditionally quantify training loss as a function of dataset size and model parameters, providing compute-optimal estimates but often neglecting the impact of data quality on model generalization. In this paper, we extend the conventional understanding of scaling law by offering a microscopic view of data quality within the original formulation -- effective training tokens -- which we posit to be a critical determinant of performance for parameter-constrained language models. Specifically, we formulate the proposed term of effective training tokens to be a combination of two readily-computed indicators of text: (i) text diversity and (ii) syntheticity as measured by a teacher model. We pretrained over $200$ models of 25M to 1.5B parameters on a diverse set of sampled, synthetic data, and estimated the constants that relate text quality, model size, training tokens, and eight reasoning task accuracy scores. We demonstrated the estimated constants yield +0.83 Pearson correlation with true accuracies, and analyzed it in scenarios involving widely-used data techniques such as data sampling and synthesis which aim to improve data quality.<|reference_end|> | arxiv | @article{chang2024scaling,
title={Scaling Parameter-Constrained Language Models with Quality Data},
author={Ernie Chang, Matteo Paltenghi, Yang Li, Pin-Jie Lin, Changsheng Zhao,
Patrick Huber, Zechun Liu, Rastislav Rabatin, Yangyang Shi, Vikas Chandra},
journal={arXiv preprint arXiv:2410.03083},
year={2024},
archivePrefix={arXiv},
eprint={2410.03083},
primaryClass={cs.CL cs.AI}
} | chang2024scaling |
arxiv-665417 | 2410.03085 | Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach | <|reference_start|>Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach: Constrained optimization problems arise in various engineering system operations such as inventory management and electric power grids. However, the requirement to repeatedly solve such optimization problems with uncertain parameters poses a significant computational challenge. This work introduces a learning scheme using Bayesian Neural Networks (BNNs) to solve constrained optimization problems under limited labeled data and restricted model training times. We propose a semi-supervised BNN for this practical but complex regime, wherein training commences in a sandwiched fashion, alternating between a supervised learning step (using labeled data) for minimizing cost, and an unsupervised learning step (using unlabeled data) for enforcing constraint feasibility. Both supervised and unsupervised steps use a Bayesian approach, where Stochastic Variational Inference is employed for approximate Bayesian inference. We show that the proposed semi-supervised learning method outperforms conventional BNN and deep neural network (DNN) architectures on important non-convex constrained optimization problems from energy network operations, achieving up to a tenfold reduction in expected maximum equality gap and halving the optimality and inequality (feasibility) gaps, without requiring any correction or projection step. By leveraging the BNN's ability to provide posterior samples at minimal computational cost, we demonstrate that a Selection via Posterior (SvP) scheme can further reduce equality gaps by more than 10%. We also provide tight and practically meaningful probabilistic confidence bounds that can be constructed using a low number of labeled testing data and readily adapted to other applications.<|reference_end|> | arxiv | @article{pareek2024optimization,
title={Optimization Proxies using Limited Labeled Data and Training Time -- A
Semi-Supervised Bayesian Neural Network Approach},
author={Parikshit Pareek, Kaarthik Sundar, Deepjyoti Deka and Sidhant Misra},
journal={arXiv preprint arXiv:2410.03085},
year={2024},
archivePrefix={arXiv},
eprint={2410.03085},
primaryClass={cs.LG cs.SY eess.SY}
} | pareek2024optimization |
arxiv-665418 | 2410.03086 | Design and Evaluation of a Compliant Quasi Direct Drive End-effector for Safe Robotic Ultrasound Imaging | <|reference_start|>Design and Evaluation of a Compliant Quasi Direct Drive End-effector for Safe Robotic Ultrasound Imaging: Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant human-robot interaction (HRI) during probe contact poses a significant challenge. Most existing systems either have high mechanical stiffness or are compliant but lack sufficient force and precision. This paper presents a novel single-degree-of-freedom end-effector for safe and accurate robotic ultrasound imaging, using a quasi-direct drive actuator to achieve both passive mechanical compliance and precise active force regulation, even during motion. The end-effector demonstrates an effective force control bandwidth of 100 Hz and can apply forces ranging from 2.5N to 15N. To validate the end-effector's performance, we developed a novel ex vivo actuating platform, enabling compliance testing of the end-effector on simulated abdominal breathing and sudden patient movements. Experiments demonstrate that the end-effector can maintain consistent probe contact during simulated respiratory motion at 2.5N, 5N, 10N, and 15N, with an average force tracking RMS error of 0.83N compared to 4.70N on a UR3e robot arm using conventional force control. This system represents the first compliant ultrasound end-effector tested on a tissue platform simulating dynamic movement. The proposed solution provides a novel approach for designing and evaluating compliant robotic ultrasound systems, advancing the path for more compliant and patient-friendly robotic ultrasound systems in clinical settings.<|reference_end|> | arxiv | @article{chen2024design,
title={Design and Evaluation of a Compliant Quasi Direct Drive End-effector for
Safe Robotic Ultrasound Imaging},
author={Danyi Chen, Ravi Prakash, Zacharias Chen, Sarah Dias, Vincent Wang,
Leila Bridgeman, Siobhan Oca},
journal={arXiv preprint arXiv:2410.03086},
year={2024},
archivePrefix={arXiv},
eprint={2410.03086},
primaryClass={cs.RO}
} | chen2024design |
arxiv-665419 | 2410.03090 | UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference | <|reference_start|>UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference: Deploying large language models (LLMs) is challenging due to their high memory and computational demands, especially during long-context inference. While key-value (KV) caching accelerates inference by reusing previously computed keys and values, it also introduces significant memory overhead. Existing KV cache compression methods such as eviction and merging typically compress the KV cache after it is generated and overlook the eviction of hidden states, failing to improve the speed of the prefilling stage. Additionally, applying a uniform compression rate across different attention heads can harm crucial retrieval heads in needle-in-a-haystack tasks due to excessive compression. In this paper, we propose UNComp, an uncertainty-aware compression scheme that leverages matrix entropy to estimate model uncertainty across layers and heads at the token sequence level. By grouping layers and heads based on their uncertainty, UNComp adaptively compresses both the hidden states and the KV cache. Our method achieves a 1.6x speedup in the prefilling stage and reduces the KV cache to 4.74% of its original size, resulting in a 6.4x increase in throughput and a 1.4x speedup in inference with only a 1.41% performance loss. Remarkably, in needle-in-a-haystack tasks, UNComp outperforms the full-size KV cache even when compressed to 9.38% of its original size. Our approach offers an efficient, training-free Grouped-Query Attention paradigm that can be seamlessly integrated into existing KV cache schemes.<|reference_end|> | arxiv | @article{xiong2024uncomp:,
title={UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large
Language Model Inference},
author={Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan,
Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Lingpeng Kong, Ngai Wong},
journal={arXiv preprint arXiv:2410.03090},
year={2024},
archivePrefix={arXiv},
eprint={2410.03090},
primaryClass={cs.CL cs.LG}
} | xiong2024uncomp: |
arxiv-665420 | 2410.03092 | Strategic Insights from Simulation Gaming of AI Race Dynamics | <|reference_start|>Strategic Insights from Simulation Gaming of AI Race Dynamics: We present insights from "Intelligence Rising", a scenario exploration exercise about possible AI futures. Drawing on the experiences of facilitators who have overseen 43 games over a four-year period, we illuminate recurring patterns, strategies, and decision-making processes observed during gameplay. Our analysis reveals key strategic considerations about AI development trajectories in this simulated environment, including: the destabilising effects of AI races, the crucial role of international cooperation in mitigating catastrophic risks, the challenges of aligning corporate and national interests, and the potential for rapid, transformative change in AI capabilities. We highlight places where we believe the game has been effective in exposing participants to the complexities and uncertainties inherent in AI governance. Key recurring gameplay themes include the emergence of international agreements, challenges to the robustness of such agreements, the critical role of cybersecurity in AI development, and the potential for unexpected crises to dramatically alter AI trajectories. By documenting these insights, we aim to provide valuable foresight for policymakers, industry leaders, and researchers navigating the complex landscape of AI development and governance.<|reference_end|> | arxiv | @article{gruetzemacher2024strategic,
title={Strategic Insights from Simulation Gaming of AI Race Dynamics},
author={Ross Gruetzemacher, Shahar Avin, James Fox, Alexander K Saeri},
journal={arXiv preprint arXiv:2410.03092},
year={2024},
archivePrefix={arXiv},
eprint={2410.03092},
primaryClass={cs.CY cs.AI}
} | gruetzemacher2024strategic |
arxiv-665421 | 2410.03093 | Data Playwright: Authoring Data Videos with Annotated Narration | <|reference_start|>Data Playwright: Authoring Data Videos with Annotated Narration: Creating data videos that effectively narrate stories with animated visuals requires substantial effort and expertise. A promising research trend is leveraging the easy-to-use natural language (NL) interaction to automatically synthesize data video components from narrative content like text narrations, or NL commands that specify user-required designs. Nevertheless, previous research has overlooked the integration of narrative content and specific design authoring commands, leading to generated results that lack customization or fail to seamlessly fit into the narrative context. To address these issues, we introduce a novel paradigm for creating data videos, which seamlessly integrates users' authoring and narrative intents in a unified format called annotated narration, allowing users to incorporate NL commands for design authoring as inline annotations within the narration text. Informed by a formative study on users' preference for annotated narration, we develop a prototype system named Data Playwright that embodies this paradigm for effective creation of data videos. Within Data Playwright, users can write annotated narration based on uploaded visualizations. The system's interpreter automatically understands users' inputs and synthesizes data videos with narration-animation interplay, powered by large language models. Finally, users can preview and fine-tune the video. A user study demonstrated that participants can effectively create data videos with Data Playwright by effortlessly articulating their desired outcomes through annotated narration.<|reference_end|> | arxiv | @article{shen2024data,
title={Data Playwright: Authoring Data Videos with Annotated Narration},
author={Leixian Shen, Haotian Li, Yun Wang, Tianqi Luo, Yuyu Luo, Huamin Qu},
journal={arXiv preprint arXiv:2410.03093},
year={2024},
archivePrefix={arXiv},
eprint={2410.03093},
primaryClass={cs.HC}
} | shen2024data |
arxiv-665422 | 2410.03094 | Entanglement-induced provable and robust quantum learning advantages | <|reference_start|>Entanglement-induced provable and robust quantum learning advantages: Quantum computing holds the unparalleled potentials to enhance, speed up or innovate machine learning. However, an unambiguous demonstration of quantum learning advantage has not been achieved so far. Here, we rigorously establish a noise-robust, unconditional quantum learning advantage in terms of expressivity, inference speed, and training efficiency, compared to commonly-used classical machine learning models. Our proof is information-theoretic and pinpoints the origin of this advantage: quantum entanglement can be used to reduce the communication required by non-local machine learning tasks. In particular, we design a fully classical task that can be solved with unit accuracy by a quantum model with a constant number of variational parameters using entanglement resources, whereas commonly-used classical models must scale at least linearly with the size of the task to achieve a larger-than-exponentially-small accuracy. We further show that the quantum model can be trained with constant time and a number of samples inversely proportional to the problem size. We prove that this advantage is robust against constant depolarization noise. We show through numerical simulations that even though the classical models can have improved performance as their sizes are increased, they would suffer from overfitting. The constant-versus-linear separation, bolstered by the overfitting problem, makes it possible to demonstrate the quantum advantage with relatively small system sizes. We demonstrate, through both numerical simulations and trapped-ion experiments on IonQ Aria, the desired quantum-classical learning separation. Our results provide a valuable guide for demonstrating quantum learning advantages in practical applications with current noisy intermediate-scale quantum devices.<|reference_end|> | arxiv | @article{zhao2024entanglement-induced,
title={Entanglement-induced provable and robust quantum learning advantages},
author={Haimeng Zhao and Dong-Ling Deng},
journal={arXiv preprint arXiv:2410.03094},
year={2024},
archivePrefix={arXiv},
eprint={2410.03094},
primaryClass={quant-ph cs.CC cs.LG}
} | zhao2024entanglement-induced |
arxiv-665423 | 2410.03097 | Combing Text-based and Drag-based Editing for Precise and Flexible Image Editing | <|reference_start|>Combing Text-based and Drag-based Editing for Precise and Flexible Image Editing: Precise and flexible image editing remains a fundamental challenge in computer vision. Based on the modified areas, most editing methods can be divided into two main types: global editing and local editing. In this paper, we choose the two most common editing approaches (ie text-based editing and drag-based editing) and analyze their drawbacks. Specifically, text-based methods often fail to describe the desired modifications precisely, while drag-based methods suffer from ambiguity. To address these issues, we proposed \textbf{CLIPDrag}, a novel image editing method that is the first to combine text and drag signals for precise and ambiguity-free manipulations on diffusion models. To fully leverage these two signals, we treat text signals as global guidance and drag points as local information. Then we introduce a novel global-local motion supervision method to integrate text signals into existing drag-based methods by adapting a pre-trained language-vision model like CLIP. Furthermore, we also address the problem of slow convergence in CLIPDrag by presenting a fast point-tracking method that enforces drag points moving toward correct directions. Extensive experiments demonstrate that CLIPDrag outperforms existing single drag-based methods or text-based methods.<|reference_end|> | arxiv | @article{jiang2024combing,
title={Combing Text-based and Drag-based Editing for Precise and Flexible Image
Editing},
author={Ziqi Jiang, Zhen Wang and Long Chen},
journal={arXiv preprint arXiv:2410.03097},
year={2024},
archivePrefix={arXiv},
eprint={2410.03097},
primaryClass={cs.CV cs.AI}
} | jiang2024combing |
arxiv-665424 | 2410.03098 | Forest Proximities for Time Series | <|reference_start|>Forest Proximities for Time Series: RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an accurate and efficient time series classification model. We use the forest proximities in connection with Multi-Dimensional Scaling to obtain vector embeddings of univariate time series, comparing the embeddings to those obtained using various time series distance measures. We also use the forest proximities alongside Local Outlier Factors to investigate the connection between misclassified points and outliers, comparing with nearest neighbor classifiers which use time series distance measures. We show that the forest proximities may exhibit a stronger connection between misclassified points and outliers than nearest neighbor classifiers.<|reference_end|> | arxiv | @article{shaw2024forest,
title={Forest Proximities for Time Series},
author={Ben Shaw, Jake Rhodes, Soukaina Filali Boubrahimi, Kevin R. Moon},
journal={arXiv preprint arXiv:2410.03098},
year={2024},
archivePrefix={arXiv},
eprint={2410.03098},
primaryClass={stat.ML cs.LG}
} | shaw2024forest |
arxiv-665425 | 2410.03099 | CoCoHD: Congress Committee Hearing Dataset | <|reference_start|>CoCoHD: Congress Committee Hearing Dataset: U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the Congress Committee Hearing Dataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee's stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.<|reference_end|> | arxiv | @article{hiray2024cocohd:,
title={CoCoHD: Congress Committee Hearing Dataset},
author={Arnav Hiray, Yunsong Liu, Mingxiao Song, Agam Shah, Sudheer Chava},
journal={arXiv preprint arXiv:2410.03099},
year={2024},
archivePrefix={arXiv},
eprint={2410.03099},
primaryClass={cs.CL}
} | hiray2024cocohd: |
arxiv-665426 | 2410.03102 | Examining Racial Stereotypes in YouTube Autocomplete Suggestions | <|reference_start|>Examining Racial Stereotypes in YouTube Autocomplete Suggestions: Autocomplete is a popular search feature that predicts queries based on user input and guides users to a set of potentially relevant suggestions. In this study, we examine how YouTube autocompletes serve as an information source for users exploring information about race. We perform an algorithm output audit of autocomplete suggestions for input queries about four racial groups and examine the stereotypes they embody. Using critical discourse analysis, we identify five major sociocultural contexts in which racial biases manifest -- Appearance, Ability, Culture, Social Equity, and Manner. Our results show evidence of aggregated discrimination and interracial tensions in the autocompletes we collected and highlight their potential risks in othering racial minorities. We call for urgent innovations in content moderation policy design and enforcement to address these biases in search outputs.<|reference_end|> | arxiv | @article{ha2024examining,
title={Examining Racial Stereotypes in YouTube Autocomplete Suggestions},
author={Eunbin Ha, Haein Kong, Shagun Jhaver},
journal={arXiv preprint arXiv:2410.03102},
year={2024},
archivePrefix={arXiv},
eprint={2410.03102},
primaryClass={cs.CY}
} | ha2024examining |
arxiv-665427 | 2410.03103 | Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning | <|reference_start|>Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning: Fill-in-the-Middle (FIM) has become integral to code language models, enabling generation of missing code given both left and right contexts. However, the current FIM training paradigm, which reorders original training sequences and then performs regular next-token prediction (NTP), often leads to models struggling to generate content that aligns smoothly with the surrounding context. Crucially, while existing works rely on rule-based post-processing to circumvent this weakness, such methods are not practically usable in open-domain code completion tasks as they depend on restrictive, dataset-specific assumptions (e.g., generating the same number of lines as in the ground truth). Moreover, model performance on FIM tasks deteriorates significantly without these unrealistic assumptions. We hypothesize that NTP alone is insufficient for models to learn effective planning conditioned on the distant right context, a critical factor for successful code infilling. To overcome this, we propose Horizon-Length Prediction (HLP), a novel training objective that teaches models to predict the number of remaining middle tokens (i.e., horizon length) at each step. HLP advances FIM with lookahead planning, enabling models to inherently learn infilling boundaries for arbitrary left and right contexts without relying on dataset-specific post-processing. Our evaluation across different models and sizes shows that HLP significantly improves FIM performance by up to 24% relatively on diverse benchmarks, across file-level and repository-level, and without resorting to unrealistic post-processing methods. Furthermore, the enhanced planning capability gained through HLP boosts model performance on code reasoning. Importantly, HLP only incurs negligible training overhead and no additional inference cost, ensuring its practicality for real-world scenarios.<|reference_end|> | arxiv | @article{ding2024horizon-length,
title={Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for
Code Generation with Lookahead Planning},
author={Yifeng Ding, Hantian Ding, Shiqi Wang, Qing Sun, Varun Kumar, Zijian
Wang},
journal={arXiv preprint arXiv:2410.03103},
year={2024},
archivePrefix={arXiv},
eprint={2410.03103},
primaryClass={cs.LG cs.CL cs.SE}
} | ding2024horizon-length |
arxiv-665428 | 2410.03104 | Calibration of NYURay for Ray Tracing using 28, 73, and 142 GHz Channel Measurements conducted in Indoor, Outdoor, and Factory Scenarios | <|reference_start|>Calibration of NYURay for Ray Tracing using 28, 73, and 142 GHz Channel Measurements conducted in Indoor, Outdoor, and Factory Scenarios: Site-specific wireless channel simulations via ray tracers can be used to effectively study wireless, decreasing the need for extensive site-specific radio propagation measurements. To ensure that ray tracer simulations faithfully reproduce wireless channels, calibration of simulation results against real-world measurements is required. In this study we introduce NYURay, a 3D ray tracer specifically tailored for mmWave and sub-THz frequencies. To reliably generate site-specific wireless channel parameters, NYURay is calibrated using radio propagation measurements conducted at 28, 73, and 142 GHz in diverse scenarios such as outdoor areas, indoor offices, and factories. Traditional ray tracing calibration assumes angle-dependent reflection, requiring slow iterative optimization techniques with no closed form solution. We propose a simpler and quicker novel calibration method that assumes angle-independent reflection. The effectiveness of the proposed calibration approach is demonstrated using NYURay. When comparing the directional multipath power predicted by NYURay to the actual measured power, the standard deviation in error was less than 3 dB in indoor office environments and less than 2 dB in outdoor and factory environments. The root mean square (RMS) delay spread and angular spread was underpredicted by NYURay due to incomplete environmental maps available for calibration, however an overall agreement between the measured and simulated values was observed. These results highlight the high level of accuracy NYURay provides in generating the site-specific real-world wireless channel, that could be used to generate synthetic data for machine learning.<|reference_end|> | arxiv | @article{kanhere2024calibration,
title={Calibration of NYURay for Ray Tracing using 28, 73, and 142 GHz Channel
Measurements conducted in Indoor, Outdoor, and Factory Scenarios},
author={O. Kanhere, H. Poddar, and T. S. Rappaport},
journal={arXiv preprint arXiv:2410.03104},
year={2024},
archivePrefix={arXiv},
eprint={2410.03104},
primaryClass={cs.IT math.IT}
} | kanhere2024calibration |
arxiv-665429 | 2410.03105 | Mamba in Vision: A Comprehensive Survey of Techniques and Applications | <|reference_start|>Mamba in Vision: A Comprehensive Survey of Techniques and Applications: Mamba is emerging as a novel approach to overcome the challenges faced by Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they often struggle to capture long-range dependencies without complex architectural modifications. In contrast, ViTs effectively model global relationships but suffer from high computational costs due to the quadratic complexity of their self-attention mechanisms. Mamba addresses these limitations by leveraging Selective Structured State Space Models to effectively capture long-range dependencies with linear computational complexity. This survey analyzes the unique contributions, computational benefits, and applications of Mamba models while also identifying challenges and potential future research directions. We provide a foundational resource for advancing the understanding and growth of Mamba models in computer vision. An overview of this work is available at https://github.com/maklachur/Mamba-in-Computer-Vision.<|reference_end|> | arxiv | @article{rahman2024mamba,
title={Mamba in Vision: A Comprehensive Survey of Techniques and Applications},
author={Md Maklachur Rahman, Abdullah Aman Tutul, Ankur Nath, Lamyanba
Laishram, Soon Ki Jung, Tracy Hammond},
journal={arXiv preprint arXiv:2410.03105},
year={2024},
archivePrefix={arXiv},
eprint={2410.03105},
primaryClass={cs.CV cs.AI cs.CL cs.LG}
} | rahman2024mamba |
arxiv-665430 | 2410.03106 | A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic Dynamic Games | <|reference_start|>A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic Dynamic Games: We present a policy iteration algorithm for the infinite-horizon N-player general-sum deterministic linear quadratic dynamic games and compare it to policy gradient methods. We demonstrate that the proposed policy iteration algorithm is distinct from the Gauss-Newton policy gradient method in the N-player game setting, in contrast to the single-player setting where under suitable choice of step size they are equivalent. We illustrate in numerical experiments that the convergence rate of the proposed policy iteration algorithm significantly surpasses that of the Gauss-Newton policy gradient method and other policy gradient variations. Furthermore, our numerical results indicate that, compared to policy gradient methods, the convergence performance of the proposed policy iteration algorithm is less sensitive to the initial policy and changes in the number of players.<|reference_end|> | arxiv | @article{guan2024a,
title={A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic
Dynamic Games},
author={Yuxiang Guan, Giulio Salizzoni, Maryam Kamgarpour, Tyler H. Summers},
journal={arXiv preprint arXiv:2410.03106},
year={2024},
archivePrefix={arXiv},
eprint={2410.03106},
primaryClass={math.OC cs.SY eess.SY}
} | guan2024a |
arxiv-665431 | 2410.03107 | MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators | <|reference_start|>MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators: Modeling the structure and events of the physical world constitutes a fundamental objective of neural networks. Among the diverse approaches, Graph Network Simulators (GNS) have emerged as the leading method for modeling physical phenomena, owing to their low computational cost and high accuracy. The datasets employed for training and evaluating physical simulation techniques are typically generated by researchers themselves, often resulting in limited data volume and quality. Consequently, this poses challenges in accurately assessing the performance of these methods. In response to this, we have constructed a high-quality physical simulation dataset encompassing 1D, 2D, and 3D scenes, along with more trajectories and time-steps compared to existing datasets. Furthermore, our work distinguishes itself by developing eight complete scenes, significantly enhancing the dataset's comprehensiveness. A key feature of our dataset is the inclusion of precise multi-body dynamics, facilitating a more realistic simulation of the physical world. Utilizing our high-quality dataset, we conducted a systematic evaluation of various existing GNS methods. Our dataset is accessible for download at https://github.com/Sherlocktein/MBDS, offering a valuable resource for researchers to enhance the training and evaluation of their methodologies.<|reference_end|> | arxiv | @article{yang2024mbds:,
title={MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks
Simulators},
author={Sheng Yang and Fengge Wu and Junsuo Zhao},
journal={arXiv preprint arXiv:2410.03107},
year={2024},
archivePrefix={arXiv},
eprint={2410.03107},
primaryClass={cs.CV cs.AI}
} | yang2024mbds: |
arxiv-665432 | 2410.03108 | A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems | <|reference_start|>A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems: This study introduces a training-free conditional diffusion model for learning unknown stochastic differential equations (SDEs) using data. The proposed approach addresses key challenges in computational efficiency and accuracy for modeling SDEs by utilizing a score-based diffusion model to approximate their stochastic flow map. Unlike the existing methods, this technique is based on an analytically derived closed-form exact score function, which can be efficiently estimated by Monte Carlo method using the trajectory data, and eliminates the need for neural network training to learn the score function. By generating labeled data through solving the corresponding reverse ordinary differential equation, the approach enables supervised learning of the flow map. Extensive numerical experiments across various SDE types, including linear, nonlinear, and multi-dimensional systems, demonstrate the versatility and effectiveness of the method. The learned models exhibit significant improvements in predicting both short-term and long-term behaviors of unknown stochastic systems, often surpassing baseline methods like GANs in estimating drift and diffusion coefficients.<|reference_end|> | arxiv | @article{liu2024a,
title={A Training-Free Conditional Diffusion Model for Learning Stochastic
Dynamical Systems},
author={Yanfang Liu, Yuan Chen, Dongbin Xiu, Guannan Zhang},
journal={arXiv preprint arXiv:2410.03108},
year={2024},
archivePrefix={arXiv},
eprint={2410.03108},
primaryClass={cs.LG math.DS}
} | liu2024a |
arxiv-665433 | 2410.03111 | LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy | <|reference_start|>LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy: The Key-Value (KV) cache is a crucial component in serving transformer-based autoregressive large language models (LLMs), enabling faster inference by storing previously computed KV vectors. However, its memory consumption scales linearly with sequence length and batch size, posing a significant bottleneck in LLM deployment. Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages, which requires extensive parameter tuning thus unsuitable for pre-trained LLMs; (2) KV cache compression at test time, primarily through token eviction policies, which often overlook inter-layer dependencies and can be task-specific. This paper introduces an orthogonal approach to KV cache compression. We propose a low-rank approximation of KV weight matrices, allowing for plug-in integration with existing transformer-based LLMs without model retraining. To effectively compress KV cache at the weight level, we adjust for layerwise sensitivity and introduce a progressive compression strategy, which is supported by our theoretical analysis on how compression errors accumulate in deep networks. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages. Extensive experiments with LLaMA models ranging from 8B to 70B parameters across various tasks show that our approach significantly reduces the GPU memory footprint while maintaining performance.<|reference_end|> | arxiv | @article{zhang2024lorc:,
title={LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive
Compression Strategy},
author={Rongzhi Zhang, Kuang Wang, Liyuan Liu, Shuohang Wang, Hao Cheng, Chao
Zhang, Yelong Shen},
journal={arXiv preprint arXiv:2410.03111},
year={2024},
archivePrefix={arXiv},
eprint={2410.03111},
primaryClass={cs.LG cs.AI cs.CL}
} | zhang2024lorc: |
arxiv-665434 | 2410.03115 | X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale | <|reference_start|>X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale: Large language models (LLMs) have achieved remarkable success across various NLP tasks, yet their focus has predominantly been on English due to English-centric pre-training and limited multilingual data. While some multilingual LLMs claim to support for hundreds of languages, models often fail to provide high-quality response for mid- and low-resource languages, leading to imbalanced performance heavily skewed in favor of high-resource languages like English and Chinese. In this paper, we prioritize quality over scaling number of languages, with a focus on multilingual machine translation task, and introduce X-ALMA, a model designed with a commitment to ensuring top-tier performance across 50 diverse languages, regardless of their resource levels. X-ALMA surpasses state-of-the-art open-source multilingual LLMs, such as Aya-101 and Aya-23, in every single translation direction on the FLORES and WMT'23 test datasets according to COMET-22. This is achieved by plug-and-play language-specific module architecture to prevent language conflicts during training and a carefully designed training regimen with novel optimization methods to maximize the translation performance. At the final stage of training regimen, our proposed Adaptive Rejection Preference Optimization (ARPO) surpasses existing preference optimization methods in translation tasks.<|reference_end|> | arxiv | @article{xu2024x-alma:,
title={X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality
Translation at Scale},
author={Haoran Xu, Kenton Murray, Philipp Koehn, Hieu Hoang, Akiko Eriguchi,
Huda Khayrallah},
journal={arXiv preprint arXiv:2410.03115},
year={2024},
archivePrefix={arXiv},
eprint={2410.03115},
primaryClass={cs.CL}
} | xu2024x-alma: |
arxiv-665435 | 2410.03117 | ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure | <|reference_start|>ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure: Reasoning is central to a wide range of intellectual activities, and while the capabilities of large language models (LLMs) continue to advance, their performance in reasoning tasks remains limited. The processes and mechanisms underlying reasoning are not yet fully understood, but key elements include path exploration, selection of relevant knowledge, and multi-step inference. Problems are solved through the synthesis of these components. In this paper, we propose a benchmark that focuses on a specific aspect of reasoning ability: the direct evaluation of multi-step inference. To this end, we design a special reasoning task where multi-step inference is specifically focused by largely eliminating path exploration and implicit knowledge utilization. Our dataset comprises pairs of explicit instructions and corresponding questions, where the procedures necessary for solving the questions are entirely detailed within the instructions. This setup allows models to solve problems solely by following the provided directives. By constructing problems that require varying numbers of steps to solve and evaluating responses at each step, we enable a thorough assessment of state-of-the-art LLMs' ability to follow instructions. To ensure the robustness of our evaluation, we include multiple distinct tasks. Furthermore, by comparing accuracy across tasks, utilizing step-aware metrics, and applying separately defined measures of complexity, we conduct experiments that offer insights into the capabilities and limitations of LLMs in reasoning tasks. Our findings have significant implications for the development of LLMs and highlight areas for future research in advancing their reasoning abilities. Our dataset is available at \url{https://huggingface.co/datasets/ifujisawa/procbench} and code at \url{https://github.com/ifujisawa/proc-bench}.<|reference_end|> | arxiv | @article{fujisawa2024procbench:,
title={ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure},
author={Ippei Fujisawa, Sensho Nobe, Hiroki Seto, Rina Onda, Yoshiaki Uchida,
Hiroki Ikoma, Pei-Chun Chien, Ryota Kanai},
journal={arXiv preprint arXiv:2410.03117},
year={2024},
archivePrefix={arXiv},
eprint={2410.03117},
primaryClass={cs.AI cs.CL cs.LG}
} | fujisawa2024procbench: |
arxiv-665436 | 2410.03118 | Precision, Stability, and Generalization: A Comprehensive Assessment of RNNs learnability capability for Classifying Counter and Dyck Languages | <|reference_start|>Precision, Stability, and Generalization: A Comprehensive Assessment of RNNs learnability capability for Classifying Counter and Dyck Languages: This study investigates the learnability of Recurrent Neural Networks (RNNs) in classifying structured formal languages, focusing on counter and Dyck languages. Traditionally, both first-order (LSTM) and second-order (O2RNN) RNNs have been considered effective for such tasks, primarily based on their theoretical expressiveness within the Chomsky hierarchy. However, our research challenges this notion by demonstrating that RNNs primarily operate as state machines, where their linguistic capabilities are heavily influenced by the precision of their embeddings and the strategies used for sampling negative examples. Our experiments revealed that performance declines significantly as the structural similarity between positive and negative examples increases. Remarkably, even a basic single-layer classifier using RNN embeddings performed better than chance. To evaluate generalization, we trained models on strings up to a length of 40 and tested them on strings from lengths 41 to 500, using 10 unique seeds to ensure statistical robustness. Stability comparisons between LSTM and O2RNN models showed that O2RNNs generally offer greater stability across various scenarios. We further explore the impact of different initialization strategies revealing that our hypothesis is consistent with various RNNs. Overall, this research questions established beliefs about RNNs' computational capabilities, highlighting the importance of data structure and sampling techniques in assessing neural networks' potential for language classification tasks. It emphasizes that stronger constraints on expressivity are crucial for understanding true learnability, as mere expressivity does not capture the essence of learning.<|reference_end|> | arxiv | @article{dave2024precision,,
title={Precision, Stability, and Generalization: A Comprehensive Assessment of
RNNs learnability capability for Classifying Counter and Dyck Languages},
author={Neisarg Dave, Daniel Kifer, Lee Giles, Ankur Mali},
journal={arXiv preprint arXiv:2410.03118},
year={2024},
archivePrefix={arXiv},
eprint={2410.03118},
primaryClass={cs.CL}
} | dave2024precision, |
arxiv-665437 | 2410.03119 | Spatial-aware decision-making with ring attractors in reinforcement learning systems | <|reference_start|>Spatial-aware decision-making with ring attractors in reinforcement learning systems: This paper explores the integration of ring attractors, a mathematical model inspired by neural circuit dynamics, into the reinforcement learning (RL) action selection process. Ring attractors, as specialized brain-inspired structures that encode spatial information and uncertainty, offer a biologically plausible mechanism to improve learning speed and predictive performance. They do so by explicitly encoding the action space, facilitating the organization of neural activity, and enabling the distribution of spatial representations across the neural network in the context of deep RL. The application of ring attractors in the RL action selection process involves mapping actions to specific locations on the ring and decoding the selected action based on neural activity. We investigate the application of ring attractors by both building them as exogenous models and integrating them as part of a Deep Learning policy algorithm. Our results show a significant improvement in state-of-the-art models for the Atari 100k benchmark. Notably, our integrated approach improves the performance of state-of-the-art models by half, representing a 53\% increase over selected baselines.<|reference_end|> | arxiv | @article{saura2024spatial-aware,
title={Spatial-aware decision-making with ring attractors in reinforcement
learning systems},
author={Marcos Negre Saura, Richard Allmendinger, Theodore Papamarkou, Wei Pan},
journal={arXiv preprint arXiv:2410.03119},
year={2024},
archivePrefix={arXiv},
eprint={2410.03119},
primaryClass={cs.LG}
} | saura2024spatial-aware |
arxiv-665438 | 2410.03120 | Solving the Phase Ordering Problem $\ne$ Generating the Globally Optimal Code | <|reference_start|>Solving the Phase Ordering Problem $\ne$ Generating the Globally Optimal Code: Phase ordering problem has been a long-standing challenge in compiler optimizations. Over the past four decades, a significant amount of effort has been devoted, and indeed, substantial progress has been made. However, in this paper, we raise questions about the overall significance of solving the phase ordering problem in the first place, as pursuing a solution to this problem may not align with the fundamental goal of compiler optimizations, i.e., generating the globally optimal code among all programs that compilers deem semantically equivalent to an input program. Our findings, supported by both theoretical and empirical evidence, show that solving the phase ordering problem is not equivalent to generating such globally optimal code. The fundamental reason that applying the optimal phase ordering may still result in suboptimal code is the exclusion of programs of less efficiency during the optimization process. Motivated by this insight, we propose a theoretical approach, called \textit{infinitive iterative bi-directional optimizations} (\textit{IIBO}), which is guaranteed to converge to the globally optimal code for any input program. We realize IIBO into a practical algorithm and apply it to optimize real-world programs. Results show that IIBO frequently generates more efficient code than GCC/LLVM, two state-of-the-art industry compilers, as well as exhaustive search, which can be deemed the solution to the phasing ordering problem.% input programs. Given the significance and impact of our results, we are currently in active discussions with LLVM engineers on the possible incorporation of our findings into their next release. In general, we expect our work to inspire new design principles for compiler development in the pursuit of generating the globally optimal code.<|reference_end|> | arxiv | @article{wang2024beyond,
title={Beyond the Phase Ordering Problem: Finding the Globally Optimal Code
w.r.t. Optimization Phases},
author={Yu Wang, Hongyu Chen, Ke Wang},
journal={arXiv preprint arXiv:2410.03120},
year={2024},
archivePrefix={arXiv},
eprint={2410.03120},
primaryClass={cs.PL}
} | wang2024beyond |
arxiv-665439 | 2410.03122 | RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning | <|reference_start|>RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning: The ripple effect poses a significant challenge in knowledge editing for large language models. Namely, when a single fact is edited, the model struggles to accurately update the related facts in a sequence, which is evaluated by multi-hop questions linked to a chain of related facts. Recent strategies have moved away from traditional parameter updates to more flexible, less computation-intensive methods, proven to be more effective in addressing the ripple effect. In-context learning (ICL) editing uses a simple demonstration `Imagine that + new fact` to guide LLMs, but struggles with complex multi-hop questions as the new fact alone fails to specify the chain of facts involved in such scenarios. Besides, memory-based editing maintains additional storage for all edits and related facts, requiring continuous updates to stay effective. As a result of these design limitations, the challenge remains, with the highest accuracy being only 33.8% on the MQuAKE-cf benchmarks for Vicuna-7B. To address this, we propose RippleCOT, a novel ICL editing approach integrating Chain-of-Thought (COT) reasoning. RippleCOT structures demonstrations as `newfact, question, thought, answer`, incorporating a thought component to identify and decompose the multi-hop logic within questions. This approach effectively guides the model through complex multi-hop questions with chains of related facts. Comprehensive experiments demonstrate that RippleCOT significantly outperforms the state-of-the-art on the ripple effect, achieving accuracy gains ranging from 7.8% to 87.1%.<|reference_end|> | arxiv | @article{zhao2024ripplecot:,
title={RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language
Models via Chain-of-Thought In-Context Learning},
author={Zihao Zhao, Yuchen Yang, Yijiang Li, Yinzhi Cao},
journal={arXiv preprint arXiv:2410.03122},
year={2024},
archivePrefix={arXiv},
eprint={2410.03122},
primaryClass={cs.CL cs.AI cs.LG}
} | zhao2024ripplecot: |
arxiv-665440 | 2410.03123 | Shrinking: Reconstruction of Parameterized Surfaces from Signed Distance Fields | <|reference_start|>Shrinking: Reconstruction of Parameterized Surfaces from Signed Distance Fields: We propose a novel method for reconstructing explicit parameterized surfaces from Signed Distance Fields (SDFs), a widely used implicit neural representation (INR) for 3D surfaces. While traditional reconstruction methods like Marching Cubes extract discrete meshes that lose the continuous and differentiable properties of INRs, our approach iteratively contracts a parameterized initial sphere to conform to the target SDF shape, preserving differentiability and surface parameterization throughout. This enables downstream applications such as texture mapping, geometry processing, animation, and finite element analysis. Evaluated on the typical geometric shapes and parts of the ABC dataset, our method achieves competitive reconstruction quality, maintaining smoothness and differentiability crucial for advanced computer graphics and geometric deep learning applications.<|reference_end|> | arxiv | @article{yin2024shrinking:,
title={Shrinking: Reconstruction of Parameterized Surfaces from Signed Distance
Fields},
author={Haotian Yin, Przemyslaw Musialski},
journal={arXiv preprint arXiv:2410.03123},
year={2024},
archivePrefix={arXiv},
eprint={2410.03123},
primaryClass={cs.GR cs.LG}
} | yin2024shrinking: |
arxiv-665441 | 2410.03124 | On Unsupervised Prompt Learning for Classification with Black-box Language Models | <|reference_start|>On Unsupervised Prompt Learning for Classification with Black-box Language Models: Large language models (LLMs) have achieved impressive success in text-formatted learning problems, and most popular LLMs have been deployed in a black-box fashion. Meanwhile, fine-tuning is usually necessary for a specific downstream task to obtain better performance, and this functionality is provided by the owners of the black-box LLMs. To fine-tune a black-box LLM, labeled data are always required to adjust the model parameters. However, in many real-world applications, LLMs can label textual datasets with even better quality than skilled human annotators, motivating us to explore the possibility of fine-tuning black-box LLMs with unlabeled data. In this paper, we propose unsupervised prompt learning for classification with black-box LLMs, where the learning parameters are the prompt itself and the pseudo labels of unlabeled data. Specifically, the prompt is modeled as a sequence of discrete tokens, and every token has its own to-be-learned categorical distribution. On the other hand, for learning the pseudo labels, we are the first to consider the in-context learning (ICL) capabilities of LLMs: we first identify reliable pseudo-labeled data using the LLM, and then assign pseudo labels to other unlabeled data based on the prompt, allowing the pseudo-labeled data to serve as in-context demonstrations alongside the prompt. Those in-context demonstrations matter: previously, they are involved when the prompt is used for prediction while they are not involved when the prompt is trained; thus, taking them into account during training makes the prompt-learning and prompt-using stages more consistent. Experiments on benchmark datasets show the effectiveness of our proposed algorithm. After unsupervised prompt learning, we can use the pseudo-labeled dataset for further fine-tuning by the owners of the black-box LLMs.<|reference_end|> | arxiv | @article{zhang2024on,
title={On Unsupervised Prompt Learning for Classification with Black-box
Language Models},
author={Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, and Masashi
Sugiyama},
journal={arXiv preprint arXiv:2410.03124},
year={2024},
archivePrefix={arXiv},
eprint={2410.03124},
primaryClass={cs.CL cs.LG}
} | zhang2024on |
arxiv-665442 | 2410.03126 | Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist | <|reference_start|>Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist: We explore how an AI model's decision fairness affects people's engagement with and perceived fairness of the model if they are subject to its decisions, but could repeatedly and strategically respond to these decisions. Two types of strategic responses are considered -- people could determine whether to continue interacting with the model, and whether to invest in themselves to improve their chance of future favorable decisions from the model. Via three human-subject experiments, we found that in decision subjects' strategic, repeated interactions with an AI model, the model's decision fairness does not change their willingness to interact with the model or to improve themselves, even when the model exhibits unfairness on salient protected attributes. However, decision subjects still perceive the AI model to be less fair when it systematically biases against their group, especially if the difficulty of improving one's qualification for the favorable decision is larger for the lowly-qualified people.<|reference_end|> | arxiv | @article{gemalmaz2024understanding,
title={Understanding Decision Subjects' Engagement with and Perceived Fairness
of AI Models When Opportunities of Qualification Improvement Exist},
author={Meric Altug Gemalmaz, Ming Yin},
journal={arXiv preprint arXiv:2410.03126},
year={2024},
archivePrefix={arXiv},
eprint={2410.03126},
primaryClass={cs.HC cs.AI}
} | gemalmaz2024understanding |
arxiv-665443 | 2410.03129 | ARB-LLM: Alternating Refined Binarizations for Large Language Models | <|reference_start|>ARB-LLM: Alternating Refined Binarizations for Large Language Models: Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. Binarization, as an effective compression technique, can shrink model weights to just 1 bit, significantly reducing the high demands on computation and memory. However, current binarization methods struggle to narrow the distribution gap between binarized and full-precision weights, while also overlooking the column deviation in LLM weight distribution. To tackle these issues, we propose ARB-LLM, a novel 1-bit post-training quantization (PTQ) technique tailored for LLMs. To narrow the distribution shift between binarized and full-precision weights, we first design an alternating refined binarization (ARB) algorithm to progressively update the binarization parameters, which significantly reduces the quantization error. Moreover, considering the pivot role of calibration data and the column deviation in LLM weights, we further extend ARB to ARB-X and ARB-RC. In addition, we refine the weight partition strategy with column-group bitmap (CGB), which further enhance performance. Equipping ARB-X and ARB-RC with CGB, we obtain ARB-LLM$_\text{X}$ and ARB-LLM$_\text{RC}$ respectively, which significantly outperform state-of-the-art (SOTA) binarization methods for LLMs. As a binary PTQ method, our ARB-LLM$_\text{RC}$ is the first to surpass FP16 models of the same size. The code and models will be available at https://github.com/ZHITENGLI/ARB-LLM.<|reference_end|> | arxiv | @article{li2024arb-llm:,
title={ARB-LLM: Alternating Refined Binarizations for Large Language Models},
author={Zhiteng Li, Xianglong Yan, Tianao Zhang, Haotong Qin, Dong Xie, Jiang
Tian, zhongchao shi, Linghe Kong, Yulun Zhang, Xiaokang Yang},
journal={arXiv preprint arXiv:2410.03129},
year={2024},
archivePrefix={arXiv},
eprint={2410.03129},
primaryClass={cs.CV cs.AI cs.CL cs.LG}
} | li2024arb-llm: |
arxiv-665444 | 2410.03131 | AIME: AI System Optimization via Multiple LLM Evaluators | <|reference_start|>AIME: AI System Optimization via Multiple LLM Evaluators: Text-based AI system optimization typically involves a feedback loop scheme where a single LLM generates an evaluation in natural language of the current output to improve the next iteration's output. However, in this work, we empirically demonstrate that for a practical and complex task (code generation) with multiple criteria to evaluate, utilizing only one LLM evaluator tends to let errors in generated code go undetected, thus leading to incorrect evaluations and ultimately suboptimal test case performance. Motivated by this failure case, we assume there exists an optimal evaluation policy that samples an evaluation between response and ground truth. We then theoretically prove that a linear combination of multiple evaluators can approximate this optimal policy. From this insight, we propose AI system optimization via Multiple LLM Evaluators (AIME). AIME is an evaluation protocol that utilizes multiple LLMs that each independently generate an evaluation on separate criteria and then combine them via concatenation. We provide an extensive empirical study showing AIME outperforming baseline methods in code generation tasks, with up to $62\%$ higher error detection rate and up to $16\%$ higher success rate than a single LLM evaluation protocol on LeetCodeHard and HumanEval datasets. We also show that the selection of the number of evaluators and which criteria to utilize is non-trivial as it can impact pact success rate by up to $12\%$.<|reference_end|> | arxiv | @article{patel2024aime:,
title={AIME: AI System Optimization via Multiple LLM Evaluators},
author={Bhrij Patel, Souradip Chakraborty, Wesley A. Suttle, Mengdi Wang,
Amrit Singh Bedi, Dinesh Manocha},
journal={arXiv preprint arXiv:2410.03131},
year={2024},
archivePrefix={arXiv},
eprint={2410.03131},
primaryClass={cs.AI cs.CL cs.LG}
} | patel2024aime: |
arxiv-665445 | 2410.03132 | Autoregressive Action Sequence Learning for Robotic Manipulation | <|reference_start|>Autoregressive Action Sequence Learning for Robotic Manipulation: Autoregressive models have demonstrated remarkable success in natural language processing. In this work, we design a simple yet effective autoregressive architecture for robotic manipulation tasks. We propose the Chunking Causal Transformer (CCT), which extends the next-single-token prediction of causal transformers to support multi-token prediction in a single pass. Further, we design a novel attention interleaving strategy that allows CCT to be trained efficiently with teacher-forcing. Based on CCT, we propose the Autoregressive Policy (ARP) model, which learns to generate action sequences autoregressively. We find that action sequence learning enables better leverage of the underlying causal relationships in robotic tasks. We evaluate ARP across diverse robotic manipulation environments, including Push-T, ALOHA, and RLBench, and show that it outperforms the state-of-the-art methods in all tested environments, while being more efficient in computation and parameter sizes. Video demonstrations, our source code, and the models of ARP can be found at http://github.com/mlzxy/arp.<|reference_end|> | arxiv | @article{zhang2024autoregressive,
title={Autoregressive Action Sequence Learning for Robotic Manipulation},
author={Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, Abdeslam Boularias},
journal={arXiv preprint arXiv:2410.03132},
year={2024},
archivePrefix={arXiv},
eprint={2410.03132},
primaryClass={cs.RO cs.AI cs.LG}
} | zhang2024autoregressive |
arxiv-665446 | 2410.03134 | Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models | <|reference_start|>Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models: Remaining useful life (RUL) prediction is crucial for maintaining modern industrial systems, where equipment reliability and operational safety are paramount. Traditional methods, based on small-scale deep learning or physical/statistical models, often struggle with complex, multidimensional sensor data and varying operating conditions, limiting their generalization capabilities. To address these challenges, this paper introduces an innovative regression framework utilizing large language models (LLMs) for RUL prediction. By leveraging the modeling power of LLMs pre-trained on corpus data, the proposed model can effectively capture complex temporal dependencies and improve prediction accuracy. Extensive experiments on the Turbofan engine's RUL prediction task show that the proposed model surpasses state-of-the-art (SOTA) methods on the challenging FD002 and FD004 subsets and achieves near-SOTA results on the other subsets. Notably, different from previous research, our framework uses the same sliding window length and all sensor signals for all subsets, demonstrating strong consistency and generalization. Moreover, transfer learning experiments reveal that with minimal target domain data for fine-tuning, the model outperforms SOTA methods trained on full target domain data. This research highlights the significant potential of LLMs in industrial signal processing and RUL prediction, offering a forward-looking solution for health management in future intelligent industrial systems.<|reference_end|> | arxiv | @article{chen2024remaining,
title={Remaining Useful Life Prediction: A Study on Multidimensional Industrial
Signal Processing and Efficient Transfer Learning Based on Large Language
Models},
author={Yan Chen, Cheng Liu},
journal={arXiv preprint arXiv:2410.03134},
year={2024},
archivePrefix={arXiv},
eprint={2410.03134},
primaryClass={cs.LG cs.AI eess.SP}
} | chen2024remaining |
arxiv-665447 | 2410.03136 | Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model | <|reference_start|>Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model: Enhancing the reasoning capabilities of large language models (LLMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making. Humans excel at these tasks by leveraging deliberate planning with an internal world model to simulate the potential outcomes of various actions. Inspired by this, we propose a novel multi-step reasoning framework for LLMs, referred to as Structure-aware Planning with Accurate World Model (SWAP). Unlike previous approaches that rely solely on Chain-of-Thought (CoT) reasoning in natural language, SWAP incorporates structural information to guide the reasoning process via a world model and provides a soft verification mechanism over the steps. Moreover, SWAP overcomes the challenge of accurate world state predictions in complex reasoning tasks by introducing a Generator-Discriminator architecture, which enables more reliable world modeling. Specifically, the generator predicts the next state, and the discriminator ensures alignment with the logical consistency required by the problem context. SWAP also encourages the policy model to explore a broad range of potential actions to prevent premature convergence. By resolving the bottlenecks of generation diversity for both actions and states using diversity-based modeling (DBM) and improving discrimination accuracy through contrastive ranking (CR), SWAP significantly enhances the reasoning performance of LLMs. We evaluate SWAP across diverse reasoning-intensive benchmarks including math reasoning, logical reasoning, and coding tasks. Extensive experiments demonstrate that SWAP achieves substantial improvements over the baselines and consistently outperforms existing LLMs of similar sizes.<|reference_end|> | arxiv | @article{xiong2024deliberate,
title={Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate
World Model},
author={Siheng Xiong, Ali Payani, Yuan Yang, Faramarz Fekri},
journal={arXiv preprint arXiv:2410.03136},
year={2024},
archivePrefix={arXiv},
eprint={2410.03136},
primaryClass={cs.CL}
} | xiong2024deliberate |
arxiv-665448 | 2410.03137 | SAG: Style-Aligned Article Generation via Model Collaboration | <|reference_start|>SAG: Style-Aligned Article Generation via Model Collaboration: Large language models (LLMs) have increased the demand for personalized and stylish content generation. However, closed-source models like GPT-4 present limitations in optimization opportunities, while the substantial training costs and inflexibility of open-source alternatives, such as Qwen-72B, pose considerable challenges. Conversely, small language models (SLMs) struggle with understanding complex instructions and transferring learned capabilities to new contexts, often exhibiting more pronounced limitations. In this paper, we present a novel collaborative training framework that leverages the strengths of both LLMs and SLMs for style article generation, surpassing the performance of either model alone. We freeze the LLMs to harness their robust instruction-following capabilities and subsequently apply supervised fine-tuning on the SLM using style-specific data. Additionally, we introduce a self-improvement method to enhance style consistency. Our new benchmark, NoteBench, thoroughly evaluates style-aligned generation. Extensive experiments show that our approach achieves state-of-the-art performance, with improvements of 0.78 in ROUGE-L and 0.55 in BLEU-4 scores compared to GPT-4, while maintaining a low hallucination rate regarding factual and faithfulness.<|reference_end|> | arxiv | @article{xu2024sag:,
title={SAG: Style-Aligned Article Generation via Model Collaboration},
author={Chenning Xu, Fangxun Shu, Dian Jin, Jinghao Wei, Hao Jiang},
journal={arXiv preprint arXiv:2410.03137},
year={2024},
archivePrefix={arXiv},
eprint={2410.03137},
primaryClass={cs.CL}
} | xu2024sag: |
arxiv-665449 | 2410.03138 | Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity | <|reference_start|>Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity: Recent advancements in large language models (LLMs) have demonstrated impressive performance in generating molecular structures as drug candidates, which offers significant potential to accelerate drug discovery. However, the current LLMs overlook a critical requirement for drug discovery: proposing a diverse set of molecules. This diversity is essential for improving the chances of finding a viable drug, as it provides alternative molecules that may succeed where others fail in wet-lab or clinical validations. Despite such a need for diversity, the LLMs often output structurally similar molecules from a given prompt. While decoding schemes like beam search may enhance textual diversity, this often does not align with molecular structural diversity. In response, we propose a new method for fine-tuning molecular generative LLMs to autoregressively generate a set of structurally diverse molecules, where each molecule is generated by conditioning on the previously generated molecules. Our approach consists of two stages: (1) supervised fine-tuning to adapt LLMs to autoregressively generate molecules in a sequence and (2) reinforcement learning to maximize structural diversity within the generated molecules. Our experiments show that (1) our fine-tuning approach enables the LLMs to better discover diverse molecules compared to existing decoding schemes and (2) our fine-tuned model outperforms other representative LLMs in generating diverse molecules, including the ones fine-tuned on chemical domains.<|reference_end|> | arxiv | @article{jang2024can,
title={Can LLMs Generate Diverse Molecules? Towards Alignment with Structural
Diversity},
author={Hyosoon Jang, Yunhui Jang, Jaehyung Kim, Sungsoo Ahn},
journal={arXiv preprint arXiv:2410.03138},
year={2024},
archivePrefix={arXiv},
eprint={2410.03138},
primaryClass={cs.LG q-bio.QM}
} | jang2024can |
arxiv-665450 | 2410.03139 | How does the teacher rate? Observations from the NeuroPiano dataset | <|reference_start|>How does the teacher rate? Observations from the NeuroPiano dataset: This paper provides a detailed analysis of the NeuroPiano dataset, which comprise 104 audio recordings of student piano performances accompanied with 2255 textual feedback and ratings given by professional pianists. We offer a statistical overview of the dataset, focusing on the standardization of annotations and inter-annotator agreement across 12 evaluative questions concerning performance quality. We also explore the predictive relationship between audio features and teacher ratings via machine learning, as well as annotations provided for text analysis of the responses.<|reference_end|> | arxiv | @article{zhang2024how,
title={How does the teacher rate? Observations from the NeuroPiano dataset},
author={Huan Zhang, Vincent Cheung, Hayato Nishioka, Simon Dixon, Shinichi
Furuya},
journal={arXiv preprint arXiv:2410.03139},
year={2024},
archivePrefix={arXiv},
eprint={2410.03139},
primaryClass={eess.AS cs.SD}
} | zhang2024how |
arxiv-665451 | 2410.03140 | In-context Learning in Presence of Spurious Correlations | <|reference_start|>In-context Learning in Presence of Spurious Correlations: Large language models exhibit a remarkable capacity for in-context learning, where they learn to solve tasks given a few examples. Recent work has shown that transformers can be trained to perform simple regression tasks in-context. This work explores the possibility of training an in-context learner for classification tasks involving spurious features. We find that the conventional approach of training in-context learners is susceptible to spurious features. Moreover, when the meta-training dataset includes instances of only one task, the conventional approach leads to task memorization and fails to produce a model that leverages context for predictions. Based on these observations, we propose a novel technique to train such a learner for a given classification task. Remarkably, this in-context learner matches and sometimes outperforms strong methods like ERM and GroupDRO. However, unlike these algorithms, it does not generalize well to other tasks. We show that it is possible to obtain an in-context learner that generalizes to unseen tasks by training on a diverse dataset of synthetic in-context learning instances.<|reference_end|> | arxiv | @article{harutyunyan2024in-context,
title={In-context Learning in Presence of Spurious Correlations},
author={Hrayr Harutyunyan, Rafayel Darbinyan, Samvel Karapetyan, Hrant
Khachatrian},
journal={arXiv preprint arXiv:2410.03140},
year={2024},
archivePrefix={arXiv},
eprint={2410.03140},
primaryClass={cs.LG cs.CL}
} | harutyunyan2024in-context |
arxiv-665452 | 2410.03141 | Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging | <|reference_start|>Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging: Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management. This study employed various machine learning techniques to detect the presence of RSD in different sugarcane varieties, using vegetation indices derived from freely available satellite-based spectral data. Our results show that the Support Vector Machine with a Radial Basis Function Kernel (SVM-RBF) was the most effective algorithm, achieving classification accuracy between 85.64% and 96.55%, depending on the variety. Gradient Boosting and Random Forest also demonstrated high performance achieving accuracy between 83.33% to 96.55%, while Logistic Regression and Quadratic Discriminant Analysis showed variable results across different varieties. The inclusion of sugarcane variety and vegetation indices was important in the detection of RSD. This agreed with what was identified in the current literature. Our study highlights the potential of satellite-based remote sensing as a cost-effective and efficient method for large-scale sugarcane disease detection alternative to traditional manual laboratory testing methods.<|reference_end|> | arxiv | @article{waters2024machine,
title={Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With
Freely Available Satellite Based Multispectral Imaging},
author={Ethan Kane Waters, Carla Chia-ming Chen and Mostafa Rahimi Azghadi},
journal={arXiv preprint arXiv:2410.03141},
year={2024},
archivePrefix={arXiv},
eprint={2410.03141},
primaryClass={cs.LG cs.CV eess.IV}
} | waters2024machine |
arxiv-665453 | 2410.03143 | ECHOPulse: ECG controlled echocardio-grams video generation | <|reference_start|>ECHOPulse: ECG controlled echocardio-grams video generation: Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPULSE, an ECG-conditioned ECHO video generation model. ECHOPULSE introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPULSE not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Additionally, ECHOPULSE can be easily generalized to other modality generation tasks, such as cardiac MRI, fMRI, and 3D CT generation. Demo can seen from \url{https://github.com/levyisthebest/ECHOPulse_Prelease}.<|reference_end|> | arxiv | @article{li2024echopulse:,
title={ECHOPulse: ECG controlled echocardio-grams video generation},
author={Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin,
Sifan Song, Yucheng Shi, Tianming Liu, Quanzheng Li and Xiang Li},
journal={arXiv preprint arXiv:2410.03143},
year={2024},
archivePrefix={arXiv},
eprint={2410.03143},
primaryClass={eess.IV cs.CV cs.LG}
} | li2024echopulse: |
arxiv-665454 | 2410.03145 | Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback | <|reference_start|>Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback: Large language models (LLMs) fine-tuned with alignment techniques, such as reinforcement learning from human feedback, have been instrumental in developing some of the most capable AI systems to date. Despite their success, existing methods typically rely on simple binary labels, such as those indicating preferred outputs in pairwise preferences, which fail to capture the subtle differences in relative quality between pairs. To address this limitation, we introduce an approach called Margin Matching Preference Optimization (MMPO), which incorporates relative quality margins into optimization, leading to improved LLM policies and reward models. Specifically, given quality margins in pairwise preferences, we design soft target probabilities based on the Bradley-Terry model, which are then used to train models with the standard cross-entropy objective. Experiments with both human and AI feedback data demonstrate that MMPO consistently outperforms baseline methods, often by a substantial margin, on popular benchmarks including MT-bench and RewardBench. Notably, the 7B model trained with MMPO achieves state-of-the-art performance on RewardBench as of June 2024, outperforming other models of the same scale. Our analysis also shows that MMPO is more robust to overfitting, leading to better-calibrated models.<|reference_end|> | arxiv | @article{kim2024margin,
title={Margin Matching Preference Optimization: Enhanced Model Alignment with
Granular Feedback},
author={Kyuyoung Kim, Ah Jeong Seo, Hao Liu, Jinwoo Shin, Kimin Lee},
journal={arXiv preprint arXiv:2410.03145},
year={2024},
archivePrefix={arXiv},
eprint={2410.03145},
primaryClass={cs.CL}
} | kim2024margin |
arxiv-665455 | 2410.03146 | Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel Approach using Gloss-based Annotation | <|reference_start|>Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel Approach using Gloss-based Annotation: This paper presents an innovative approach called BGTAI to simplify multimodal understanding by utilizing gloss-based annotation as an intermediate step in aligning Text and Audio with Images. While the dynamic temporal factors in textual and audio inputs contain various predicate adjectives that influence the meaning of the entire sentence, images, on the other hand, present static scenes. By representing text and audio as gloss notations that omit complex semantic nuances, a better alignment with images can potentially be achieved. This study explores the feasibility of this idea, specifically, we first propose the first Langue2Gloss model and then integrate it into the multimodal model UniBriVL for joint training. To strengthen the adaptability of gloss with text/audio and overcome the efficiency and instability issues in multimodal training, we propose a DS-Net (Data-Pair Selection Network), an Result Filter module, and a novel SP-Loss function. Our approach outperforms previous multimodal models in the main experiments, demonstrating its efficacy in enhancing multimodal representations and improving compatibility among text, audio, visual, and any sequence modalities.<|reference_end|> | arxiv | @article{fang2024bridging,
title={Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel
Approach using Gloss-based Annotation},
author={Sen Fang, Sizhou Chen, Yalin Feng, Xiaofeng Zhang, Teik Toe Teoh},
journal={arXiv preprint arXiv:2410.03146},
year={2024},
archivePrefix={arXiv},
eprint={2410.03146},
primaryClass={cs.CV}
} | fang2024bridging |
arxiv-665456 | 2410.03147 | Analysis and Detection of Differences in Spoken User Behaviors between Autonomous and Wizard-of-Oz Systems | <|reference_start|>Analysis and Detection of Differences in Spoken User Behaviors between Autonomous and Wizard-of-Oz Systems: This study examined users' behavioral differences in a large corpus of Japanese human-robot interactions, comparing interactions between a tele-operated robot and an autonomous dialogue system. We analyzed user spoken behaviors in both attentive listening and job interview dialogue scenarios. Results revealed significant differences in metrics such as speech length, speaking rate, fillers, backchannels, disfluencies, and laughter between operator-controlled and autonomous conditions. Furthermore, we developed predictive models to distinguish between operator and autonomous system conditions. Our models demonstrated higher accuracy and precision compared to the baseline model, with several models also achieving a higher F1 score than the baseline.<|reference_end|> | arxiv | @article{elmers2024analysis,
title={Analysis and Detection of Differences in Spoken User Behaviors between
Autonomous and Wizard-of-Oz Systems},
author={Mikey Elmers, Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara},
journal={arXiv preprint arXiv:2410.03147},
year={2024},
archivePrefix={arXiv},
eprint={2410.03147},
primaryClass={cs.CL cs.HC cs.RO}
} | elmers2024analysis |
arxiv-665457 | 2410.03148 | Memory-distributed level set-based inverse homogenisation of three-dimensional piezoelectric materials | <|reference_start|>Memory-distributed level set-based inverse homogenisation of three-dimensional piezoelectric materials: In this paper we use level set-based topology optimisation to design three-dimensional periodic piezoelectric materials with enhanced properties. Our methodology is fully memory-distributed and written in Julia using the package GridapTopOpt. We compare and assess several existing iterative solvers with respect to their weak scalability and find that an approximate Schur complement preconditioned GMRES method demonstrates the best performance and scalability for solving the piezoelectric homogenisation equations. We use the developed techniques to computationally design high-resolution piezoelectric metamaterials with enhanced stiffness and piezoelectric properties that yield new insights into material design for sensor, hydrophone, and actuator applications. We suggest two robust structures with simple geometric features that exhibit enhanced piezoelectric properties several times larger than those of the base material. We find that level set-based topology optimisation is well suited to problems involving piezoelectricity and has the advantage of avoiding large regions of intermediate density material.<|reference_end|> | arxiv | @article{wegert2024memory-distributed,
title={Memory-distributed level set-based inverse homogenisation of
three-dimensional piezoelectric materials},
author={Zachary J. Wegert, Anthony P. Roberts, Vivien J. Challis},
journal={arXiv preprint arXiv:2410.03148},
year={2024},
archivePrefix={arXiv},
eprint={2410.03148},
primaryClass={cs.CE cs.DC}
} | wegert2024memory-distributed |
arxiv-665458 | 2410.03151 | Media Framing through the Lens of Event-Centric Narratives | <|reference_start|>Media Framing through the Lens of Event-Centric Narratives: From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.<|reference_end|> | arxiv | @article{das2024media,
title={Media Framing through the Lens of Event-Centric Narratives},
author={Rohan Das, Aditya Chandra, I-Ta Lee, Maria Leonor Pacheco},
journal={arXiv preprint arXiv:2410.03151},
year={2024},
archivePrefix={arXiv},
eprint={2410.03151},
primaryClass={cs.CL cs.SI}
} | das2024media |
arxiv-665459 | 2410.03152 | Sampling-Based Model Predictive Control for Volumetric Ablation in Robotic Laser Surgery | <|reference_start|>Sampling-Based Model Predictive Control for Volumetric Ablation in Robotic Laser Surgery: Laser-based surgical ablation relies heavily on surgeon involvement, restricting precision to the limits of human error. The interaction between laser and tissue is governed by various laser parameters that control the laser irradiance on the tissue, including the laser power, distance, spot size, orientation, and exposure time. This complex interaction lends itself to robotic automation, allowing the surgeon to focus on high-level tasks, such as choosing the region and method of ablation, while the lower-level ablation plan can be handled autonomously. This paper describes a sampling-based model predictive control (MPC) scheme to plan ablation sequences for arbitrary tissue volumes. Using a steady-state point ablation model to simulate a single laser-tissue interaction, a random search technique explores the reachable state space while preserving sensitive tissue regions. The sampled MPC strategy provides an ablation sequence that accounts for parameter uncertainty without violating constraints, such as avoiding critical nerve bundles or blood vessels.<|reference_end|> | arxiv | @article{wang2024sampling-based,
title={Sampling-Based Model Predictive Control for Volumetric Ablation in
Robotic Laser Surgery},
author={Vincent Y. Wang, Ravi Prakash, Siobhan R. Oca, Ethan J. LoCicero,
Patrick J. Codd, Leila J. Bridgeman},
journal={arXiv preprint arXiv:2410.03152},
year={2024},
archivePrefix={arXiv},
eprint={2410.03152},
primaryClass={cs.RO}
} | wang2024sampling-based |
arxiv-665460 | 2410.03154 | Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights | <|reference_start|>Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights: This study explores the learnability of memory-less and memory-augmented RNNs, which are theoretically equivalent to Pushdown Automata. Empirical results show that these models often fail to generalize on longer sequences, relying more on precision than mastering symbolic grammar. Experiments on fully trained and component-frozen models reveal that freezing the memory component significantly improves performance, achieving state-of-the-art results on the Penn Treebank dataset (test perplexity reduced from 123.5 to 120.5). Models with frozen memory retained up to 90% of initial performance on longer sequences, compared to a 60% drop in standard models. Theoretical analysis suggests that freezing memory stabilizes temporal dependencies, leading to robust convergence. These findings stress the need for stable memory designs and long-sequence evaluations to understand RNNs true learnability limits.<|reference_end|> | arxiv | @article{das2024exploring,
title={Exploring Learnability in Memory-Augmented Recurrent Neural Networks:
Precision, Stability, and Empirical Insights},
author={Shrabon Das, Ankur Mali},
journal={arXiv preprint arXiv:2410.03154},
year={2024},
archivePrefix={arXiv},
eprint={2410.03154},
primaryClass={cs.CL}
} | das2024exploring |
arxiv-665461 | 2410.03156 | MELODI: Exploring Memory Compression for Long Contexts | <|reference_start|>MELODI: Exploring Memory Compression for Long Contexts: We present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across both network layers and context windows. Specifically, the short-term memory is achieved through recurrent compression of context windows across multiple layers, ensuring smooth transitions between windows. In contrast, the long-term memory performs further compression within a single middle layer and aggregates information across context windows, effectively consolidating crucial information from the entire history. Compared to a strong baseline - the Memorizing Transformer employing dense attention over a large long-term memory (64K key-value pairs) - our method demonstrates superior performance on various long-context datasets while remarkably reducing the memory footprint by a factor of 8.<|reference_end|> | arxiv | @article{chen2024melodi:,
title={MELODI: Exploring Memory Compression for Long Contexts},
author={Yinpeng Chen, DeLesley Hutchins, Aren Jansen, Andrey Zhmoginov, David
Racz, Jesper Andersen},
journal={arXiv preprint arXiv:2410.03156},
year={2024},
archivePrefix={arXiv},
eprint={2410.03156},
primaryClass={cs.LG cs.AI}
} | chen2024melodi: |
arxiv-665462 | 2410.03158 | Mathematical Formalism for Memory Compression in Selective State Space Models | <|reference_start|>Mathematical Formalism for Memory Compression in Selective State Space Models: State space models (SSMs) have emerged as a powerful framework for modelling long-range dependencies in sequence data. Unlike traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), SSMs offer a structured and stable approach to sequence modelling, leveraging principles from control theory and dynamical systems. However, a key challenge in sequence modelling is compressing long-term dependencies into a compact hidden state representation without losing critical information. In this paper, we develop a rigorous mathematical framework for understanding memory compression in selective state space models. We introduce a selective gating mechanism that dynamically filters and updates the hidden state based on input relevance, allowing for efficient memory compression. We formalize the trade-off between memory efficiency and information retention using information-theoretic tools, such as mutual information and rate-distortion theory. Our analysis provides theoretical bounds on the amount of information that can be compressed without sacrificing model performance. We also derive theorems that prove the stability and convergence of the hidden state in selective SSMs, ensuring reliable long-term memory retention. Computational complexity analysis reveals that selective SSMs offer significant improvements in memory efficiency and processing speed compared to traditional RNN-based models. Through empirical validation on sequence modelling tasks such as time-series forecasting and natural language processing, we demonstrate that selective SSMs achieve state-of-the-art performance while using less memory and computational resources.<|reference_end|> | arxiv | @article{bhat2024mathematical,
title={Mathematical Formalism for Memory Compression in Selective State Space
Models},
author={Siddhanth Bhat},
journal={arXiv preprint arXiv:2410.03158},
year={2024},
archivePrefix={arXiv},
eprint={2410.03158},
primaryClass={cs.LG cs.AI cs.CC}
} | bhat2024mathematical |
arxiv-665463 | 2410.03159 | Autoregressive Moving-average Attention Mechanism for Time Series Forecasting | <|reference_start|>Autoregressive Moving-average Attention Mechanism for Time Series Forecasting: We propose an Autoregressive (AR) Moving-average (MA) attention structure that can adapt to various linear attention mechanisms, enhancing their ability to capture long-range and local temporal patterns in time series. In this paper, we first demonstrate that, for the time series forecasting (TSF) task, the previously overlooked decoder-only autoregressive Transformer model can achieve results comparable to the best baselines when appropriate tokenization and training methods are applied. Moreover, inspired by the ARMA model from statistics and recent advances in linear attention, we introduce the full ARMA structure into existing autoregressive attention mechanisms. By using an indirect MA weight generation method, we incorporate the MA term while maintaining the time complexity and parameter size of the underlying efficient attention models. We further explore how indirect parameter generation can produce implicit MA weights that align with the modeling requirements for local temporal impacts. Experimental results show that incorporating the ARMA structure consistently improves the performance of various AR attentions on TSF tasks, achieving state-of-the-art results.<|reference_end|> | arxiv | @article{lu2024autoregressive,
title={Autoregressive Moving-average Attention Mechanism for Time Series
Forecasting},
author={Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang},
journal={arXiv preprint arXiv:2410.03159},
year={2024},
archivePrefix={arXiv},
eprint={2410.03159},
primaryClass={cs.LG cs.AI stat.ML}
} | lu2024autoregressive |
arxiv-665464 | 2410.03160 | Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach | <|reference_start|>Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach: Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar timestep variable applied at the clip level, which limits their ability to model complex temporal dependencies needed for various tasks like image-to-video generation. To address this limitation, we propose a frame-aware video diffusion model~(FVDM), which introduces a novel vectorized timestep variable~(VTV). Unlike conventional VDMs, our approach allows each frame to follow an independent noise schedule, enhancing the model's capacity to capture fine-grained temporal dependencies. FVDM's flexibility is demonstrated across multiple tasks, including standard video generation, image-to-video generation, video interpolation, and long video synthesis. Through a diverse set of VTV configurations, we achieve superior quality in generated videos, overcoming challenges such as catastrophic forgetting during fine-tuning and limited generalizability in zero-shot methods.Our empirical evaluations show that FVDM outperforms state-of-the-art methods in video generation quality, while also excelling in extended tasks. By addressing fundamental shortcomings in existing VDMs, FVDM sets a new paradigm in video synthesis, offering a robust framework with significant implications for generative modeling and multimedia applications.<|reference_end|> | arxiv | @article{liu2024redefining,
title={Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep
Approach},
author={Yaofang Liu, Yumeng Ren, Xiaodong Cun, Aitor Artola, Yang Liu, Tieyong
Zeng, Raymond H. Chan, Jean-michel Morel},
journal={arXiv preprint arXiv:2410.03160},
year={2024},
archivePrefix={arXiv},
eprint={2410.03160},
primaryClass={cs.CV cs.LG}
} | liu2024redefining |
arxiv-665465 | 2410.03161 | Adaptive Masking Enhances Visual Grounding | <|reference_start|>Adaptive Masking Enhances Visual Grounding: In recent years, zero-shot and few-shot learning in visual grounding have garnered considerable attention, largely due to the success of large-scale vision-language pre-training on expansive datasets such as LAION-5B and DataComp-1B. However, the continuous expansion of these datasets presents significant challenges, particularly with respect to data availability and computational overhead, thus creating a bottleneck in the advancement of low-shot learning capabilities. In this paper, we propose IMAGE, Interpretative MAsking with Gaussian radiation modEling, aimed at enhancing vocabulary grounding in low-shot learning scenarios without necessitating an increase in dataset size. Drawing inspiration from cognitive science and the recent success of masked autoencoders (MAE), our method leverages adaptive masking on salient regions of the feature maps generated by the vision backbone. This enables the model to learn robust, generalized representations through the reconstruction of occluded information, thereby facilitating effective attention to both local and global features. We evaluate the efficacy of our approach on benchmark datasets, including COCO and ODinW, demonstrating its superior performance in zero-shot and few-shot tasks. Experimental results consistently show that IMAGE outperforms baseline models, achieving enhanced generalization and improved performance in low-shot scenarios. These findings highlight the potential of adaptive feature manipulation through attention mechanisms and Gaussian modeling as a promising alternative to approaches that rely on the continual scaling of dataset sizes for the advancement of zero-shot and few-shot learning. Our code is publicly available at https://github.com/git-lenny/IMAGE.<|reference_end|> | arxiv | @article{jia2024adaptive,
title={Adaptive Masking Enhances Visual Grounding},
author={Sen Jia, Lei Li},
journal={arXiv preprint arXiv:2410.03161},
year={2024},
archivePrefix={arXiv},
eprint={2410.03161},
primaryClass={cs.AI}
} | jia2024adaptive |
arxiv-665466 | 2410.03168 | Can Watermarked LLMs be Identified by Users via Crafted Prompts? | <|reference_start|>Can Watermarked LLMs be Identified by Users via Crafted Prompts?: Text watermarking for Large Language Models (LLMs) has made significant progress in detecting LLM outputs and preventing misuse. Current watermarking techniques offer high detectability, minimal impact on text quality, and robustness to text editing. However, current researches lack investigation into the imperceptibility of watermarking techniques in LLM services. This is crucial as LLM providers may not want to disclose the presence of watermarks in real-world scenarios, as it could reduce user willingness to use the service and make watermarks more vulnerable to attacks. This work is the first to investigate the imperceptibility of watermarked LLMs. We design an identification algorithm called Water-Probe that detects watermarks through well-designed prompts to the LLM. Our key motivation is that current watermarked LLMs expose consistent biases under the same watermark key, resulting in similar differences across prompts under different watermark keys. Experiments show that almost all mainstream watermarking algorithms are easily identified with our well-designed prompts, while Water-Probe demonstrates a minimal false positive rate for non-watermarked LLMs. Finally, we propose that the key to enhancing the imperceptibility of watermarked LLMs is to increase the randomness of watermark key selection. Based on this, we introduce the Water-Bag strategy, which significantly improves watermark imperceptibility by merging multiple watermark keys.<|reference_end|> | arxiv | @article{liu2024can,
title={Can Watermarked LLMs be Identified by Users via Crafted Prompts?},
author={Aiwei Liu, Sheng Guan, Yiming Liu, Leyi Pan, Yifei Zhang, Liancheng
Fang, Lijie Wen, Philip S. Yu, Xuming Hu},
journal={arXiv preprint arXiv:2410.03168},
year={2024},
archivePrefix={arXiv},
eprint={2410.03168},
primaryClass={cs.CR cs.CL}
} | liu2024can |
arxiv-665467 | 2410.03170 | Autoregressive Large Language Models are Computationally Universal | <|reference_start|>Autoregressive Large Language Models are Computationally Universal: We show that autoregressive decoding of a transformer-based language model can realize universal computation, without external intervention or modification of the model's weights. Establishing this result requires understanding how a language model can process arbitrarily long inputs using a bounded context. For this purpose, we consider a generalization of autoregressive decoding where, given a long input, emitted tokens are appended to the end of the sequence as the context window advances. We first show that the resulting system corresponds to a classical model of computation, a Lag system, that has long been known to be computationally universal. By leveraging a new proof, we show that a universal Turing machine can be simulated by a Lag system with 2027 production rules. We then investigate whether an existing large language model can simulate the behaviour of such a universal Lag system. We give an affirmative answer by showing that a single system-prompt can be developed for gemini-1.5-pro-001 that drives the model, under deterministic (greedy) decoding, to correctly apply each of the 2027 production rules. We conclude that, by the Church-Turing thesis, prompted gemini-1.5-pro-001 with extended autoregressive (greedy) decoding is a general purpose computer.<|reference_end|> | arxiv | @article{schuurmans2024autoregressive,
title={Autoregressive Large Language Models are Computationally Universal},
author={Dale Schuurmans, Hanjun Dai, Francesco Zanini},
journal={arXiv preprint arXiv:2410.03170},
year={2024},
archivePrefix={arXiv},
eprint={2410.03170},
primaryClass={cs.CL}
} | schuurmans2024autoregressive |
arxiv-665468 | 2410.03171 | Selective Transformer for Hyperspectral Image Classification | <|reference_start|>Selective Transformer for Hyperspectral Image Classification: Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification. However, existing Transformer models face two key challenges when dealing with HSI scenes characterized by diverse land cover types and rich spectral information: (1) fixed receptive field representation overlooks effective contextual information; (2) redundant self-attention feature representation. To address these limitations, we propose a novel Selective Transformer (SFormer) for HSI classification. The SFormer is designed to dynamically select receptive fields for capturing both spatial and spectral contextual information, while mitigating the impact of redundant data by prioritizing the most relevant features. This enables a highly accurate classification of the land covers of the HSI. Specifically, a Kernel Selective Transformer Block (KSTB) is first utilized to dynamically select an appropriate receptive field range to effectively extract spatial-spectral features. Furthermore, to capture the most crucial tokens, a Token Selective Transformer Block (TSTB) is introduced, which selects the most relevant tokens based on the ranking of attention scores for each query. Extensive experiments on four benchmark HSI datasets demonstrate that the proposed SFormer outperforms the state-of-the-art HSI classification models. The codes will be released.<|reference_end|> | arxiv | @article{xu2024selective,
title={Selective Transformer for Hyperspectral Image Classification},
author={Yichu Xu, Di Wang, Lefei Zhang, Liangpei Zhang},
journal={arXiv preprint arXiv:2410.03171},
year={2024},
archivePrefix={arXiv},
eprint={2410.03171},
primaryClass={cs.CV}
} | xu2024selective |
arxiv-665469 | 2410.03173 | Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms | <|reference_start|>Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms: Exploration of complex high-dimensional spaces presents significant challenges in fields such as molecular discovery, process optimization, and supply chain management. Genetic Algorithms (GAs), while offering significant power for creating new candidate spaces, often entail high computational demands due to the need for evaluation of each new proposed solution. On the other hand, Deep Kernel Learning (DKL) efficiently navigates the spaces of preselected candidate structures but lacks generative capabilities. This study introduces an approach that amalgamates the generative power of GAs to create new candidates with the efficiency of DKL-based surrogate models to rapidly ascertain the behavior of new candidate spaces. This DKL-GA framework can be further used to build Bayesian Optimization (BO) workflows. We demonstrate the effectiveness of this approach through the optimization of the FerroSIM model, showcasing its broad applicability to diverse challenges, including molecular discovery and battery charging optimization.<|reference_end|> | arxiv | @article{valleti2024rapid,
title={Rapid optimization in high dimensional space by deep kernel learning
augmented genetic algorithms},
author={Mani Valleti, Aditya Raghavan, Sergei V. Kalinin},
journal={arXiv preprint arXiv:2410.03173},
year={2024},
archivePrefix={arXiv},
eprint={2410.03173},
primaryClass={cs.LG cond-mat.mtrl-sci physics.comp-ph physics.data-an}
} | valleti2024rapid |
arxiv-665470 | 2410.03174 | HRVMamba: High-Resolution Visual State Space Model for Dense Prediction | <|reference_start|>HRVMamba: High-Resolution Visual State Space Model for Dense Prediction: Recently, State Space Models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have demonstrated significant potential in computer vision tasks due to their linear computational complexity with respect to token length and their global receptive field. However, Mamba's performance on dense prediction tasks, including human pose estimation and semantic segmentation, has been constrained by three key challenges: insufficient inductive bias, long-range forgetting, and low-resolution output representation. To address these challenges, we introduce the Dynamic Visual State Space (DVSS) block, which utilizes multi-scale convolutional kernels to extract local features across different scales and enhance inductive bias, and employs deformable convolution to mitigate the long-range forgetting problem while enabling adaptive spatial aggregation based on input and task-specific information. By leveraging the multi-resolution parallel design proposed in HRNet, we introduce High-Resolution Visual State Space Model (HRVMamba) based on the DVSS block, which preserves high-resolution representations throughout the entire process while promoting effective multi-scale feature learning. Extensive experiments highlight HRVMamba's impressive performance on dense prediction tasks, achieving competitive results against existing benchmark models without bells and whistles. Code is available at https://github.com/zhanghao5201/HRVMamba.<|reference_end|> | arxiv | @article{zhang2024hrvmamba:,
title={HRVMamba: High-Resolution Visual State Space Model for Dense Prediction},
author={Hao Zhang, Yongqiang Ma, Wenqi Shao, Ping Luo, Nanning Zheng, Kaipeng
Zhang},
journal={arXiv preprint arXiv:2410.03174},
year={2024},
archivePrefix={arXiv},
eprint={2410.03174},
primaryClass={cs.CV}
} | zhang2024hrvmamba: |
arxiv-665471 | 2410.03176 | Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models | <|reference_start|>Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models: Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, there is no clear conclusion as to which part of the model these hallucinations originate from. In this paper, we present an in-depth investigation into the object hallucination problem specifically within the CLIP model, which serves as the backbone for many state-of-the-art vision-language systems. We unveil that even in isolation, the CLIP model is prone to object hallucinations, suggesting that the hallucination problem is not solely due to the interaction between vision and language modalities. To address this, we propose a counterfactual data augmentation method by creating negative samples with a variety of hallucination issues. We demonstrate that our method can effectively mitigate object hallucinations for CLIP model, and we show the the enhanced model can be employed as a visual encoder, effectively alleviating the object hallucination issue in LVLMs.<|reference_end|> | arxiv | @article{liu2024investigating,
title={Investigating and Mitigating Object Hallucinations in Pretrained
Vision-Language (CLIP) Models},
author={Yufang Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Aimin Zhou},
journal={arXiv preprint arXiv:2410.03176},
year={2024},
archivePrefix={arXiv},
eprint={2410.03176},
primaryClass={cs.CV cs.AI}
} | liu2024investigating |
arxiv-665472 | 2410.03177 | Hybrid Centralized-Distributed Resource Allocation Based on Deep Reinforcement Learning for Cooperative D2D Communications | <|reference_start|>Hybrid Centralized-Distributed Resource Allocation Based on Deep Reinforcement Learning for Cooperative D2D Communications: Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely investigated and applied as a critical technology in 5G New Radio (NR). In addition to conventional overlay and underlay D2D communications, cooperative D2D communication, which can achieve a win-win situation between cellular users (CUs) and D2D users (DUs) through cooperative relaying technique, has attracted extensive attention from academic and industrial circles in the past decade. This paper delves into optimizing joint spectrum allocation, power control, and link-matching between multiple CUs and DUs for cooperative D2D communications, using weighted sum energy efficiency (WSEE) as the performance metric to address the challenges of green communication and sustainable development. This integer programming problem can be decomposed into a classic weighted bipartite graph matching and a series of nonconvex spectrum allocation and power control problems between potentially matched cellular and D2D link pairs. To address this issue, we propose a hybrid centralized-distributed scheme based on deep reinforcement learning (DRL) and the Kuhn-Munkres (KM) algorithm. Leveraging the latter, the CUs and DUs autonomously optimize spectrum allocation and power control by only utilizing local information. Then, the base station (BS) determines the link matching. Simulation results reveal that it achieves near-optimal performance and significantly enhances the network convergence speed with low signaling overheads. In addition, we also propose and utilize cooperative link sets for corresponding D2D links to accelerate the proposed scheme and reduce signaling exchange further.<|reference_end|> | arxiv | @article{yu2024hybrid,
title={Hybrid Centralized-Distributed Resource Allocation Based on Deep
Reinforcement Learning for Cooperative D2D Communications},
author={Yang Yu, Xiaoqing Tang},
journal={arXiv preprint arXiv:2410.03177},
year={2024},
archivePrefix={arXiv},
eprint={2410.03177},
primaryClass={cs.NI}
} | yu2024hybrid |
arxiv-665473 | 2410.03178 | Optimal Control in Both Steady State and Transient Process with Unknown Disturbances | <|reference_start|>Optimal Control in Both Steady State and Transient Process with Unknown Disturbances: The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physical states to the optimal solution in the steady state, without considering the performance during the transient process. In this paper, we simultaneously consider the performance in both the steady state and the transient process of a linear time-invariant system with unknown disturbances. The performance of the transient process is illustrated by the concept of overtaking optimality. An overtaking optimal controller with known disturbances is derived to achieve the transient overtaking optimality while guaranteeing steady-state performance. Then, we propose a disturbance independent near-optimal controller, which can achieve optimal steady-state performance and approach the overtaking optimal performance in the transient process. The system performance gap between the overtaking optimal controller and the proposed controller proves to be inversely proportional to the control gains. A case study on a power system with four buses is used to validate the effectiveness of the two controllers.<|reference_end|> | arxiv | @article{li2024optimal,
title={Optimal Control in Both Steady State and Transient Process with Unknown
Disturbances},
author={Ming Li, Zhaojian Wang, Feng Liu, Ming Cao, Bo Yang},
journal={arXiv preprint arXiv:2410.03178},
year={2024},
archivePrefix={arXiv},
eprint={2410.03178},
primaryClass={eess.SY cs.SY}
} | li2024optimal |
arxiv-665474 | 2410.03180 | Specification Slicing for VDM-SL | <|reference_start|>Specification Slicing for VDM-SL: The executable specification is one of the powerful tools in lightweight formal software development. VDM-SL allows the explicit and executable definition of operations that reference and update internal state through imperative statements. While the extensive executable subset of VDM-SL enables validation and testing in the specification phase, it also brings difficulties in reading and debugging as in imperative programming. In this paper, we define specification slicing for VDM-SL based on program slicing, a technique used for debugging and maintaining program source code in implementation languages. We then present and discuss its applications. The slicer for VDM-SL is implemented on ViennaTalk and can be used on browsers and debuggers describing the VDM-SL specification.<|reference_end|> | arxiv | @article{oda2024specification,
title={Specification Slicing for VDM-SL},
author={Tomohiro Oda and Han-Myung Chang},
journal={arXiv preprint arXiv:2410.03180},
year={2024},
number={OVT22/2024/02},
archivePrefix={arXiv},
eprint={2410.03180},
primaryClass={cs.SE}
} | oda2024specification |
arxiv-665475 | 2410.03181 | Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas | <|reference_start|>Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas: This study is the first to explore whether multi-modal large language models (LLMs) can align their behaviors with visual personas, addressing a significant gap in the literature that predominantly focuses on text-based personas. We developed a novel dataset of 5K fictional avatar images for assignment as visual personas to LLMs, and analyzed their negotiation behaviors based on the visual traits depicted in these images, with a particular focus on aggressiveness. The results indicate that LLMs assess the aggressiveness of images in a manner similar to humans and output more aggressive negotiation behaviors when prompted with an aggressive visual persona. Interestingly, the LLM exhibited more aggressive negotiation behaviors when the opponent's image appeared less aggressive than their own, and less aggressive behaviors when the opponents image appeared more aggressive.<|reference_end|> | arxiv | @article{sun2024kiss,
title={Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large
Language Models with Assigned Visual Personas},
author={Seungjong Sun, Eungu Lee, Seo Yeon Baek, Seunghyun Hwang, Wonbyung
Lee, Dongyan Nan, Bernard J. Jansen, Jang Hyun Kim},
journal={arXiv preprint arXiv:2410.03181},
year={2024},
archivePrefix={arXiv},
eprint={2410.03181},
primaryClass={cs.CL}
} | sun2024kiss |
arxiv-665476 | 2410.03182 | Generating bilingual example sentences with large language models as lexicography assistants | <|reference_start|>Generating bilingual example sentences with large language models as lexicography assistants: We present a study of LLMs' performance in generating and rating example sentences for bilingual dictionaries across languages with varying resource levels: French (high-resource), Indonesian (mid-resource), and Tetun (low-resource), with English as the target language. We evaluate the quality of LLM-generated examples against the GDEX (Good Dictionary EXample) criteria: typicality, informativeness, and intelligibility. Our findings reveal that while LLMs can generate reasonably good dictionary examples, their performance degrades significantly for lower-resourced languages. We also observe high variability in human preferences for example quality, reflected in low inter-annotator agreement rates. To address this, we demonstrate that in-context learning can successfully align LLMs with individual annotator preferences. Additionally, we explore the use of pre-trained language models for automated rating of examples, finding that sentence perplexity serves as a good proxy for typicality and intelligibility in higher-resourced languages. Our study also contributes a novel dataset of 600 ratings for LLM-generated sentence pairs, and provides insights into the potential of LLMs in reducing the cost of lexicographic work, particularly for low-resource languages.<|reference_end|> | arxiv | @article{merx2024generating,
title={Generating bilingual example sentences with large language models as
lexicography assistants},
author={Raphael Merx, Ekaterina Vylomova, Kemal Kurniawan},
journal={arXiv preprint arXiv:2410.03182},
year={2024},
archivePrefix={arXiv},
eprint={2410.03182},
primaryClass={cs.CL}
} | merx2024generating |
arxiv-665477 | 2410.03183 | Research Directions for Verifiable Crypto-Physically Secure TEEs | <|reference_start|>Research Directions for Verifiable Crypto-Physically Secure TEEs: A niche corner of the Web3 world is increasingly making use of hardware-based Trusted Execution Environments (TEEs) to build decentralized infrastructure. One of the motivations to use TEEs is to go beyond the current performance limitations of cryptography-based alternatives such as zero-knowledge proofs (ZKP), fully homomorphic encryption (FHE), and multi-party computation (MPC). Despite their appealing advantages, current TEEs suffer from serious limitations as they are not secure against physical attacks, and their attestation mechanism is rooted in the chip manufacturer's trust. As a result, Web3 applications have to rely on cloud infrastruture to act as trusted guardians of hardware-based TEEs and have to accept to trust chip manufacturers. This work aims at exploring how we could potentially architect and implement chips that would be secure against physical attacks and would not require putting trust in chip manufacturers. One goal of this work is to motivate the Web3 movement to acknowledge and leverage the substantial amount of relevant hardware research that already exists. In brief, a combination of: (1) physical unclonable functions (PUFs) to secure the root-of-trust; (2) masking and redundancy techniques to secure computations; (3) open source hardware and imaging techniques to verify that a chip matches its expected design; can help move towards attesting that a given TEE can be trusted without the need to trust a cloud provider and a chip manufacturer.<|reference_end|> | arxiv | @article{bellemare2024research,
title={Research Directions for Verifiable Crypto-Physically Secure TEEs},
author={Sylvain Bellemare},
journal={arXiv preprint arXiv:2410.03183},
year={2024},
archivePrefix={arXiv},
eprint={2410.03183},
primaryClass={cs.CR cs.AR cs.ET}
} | bellemare2024research |
arxiv-665478 | 2410.03185 | EXAQ: Exponent Aware Quantization For LLMs Acceleration | <|reference_start|>EXAQ: Exponent Aware Quantization For LLMs Acceleration: Quantization has established itself as the primary approach for decreasing the computational and storage expenses associated with Large Language Models (LLMs) inference. The majority of current research emphasizes quantizing weights and activations to enable low-bit general-matrix-multiply (GEMM) operations, with the remaining non-linear operations executed at higher precision. In our study, we discovered that following the application of these techniques, the primary bottleneck in LLMs inference lies in the softmax layer. The softmax operation comprises three phases: exponent calculation, accumulation, and normalization, Our work focuses on optimizing the first two phases. We propose an analytical approach to determine the optimal clipping value for the input to the softmax function, enabling sub-4-bit quantization for LLMs inference. This method accelerates the calculations of both $e^x$ and $\sum(e^x)$ with minimal to no accuracy degradation. For example, in LLaMA1-30B, we achieve baseline performance with 2-bit quantization on the well-known "Physical Interaction: Question Answering" (PIQA) dataset evaluation. This ultra-low bit quantization allows, for the first time, an acceleration of approximately 4x in the accumulation phase. The combination of accelerating both $e^x$ and $\sum(e^x)$ results in a 36.9% acceleration in the softmax operation.<|reference_end|> | arxiv | @article{shkolnik2024exaq:,
title={EXAQ: Exponent Aware Quantization For LLMs Acceleration},
author={Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron
Banner, Kfir Yehuda Levy},
journal={arXiv preprint arXiv:2410.03185},
year={2024},
archivePrefix={arXiv},
eprint={2410.03185},
primaryClass={cs.LG cs.AI cs.PF}
} | shkolnik2024exaq: |
arxiv-665479 | 2410.03187 | Autonomous Character-Scene Interaction Synthesis from Text Instruction | <|reference_start|>Autonomous Character-Scene Interaction Synthesis from Text Instruction: Synthesizing human motions in 3D environments, particularly those with complex activities such as locomotion, hand-reaching, and human-object interaction, presents substantial demands for user-defined waypoints and stage transitions. These requirements pose challenges for current models, leading to a notable gap in automating the animation of characters from simple human inputs. This paper addresses this challenge by introducing a comprehensive framework for synthesizing multi-stage scene-aware interaction motions directly from a single text instruction and goal location. Our approach employs an auto-regressive diffusion model to synthesize the next motion segment, along with an autonomous scheduler predicting the transition for each action stage. To ensure that the synthesized motions are seamlessly integrated within the environment, we propose a scene representation that considers the local perception both at the start and the goal location. We further enhance the coherence of the generated motion by integrating frame embeddings with language input. Additionally, to support model training, we present a comprehensive motion-captured dataset comprising 16 hours of motion sequences in 120 indoor scenes covering 40 types of motions, each annotated with precise language descriptions. Experimental results demonstrate the efficacy of our method in generating high-quality, multi-stage motions closely aligned with environmental and textual conditions.<|reference_end|> | arxiv | @article{jiang2024autonomous,
title={Autonomous Character-Scene Interaction Synthesis from Text Instruction},
author={Nan Jiang, Zimo He, Zi Wang, Hongjie Li, Yixin Chen, Siyuan Huang,
Yixin Zhu},
journal={arXiv preprint arXiv:2410.03187},
year={2024},
archivePrefix={arXiv},
eprint={2410.03187},
primaryClass={cs.CV}
} | jiang2024autonomous |
arxiv-665480 | 2410.03188 | Looking into Concept Explanation Methods for Diabetic Retinopathy Classification | <|reference_start|>Looking into Concept Explanation Methods for Diabetic Retinopathy Classification: Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user's preferences into account.<|reference_end|> | arxiv | @article{storås2024looking,
title={Looking into Concept Explanation Methods for Diabetic Retinopathy
Classification},
author={Andrea M. Stor{aa}s and Josefine V. Sundgaard},
journal={Machine.Learning.for.Biomedical.Imaging. 2 (2024)},
year={2024},
doi={10.59275/j.melba.2024-e7fd},
archivePrefix={arXiv},
eprint={2410.03188},
primaryClass={cs.CV cs.AI}
} | storås2024looking |
arxiv-665481 | 2410.03189 | Generalizable Prompt Tuning for Vision-Language Models | <|reference_start|>Generalizable Prompt Tuning for Vision-Language Models: Prompt tuning for vision-language models such as CLIP involves optimizing the text prompts used to generate image-text pairs for specific downstream tasks. While hand-crafted or template-based prompts are generally applicable to a wider range of unseen classes, they tend to perform poorly in downstream tasks (i.e., seen classes). Learnable soft prompts, on the other hand, often perform well in downstream tasks but lack generalizability. Additionally, prior research has predominantly concentrated on the textual modality, with very few studies attempting to explore the prompt's generalization potential from the visual modality. Keeping these limitations in mind, we investigate how to prompt tuning to obtain both a competitive downstream performance and generalization. The study shows that by treating soft and hand-crafted prompts as dual views of the textual modality, and maximizing their mutual information, we can better ensemble task-specific and general semantic information. Moreover, to generate more expressive prompts, the study introduces a class-wise augmentation from the visual modality, resulting in significant robustness to a wider range of unseen classes. Extensive evaluations on several benchmarks report that the proposed approach achieves competitive results in terms of both task-specific performance and general abilities.<|reference_end|> | arxiv | @article{zhang2024generalizable,
title={Generalizable Prompt Tuning for Vision-Language Models},
author={Qian Zhang},
journal={arXiv preprint arXiv:2410.03189},
year={2024},
archivePrefix={arXiv},
eprint={2410.03189},
primaryClass={cs.CV}
} | zhang2024generalizable |
arxiv-665482 | 2410.03190 | Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization | <|reference_start|>Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization: Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer inference steps. While such models are attractive for applications due to the low inference cost and latency, fine-tuning them with a naive diffusion objective would result in degraded and blurry outputs. An intuitive alternative is to repeat the diffusion distillation process with a fine-tuned teacher model, which produces good results but is cumbersome and computationally intensive; the distillation training usually requires magnitude higher of training compute compared to fine-tuning for specific image styles. In this paper, we present an algorithm named pairwise sample optimization (PSO), which enables the direct fine-tuning of an arbitrary timestep-distilled diffusion model. PSO introduces additional reference images sampled from the current time-step distilled model, and increases the relative likelihood margin between the training images and reference images. This enables the model to retain its few-step generation ability, while allowing for fine-tuning of its output distribution. We also demonstrate that PSO is a generalized formulation which can be flexibly extended to both offline-sampled and online-sampled pairwise data, covering various popular objectives for diffusion model preference optimization. We evaluate PSO in both preference optimization and other fine-tuning tasks, including style transfer and concept customization. We show that PSO can directly adapt distilled models to human-preferred generation with both offline and online-generated pairwise preference image data. PSO also demonstrates effectiveness in style transfer and concept customization by directly tuning timestep-distilled diffusion models.<|reference_end|> | arxiv | @article{miao2024tuning,
title={Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample
Optimization},
author={Zichen Miao, Zhengyuan Yang, Kevin Lin, Ze Wang, Zicheng Liu, Lijuan
Wang, Qiang Qiu},
journal={arXiv preprint arXiv:2410.03190},
year={2024},
archivePrefix={arXiv},
eprint={2410.03190},
primaryClass={cs.CV}
} | miao2024tuning |
arxiv-665483 | 2410.03191 | Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data | <|reference_start|>Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data: Epilepsy affects over 50 million people globally, with EEG/MEG-based spike detection playing a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training, limiting the number of professionals available to analyze EEG/MEG data. To address this, various algorithmic approaches have been developed. However, current methods face challenges in handling varying channel configurations and in identifying the specific channels where spikes originate. This paper introduces a novel Nested Deep Learning (NDL) framework designed to overcome these limitations. NDL applies a weighted combination of signals across all channels, ensuring adaptability to different channel setups, and allows clinicians to identify key channels more accurately. Through theoretical analysis and empirical validation on real EEG/MEG datasets, NDL demonstrates superior accuracy in spike detection and channel localization compared to traditional methods. The results show that NDL improves prediction accuracy, supports cross-modality data integration, and can be fine-tuned for various neurophysiological applications.<|reference_end|> | arxiv | @article{wei2024nested,
title={Nested Deep Learning Model Towards A Foundation Model for Brain Signal
Data},
author={Fangyi Wei, Jiajie Mo, Kai Zhang, Haipeng Shen, Srikantan Nagarajan,
Fei Jiang},
journal={arXiv preprint arXiv:2410.03191},
year={2024},
archivePrefix={arXiv},
eprint={2410.03191},
primaryClass={stat.ML cs.LG}
} | wei2024nested |
arxiv-665484 | 2410.03192 | MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech | <|reference_start|>MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech: Text-to-speech (TTS) systems that scale up the amount of training data have achieved significant improvements in zero-shot speech synthesis. However, these systems have certain limitations: they require a large amount of training data, which increases costs, and often overlook prosody similarity. To address these issues, we propose MultiVerse, a zero-shot multi-task TTS system that is able to perform TTS or speech style transfer in zero-shot and cross-lingual conditions. MultiVerse requires much less training data than traditional data-driven approaches. To ensure zero-shot performance even with limited data, we leverage source-filter theory-based disentanglement, utilizing the prompt for modeling filter-related and source-related representations. Additionally, to further enhance prosody similarity, we adopt a prosody modeling approach combining prompt-based autoregressive and non-autoregressive methods. Evaluations demonstrate the remarkable zero-shot multi-task TTS performance of MultiVerse and show that MultiVerse not only achieves zero-shot TTS performance comparable to data-driven TTS systems with much less data, but also significantly outperforms other zero-shot TTS systems trained with the same small amount of data. In particular, our novel prosody modeling technique significantly contributes to MultiVerse's ability to generate speech with high prosody similarity to the given prompts. Our samples are available at https://nc-ai.github.io/speech/publications/multiverse/index.html<|reference_end|> | arxiv | @article{bak2024multiverse:,
title={MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech},
author={Taejun Bak, Youngsik Eom, SeungJae Choi, Young-Sun Joo},
journal={arXiv preprint arXiv:2410.03192},
year={2024},
archivePrefix={arXiv},
eprint={2410.03192},
primaryClass={eess.AS cs.AI cs.SD}
} | bak2024multiverse: |
arxiv-665485 | 2410.03194 | Parallel Corpus Augmentation using Masked Language Models | <|reference_start|>Parallel Corpus Augmentation using Masked Language Models: In this paper we propose a novel method of augmenting parallel text corpora which promises good quality and is also capable of producing many fold larger corpora than the seed corpus we start with. We do not need any additional monolingual corpora. We use Multi-Lingual Masked Language Model to mask and predict alternative words in context and we use Sentence Embeddings to check and select sentence pairs which are likely to be translations of each other. We cross check our method using metrics for MT Quality Estimation. We believe this method can greatly alleviate the data scarcity problem for all language pairs for which a reasonable seed corpus is available.<|reference_end|> | arxiv | @article{kumari2024parallel,
title={Parallel Corpus Augmentation using Masked Language Models},
author={Vibhuti Kumari and Narayana Murthy Kavi},
journal={arXiv preprint arXiv:2410.03194},
year={2024},
archivePrefix={arXiv},
eprint={2410.03194},
primaryClass={cs.CL}
} | kumari2024parallel |
arxiv-665486 | 2410.03195 | The Potential of Citizen Platforms for Requirements Engineering of Large Socio-Technical Software Systems | <|reference_start|>The Potential of Citizen Platforms for Requirements Engineering of Large Socio-Technical Software Systems: Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.<|reference_end|> | arxiv | @article{ruohonen2024the,
title={The Potential of Citizen Platforms for Requirements Engineering of Large
Socio-Technical Software Systems},
author={Jukka Ruohonen and Kalle Hjerppe},
journal={arXiv preprint arXiv:2410.03195},
year={2024},
archivePrefix={arXiv},
eprint={2410.03195},
primaryClass={cs.SE cs.CY}
} | ruohonen2024the |
arxiv-665487 | 2410.03197 | Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages | <|reference_start|>Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages: Automatic question generation (QG) serves a wide range of purposes, such as augmenting question-answering (QA) corpora, enhancing chatbot systems, and developing educational materials. Despite its importance, most existing datasets predominantly focus on English, resulting in a considerable gap in data availability for other languages. Cross-lingual transfer for QG (XLT-QG) addresses this limitation by allowing models trained on high-resource language datasets to generate questions in low-resource languages. In this paper, we propose a simple and efficient XLT-QG method that operates without the need for monolingual, parallel, or labeled data in the target language, utilizing a small language model. Our model, trained solely on English QA datasets, learns interrogative structures from a limited set of question exemplars, which are then applied to generate questions in the target language. Experimental results show that our method outperforms several XLT-QG baselines and achieves performance comparable to GPT-3.5-turbo across different languages. Additionally, the synthetic data generated by our model proves beneficial for training multilingual QA models. With significantly fewer parameters than large language models and without requiring additional training for target languages, our approach offers an effective solution for QG and QA tasks across various languages.<|reference_end|> | arxiv | @article{hwang2024cross-lingual,
title={Cross-lingual Transfer for Automatic Question Generation by Learning
Interrogative Structures in Target Languages},
author={Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee},
journal={arXiv preprint arXiv:2410.03197},
year={2024},
archivePrefix={arXiv},
eprint={2410.03197},
primaryClass={cs.CL}
} | hwang2024cross-lingual |
arxiv-665488 | 2410.03198 | PersoBench: Benchmarking Personalized Response Generation in Large Language Models | <|reference_start|>PersoBench: Benchmarking Personalized Response Generation in Large Language Models: While large language models (LLMs) have exhibited impressive conversational capabilities, their proficiency in delivering personalized responses remains unclear. Although recent benchmarks automatically evaluate persona consistency in role-playing contexts using LLM-based judgment, the evaluation of personalization in response generation remains underexplored. To address this gap, we present a new benchmark, PersoBench, to evaluate the personalization ability of LLMs in persona-aware dialogue generation within a zero-shot setting. We assess the performance of three open-source and three closed-source LLMs using well-known datasets and a range of metrics. Our analysis, conducted on three well-known persona-aware datasets, evaluates multiple dimensions of response quality, including fluency, diversity, coherence, and personalization, across both standard and chain-of-thought prompting methods. Our findings reveal that while LLMs excel at generating fluent and diverse responses, they are far from satisfactory in delivering personalized and coherent responses considering both the conversation context and the provided personas. Our benchmark implementation is available at https://github.com/salehafzoon/PersoBench.<|reference_end|> | arxiv | @article{afzoon2024persobench:,
title={PersoBench: Benchmarking Personalized Response Generation in Large
Language Models},
author={Saleh Afzoon, Usman Naseem, Amin Beheshti, Zahra Jamali},
journal={arXiv preprint arXiv:2410.03198},
year={2024},
archivePrefix={arXiv},
eprint={2410.03198},
primaryClass={cs.CL}
} | afzoon2024persobench: |
arxiv-665489 | 2410.03202 | Learning test generators for cyber-physical systems | <|reference_start|>Learning test generators for cyber-physical systems: Black-box runtime verification methods for cyber-physical systems can be used to discover errors in systems whose inputs and outputs are expressed as signals over time and their correctness requirements are specified in a temporal logic. Existing methods, such as requirement falsification, often focus on finding a single input that is a counterexample to system correctness. In this paper, we study how to create test generators that can produce multiple and diverse counterexamples for a single requirement. Several counterexamples expose system failures in varying input conditions and support the root cause analysis of the faults. We present the WOGAN algorithm to create such test generators automatically. The algorithm works by training iteratively a Wasserstein generative adversarial network that models the target distribution of the uniform distribution on the set of counterexamples. WOGAN is an algorithm that trains generative models that act as test generators for runtime verification. The training is performed online without the need for a previous model or dataset. We also propose criteria to evaluate such test generators. We evaluate the trained generators on several well-known problems including the ARCH-COMP falsification benchmarks. Our experimental results indicate that generators trained by the WOGAN algorithm are as effective as state-of-the-art requirement falsification algorithms while producing tests that are as diverse as a sample from uniform random sampling. We conclude that WOGAN is a viable method to produce test generators automatically and that these test generators can generate multiple and diverse counterexamples for the runtime verification of cyber-physical systems.<|reference_end|> | arxiv | @article{peltomäki2024learning,
title={Learning test generators for cyber-physical systems},
author={Jarkko Peltom"aki, Ivan Porres},
journal={arXiv preprint arXiv:2410.03202},
year={2024},
archivePrefix={arXiv},
eprint={2410.03202},
primaryClass={cs.LG cs.SE}
} | peltomäki2024learning |
arxiv-665490 | 2410.03203 | Learning Semantic Structure through First-Order-Logic Translation | <|reference_start|>Learning Semantic Structure through First-Order-Logic Translation: In this paper, we study whether transformer-based language models can extract predicate argument structure from simple sentences. We firstly show that language models sometimes confuse which predicates apply to which objects. To mitigate this, we explore two tasks: question answering (Q/A), and first order logic (FOL) translation, and two regimes, prompting and finetuning. In FOL translation, we finetune several large language models on synthetic datasets designed to gauge their generalization abilities. For Q/A, we finetune encoder models like BERT and RoBERTa and use prompting for LLMs. The results show that FOL translation for LLMs is better suited to learn predicate argument structure.<|reference_end|> | arxiv | @article{chaturvedi2024learning,
title={Learning Semantic Structure through First-Order-Logic Translation},
author={Akshay Chaturvedi, Nicholas Asher},
journal={arXiv preprint arXiv:2410.03203},
year={2024},
archivePrefix={arXiv},
eprint={2410.03203},
primaryClass={cs.CL cs.LG}
} | chaturvedi2024learning |
arxiv-665491 | 2410.03204 | Optimized Topology Control for IoT Networks using Graph-based Localization | <|reference_start|>Optimized Topology Control for IoT Networks using Graph-based Localization: The key research question we are addressing in this paper, is how local distance information can be integrated into the global structure determination, in the form of network graphs realization for IoT networks. IoT networks will be pervading every walk of life over the next few years with the aim of improving quality of life and enhancing surrounding living conditions, while balancing available resources, like energy and computational power. As we deal with massive number of heterogeneous devices contributing to each IoT network, it is of paramount importance that the IoT network topology can be designed and controlled in such a way that coverage and throughput can be maximized using a minimum number of devices, while tackling challenges like poor link quality and interference. We tackle the above-mentioned problem of topology design and control through our designed graph-realization concept. End-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph. The stitching is done in a way that transmit power and information rate are optimized while reducing error probability.<|reference_end|> | arxiv | @article{dey2024optimized,
title={Optimized Topology Control for IoT Networks using Graph-based
Localization},
author={Indrakshi Dey and Nicola Marchetti},
journal={arXiv preprint arXiv:2410.03204},
year={2024},
archivePrefix={arXiv},
eprint={2410.03204},
primaryClass={eess.SY cs.SY}
} | dey2024optimized |
arxiv-665492 | 2410.03205 | A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems | <|reference_start|>A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems: In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.<|reference_end|> | arxiv | @article{osaba2024a,
title={A Tutorial on the Design, Experimentation and Application of
Metaheuristic Algorithms to Real-World Optimization Problems},
author={Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser, Antonio J.
Nebro, Daniel Molina, Antonio LaTorre, Ponnuthurai N.Suganthan, Carlos A.
Coello Coello, Francisco Herrera},
journal={Swarm and Evolutionary Computation, vol. 64, p. 100888, Jul. 2021},
year={2024},
doi={10.1016/j.swevo.2021.100888},
archivePrefix={arXiv},
eprint={2410.03205},
primaryClass={cs.NE cs.AI}
} | osaba2024a |
arxiv-665493 | 2410.03207 | StoryNavi: On-Demand Narrative-Driven Reconstruction of Video Play With Generative AI | <|reference_start|>StoryNavi: On-Demand Narrative-Driven Reconstruction of Video Play With Generative AI: Manually navigating lengthy videos to seek information or answer questions can be a tedious and time-consuming task for users. We introduce StoryNavi, a novel system powered by VLLMs for generating customised video play experiences by retrieving materials from original videos. It directly answers users' query by constructing non-linear sequence with identified relevant clips to form a cohesive narrative. StoryNavi offers two modes of playback of the constructed video plays: 1) video-centric, which plays original audio and skips irrelevant segments, and 2) narrative-centric, narration guides the experience, and the original audio is muted. Our technical evaluation showed adequate retrieval performance compared to human retrieval. Our user evaluation shows that maintaining narrative coherence significantly enhances user engagement when viewing disjointed video segments. However, factors like video genre, content, and the query itself may lead to varying user preferences for the playback mode.<|reference_end|> | arxiv | @article{xu2024storynavi:,
title={StoryNavi: On-Demand Narrative-Driven Reconstruction of Video Play With
Generative AI},
author={Alston Lantian Xu, Tianwei Ma, Tianmeng Liu, Can Liu, Alvaro
Cassinelli},
journal={arXiv preprint arXiv:2410.03207},
year={2024},
archivePrefix={arXiv},
eprint={2410.03207},
primaryClass={cs.HC}
} | xu2024storynavi: |
arxiv-665494 | 2410.03208 | SPHINX: Structural Prediction using Hypergraph Inference Network | <|reference_start|>SPHINX: Structural Prediction using Hypergraph Inference Network: The importance of higher-order relations is widely recognized in a large number of real-world systems. However, annotating them is a tedious and sometimes impossible task. Consequently, current approaches for data modelling either ignore the higher-order interactions altogether or simplify them into pairwise connections. In order to facilitate higher-order processing, even when a hypergraph structure is not available, we introduce Structural Prediction using Hypergraph Inference Network (SPHINX), a model that learns to infer a latent hypergraph structure in an unsupervised way, solely from the final node-level signal. The model consists of a soft, differentiable clustering method used to sequentially predict, for each hyperedge, the probability distribution over the nodes and a sampling algorithm that converts them into an explicit hypergraph structure. We show that the recent advancement in k-subset sampling represents a suitable tool for producing discrete hypergraph structures, addressing some of the training instabilities exhibited by prior works. The resulting model can generate the higher-order structure necessary for any modern hypergraph neural network, facilitating the capture of higher-order interaction in domains where annotating them is difficult. Through extensive ablation studies and experiments conducted on two challenging datasets for trajectory prediction, we demonstrate that our model is capable of inferring suitable latent hypergraphs, that are interpretable and enhance the final performance.<|reference_end|> | arxiv | @article{duta2024sphinx:,
title={SPHINX: Structural Prediction using Hypergraph Inference Network},
author={Iulia Duta and Pietro Li`o},
journal={arXiv preprint arXiv:2410.03208},
year={2024},
archivePrefix={arXiv},
eprint={2410.03208},
primaryClass={cs.LG}
} | duta2024sphinx: |
arxiv-665495 | 2410.03210 | Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness | <|reference_start|>Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness: Frameworks and DSLs auto-generating code have traditionally relied on human experts developing them to have in place rigorous methods to assure the legality of the applied code transformations. Machine Learning (ML) is gaining wider adoption as a means to auto-generate code optimised for the hardware target. However, ML solutions, and in particular black-box DNNs, provide no such guarantees on legality. In this paper we propose a library, Tadashi, which leverages the polyhedral model to empower researchers seeking to curate datasets crucial for applying ML in code-generation. Tadashi provides the ability to reliably and practically check the legality of candidate transformations on polyhedral schedules applied on a baseline reference code. We provide a proof that our library guarantees the legality of generated transformations, and demonstrate its lightweight practical cost. Tadashi is available at https://github.com/vatai/tadashi/.<|reference_end|> | arxiv | @article{vatai2024tadashi:,
title={Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed
Correctness},
author={Emil Vatai, Aleksandr Drozd, Ivan R. Ivanov, Yinghao Ren and Mohamed
Wahib},
journal={arXiv preprint arXiv:2410.03210},
year={2024},
archivePrefix={arXiv},
eprint={2410.03210},
primaryClass={cs.LG}
} | vatai2024tadashi: |
arxiv-665496 | 2410.03211 | CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments | <|reference_start|>CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments: Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult due to limited human supervision and the reliance on self-labeling by patients, making data collection and supervised learning particularly challenging. To address this issue, we introduce CUDLE (Cannabis Use Detection with Label Efficiency), a novel framework that leverages self-supervised learning with real-world wearable sensor data to tackle a pressing healthcare challenge: the automatic detection of cannabis consumption in free-living environments. CUDLE identifies cannabis consumption moments using sensor-derived data through a contrastive learning framework. It first learns robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, enabling CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 hours of wearable sensor data alongside user-reported cannabis use moments through EMA (Ecological Momentary Assessment) methods. Our extensive analysis using the collected data shows that CUDLE achieves a higher accuracy of 73.4%, compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% less labels, but also reaches peak performance with far fewer subjects.<|reference_end|> | arxiv | @article{azghan2024cudle:,
title={CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in
Uncontrolled Environments},
author={Reza Rahimi Azghan, Nicholas C. Glodosky, Ramesh Kumar Sah, Carrie
Cuttler, Ryan McLaughlin, Michael J. Cleveland, Hassan Ghasemzadeh},
journal={arXiv preprint arXiv:2410.03211},
year={2024},
archivePrefix={arXiv},
eprint={2410.03211},
primaryClass={cs.LG}
} | azghan2024cudle: |
arxiv-665497 | 2410.03212 | Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models | <|reference_start|>Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models: Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. Despite this progress, the vast scale of tool retrieval remains challenging due to stringent input length constraints. In response, we propose a pre-retrieval strategy from an extensive repository, effectively framing the problem as the massive tool retrieval (MTR) task. We introduce the MTRB (massive tool retrieval benchmark) to evaluate real-world tool-augmented LLM scenarios with a large number of tools. This benchmark is designed for low-resource scenarios and includes a diverse collection of tools with descriptions refined for consistency and clarity. It consists of three subsets, each containing 90 test samples and 10 training samples. To handle the low-resource MTR task, we raise a new query-tool alignment (QTA) framework leverages LLMs to enhance query-tool alignment by rewriting user queries through ranking functions and the direct preference optimization (DPO) method. This approach consistently outperforms existing state-of-the-art models in top-5 and top-10 retrieval tasks across the MTRB benchmark, with improvements up to 93.28% based on the metric Sufficiency@k, which measures the adequacy of tool retrieval within the first k results. Furthermore, ablation studies validate the efficacy of our framework, highlighting its capacity to optimize performance even with limited annotated samples. Specifically, our framework achieves up to 78.53% performance improvement in Sufficiency@k with just a single annotated sample. Additionally, QTA exhibits strong cross-dataset generalizability, emphasizing its potential for real-world applications.<|reference_end|> | arxiv | @article{zhang2024data-efficient,
title={Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach
for Query-Tool Alignment with Language Models},
author={Yuxiang Zhang, Xin Fan, Junjie Wang, Chongxian Chen, Fan Mo, Tetsuya
Sakai, Hayato Yamana},
journal={arXiv preprint arXiv:2410.03212},
year={2024},
archivePrefix={arXiv},
eprint={2410.03212},
primaryClass={cs.IR}
} | zhang2024data-efficient |
arxiv-665498 | 2410.03213 | Computing largest minimum color-spanning intervals of imprecise points | <|reference_start|>Computing largest minimum color-spanning intervals of imprecise points: We study a geometric facility location problem under imprecision. Given $n$ unit intervals in the real line, each with one of $k$ colors, the goal is to place one point in each interval such that the resulting \emph{minimum color-spanning interval} is as large as possible. A minimum color-spanning interval is an interval of minimum size that contains at least one point from a given interval of each color. We prove that if the input intervals are pairwise disjoint, the problem can be solved in $O(n)$ time, even for intervals of arbitrary length. For overlapping intervals, the problem becomes much more difficult. Nevertheless, we show that it can be solved in $O(n \log^2 n)$ time when $k=2$, by exploiting several structural properties of candidate solutions, combined with a number of advanced algorithmic techniques. Interestingly, this shows a sharp contrast with the 2-dimensional version of the problem, recently shown to be NP-hard.<|reference_end|> | arxiv | @article{acharyya2024computing,
title={Computing largest minimum color-spanning intervals of imprecise points},
author={Ankush Acharyya, Vahideh Keikha, Maria Saumell, Rodrigo I. Silveira},
journal={arXiv preprint arXiv:2410.03213},
year={2024},
archivePrefix={arXiv},
eprint={2410.03213},
primaryClass={cs.CG cs.DS}
} | acharyya2024computing |
arxiv-665499 | 2410.03215 | NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task | <|reference_start|>NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task: In this paper, we describe our system for the WMT 24 shared task of Low-Resource Indic Language Translation. We consider eng $\leftrightarrow$ {as, kha, lus, mni} as participating language pairs. In this shared task, we explore the finetuning of a pre-trained model motivated by the pre-trained objective of aligning embeddings closer by alignment augmentation \cite{lin-etal-2020-pre} for 22 scheduled Indian languages. Our primary system is based on language-specific finetuning on a pre-trained model. We achieve chrF2 scores of 50.6, 42.3, 54.9, and 66.3 on the official public test set for eng$\rightarrow$as, eng$\rightarrow$kha, eng$\rightarrow$lus, eng$\rightarrow$mni respectively. We also explore multilingual training with/without language grouping and layer-freezing. Our code, models, and generated translations are available here: https://github.com/pramitsahoo/WMT2024-LRILT.<|reference_end|> | arxiv | @article{sahoo2024nlip_lab-iith,
title={NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task},
author={Pramit Sahoo, Maharaj Brahma, Maunendra Sankar Desarkar},
journal={arXiv preprint arXiv:2410.03215},
year={2024},
archivePrefix={arXiv},
eprint={2410.03215},
primaryClass={cs.CL cs.AI}
} | sahoo2024nlip_lab-iith |
arxiv-665500 | 2410.03217 | An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management | <|reference_start|>An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management: Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security.<|reference_end|> | arxiv | @article{gupta2024an,
title={An Intelligent Quantum Cyber-Security Framework for Healthcare Data
Management},
author={Kishu Gupta, Deepika Saxena, Pooja Rani, Jitendra Kumar, Aaisha
Makkar, Ashutosh Kumar Singh, Chung-Nan Lee},
journal={IEEE Transactions on Automation Science and Engineering (2024)},
year={2024},
doi={10.1109/TASE.2024.3456209},
archivePrefix={arXiv},
eprint={2410.03217},
primaryClass={cs.CR}
} | gupta2024an |
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