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arxiv-662901
|
2409.18962
|
Exploring Token Pruning in Vision State Space Models
|
<|reference_start|>Exploring Token Pruning in Vision State Space Models: State Space Models (SSMs) have the advantage of keeping linear computational complexity compared to attention modules in transformers, and have been applied to vision tasks as a new type of powerful vision foundation model. Inspired by the observations that the final prediction in vision transformers (ViTs) is only based on a subset of most informative tokens, we take the novel step of enhancing the efficiency of SSM-based vision models through token-based pruning. However, direct applications of existing token pruning techniques designed for ViTs fail to deliver good performance, even with extensive fine-tuning. To address this issue, we revisit the unique computational characteristics of SSMs and discover that naive application disrupts the sequential token positions. This insight motivates us to design a novel and general token pruning method specifically for SSM-based vision models. We first introduce a pruning-aware hidden state alignment method to stabilize the neighborhood of remaining tokens for performance enhancement. Besides, based on our detailed analysis, we propose a token importance evaluation method adapted for SSM models, to guide the token pruning. With efficient implementation and practical acceleration methods, our method brings actual speedup. Extensive experiments demonstrate that our approach can achieve significant computation reduction with minimal impact on performance across different tasks. Notably, we achieve 81.7\% accuracy on ImageNet with a 41.6\% reduction in the FLOPs for pruned PlainMamba-L3. Furthermore, our work provides deeper insights into understanding the behavior of SSM-based vision models for future research.<|reference_end|>
|
arxiv
|
@article{zhan2024exploring,
title={Exploring Token Pruning in Vision State Space Models},
author={Zheng Zhan, Zhenglun Kong, Yifan Gong, Yushu Wu, Zichong Meng, Hangyu
Zheng, Xuan Shen, Stratis Ioannidis, Wei Niu, Pu Zhao, Yanzhi Wang},
journal={arXiv preprint arXiv:2409.18962},
year={2024},
archivePrefix={arXiv},
eprint={2409.18962},
primaryClass={cs.CV cs.AI cs.LG}
}
|
zhan2024exploring
|
arxiv-662902
|
2409.18964
|
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
|
<|reference_start|>PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation: We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with a data-driven video generation process, enabling plausible image-space dynamics. At the heart of our system are three core components: (i) an image understanding module that effectively captures the geometry, materials, and physical parameters of the image; (ii) an image-space dynamics simulation model that utilizes rigid-body physics and inferred parameters to simulate realistic behaviors; and (iii) an image-based rendering and refinement module that leverages generative video diffusion to produce realistic video footage featuring the simulated motion. The resulting videos are realistic in both physics and appearance and are even precisely controllable, showcasing superior results over existing data-driven image-to-video generation works through quantitative comparison and comprehensive user study. PhysGen's resulting videos can be used for various downstream applications, such as turning an image into a realistic animation or allowing users to interact with the image and create various dynamics. Project page: https://stevenlsw.github.io/physgen/<|reference_end|>
|
arxiv
|
@article{liu2024physgen:,
title={PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation},
author={Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, Shenlong Wang},
journal={arXiv preprint arXiv:2409.18964},
year={2024},
archivePrefix={arXiv},
eprint={2409.18964},
primaryClass={cs.CV cs.AI cs.LG}
}
|
liu2024physgen:
|
arxiv-662903
|
2409.18967
|
Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
|
<|reference_start|>Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis: Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.<|reference_end|>
|
arxiv
|
@article{zhao2024brain,
title={Brain Network Diffusion-Driven fMRI Connectivity Augmentation for
Enhanced Autism Spectrum Disorder Diagnosis},
author={Haokai Zhao, Haowei Lou, Lina Yao, Yu Zhang},
journal={arXiv preprint arXiv:2409.18967},
year={2024},
archivePrefix={arXiv},
eprint={2409.18967},
primaryClass={q-bio.NC cs.CV}
}
|
zhao2024brain
|
arxiv-662904
|
2409.18968
|
Safety challenges of AI in medicine
|
<|reference_start|>Safety challenges of AI in medicine: Recent advancements in artificial intelligence (AI), particularly in deep learning and large language models (LLMs), have accelerated their integration into medicine. However, these developments have also raised public concerns about the safe application of AI. In healthcare, these concerns are especially pertinent, as the ethical and secure deployment of AI is crucial for protecting patient health and privacy. This review examines potential risks in AI practices that may compromise safety in medicine, including reduced performance across diverse populations, inconsistent operational stability, the need for high-quality data for effective model tuning, and the risk of data breaches during model development and deployment. For medical practitioners, patients, and researchers, LLMs provide a convenient way to interact with AI and data through language. However, their emergence has also amplified safety concerns, particularly due to issues like hallucination. Second part of this article explores safety issues specific to LLMs in medical contexts, including limitations in processing complex logic, challenges in aligning AI objectives with human values, the illusion of understanding, and concerns about diversity. Thoughtful development of safe AI could accelerate its adoption in real-world medical settings.<|reference_end|>
|
arxiv
|
@article{wang2024safety,
title={Safety challenges of AI in medicine},
author={Xiaoye Wang, Nicole Xi Zhang, Hongyu He, Trang Nguyen, Kun-Hsing Yu,
Hao Deng, Cynthia Brandt, Danielle S. Bitterman, Ling Pan, Ching-Yu Cheng,
James Zou, Dianbo Liu},
journal={arXiv preprint arXiv:2409.18968},
year={2024},
archivePrefix={arXiv},
eprint={2409.18968},
primaryClass={cs.CY cs.AI cs.LG}
}
|
wang2024safety
|
arxiv-662905
|
2409.18969
|
Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources
|
<|reference_start|>Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources: The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and BERT-based-case-SQuad2 predictions. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses BERT to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts.<|reference_end|>
|
arxiv
|
@article{fondi2024integrating,
title={Integrating SPARQL and LLMs for Question Answering over Scholarly Data
Sources},
author={Fomubad Borista Fondi, Azanzi Jiomekong Fidel},
journal={arXiv preprint arXiv:2409.18969},
year={2024},
archivePrefix={arXiv},
eprint={2409.18969},
primaryClass={cs.IR cs.AI}
}
|
fondi2024integrating
|
arxiv-662906
|
2409.18970
|
Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions
|
<|reference_start|>Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions: Value at Risk (VaR) and stress testing are two of the most widely used approaches in portfolio risk management to estimate potential market value losses under adverse market moves. VaR quantifies potential loss in value over a specified horizon (such as one day or ten days) at a desired confidence level (such as 95'th percentile). In scenario design and stress testing, the goal is to construct extreme market scenarios such as those involving severe recession or a specific event of concern (such as a rapid increase in rates or a geopolitical event), and quantify potential impact of such scenarios on the portfolio. The goal of this paper is to propose an approach for incorporating prevailing market conditions in stress scenario design and estimation of VaR so that they provide more accurate and realistic insights about portfolio risk over the near term. The proposed approach is based on historical data where historical observations of market changes are given more weight if a certain period in history is "more similar" to the prevailing market conditions. Clusters of market conditions are identified using a Machine Learning approach called Variational Inference (VI) where for each cluster future changes in portfolio value are similar. VI based algorithm uses optimization techniques to obtain analytical approximations of the posterior probability density of cluster assignments (market regimes) and probabilities of different outcomes for changes in portfolio value. Covid related volatile period around the year 2020 is used to illustrate the performance of the proposed approach and in particular show how VaR and stress scenarios adapt quickly to changing market conditions. Another advantage of the proposed approach is that classification of market conditions into clusters can provide useful insights about portfolio performance under different market conditions.<|reference_end|>
|
arxiv
|
@article{nagpal2024portfolio,
title={Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current
Market Conditions},
author={Krishan Mohan Nagpal},
journal={arXiv preprint arXiv:2409.18970},
year={2024},
archivePrefix={arXiv},
eprint={2409.18970},
primaryClass={q-fin.CP cs.LG}
}
|
nagpal2024portfolio
|
arxiv-662907
|
2409.18971
|
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better
|
<|reference_start|>Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better: In this paper, we present our solutions for emotion recognition in the sub-challenges of Multimodal Emotion Recognition Challenge (MER2024). To mitigate the modal competition issue between audio and text, we adopt an early fusion strategy based on a large language model, where joint training of audio and text is conducted initially. And the joint Audio-Text modal feature will be late-fused with other unimodal features. In order to solve the problems of data insufficiency and class imbalance, We use multiple turns of multi-model voting for data mining. Moreover, to enhance the quality of audio features, we employ speech source separation to preprocess audios. Our model ranks \textbf{2nd} in both MER2024-SEMI and MER2024-NOISE, validating our method's effectiveness.<|reference_end|>
|
arxiv
|
@article{ge2024early,
title={Early Joint Learning of Emotion Information Makes MultiModal Model
Understand You Better},
author={Mengying Ge, Mingyang Li, Dongkai Tang, Pengbo Li, Kuo Liu, Shuhao
Deng, Songbai Pu, Long Liu, Yang Song, Tao Zhang},
journal={arXiv preprint arXiv:2409.18971},
year={2024},
archivePrefix={arXiv},
eprint={2409.18971},
primaryClass={cs.MM cs.AI cs.SD eess.AS}
}
|
ge2024early
|
arxiv-662908
|
2409.18972
|
Instance Configuration for Sustainable Job Shop Scheduling
|
<|reference_start|>Instance Configuration for Sustainable Job Shop Scheduling: The Job Shop Scheduling Problem (JSP) is a pivotal challenge in operations research and is essential for evaluating the effectiveness and performance of scheduling algorithms. Scheduling problems are a crucial domain in combinatorial optimization, where resources (machines) are allocated to job tasks to minimize the completion time (makespan) alongside other objectives like energy consumption. This research delves into the intricacies of JSP, focusing on optimizing performance metrics and minimizing energy consumption while considering various constraints such as deadlines and release dates. Recognizing the multi-dimensional nature of benchmarking in JSP, this study underscores the significance of reference libraries and datasets like JSPLIB in enriching algorithm evaluation. The research highlights the importance of problem instance characteristics, including job and machine numbers, processing times, and machine availability, emphasizing the complexities introduced by energy consumption considerations. An innovative instance configurator is proposed, equipped with parameters such as the number of jobs, machines, tasks, and speeds, alongside distributions for processing times and energy consumption. The generated instances encompass various configurations, reflecting real-world scenarios and operational constraints. These instances facilitate comprehensive benchmarking and evaluation of scheduling algorithms, particularly in contexts of energy efficiency. A comprehensive set of 500 test instances has been generated and made publicly available, promoting further research and benchmarking in JSP. These instances enable robust analyses and foster collaboration in developing advanced, energy-efficient scheduling solutions by providing diverse scenarios.<|reference_end|>
|
arxiv
|
@article{perez2024instance,
title={Instance Configuration for Sustainable Job Shop Scheduling},
author={Christian Perez, Carlos March and Miguel A. Salido},
journal={arXiv preprint arXiv:2409.18972},
year={2024},
archivePrefix={arXiv},
eprint={2409.18972},
primaryClass={cs.DC math.OC}
}
|
perez2024instance
|
arxiv-662909
|
2409.18973
|
EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG
|
<|reference_start|>EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG: Motor pattern recognition paradigms are the main forms of Brain-Computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent Channel-Specific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEG-EMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability.<|reference_end|>
|
arxiv
|
@article{he2024eeg-emg,
title={EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of
EEG and EMG},
author={ZhengXiao He, Minghong Cai, Letian Li, Siyuan Tian, Ren-Jie Dai},
journal={arXiv preprint arXiv:2409.18973},
year={2024},
archivePrefix={arXiv},
eprint={2409.18973},
primaryClass={eess.SP cs.AI q-bio.NC}
}
|
he2024eeg-emg
|
arxiv-662910
|
2409.18974
|
Neural Product Importance Sampling via Warp Composition
|
<|reference_start|>Neural Product Importance Sampling via Warp Composition: Achieving high efficiency in modern photorealistic rendering hinges on using Monte Carlo sampling distributions that closely approximate the illumination integral estimated for every pixel. Samples are typically generated from a set of simple distributions, each targeting a different factor in the integrand, which are combined via multiple importance sampling. The resulting mixture distribution can be far from the actual product of all factors, leading to sub-optimal variance even for direct-illumination estimation. We present a learning-based method that uses normalizing flows to efficiently importance sample illumination product integrals, e.g., the product of environment lighting and material terms. Our sampler composes a flow head warp with an emitter tail warp. The small conditional head warp is represented by a neural spline flow, while the large unconditional tail is discretized per environment map and its evaluation is instant. If the conditioning is low-dimensional, the head warp can be also discretized to achieve even better performance. We demonstrate variance reduction over prior methods on a range of applications comprising complex geometry, materials and illumination.<|reference_end|>
|
arxiv
|
@article{litalien2024neural,
title={Neural Product Importance Sampling via Warp Composition},
author={Joey Litalien, Milov{s} Hav{s}an, Fujun Luan, Krishna Mullia, and
Iliyan Georgiev},
journal={arXiv preprint arXiv:2409.18974},
year={2024},
doi={10.1145/3680528.3687566},
archivePrefix={arXiv},
eprint={2409.18974},
primaryClass={cs.CV cs.GR}
}
|
litalien2024neural
|
arxiv-662911
|
2409.18976
|
Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns
|
<|reference_start|>Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns: The proliferation of complex online media has accelerated the process of ideology formation, influenced by stakeholders through advertising channels. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. There are technical challenges in describing the optimal budget allocation between channels over time, which involves defining the finite vector structure of controls on the chart. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes like revenue and ROI. Therefore, the strategy for media budget allocation is primarily an exercise focused on cost and achieving goals, by identifying a specific framework for a media program. Numerous researchers optimize the achievement and frequency of media selection models to aid superior planning decisions amid complexity and vast information availability. In this study, we present a planning model using the media mix model for advertising construction campaigns. Additionally, a decision-making strategy centered on FMEA identifies and prioritizes financial risk factors of the media system in companies. Despite some limitations, this research proposes a decision-making approach based on Z-number theory. To address the drawbacks of the RPN score, the suggested decision-making methodology integrates Z-SWARA and Z-WASPAS techniques with the FMEA method.<|reference_end|>
|
arxiv
|
@article{lonbar2024prioritizing,
title={Prioritizing Risk Factors in Media Entrepreneurship on Social Networks:
Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk
Management in Advertising Construction Campaigns},
author={Ahmad Gholizadeh Lonbar, Hamidreza Hasanzadeh, Fahimeh Asgari, Hajar
Kazemi Naeini, Roya Shomali, Saeed Asadi},
journal={arXiv preprint arXiv:2409.18976},
year={2024},
archivePrefix={arXiv},
eprint={2409.18976},
primaryClass={cs.CY cs.SI}
}
|
lonbar2024prioritizing
|
arxiv-662912
|
2409.18977
|
Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading
|
<|reference_start|>Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading: In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11\%, increasing the final user utility function value by 0.24\%, and decreasing the variance of results by 95.45\%.<|reference_end|>
|
arxiv
|
@article{xia2024dynamic,
title={Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge
Offloading},
author={Yun Xia, Hai Xue, Di Zhang, Shahid Mumtaz, Xiaolong Xu, Joel J. P. C.
Rodrigues},
journal={arXiv preprint arXiv:2409.18977},
year={2024},
archivePrefix={arXiv},
eprint={2409.18977},
primaryClass={cs.NI cs.GT}
}
|
xia2024dynamic
|
arxiv-662913
|
2409.18978
|
Pronoun Logic
|
<|reference_start|>Pronoun Logic: Particularly in transgender and nonbinary (TGNB) communities, it is an increasingly common practice to publicly share one's personal pronouns so that we may be gendered correctly in others' speech. Many of us have nuanced desires for how we are gendered, leading us to use more complex descriptions of our wishes; for example, the descriptor 'she/they'. We observe that these descriptions of our wishes have the structure of a little language all their own. We thus propose formal logic as a tool for expressing one's personal pronouns and potentially other aspects of gender. We explore three potential logical foundations (linear logic, temporal logic, and free logic with definite descriptions) and their trade-offs. Our foremost motivation for this proposal is play, affirming that one can be both a logician and TGNB at the same time. We present formalization as something that can continue to evolve over time with society's understanding of gender. This implies that outreach is a major potential application: we can show TGNB youth that they belong in logic and have a unique contribution to make. Tools for evaluating whether one's pronouns are respected are an application as well.<|reference_end|>
|
arxiv
|
@article{bohrer2024pronoun,
title={Pronoun Logic},
author={Rose Bohrer and Ashe Neth},
journal={arXiv preprint arXiv:2409.18978},
year={2024},
archivePrefix={arXiv},
eprint={2409.18978},
primaryClass={cs.CL}
}
|
bohrer2024pronoun
|
arxiv-662914
|
2409.18980
|
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
|
<|reference_start|>IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web: Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benchmark specifically for assessing the Image-to-Web conversion proficiency of these large models. Primarily, it is essential to ensure the integrity of the web elements generated. These elements comprise visible and invisible categories. Previous evaluation methods (e.g., BLEU) are notably susceptible to significant alterations due to the presence of invisible elements in Web. Furthermore, it is crucial to measure the layout information of web pages, referring to the positional relationships between elements, which is overlooked by previous work. To address challenges, we have curated and aligned a benchmark of images and corresponding web codes (IW-Bench). Specifically, we propose the Element Accuracy, which tests the completeness of the elements by parsing the Document Object Model (DOM) tree. Layout Accuracy is also proposed to analyze the positional relationships of elements by converting DOM tree into a common subsequence. Besides, we design a five-hop multimodal Chain-of-Thought Prompting for better performance, which contains five hop: 1) SoM prompt injection. 2) Inferring Elements. 3) Inferring Layout. 4) Inferring Web code. 5) Reflection. Our benchmark comprises 1200 pairs of images and web codes with varying levels of difficulty. We have conducted extensive experiments on existing large multimodal models, offering insights into their performance and areas for improvement in image-to-web domain.<|reference_end|>
|
arxiv
|
@article{guo2024iw-bench:,
title={IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web},
author={Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia
Du, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li},
journal={arXiv preprint arXiv:2409.18980},
year={2024},
archivePrefix={arXiv},
eprint={2409.18980},
primaryClass={cs.CL cs.AI cs.CV}
}
|
guo2024iw-bench:
|
arxiv-662915
|
2409.18982
|
Aligning Robot Navigation Behaviors with Human Intentions and Preferences
|
<|reference_start|>Aligning Robot Navigation Behaviors with Human Intentions and Preferences: Recent advances in the field of machine learning have led to new ways for mobile robots to acquire advanced navigational capabilities. However, these learning-based methods raise the possibility that learned navigation behaviors may not align with the intentions and preferences of people, a problem known as value misalignment. To mitigate this risk, this dissertation aims to answer the question: "How can we use machine learning methods to align the navigational behaviors of autonomous mobile robots with human intentions and preferences?" First, this dissertation addresses this question by introducing a new approach to learning navigation behaviors by imitating human-provided demonstrations of the intended navigation task. This contribution allows mobile robots to acquire autonomous visual navigation capabilities through imitation, using a novel objective function that encourages the agent to align with the human's navigation objectives and penalizes misalignment. Second, this dissertation introduces two algorithms to enhance terrain-aware off-road navigation for mobile robots by learning visual terrain awareness in a self-supervised manner. This contribution enables mobile robots to respect a human operator's preferences for navigating different terrains in urban outdoor environments, while extrapolating these preferences to visually novel terrains by leveraging multi-modal representations. Finally, in the context of robot navigation in human-occupied environments, this dissertation introduces a dataset and an algorithm for robot navigation in a socially compliant manner in both indoor and outdoor environments. In summary, the contributions in this dissertation take significant steps toward addressing the value alignment problem in autonomous navigation, enabling mobile robots to navigate autonomously with objectives that align with human intentions and preferences.<|reference_end|>
|
arxiv
|
@article{karnan2024aligning,
title={Aligning Robot Navigation Behaviors with Human Intentions and
Preferences},
author={Haresh Karnan},
journal={The University of Texas at Austin, May 2024},
year={2024},
archivePrefix={arXiv},
eprint={2409.18982},
primaryClass={cs.RO cs.AI cs.LG}
}
|
karnan2024aligning
|
arxiv-662916
|
2409.18984
|
Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
|
<|reference_start|>Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language: This work investigates the identification of Charismatic Leadership Tactics (CLTs) in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Based on an own extensive corpus of CLTs generated and curated for this task, our methodology entails training a machine learning model that is capable of accurately identifying the presence of these tactics in natural language. A performance evaluation is conducted to assess the effectiveness of our model in detecting CLTs. We find that the total accuracy over the detection of all CLTs is 98.96\% The results of this study have significant implications for research in psychology and management, offering potential methods to simplify the currently elaborate assessment of charisma in texts.<|reference_end|>
|
arxiv
|
@article{saeid2024harnessing,
title={Harnessing Large Language Models: Fine-tuned BERT for Detecting
Charismatic Leadership Tactics in Natural Language},
author={Yasser Saeid, Felix Neub"urger, Stefanie Kr"ugl, Helena H"uster,
Thomas Kopinski, Ralf Lanwehr},
journal={arXiv preprint arXiv:2409.18984},
year={2024},
archivePrefix={arXiv},
eprint={2409.18984},
primaryClass={cs.CL cs.AI cs.LG}
}
|
saeid2024harnessing
|
arxiv-662917
|
2409.18986
|
Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test Interpretation in Clinical Medicine
|
<|reference_start|>Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test Interpretation in Clinical Medicine: Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using Retrieval-Augmented Generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 68 lab tests-30 with conditional factors and 38 without. For tests with factors, normal ranges depend on patient-specific information. Our results show that GPT-4-turbo with RAG achieved a 0.95 F1 score for factor retrieval and 0.993 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 29.1% in factor retrieval and showed 60.9% and 52.9% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.<|reference_end|>
|
arxiv
|
@article{wang2024lab-ai,
title={Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test
Interpretation in Clinical Medicine},
author={Xiaoyu Wang, Haoyong Ouyang, Balu Bhasuran, Xiao Luo, Karim Hanna, Mia
Liza A. Lustria, Zhe He},
journal={arXiv preprint arXiv:2409.18986},
year={2024},
archivePrefix={arXiv},
eprint={2409.18986},
primaryClass={cs.CL cs.AI cs.IR}
}
|
wang2024lab-ai
|
arxiv-662918
|
2409.18987
|
Efficient and Personalized Mobile Health Event Prediction via Small Language Models
|
<|reference_start|>Efficient and Personalized Mobile Health Event Prediction via Small Language Models: Healthcare monitoring is crucial for early detection, timely intervention, and the ongoing management of health conditions, ultimately improving individuals' quality of life. Recent research shows that Large Language Models (LLMs) have demonstrated impressive performance in supporting healthcare tasks. However, existing LLM-based healthcare solutions typically rely on cloud-based systems, which raise privacy concerns and increase the risk of personal information leakage. As a result, there is growing interest in running these models locally on devices like mobile phones and wearables to protect users' privacy. Small Language Models (SLMs) are potential candidates to solve privacy and computational issues, as they are more efficient and better suited for local deployment. However, the performance of SLMs in healthcare domains has not yet been investigated. This paper examines the capability of SLMs to accurately analyze health data, such as steps, calories, sleep minutes, and other vital statistics, to assess an individual's health status. Our results show that, TinyLlama, which has 1.1 billion parameters, utilizes 4.31 GB memory, and has 0.48s latency, showing the best performance compared other four state-of-the-art (SOTA) SLMs on various healthcare applications. Our results indicate that SLMs could potentially be deployed on wearable or mobile devices for real-time health monitoring, providing a practical solution for efficient and privacy-preserving healthcare.<|reference_end|>
|
arxiv
|
@article{wang2024efficient,
title={Efficient and Personalized Mobile Health Event Prediction via Small
Language Models},
author={Xin Wang, Ting Dang, Vassilis Kostakos, and Hong Jia},
journal={arXiv preprint arXiv:2409.18987},
year={2024},
archivePrefix={arXiv},
eprint={2409.18987},
primaryClass={cs.CL cs.AI cs.CY cs.LG}
}
|
wang2024efficient
|
arxiv-662919
|
2409.18988
|
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
|
<|reference_start|>A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy: Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the International Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions.<|reference_end|>
|
arxiv
|
@article{li2024a,
title={A Unified Framework to Classify Business Activities into International
Standard Industrial Classification through Large Language Models for Circular
Economy},
author={Xiang Li, Lan Zhao, Junhao Ren, Yajuan Sun, Chuan Fu Tan, Zhiquan Yeo,
Gaoxi Xiao},
journal={arXiv preprint arXiv:2409.18988},
year={2024},
archivePrefix={arXiv},
eprint={2409.18988},
primaryClass={cs.CL cs.AI econ.GN q-fin.EC}
}
|
li2024a
|
arxiv-662920
|
2409.18989
|
SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks
|
<|reference_start|>SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks: This paper introduces SC-Phi2, a fine-tuned StarCraft II small language model for macromanagement tasks. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less power and memory to run. To teach Microsoft's Phi2 model about StarCraft, we create a new SC2 text dataset with information about StarCraft races, roles, and actions and use it to fine-tune Phi-2 with self-supervised learning. We pair this language model with a Vision Transformer (ViT) from the pre-trained BLIP-2 (Bootstrapping Language Image Pre-training) model, fine-tuning it on the MSC replay dataset. This enables us to construct dynamic prompts that include visual game state information. Unlike the large models used in StarCraft LLMs such as GPT-3.5, Phi2 is trained primarily on textbook data and contains little inherent knowledge of StarCraft II beyond what is provided by our training process. By using LoRA (Low-rank Adaptation) and quantization, our model can be trained on a single GPU. We demonstrate that our model performs well at micromanagement tasks such as build order and global state prediction with a small number of parameters.<|reference_end|>
|
arxiv
|
@article{khan2024sc-phi2:,
title={SC-Phi2: A Fine-tuned Small Language Model for StarCraft II
Macromanagement Tasks},
author={Muhammad Junaid Khan and Gita Sukthankar},
journal={arXiv preprint arXiv:2409.18989},
year={2024},
archivePrefix={arXiv},
eprint={2409.18989},
primaryClass={cs.CL cs.AI}
}
|
khan2024sc-phi2:
|
arxiv-662921
|
2409.18991
|
Surveying the MLLM Landscape: A Meta-Review of Current Surveys
|
<|reference_start|>Surveying the MLLM Landscape: A Meta-Review of Current Surveys: The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field.<|reference_end|>
|
arxiv
|
@article{li2024surveying,
title={Surveying the MLLM Landscape: A Meta-Review of Current Surveys},
author={Ming Li, Keyu Chen, Ziqian Bi, Ming Liu, Benji Peng, Qian Niu, Junyu
Liu, Jinlang Wang, Sen Zhang, Xuanhe Pan, Jiawei Xu, Pohsun Feng},
journal={arXiv preprint arXiv:2409.18991},
year={2024},
archivePrefix={arXiv},
eprint={2409.18991},
primaryClass={cs.CL}
}
|
li2024surveying
|
arxiv-662922
|
2409.18992
|
A Review of Mechanistic Models of Event Comprehension
|
<|reference_start|>A Review of Mechanistic Models of Event Comprehension: This review examines theoretical assumptions and computational models of event comprehension, tracing the evolution from discourse comprehension theories to contemporary event cognition frameworks. The review covers key discourse comprehension accounts, including Construction-Integration, Event Indexing, Causal Network, and Resonance models, highlighting their contributions to understanding cognitive processes in comprehension. I then discuss contemporary theoretical frameworks of event comprehension, including Event Segmentation Theory (Zacks et al., 2007), the Event Horizon Model (Radvansky & Zacks, 2014), and Hierarchical Generative Framework (Kuperberg, 2021), which emphasize prediction, causality, and multilevel representations in event understanding. Building on these theories, I evaluate five computational models of event comprehension: REPRISE (Butz et al., 2019), Structured Event Memory (SEM; Franklin et al., 2020), the Lu model (Lu et al., 2022), the Gumbsch model (Gumbsch et al., 2022), and the Elman and McRae model (2019). The analysis focuses on their approaches to hierarchical processing, prediction mechanisms, and representation learning. Key themes that emerge include the use of hierarchical structures as inductive biases, the importance of prediction in comprehension, and diverse strategies for learning event dynamics. The review identifies critical areas for future research, including the need for more sophisticated approaches to learning structured representations, integrating episodic memory mechanisms, and developing adaptive updating algorithms for working event models. By synthesizing insights from both theoretical frameworks and computational implementations, this review aims to advance our understanding of human event comprehension and guide future modeling efforts in cognitive science.<|reference_end|>
|
arxiv
|
@article{nguyen2024a,
title={A Review of Mechanistic Models of Event Comprehension},
author={Tan T. Nguyen},
journal={arXiv preprint arXiv:2409.18992},
year={2024},
archivePrefix={arXiv},
eprint={2409.18992},
primaryClass={cs.CL cs.AI cs.CV}
}
|
nguyen2024a
|
arxiv-662923
|
2409.18995
|
Systematic Characterization of the Effectiveness of Alignment in Large Language Models for Categorical Decisions
|
<|reference_start|>Systematic Characterization of the Effectiveness of Alignment in Large Language Models for Categorical Decisions: As large language models (LLMs) are deployed in high-stakes domains like healthcare, understanding how well their decision-making aligns with human preferences and values becomes crucial, especially when we recognize that there is no single gold standard for these preferences. This paper applies a systematic methodology for evaluating preference alignment in LLMs on categorical decision-making with medical triage as a domain-specific use case. It also measures how effectively an alignment procedure will change the alignment of a specific model. Key to this methodology is a novel simple measure, the Alignment Compliance Index (ACI), that quantifies how effectively a LLM can be aligned to a given preference function or gold standard. Since the ACI measures the effect rather than the process of alignment, it is applicable to alignment methods beyond the in-context learning used in this study. Using a dataset of simulated patient pairs, three frontier LLMs (GPT4o, Claude 3.5 Sonnet, and Gemini Advanced) were assessed on their ability to make triage decisions consistent with an expert clinician's preferences. The models' performance before and after alignment attempts was evaluated using various prompting strategies. The results reveal significant variability in alignment effectiveness across models and alignment approaches. Notably, models that performed well, as measured by ACI, pre-alignment sometimes degraded post-alignment, and small changes in the target preference function led to large shifts in model rankings. The implicit ethical principles, as understood by humans, underlying the LLMs' decisions were also explored through targeted questioning. This study motivates the use of a practical set of methods and the ACI, in the near term, to understand the correspondence between the variety of human and LLM decision-making values in categorical decision-making such as triage.<|reference_end|>
|
arxiv
|
@article{kohane2024systematic,
title={Systematic Characterization of the Effectiveness of Alignment in Large
Language Models for Categorical Decisions},
author={Isaac Kohane},
journal={arXiv preprint arXiv:2409.18995},
year={2024},
archivePrefix={arXiv},
eprint={2409.18995},
primaryClass={cs.CL cs.AI}
}
|
kohane2024systematic
|
arxiv-662924
|
2409.18996
|
From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models
|
<|reference_start|>From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models: Cross-modal reasoning (CMR), the intricate process of synthesizing and drawing inferences across divergent sensory modalities, is increasingly recognized as a crucial capability in the progression toward more sophisticated and anthropomorphic artificial intelligence systems. Large Language Models (LLMs) represent a class of AI algorithms specifically engineered to parse, produce, and engage with human language on an extensive scale. The recent trend of deploying LLMs to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy. Moreover, the survey delves into the principal design strategies and operational techniques of prototypical models within this domain. Additionally, it articulates the prevailing challenges associated with the integration of LLMs in CMR and identifies prospective research directions. To sum up, this survey endeavors to expedite progress within this burgeoning field by endowing scholars with a holistic and detailed vista, showcasing the vanguard of current research whilst pinpointing potential avenues for advancement. An associated GitHub repository that collects the relevant papers can be found at https://github.com/ZuyiZhou/Awesome-Cross-modal-Reasoning-with-LLMs<|reference_end|>
|
arxiv
|
@article{qian2024from,
title={From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal
Reasoning with Large Language Models},
author={Shengsheng Qian, Zuyi Zhou, Dizhan Xue, Bing Wang, and Changsheng Xu},
journal={arXiv preprint arXiv:2409.18996},
year={2024},
archivePrefix={arXiv},
eprint={2409.18996},
primaryClass={cs.CL cs.AI cs.CV cs.LG cs.MM}
}
|
qian2024from
|
arxiv-662925
|
2409.18997
|
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent
|
<|reference_start|>PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent: Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce propainsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. propainsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present propagaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but training with propagaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span IoU in technique identification and 66.2% higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, propagaze complements limited human-annotated data in data-sparse and cross-domain scenarios, showing its potential for comprehensive and generalizable propaganda analysis.<|reference_end|>
|
arxiv
|
@article{liu2024propainsight:,
title={PropaInsight: Toward Deeper Understanding of Propaganda in Terms of
Techniques, Appeals, and Intent},
author={Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, May Fung,
Preslav Nakov, Julia Hirschberg, Heng Ji},
journal={arXiv preprint arXiv:2409.18997},
year={2024},
archivePrefix={arXiv},
eprint={2409.18997},
primaryClass={cs.CL cs.AI cs.SI}
}
|
liu2024propainsight:
|
arxiv-662926
|
2409.18998
|
Controlled LLM-based Reasoning for Clinical Trial Retrieval
|
<|reference_start|>Controlled LLM-based Reasoning for Clinical Trial Retrieval: Matching patients to clinical trials demands a systematic and reasoned interpretation of documents which require significant expert-level background knowledge, over a complex set of well-defined eligibility criteria. Moreover, this interpretation process needs to operate at scale, over vast knowledge bases of trials. In this paper, we propose a scalable method that extends the capabilities of LLMs in the direction of systematizing the reasoning over sets of medical eligibility criteria, evaluating it in the context of real-world cases. The proposed method overlays a Set-guided reasoning method for LLMs. The proposed framework is evaluated on TREC 2022 Clinical Trials, achieving results superior to the state-of-the-art: NDCG@10 of 0.693 and Precision@10 of 0.73.<|reference_end|>
|
arxiv
|
@article{jullien2024controlled,
title={Controlled LLM-based Reasoning for Clinical Trial Retrieval},
author={Mael Jullien and Alex Bogatu and Harriet Unsworth and Andre Freitas},
journal={arXiv preprint arXiv:2409.18998},
year={2024},
archivePrefix={arXiv},
eprint={2409.18998},
primaryClass={cs.CL cs.AI}
}
|
jullien2024controlled
|
arxiv-662927
|
2409.18999
|
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation
|
<|reference_start|>Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation: In the rapidly evolving field of financial sentiment analysis, the efficiency and accuracy of predictive models are critical due to their significant impact on financial markets. Transformer based models like BERT and large language models (LLMs) like GPT-4, have advanced NLP tasks considerably. Despite their advantages, BERT-based models face challenges with computational intensity in edge computing environments, and the substantial size and compute requirements of LLMs limit their practical deployment. This study proposes leveraging the generative capabilities of LLMs, such as GPT-4 Omni, to create synthetic, domain-specific training data. This approach addresses the challenge of data scarcity and enhances the performance of smaller models by making them competitive with their larger counterparts. The research specifically aims to enhance FinBERT, a BERT model fine-tuned for financial sentiment analysis, and develop TinyFinBERT, a compact transformer model, through a structured, two-tiered knowledge distillation strategy. Using data augmented by GPT-4 Omni, which involves generating new training examples and transforming existing data, we significantly improved the accuracy of FinBERT, preparing it to serve as a teacher model. This enhanced FinBERT then distilled knowledge to TinyFinBERT, employing both GPT-4 Omni and GPT-3.5 Turbo augmented data. The distillation strategy incorporated both logit and intermediate layer distillation. The training and evaluation of TinyFinBERT utilized the PhraseBank dataset and the FiQA 2018 Task1 dataset, achieving performance comparable to FinBERT while being substantially smaller and more efficient. This research demonstrates how LLMs can effectively contribute to the advancement of financial sentiment analysis by enhancing the capabilities of smaller, more efficient models through innovative data augmentation and distillation techniques.<|reference_end|>
|
arxiv
|
@article{thomas2024enhancing,
title={Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented
FinBERT Distillation},
author={Graison Jos Thomas},
journal={arXiv preprint arXiv:2409.18999},
year={2024},
archivePrefix={arXiv},
eprint={2409.18999},
primaryClass={cs.CL cs.LG}
}
|
thomas2024enhancing
|
arxiv-662928
|
2409.19001
|
Pay Attention to What Matters
|
<|reference_start|>Pay Attention to What Matters: Despite the remarkable success of Large Language Models (LLMs), they still exhibit a limited capability to align their outputs to the user instructions. In this work, we introduce a simple and effective method, which we name GUIDE, that mechanistically increases attention scores in instruction tokens. To support this operation, we present Influence, a novel metric that highlights how the user's instructions propagate through the transformer layers and impact the LLM output. Our results show that GUIDE improves the accuracy of following instructions 29.4 % to 60.4%, outperforming natural prompting alternatives and Supervised Fine-Tuning up to 1M tokens.<|reference_end|>
|
arxiv
|
@article{silva2024pay,
title={Pay Attention to What Matters},
author={Pedro Luiz Silva, Antonio de Domenico, Ali Maatouk, Fadhel Ayed},
journal={arXiv preprint arXiv:2409.19001},
year={2024},
archivePrefix={arXiv},
eprint={2409.19001},
primaryClass={cs.CL cs.AI}
}
|
silva2024pay
|
arxiv-662929
|
2409.19005
|
What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications
|
<|reference_start|>What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications: The concept of digital twins has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners. We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of digital twins from a corpus of 15,000 full-text articles spanning diverse disciplines in the built environment. The study compares these findings with insights from an expert survey that included 52 experts. The study identifies concurrence on the components that comprise a 'Digital Twin' from a practical perspective across various domains, contrasting them with those that do not, to identify deviations. We investigate the evolution of digital twin definitions over time and across different scales, including manufacturing, building, and urban/geospatial perspectives. We extracted the main components of Digital Twins using Text Frequency Analysis and N-gram analysis. Subsequently, we identified components that appeared in the literature and conducted a Chi-square test to assess the significance of each component in different domains. Our findings indicate that definitions differ based on the field of research in which they are conceived, but with many similarities across domains. One significant generalizable differentiation is related to whether a digital twin was used for High-Performance Real-Time (HPRT) or Long-Term Decision Support (LTDS) applications. We synthesized and contrasted the most representative definitions in each domain, culminating in a novel, data-driven definition specifically tailored for each context.<|reference_end|>
|
arxiv
|
@article{abdelrahman2024what,
title={What is a Digital Twin Anyway? Deriving the Definition for the Built
Environment from over 15,000 Scientific Publications},
author={Mahmoud Abdelrahman, Edgardo Macatulad, Binyu Lei, Matias Quintana,
Clayton Miller, Filip Biljecki},
journal={arXiv preprint arXiv:2409.19005},
year={2024},
archivePrefix={arXiv},
eprint={2409.19005},
primaryClass={cs.CL}
}
|
abdelrahman2024what
|
arxiv-662930
|
2409.19006
|
Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis
|
<|reference_start|>Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis: Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property rights, play a critical role in technological innovation. The growing complexity of patent documents and the surge in patent applications have created a need for automated solutions in patent analysis. In this work, we present PatExpert, an autonomous multi-agent conversational framework designed to streamline and optimize patent-related tasks. The framework consists of a metaagent that coordinates task-specific expert agents for various patent-related tasks and a critique agent for error handling and feedback provision. The meta-agent orchestrates specialized expert agents, each fine-tuned for specific tasks such as patent classification, acceptance, claim generation, abstractive summarization, multi-patent analysis, and scientific hypothesis generation. For multi-patent analysis, the framework incorporates advanced methods like Graph Retrieval-Augmented Generation (GRAG) to enhance response accuracy and relevance by combining semantic similarity with knowledge graphs. Error handling is managed by critique agents (Gold-LLM-as-a-Judge and Reward-LLM-as-a-Judge), which evaluate output responses for accuracy and provide iterative feedback. The framework also prioritizes explainability, ensuring transparent justifications for decisions made during patent analysis. Its comprehensive capabilities make it a valuable tool for automating complex patent workflows, enhancing efficiency, accuracy, and compliance in patent-related tasks. Empirical evidence demonstrates significant improvements in patent processing tasks, concluding that the framework offers a robust solution for automating and optimizing patent analysis.<|reference_end|>
|
arxiv
|
@article{srinivas2024towards,
title={Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent
Framework for Intellectual Property Management and Analysis},
author={Sakhinana Sagar Srinivas, Vijay Sri Vaikunth, Venkataramana Runkana},
journal={arXiv preprint arXiv:2409.19006},
year={2024},
archivePrefix={arXiv},
eprint={2409.19006},
primaryClass={cs.CL cs.AI cs.LG}
}
|
srinivas2024towards
|
arxiv-662931
|
2409.19007
|
Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks
|
<|reference_start|>Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks: Large language models (LLMs) are being widely researched across various disciplines, with significant recent efforts focusing on adapting LLMs for understanding of how communication networks operate. However, over-reliance on prompting techniques hinders the full exploitation of the generalization ability of these models, and the lack of efficient fine-tuning methods prevents the full realization of lightweight LLMs' potential. This paper addresses these challenges by introducing our Rephrase and Contrast (RaC) framework, an efficient fine-tuning framework. RaC enhances LLMs' comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis of correct and incorrect answers during the fine-tuning process. Experimental results demonstrate a 63.73% accuracy improvement over the foundational model when tested on a comprehensive networking problem set. Moreover, to efficiently construct the dataset for RaC fine-tuning, we develop a GPT-assisted data mining method for generating high-quality question-answer (QA) pairs; furthermore, we introduce ChoiceBoost, a data augmentation technique that expands dataset size while reducing answer-order bias. Apart from these technical innovations, we contribute to the networking community by open-sourcing valuable research resources, including: 1) the fine-tuned networking model referred to as RaC-Net, 2) the training dataset used for fine-tuning the model, 3) three testing problem sets of different difficulties to serve as benchmarks for future research, and 4) code associated with the above resources.<|reference_end|>
|
arxiv
|
@article{wang2024rephrase,
title={Rephrase and Contrast: Fine-Tuning Language Models for Enhanced
Understanding of Communication and Computer Networks},
author={Liujianfu Wang, Yuyang Du, Jingqi Lin, Kexin Chen, Soung Chang Liew},
journal={arXiv preprint arXiv:2409.19007},
year={2024},
archivePrefix={arXiv},
eprint={2409.19007},
primaryClass={cs.CL}
}
|
wang2024rephrase
|
arxiv-662932
|
2409.19010
|
A comprehensive study of on-device NLP applications -- VQA, automated Form filling, Smart Replies for Linguistic Codeswitching
|
<|reference_start|>A comprehensive study of on-device NLP applications -- VQA, automated Form filling, Smart Replies for Linguistic Codeswitching: Recent improvement in large language models, open doors for certain new experiences for on-device applications which were not possible before. In this work, we propose 3 such new experiences in 2 categories. First we discuss experiences which can be powered in screen understanding i.e. understanding whats on user screen namely - (1) visual question answering, and (2) automated form filling based on previous screen. The second category of experience which can be extended are smart replies to support for multilingual speakers with code-switching. Code-switching occurs when a speaker alternates between two or more languages. To the best of our knowledge, this is first such work to propose these tasks and solutions to each of them, to bridge the gap between latest research and real world impact of the research in on-device applications.<|reference_end|>
|
arxiv
|
@article{goyal2024a,
title={A comprehensive study of on-device NLP applications -- VQA, automated
Form filling, Smart Replies for Linguistic Codeswitching},
author={Naman Goyal},
journal={arXiv preprint arXiv:2409.19010},
year={2024},
archivePrefix={arXiv},
eprint={2409.19010},
primaryClass={cs.CL cs.AI cs.LG}
}
|
goyal2024a
|
arxiv-662933
|
2409.19011
|
Identification and Mitigating Bias in Quantum Machine Learning
|
<|reference_start|>Identification and Mitigating Bias in Quantum Machine Learning: As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and challenges that arise from the unique nature of quantum systems. This research includes work on identification, diagnosis, and response to biases in Quantum Machine Learning. This paper aims to provide an overview of three key topics: How does bias unique to Quantum Machine Learning look? Why and how can it occur? What can and should be done about it?<|reference_end|>
|
arxiv
|
@article{swaminathan2024identification,
title={Identification and Mitigating Bias in Quantum Machine Learning},
author={Nandhini Swaminathan, David Danks},
journal={arXiv preprint arXiv:2409.19011},
year={2024},
archivePrefix={arXiv},
eprint={2409.19011},
primaryClass={quant-ph cs.AI cs.LG}
}
|
swaminathan2024identification
|
arxiv-662934
|
2409.19012
|
Lost in the Logic: An Evaluation of Large Language Models' Reasoning Capabilities on LSAT Logic Games
|
<|reference_start|>Lost in the Logic: An Evaluation of Large Language Models' Reasoning Capabilities on LSAT Logic Games: In this thesis, I evaluate the performance of Large Language Models (LLMs) on the Law School Admissions Test (LSAT), specifically the Logic Games section of the test. I focus on this section because it presents a complex logical reasoning task and thus is a valuable source of data for evaluating how modern, increasingly capable LLMs can handle hard logical reasoning tasks. I construct a dataset of LSAT logic games and their associated metadata, and extensively evaluate LLMs' performance in a Chain-of-Thought prompting setting. Given the weak performance in this setting, I explore other prompting frameworks on a smaller subset of the dataset, adapting ideas from Reflexion to this task. This results in a substantially improved accuracy of 70 percent for GPT-4 and 46 percent for GPT-3.5 on this data subset, highlighting the capacity of LLMs to revise their logical errors, despite initially weak performance. Finally, I analyze the types of logic games that models perform better or worse on, as well as the types of logical errors I observe from human annotation, providing detailed insights on the logical reasoning capabilities of LLMs.<|reference_end|>
|
arxiv
|
@article{malik2024lost,
title={Lost in the Logic: An Evaluation of Large Language Models' Reasoning
Capabilities on LSAT Logic Games},
author={Saumya Malik},
journal={arXiv preprint arXiv:2409.19012},
year={2024},
archivePrefix={arXiv},
eprint={2409.19012},
primaryClass={cs.CL cs.AI}
}
|
malik2024lost
|
arxiv-662935
|
2409.19013
|
Improving Academic Skills Assessment with NLP and Ensemble Learning
|
<|reference_start|>Improving Academic Skills Assessment with NLP and Ensemble Learning: This study addresses the critical challenges of assessing foundational academic skills by leveraging advancements in natural language processing (NLP). Traditional assessment methods often struggle to provide timely and comprehensive feedback on key cognitive and linguistic aspects, such as coherence, syntax, and analytical reasoning. Our approach integrates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5, within an ensemble learning framework. These models are combined through stacking techniques using LightGBM and Ridge regression to enhance predictive accuracy. The methodology involves detailed data preprocessing, feature extraction, and pseudo-label learning to optimize model performance. By incorporating sophisticated NLP techniques and ensemble learning, this study significantly improves the accuracy and efficiency of assessments, offering a robust solution that surpasses traditional methods and opens new avenues for educational technology research focused on enhancing core academic competencies.<|reference_end|>
|
arxiv
|
@article{huang2024improving,
title={Improving Academic Skills Assessment with NLP and Ensemble Learning},
author={Xinyi Huang, Yingyi Wu, Danyang Zhang, Jiacheng Hu, Yujian Long},
journal={arXiv preprint arXiv:2409.19013},
year={2024},
archivePrefix={arXiv},
eprint={2409.19013},
primaryClass={cs.CL cs.AI cs.CY cs.LG}
}
|
huang2024improving
|
arxiv-662936
|
2409.19014
|
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark
|
<|reference_start|>FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark: Text-to-SQL technology has become crucial for translating natural language into SQL queries in various industries, enabling non-technical users to perform complex data operations. The need for accurate evaluation methods has increased as these systems have grown more sophisticated. However, we found that the Execution Accuracy (EX), the most promising evaluation metric, still shows a substantial portion of false positives and negatives compared to human evaluation. Thus, this paper introduces FLEX (False-Less EXecution), a novel approach to evaluating text-to-SQL systems using large language models (LLMs) to emulate human expert-level evaluation of SQL queries. Our method shows significantly higher agreement with human expert judgments, improving Cohen's kappa from 61 to 78.17. Re-evaluating top-performing models on the Spider and BIRD benchmarks using FLEX reveals substantial shifts in performance rankings, with an average performance decrease of 3.15 due to false positive corrections and an increase of 6.07 from addressing false negatives. This work contributes to a more accurate and nuanced evaluation of text-to-SQL systems, potentially reshaping our understanding of state-of-the-art performance in this field.<|reference_end|>
|
arxiv
|
@article{kim2024flex:,
title={FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL
Benchmark},
author={Heegyu Kim, Taeyang Jeon, Seunghwan Choi, Seungtaek Choi, Hyunsouk Cho},
journal={arXiv preprint arXiv:2409.19014},
year={2024},
archivePrefix={arXiv},
eprint={2409.19014},
primaryClass={cs.CL cs.IR cs.LG}
}
|
kim2024flex:
|
arxiv-662937
|
2409.19015
|
Textless NLP -- Zero Resource Challenge with Low Resource Compute
|
<|reference_start|>Textless NLP -- Zero Resource Challenge with Low Resource Compute: This work addresses the persistent challenges of substantial training time and GPU resource requirements even when training lightweight encoder-vocoder models for Textless NLP. We reduce training steps significantly while improving performance by a) leveraging learning rate schedulers for efficient and faster convergence b) optimizing hop length and c) tuning the interpolation scale factors for better audio quality. Additionally, we explore the latent space representation for Indian languages such as Tamil and Bengali for the acoustic unit discovery and voice conversion task. Our approach leverages a quantized encoder architecture, in conjunction with a vocoder which utilizes the proposed mixture of optimized hop length, tuned interpolation scale factors and a cyclic learning rate scheduler. We obtain consistently good results across English, Tamil and Bengali datasets. The proposed method excels in capturing complex linguistic patterns, resulting in clear reconstructed audio during voice conversion with significantly reduced training time.<|reference_end|>
|
arxiv
|
@article{ramadass2024textless,
title={Textless NLP -- Zero Resource Challenge with Low Resource Compute},
author={Krithiga Ramadass, Abrit Pal Singh, Srihari J, Sheetal Kalyani},
journal={arXiv preprint arXiv:2409.19015},
year={2024},
archivePrefix={arXiv},
eprint={2409.19015},
primaryClass={cs.CL cs.AI cs.LG cs.SD eess.AS}
}
|
ramadass2024textless
|
arxiv-662938
|
2409.19017
|
Repetition effects in a Sequential Monte Carlo sampler
|
<|reference_start|>Repetition effects in a Sequential Monte Carlo sampler: We investigate the prevalence of sample repetition in a Sequential Monte Carlo (SMC) method recently introduced for political redistricting.<|reference_end|>
|
arxiv
|
@article{cannon2024repetition,
title={Repetition effects in a Sequential Monte Carlo sampler},
author={Sarah Cannon, Daryl DeFord, Moon Duchin},
journal={arXiv preprint arXiv:2409.19017},
year={2024},
archivePrefix={arXiv},
eprint={2409.19017},
primaryClass={math.PR cs.CY math.ST stat.CO stat.TH}
}
|
cannon2024repetition
|
arxiv-662939
|
2409.19019
|
RAGProbe: An Automated Approach for Evaluating RAG Applications
|
<|reference_start|>RAGProbe: An Automated Approach for Evaluating RAG Applications: Retrieval Augmented Generation (RAG) is increasingly being used when building Generative AI applications. Evaluating these applications and RAG pipelines is mostly done manually, via a trial and error process. Automating evaluation of RAG pipelines requires overcoming challenges such as context misunderstanding, wrong format, incorrect specificity, and missing content. Prior works therefore focused on improving evaluation metrics as well as enhancing components within the pipeline using available question and answer datasets. However, they have not focused on 1) providing a schema for capturing different types of question-answer pairs or 2) creating a set of templates for generating question-answer pairs that can support automation of RAG pipeline evaluation. In this paper, we present a technique for generating variations in question-answer pairs to trigger failures in RAG pipelines. We validate 5 open-source RAG pipelines using 3 datasets. Our approach revealed the highest failure rates when prompts combine multiple questions: 91% for questions when spanning multiple documents and 78% for questions from a single document; indicating a need for developers to prioritise handling these combined questions. 60% failure rate was observed in academic domain dataset and 53% and 62% failure rates were observed in open-domain datasets. Our automated approach outperforms the existing state-of-the-art methods, by increasing the failure rate by 51% on average per dataset. Our work presents an automated approach for continuously monitoring the health of RAG pipelines, which can be integrated into existing CI/CD pipelines, allowing for improved quality.<|reference_end|>
|
arxiv
|
@article{sivasothy2024ragprobe:,
title={RAGProbe: An Automated Approach for Evaluating RAG Applications},
author={Shangeetha Sivasothy, Scott Barnett, Stefanus Kurniawan, Zafaryab
Rasool, Rajesh Vasa},
journal={arXiv preprint arXiv:2409.19019},
year={2024},
archivePrefix={arXiv},
eprint={2409.19019},
primaryClass={cs.CL cs.LG}
}
|
sivasothy2024ragprobe:
|
arxiv-662940
|
2409.19020
|
DiaSynth -- Synthetic Dialogue Generation Framework
|
<|reference_start|>DiaSynth -- Synthetic Dialogue Generation Framework: The scarcity of domain specific dialogue datasets across various domains, from academic topics to everyday conversations, limits the development of dialogue systems for various applications. Existing research is often constrained either by dialogue datasets that are too general or by niche domain dialogue datasets whose scale does not match the required scale for training dialogue systems. To address this gap, we introduce DiaSynth - a synthetic dialogue generation framework capable of generating high quality, contextually rich dialogues across a wide range of domains. Our approach differs from existing frameworks by dynamically generating dialogues that incorporate simulated personas, subtopics, and diverse conversational characteristics, using a Large Language Model (LLM) with Chain of Thought (CoT) reasoning to create contextually rich, domain-specific dialogues that closely mimic natural human interactions. DiaSynth produces tailored dialogues that emulate realistic conversations. We perform our experiments by generating synthetic data using different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained language models fine-tuned on the synthetic data outperform the base models by 16.47%, while the comparison between models fine-tuned on in-domain data and synthetic data shows that the synthetic data is able to capture 90.48% of the distribution of the in-domain data. The quality of the data generated also scales with the size of LLMs. These results validate DiaSynth's potential as a robust alternative to traditional data collection methods.<|reference_end|>
|
arxiv
|
@article{suresh2024diasynth:,
title={DiaSynth: Synthetic Dialogue Generation Framework for Low Resource
Dialogue Applications},
author={Sathya Krishnan Suresh, Wu Mengjun, Tushar Pranav, Eng Siong Chng},
journal={arXiv preprint arXiv:2409.19020},
year={2024},
archivePrefix={arXiv},
eprint={2409.19020},
primaryClass={cs.CL cs.LG}
}
|
suresh2024diasynth:
|
arxiv-662941
|
2409.19022
|
Application of AI-based Models for Online Fraud Detection and Analysis
|
<|reference_start|>Application of AI-based Models for Online Fraud Detection and Analysis: Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of technologies like AI, there is a growing concern that fraud will scale up, using sophisticated methods, like deep-fakes in phishing campaigns, all generated by language generation models like ChatGPT. However, the application of AI in detecting and analyzing online fraud remains understudied. We conduct a Systematic Literature Review on AI and NLP techniques for online fraud detection. The review adhered the PRISMA-ScR protocol, with eligibility criteria including relevance to online fraud, use of text data, and AI methodologies. We screened 2,457 academic records, 350 met our eligibility criteria, and included 223. We report the state-of-the-art NLP techniques for analysing various online fraud categories; the training data sources; the NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that current research on online fraud is divided into various scam activitiesand identify 16 different frauds that researchers focus on. This SLR enhances the academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that focusing on specific scams lacks generalization, as multiple models are required for different fraud types. The evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify issues in data limitations, training bias reporting, and selective presentation of metrics in model performance reporting, which can lead to potential biases in model evaluation.<|reference_end|>
|
arxiv
|
@article{papasavva2024application,
title={Application of AI-based Models for Online Fraud Detection and Analysis},
author={Antonis Papasavva, Shane Johnson, Ed Lowther, Samantha Lundrigan,
Enrico Mariconti, Anna Markovska, Nilufer Tuptuk},
journal={arXiv preprint arXiv:2409.19022},
year={2024},
archivePrefix={arXiv},
eprint={2409.19022},
primaryClass={cs.CL cs.AI cs.LG}
}
|
papasavva2024application
|
arxiv-662942
|
2409.19024
|
Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignment
|
<|reference_start|>Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignment: The demand for regulating potentially risky behaviors of large language models (LLMs) has ignited research on alignment methods. Since LLM alignment heavily relies on reward models for optimization or evaluation, neglecting the quality of reward models may cause unreliable results or even misalignment. Despite the vital role reward models play in alignment, previous works have consistently overlooked their performance and used off-the-shelf reward models arbitrarily without verification, rendering the reward model ``\emph{an elephant in the room}''. To this end, this work first investigates the quality of the widely-used preference dataset, HH-RLHF, and curates a clean version, CHH-RLHF. Based on CHH-RLHF, we benchmark the accuracy of a broad range of reward models used in previous alignment works, unveiling the unreliability of using them both for optimization and evaluation. Furthermore, we systematically study the impact of reward model quality on alignment performance in three reward utilization paradigms. Extensive experiments reveal that better reward models perform as better human preference proxies. This work aims to awaken people to notice this huge elephant in alignment research. We call attention to the following issues: (1) The reward model needs to be rigorously evaluated, whether for alignment optimization or evaluation. (2) Considering the role of reward models, research efforts should not only concentrate on alignment algorithm, but also on developing more reliable human proxy.<|reference_end|>
|
arxiv
|
@article{liu2024elephant,
title={Elephant in the Room: Unveiling the Impact of Reward Model Quality in
Alignment},
author={Yan Liu, Xiaoyuan Yi, Xiaokang Chen, Jing Yao, Jingwei Yi, Daoguang
Zan, Zheng Liu, Xing Xie, Tsung-Yi Ho},
journal={arXiv preprint arXiv:2409.19024},
year={2024},
archivePrefix={arXiv},
eprint={2409.19024},
primaryClass={cs.CL cs.AI}
}
|
liu2024elephant
|
arxiv-662943
|
2409.19025
|
Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing
|
<|reference_start|>Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing: There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.<|reference_end|>
|
arxiv
|
@article{troiano2024dealing,
title={Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on
Role Playing},
author={Enrica Troiano, Sofie Labat, Marco Antonio Stranisci, Viviana Patti,
Rossana Damiano, Roman Klinger},
journal={arXiv preprint arXiv:2409.19025},
year={2024},
archivePrefix={arXiv},
eprint={2409.19025},
primaryClass={cs.CL}
}
|
troiano2024dealing
|
arxiv-662944
|
2409.19027
|
Code Generation and Algorithmic Problem Solving Using Llama 31 405B
|
<|reference_start|>Code Generation and Algorithmic Problem Solving Using Llama 31 405B: Code generation by Llama 3.1 models, such as Meta's Llama 3.1 405B, represents a significant advancement in the field of artificial intelligence, particularly in natural language processing and programming automation. This paper explores the capabilities and applications of Llama-driven code generation, highlighting its ability to translate natural language prompts into executable code across multiple programming languages. Key features include contextual awareness, multi-language support, and enhanced debugging and optimization functionalities. By examining these aspects, we illustrate how Llama can serve as a versatile tool for developers of all skill levels, improving productivity and efficiency in software development. The potential implications for education, industry, and the future of coding practices are also discussed, underscoring the transformative impact of AI in programming. Experimentation shows that while Llama 3.1 405B performs well with simple algorithmic and data structure based problems, it still struggles with problems on Quantum Computing, Bioinformatics, and Artificial Intelligence.<|reference_end|>
|
arxiv
|
@article{deroy2024code,
title={Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B},
author={Aniket Deroy, Subhankar Maity},
journal={arXiv preprint arXiv:2409.19027},
year={2024},
archivePrefix={arXiv},
eprint={2409.19027},
primaryClass={cs.CL cs.SE}
}
|
deroy2024code
|
arxiv-662945
|
2409.19028
|
Exploring LLM-Driven Explanations for Quantum Algorithms
|
<|reference_start|>Exploring LLM-Driven Explanations for Quantum Algorithms: Background: Quantum computing is a rapidly growing new programming paradigm that brings significant changes to the design and implementation of algorithms. Understanding quantum algorithms requires knowledge of physics and mathematics, which can be challenging for software developers. Aims: In this work, we provide a first analysis of how LLMs can support developers' understanding of quantum code. Method: We empirically analyse and compare the quality of explanations provided by three widely adopted LLMs (Gpt3.5, Llama2, and Tinyllama) using two different human-written prompt styles for seven state-of-the-art quantum algorithms. We also analyse how consistent LLM explanations are over multiple rounds and how LLMs can improve existing descriptions of quantum algorithms. Results: Llama2 provides the highest quality explanations from scratch, while Gpt3.5 emerged as the LLM best suited to improve existing explanations. In addition, we show that adding a small amount of context to the prompt significantly improves the quality of explanations. Finally, we observe how explanations are qualitatively and syntactically consistent over multiple rounds. Conclusions: This work highlights promising results, and opens challenges for future research in the field of LLMs for quantum code explanation. Future work includes refining the methods through prompt optimisation and parsing of quantum code explanations, as well as carrying out a systematic assessment of the quality of explanations.<|reference_end|>
|
arxiv
|
@article{d'aloisio2024exploring,
title={Exploring LLM-Driven Explanations for Quantum Algorithms},
author={Giordano d'Aloisio, Sophie Fortz, Carol Hanna, Daniel Fortunato, Avner
Bensoussan, E~naut Mendiluze Usandizaga, Federica Sarro},
journal={arXiv preprint arXiv:2409.19028},
year={2024},
doi={10.1145/3674805.3690753},
archivePrefix={arXiv},
eprint={2409.19028},
primaryClass={cs.CL cs.SE quant-ph}
}
|
d'aloisio2024exploring
|
arxiv-662946
|
2409.19029
|
Enhancing Productivity with AI During the Development of an ISMS: Case Kempower
|
<|reference_start|>Enhancing Productivity with AI During the Development of an ISMS: Case Kempower: Investing in an Information Security Management System (ISMS) enhances organizational competitiveness and protects information assets. However, introducing an ISMS consumes significant resources; for instance, implementing an ISMS according to the ISO27001 standard involves documenting 116 different controls. This paper discusses how Kempower, a Finnish company, has effectively used generative AI to create and implement an ISMS, significantly reducing the resources required. This research studies how the use of generative AI can enhance the process of creating an ISMS. We conducted seven semi-structured interviews held with various stakeholders of the ISMS project, who had varying levels experience in cyber security and AI.<|reference_end|>
|
arxiv
|
@article{niemeläinen2024enhancing,
title={Enhancing Productivity with AI During the Development of an ISMS: Case
Kempower},
author={Atro Niemel"ainen, Muhammad Waseem, Tommi Mikkonen},
journal={arXiv preprint arXiv:2409.19029},
year={2024},
archivePrefix={arXiv},
eprint={2409.19029},
primaryClass={cs.CR cs.SE}
}
|
niemeläinen2024enhancing
|
arxiv-662947
|
2409.19032
|
A Systematisation of Knowledge: Connecting European Digital Identities with Web3
|
<|reference_start|>A Systematisation of Knowledge: Connecting European Digital Identities with Web3: The terms self-sovereign identity (SSI) and decentralised identity are often used interchangeably, which results in increasing ambiguity when solutions are being investigated and compared. This article aims to provide a clear distinction between the two concepts in relation to the revised Regulation as Regards establishing the European Digital Identity Framework (eIDAS 2.0) by providing a systematisation of knowledge of technological developments that led up to implementation of eIDAS 2.0. Applying an inductive exploratory approach, relevant literature was selected iteratively in waves over a nine months time frame and covers literature between 2005 and 2024. The review found that the decentralised identity sector emerged adjacent to the OpenID Connect (OIDC) paradigm of Open Authentication, whereas SSI denotes the sector's shift towards blockchain-based solutions. In this study, it is shown that the interchangeable use of SSI and decentralised identity coincides with novel protocols over OIDC. While the first part of this paper distinguishes OIDC from decentralised identity, the second part addresses the incompatibility between OIDC under eIDAS 2.0 and Web3. The paper closes by suggesting further research for establishing a digital identity bridge for connecting applications on public-permissionless ledgers with data originating from eIDAS 2.0 and being presented using OIDC.<|reference_end|>
|
arxiv
|
@article{biedermann2024a,
title={A Systematisation of Knowledge: Connecting European Digital Identities
with Web3},
author={Ben Biedermann and Matthew Scerri and Victoria Kozlova and Joshua
Ellul},
journal={2024 IEEE International Conference on Blockchain (Blockchain),
Copenhagen, Denmark, 2024, pp. 605-610},
year={2024},
doi={10.1109/blockchain62396.2024.00089},
archivePrefix={arXiv},
eprint={2409.19032},
primaryClass={cs.CR cs.DC}
}
|
biedermann2024a
|
arxiv-662948
|
2409.19037
|
Self-Replicating Mechanical Universal Turing Machine
|
<|reference_start|>Self-Replicating Mechanical Universal Turing Machine: This paper presents the implementation of a self-replicating finite-state machine (FSM) and a self-replicating Turing Machine (TM) using bio-inspired mechanisms. Building on previous work that introduced self-replicating structures capable of sorting, copying, and reading information, this study demonstrates the computational power of these mechanisms by explicitly constructing a functioning FSM and TM. This study demonstrates the universality of the system by emulating the UTM(5,5) of Neary and Woods.<|reference_end|>
|
arxiv
|
@article{lano2024self-replicating,
title={Self-Replicating Mechanical Universal Turing Machine},
author={Ralph P. Lano},
journal={arXiv preprint arXiv:2409.19037},
year={2024},
archivePrefix={arXiv},
eprint={2409.19037},
primaryClass={cs.FL cs.CL}
}
|
lano2024self-replicating
|
arxiv-662949
|
2409.19038
|
Intention-aware policy graphs: answering what, how, and why in opaque agents
|
<|reference_start|>Intention-aware policy graphs: answering what, how, and why in opaque agents: Agents are a special kind of AI-based software in that they interact in complex environments and have increased potential for emergent behaviour. Explaining such emergent behaviour is key to deploying trustworthy AI, but the increasing complexity and opaque nature of many agent implementations makes this hard. In this work, we propose a Probabilistic Graphical Model along with a pipeline for designing such model -- by which the behaviour of an agent can be deliberated about -- and for computing a robust numerical value for the intentions the agent has at any moment. We contribute measurements that evaluate the interpretability and reliability of explanations provided, and enables explainability questions such as `what do you want to do now?' (e.g. deliver soup) `how do you plan to do it?' (e.g. returning a plan that considers its skills and the world), and `why would you take this action at this state?' (e.g. explaining how that furthers or hinders its own goals). This model can be constructed by taking partial observations of the agent's actions and world states, and we provide an iterative workflow for increasing the proposed measurements through better design and/or pointing out irrational agent behaviour.<|reference_end|>
|
arxiv
|
@article{gimenez-abalos2024intention-aware,
title={Intention-aware policy graphs: answering what, how, and why in opaque
agents},
author={Victor Gimenez-Abalos, Sergio Alvarez-Napagao, Adrian Tormos, Ulises
Cort'es, Javier V'azquez-Salceda},
journal={arXiv preprint arXiv:2409.19038},
year={2024},
doi={10.5281/zenodo.13862643},
archivePrefix={arXiv},
eprint={2409.19038},
primaryClass={cs.AI cs.LG cs.MA cs.RO}
}
|
gimenez-abalos2024intention-aware
|
arxiv-662950
|
2409.19039
|
Gaussian Heritage: 3D Digitization of Cultural Heritage with Integrated Object Segmentation
|
<|reference_start|>Gaussian Heritage: 3D Digitization of Cultural Heritage with Integrated Object Segmentation: The creation of digital replicas of physical objects has valuable applications for the preservation and dissemination of tangible cultural heritage. However, existing methods are often slow, expensive, and require expert knowledge. We propose a pipeline to generate a 3D replica of a scene using only RGB images (e.g. photos of a museum) and then extract a model for each item of interest (e.g. pieces in the exhibit). We do this by leveraging the advancements in novel view synthesis and Gaussian Splatting, modified to enable efficient 3D segmentation. This approach does not need manual annotation, and the visual inputs can be captured using a standard smartphone, making it both affordable and easy to deploy. We provide an overview of the method and baseline evaluation of the accuracy of object segmentation. The code is available at https://mahtaabdn.github.io/gaussian_heritage.github.io/.<|reference_end|>
|
arxiv
|
@article{dahaghin2024gaussian,
title={Gaussian Heritage: 3D Digitization of Cultural Heritage with Integrated
Object Segmentation},
author={Mahtab Dahaghin, Myrna Castillo, Kourosh Riahidehkordi, Matteo Toso,
Alessio Del Bue},
journal={arXiv preprint arXiv:2409.19039},
year={2024},
archivePrefix={arXiv},
eprint={2409.19039},
primaryClass={cs.CV}
}
|
dahaghin2024gaussian
|
arxiv-662951
|
2409.19042
|
Probing mental health information in speech foundation models
|
<|reference_start|>Probing mental health information in speech foundation models: Non-invasive methods for diagnosing mental health conditions, such as speech analysis, offer promising potential in modern medicine. Recent advancements in machine learning, particularly speech foundation models, have shown significant promise in detecting mental health states by capturing diverse features. This study investigates which pretext tasks in these models best transfer to mental health detection and examines how different model layers encode features relevant to mental health conditions. We also probed the optimal length of audio segments and the best pooling strategies to improve detection accuracy. Using the Callyope-GP and Androids datasets, we evaluated the models' effectiveness across different languages and speech tasks, aiming to enhance the generalizability of speech-based mental health diagnostics. Our approach achieved SOTA scores in depression detection on the Androids dataset.<|reference_end|>
|
arxiv
|
@article{de gennes2024probing,
title={Probing mental health information in speech foundation models},
author={Marc de Gennes, Adrien Lesage, Martin Denais, Xuan-Nga Cao, Simon
Chang, Pierre Van Remoortere, Cyrille Dakhlia, Rachid Riad},
journal={arXiv preprint arXiv:2409.19042},
year={2024},
archivePrefix={arXiv},
eprint={2409.19042},
primaryClass={eess.AS cs.SD}
}
|
de gennes2024probing
|
arxiv-662952
|
2409.19044
|
On the Inductive Bias of Stacking Towards Improving Reasoning
|
<|reference_start|>On the Inductive Bias of Stacking Towards Improving Reasoning: Given the increasing scale of model sizes, novel training strategies like gradual stacking [Gong et al., 2019, Reddi et al., 2023] have garnered interest. Stacking enables efficient training by gradually growing the depth of a model in stages and using layers from a smaller model in an earlier stage to initialize the next stage. Although efficient for training, the model biases induced by such growing approaches are largely unexplored. In this work, we examine this fundamental aspect of gradual stacking, going beyond its efficiency benefits. We propose a variant of gradual stacking called MIDAS that can speed up language model training by up to 40%. Furthermore we discover an intriguing phenomenon: MIDAS is not only training-efficient but surprisingly also has an inductive bias towards improving downstream tasks, especially tasks that require reasoning abilities like reading comprehension and math problems, despite having similar or slightly worse perplexity compared to baseline training. To further analyze this inductive bias, we construct reasoning primitives -- simple synthetic tasks that are building blocks for reasoning -- and find that a model pretrained with stacking is significantly better than standard pretraining on these primitives, with and without fine-tuning. This provides stronger and more robust evidence for this inductive bias towards reasoning. These findings of training efficiency and inductive bias towards reasoning are verified at 1B, 2B and 8B parameter language models. Finally, we conjecture the underlying reason for this inductive bias by exploring the connection of stacking to looped models and provide strong supporting empirical analysis.<|reference_end|>
|
arxiv
|
@article{saunshi2024on,
title={On the Inductive Bias of Stacking Towards Improving Reasoning},
author={Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi,
Sashank J. Reddi, Sanjiv Kumar},
journal={arXiv preprint arXiv:2409.19044},
year={2024},
archivePrefix={arXiv},
eprint={2409.19044},
primaryClass={cs.CL cs.AI cs.LG}
}
|
saunshi2024on
|
arxiv-662953
|
2409.19051
|
Multimodal Markup Document Models for Graphic Design Completion
|
<|reference_start|>Multimodal Markup Document Models for Graphic Design Completion: This paper presents multimodal markup document models (MarkupDM) that can generate both markup language and images within interleaved multimodal documents. Unlike existing vision-and-language multimodal models, our MarkupDM tackles unique challenges critical to graphic design tasks: generating partial images that contribute to the overall appearance, often involving transparency and varying sizes, and understanding the syntax and semantics of markup languages, which play a fundamental role as a representational format of graphic designs. To address these challenges, we design an image quantizer to tokenize images of diverse sizes with transparency and modify a code language model to process markup languages and incorporate image modalities. We provide in-depth evaluations of our approach on three graphic design completion tasks: generating missing attribute values, images, and texts in graphic design templates. Results corroborate the effectiveness of our MarkupDM for graphic design tasks. We also discuss the strengths and weaknesses in detail, providing insights for future research on multimodal document generation.<|reference_end|>
|
arxiv
|
@article{kikuchi2024multimodal,
title={Multimodal Markup Document Models for Graphic Design Completion},
author={Kotaro Kikuchi, Naoto Inoue, Mayu Otani, Edgar Simo-Serra, Kota
Yamaguchi},
journal={arXiv preprint arXiv:2409.19051},
year={2024},
archivePrefix={arXiv},
eprint={2409.19051},
primaryClass={cs.CV cs.AI cs.MM}
}
|
kikuchi2024multimodal
|
arxiv-662954
|
2409.19058
|
CLLMate: A Multimodal LLM for Weather and Climate Events Forecasting
|
<|reference_start|>CLLMate: A Multimodal LLM for Weather and Climate Events Forecasting: Forecasting weather and climate events is crucial for making appropriate measures to mitigate environmental hazards and minimize associated losses. Previous research on environmental forecasting focuses on predicting numerical meteorological variables related to closed-set events rather than forecasting open-set events directly, which limits the comprehensiveness of event forecasting. We propose Weather and Climate Event Forecasting (WCEF), a new task that leverages meteorological raster data and textual event data to predict potential weather and climate events. However, due to difficulties in aligning multimodal data and the lack of sufficient supervised datasets, this task is challenging to accomplish. Therefore, we first propose a framework to align historical meteorological data with past weather and climate events using the large language model (LLM). In this framework, we construct a knowledge graph by using LLM to extract information about weather and climate events from a corpus of over 41k highly environment-focused news articles. Subsequently, we mapped these events with meteorological raster data, creating a supervised dataset, which is the largest and most novel for LLM tuning on the WCEF task. Finally, we introduced our aligned models, CLLMate (LLM for climate), a multimodal LLM to forecast weather and climate events using meteorological raster data. In evaluating CLLMate, we conducted extensive experiments. The results indicate that CLLMate surpasses both the baselines and other multimodal LLMs, showcasing the potential of utilizing LLM to align weather and climate events with meteorological data and highlighting the promising future for research on the WCEF task.<|reference_end|>
|
arxiv
|
@article{li2024cllmate:,
title={CLLMate: A Multimodal LLM for Weather and Climate Events Forecasting},
author={Haobo Li, Zhaowei Wang, Jiachen Wang, Alexis Kai Hon Lau, Huamin Qu},
journal={arXiv preprint arXiv:2409.19058},
year={2024},
archivePrefix={arXiv},
eprint={2409.19058},
primaryClass={cs.LG cs.AI cs.CL physics.ao-ph}
}
|
li2024cllmate:
|
arxiv-662955
|
2409.19060
|
CURATE: Scaling-up Differentially Private Causal Graph Discovery
|
<|reference_start|>CURATE: Scaling-up Differentially Private Causal Graph Discovery: Causal Graph Discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents joint distribution of features of a dataset. CGD-algorithms are broadly classified into two categories: (i) Constraint-based algorithms (outcome depends on conditional independence (CI) tests), (ii) Score-based algorithms (outcome depends on optimized score-function). Since, sensitive features of observational data is prone to privacy-leakage, Differential Privacy (DP) has been adopted to ensure user privacy in CGD. Adding same amount of noise in this sequential-natured estimation process affects the predictive performance of the algorithms. As initial CI tests in constraint-based algorithms and later iterations of the optimization process of score-based algorithms are crucial, they need to be more accurate, less noisy. Based on this key observation, we present CURATE (CaUsal gRaph AdapTivE privacy), a DP-CGD framework with adaptive privacy budgeting. In contrast to existing DP-CGD algorithms with uniform privacy budgeting across all iterations, CURATE allows adaptive privacy budgeting by minimizing error probability (for constraint-based), maximizing iterations of the optimization problem (for score-based) while keeping the cumulative leakage bounded. To validate our framework, we present a comprehensive set of experiments on several datasets and show that CURATE achieves higher utility compared to existing DP-CGD algorithms with less privacy-leakage.<|reference_end|>
|
arxiv
|
@article{bhattacharjee2024curate:,
title={CURATE: Scaling-up Differentially Private Causal Graph Discovery},
author={Payel Bhattacharjee, Ravi Tandon},
journal={arXiv preprint arXiv:2409.19060},
year={2024},
archivePrefix={arXiv},
eprint={2409.19060},
primaryClass={cs.CR cs.IT cs.LG math.IT stat.ME}
}
|
bhattacharjee2024curate:
|
arxiv-662956
|
2409.19062
|
Robust Proximity Operations using Probabilistic Markov Models
|
<|reference_start|>Robust Proximity Operations using Probabilistic Markov Models: A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.<|reference_end|>
|
arxiv
|
@article{parikh2024robust,
title={Robust Proximity Operations using Probabilistic Markov Models},
author={Deep Parikh, Ali Hasnain Khowaja, Manoranjan Majji},
journal={arXiv preprint arXiv:2409.19062},
year={2024},
archivePrefix={arXiv},
eprint={2409.19062},
primaryClass={cs.RO cs.SY eess.SY}
}
|
parikh2024robust
|
arxiv-662957
|
2409.19067
|
Algorithms and complexity for monitoring edge-geodetic sets in graphs
|
<|reference_start|>Algorithms and complexity for monitoring edge-geodetic sets in graphs: A monitoring edge-geodetic set of a graph is a subset $M$ of its vertices such that for every edge $e$ in the graph, deleting $e$ increases the distance between at least one pair of vertices in $M$. We study the following computational problem \textsc{MEG-set}: given a graph $G$ and an integer $k$, decide whether $G$ has a monitoring edge geodetic set of size at most $k$. We prove that the problem is NP-hard even for 2-apex 3-degenerate graphs, improving a result by Haslegrave (Discrete Applied Mathematics 2023). Additionally, we prove that the problem cannot be solved in subexponential-time, assuming the Exponential-Time Hypothesis, even for 3-degenerate graphs. Further, we prove that the optimization version of the problem is APX-hard, even for 4-degenerate graphs. Complementing these hardness results, we prove that the problem admits a polynomial-time algorithm for interval graphs, a fixed-parameter tractable algorithm for general graphs with clique-width plus diameter as the parameter, and a fixed-parameter tractable algorithm for chordal graphs with treewidth as the parameter. We also provide an approximation algorithm with factor $\ln m\cdot OPT$ and $\sqrt{n\ln m}$ for the optimization version of the problem, where $m$ is the number of edges, $n$ the number of vertices, and $OPT$ is the size of a minimum monitoring edge-geodetic set of the input graph.<|reference_end|>
|
arxiv
|
@article{foucaud2024algorithms,
title={Algorithms and complexity for monitoring edge-geodetic sets in graphs},
author={Florent Foucaud, Clara Marcille, R. B. Sandeep, Sagnik Sen and S
Taruni},
journal={arXiv preprint arXiv:2409.19067},
year={2024},
archivePrefix={arXiv},
eprint={2409.19067},
primaryClass={cs.CC cs.DM cs.DS math.CO}
}
|
foucaud2024algorithms
|
arxiv-662958
|
2409.19068
|
Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation
|
<|reference_start|>Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation: This study addresses the urban transit pattern design problem, optimizing stop sequences, headways, and fleet sizes across multiple routes simultaneously to minimize user costs (composed of riding, waiting, and transfer times) under operational constraints (e.g., vehicle capacity and fleet size). A destination-labeled multi-commodity network flow (MCNF) formulation is developed to solve the problem at a large scale more efficiently compared to the previous literature. The model allows for flexible pattern options without relying on pre-defined candidate sets and simultaneously considers multiple operational strategies such as express/local services, short-turning, and deadheading. It evaluates perceived headways of joint patterns for passengers, assigns passenger flows to each pattern accordingly, and allows transfers across patterns in different directions. The mixed-integer linear programming (MILP) model is demonstrated with a city-sized network of metro lines in Chicago, USA, achieving near-optimal solutions in hours. The total weighted journey times are reduced by 0.61% and 4.13% under single-route and multi-route scenarios respectively. The model provides transit agencies with an efficient tool for comprehensive service design and resource allocation, improving service quality and resource utilization without additional operational costs.<|reference_end|>
|
arxiv
|
@article{ng2024joint,
title={Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban
Transit Lines with Perceived Headway Consideration and Passenger Flow
Allocation},
author={Max T.M. Ng, Draco Tong, Hani S. Mahmassani, Omer Verbas, Taner
Cokyasar},
journal={arXiv preprint arXiv:2409.19068},
year={2024},
archivePrefix={arXiv},
eprint={2409.19068},
primaryClass={eess.SY cs.SY math.OC}
}
|
ng2024joint
|
arxiv-662959
|
2409.19069
|
Localizing Memorization in SSL Vision Encoders
|
<|reference_start|>Localizing Memorization in SSL Vision Encoders: Recent work on studying memorization in self-supervised learning (SSL) suggests that even though SSL encoders are trained on millions of images, they still memorize individual data points. While effort has been put into characterizing the memorized data and linking encoder memorization to downstream utility, little is known about where the memorization happens inside SSL encoders. To close this gap, we propose two metrics for localizing memorization in SSL encoders on a per-layer (layermem) and per-unit basis (unitmem). Our localization methods are independent of the downstream task, do not require any label information, and can be performed in a forward pass. By localizing memorization in various encoder architectures (convolutional and transformer-based) trained on diverse datasets with contrastive and non-contrastive SSL frameworks, we find that (1) while SSL memorization increases with layer depth, highly memorizing units are distributed across the entire encoder, (2) a significant fraction of units in SSL encoders experiences surprisingly high memorization of individual data points, which is in contrast to models trained under supervision, (3) atypical (or outlier) data points cause much higher layer and unit memorization than standard data points, and (4) in vision transformers, most memorization happens in the fully-connected layers. Finally, we show that localizing memorization in SSL has the potential to improve fine-tuning and to inform pruning strategies.<|reference_end|>
|
arxiv
|
@article{wang2024localizing,
title={Localizing Memorization in SSL Vision Encoders},
author={Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch},
journal={arXiv preprint arXiv:2409.19069},
year={2024},
archivePrefix={arXiv},
eprint={2409.19069},
primaryClass={cs.LG cs.CV}
}
|
wang2024localizing
|
arxiv-662960
|
2409.19071
|
Analog fast Fourier transforms for scalable and efficient signal processing
|
<|reference_start|>Analog fast Fourier transforms for scalable and efficient signal processing: Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this sensed data suitable for further processing $\unicode{x2013}$ such as by artificial intelligence (AI) algorithms $\unicode{x2013}$ and for transmission over communication networks. Analog in-memory computing has been shown to be a fast and energy-efficient solution for processing edge AI workloads, but not for Fourier transforms. This is because of the existence of the fast Fourier transform (FFT) algorithm, which enormously reduces the complexity of the DFT but has so far belonged only to digital processors. Here, we show that the FFT can be mapped to analog in-memory computing systems, enabling them to efficiently scale to arbitrarily large Fourier transforms without requiring large sizes or large numbers of non-volatile memory arrays. We experimentally demonstrate analog FFTs on 1D audio and 2D image signals, using a large-scale charge-trapping memory array with precisely tunable, low-conductance analog states. The scalability of both the new analog FFT approach and the charge-trapping memory device is leveraged to compute a 65,536-point analog DFT, a scale that is otherwise inaccessible by analog systems and which is $>$1000$\times$ larger than any previous analog DFT demonstration. The analog FFT also provides more numerically precise DFTs with greater tolerance to device and circuit non-idealities than a direct matrix-vector multiplication approach. We show that the extension of the FFT algorithm to analog in-memory processors leads to design considerations that differ markedly from digital implementations, and that analog Fourier transforms have a substantial power efficiency advantage at all size scales over FFTs implemented on state-of-the-art digital hardware.<|reference_end|>
|
arxiv
|
@article{xiao2024analog,
title={Analog fast Fourier transforms for scalable and efficient signal
processing},
author={T. Patrick Xiao, Ben Feinberg, David K. Richardson, Matthew Cannon,
Harsha Medu, Vineet Agrawal, Matthew J. Marinella, Sapan Agarwal, Christopher
H. Bennett},
journal={arXiv preprint arXiv:2409.19071},
year={2024},
archivePrefix={arXiv},
eprint={2409.19071},
primaryClass={cs.ET eess.SP}
}
|
xiao2024analog
|
arxiv-662961
|
2409.19074
|
Show and Guide: Instructional-Plan Grounded Vision and Language Model
|
<|reference_start|>Show and Guide: Instructional-Plan Grounded Vision and Language Model: Guiding users through complex procedural plans is an inherently multimodal task in which having visually illustrated plan steps is crucial to deliver an effective plan guidance. However, existing works on plan-following language models (LMs) often are not capable of multimodal input and output. In this work, we present MM-PlanLLM, the first multimodal LLM designed to assist users in executing instructional tasks by leveraging both textual plans and visual information. Specifically, we bring cross-modality through two key tasks: Conversational Video Moment Retrieval, where the model retrieves relevant step-video segments based on user queries, and Visually-Informed Step Generation, where the model generates the next step in a plan, conditioned on an image of the user's current progress. MM-PlanLLM is trained using a novel multitask-multistage approach, designed to gradually expose the model to multimodal instructional-plans semantic layers, achieving strong performance on both multimodal and textual dialogue in a plan-grounded setting. Furthermore, we show that the model delivers cross-modal temporal and plan-structure representations aligned between textual plan steps and instructional video moments.<|reference_end|>
|
arxiv
|
@article{glória-silva2024show,
title={Show and Guide: Instructional-Plan Grounded Vision and Language Model},
author={Diogo Gl'oria-Silva, David Semedo, Jo~ao Magalh~aes},
journal={arXiv preprint arXiv:2409.19074},
year={2024},
archivePrefix={arXiv},
eprint={2409.19074},
primaryClass={cs.CV cs.CL}
}
|
glória-silva2024show
|
arxiv-662962
|
2409.19075
|
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning
|
<|reference_start|>Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning: Meta learning has been widely used to exploit rich-resource source tasks to improve the performance of low-resource target tasks. Unfortunately, most existing meta learning approaches treat different source tasks equally, ignoring the relatedness of source tasks to the target task in knowledge transfer. To mitigate this issue, we propose a reinforcement-based multi-source meta-transfer learning framework (Meta-RTL) for low-resource commonsense reasoning. In this framework, we present a reinforcement-based approach to dynamically estimating source task weights that measure the contribution of the corresponding tasks to the target task in the meta-transfer learning. The differences between the general loss of the meta model and task-specific losses of source-specific temporal meta models on sampled target data are fed into the policy network of the reinforcement learning module as rewards. The policy network is built upon LSTMs that capture long-term dependencies on source task weight estimation across meta learning iterations. We evaluate the proposed Meta-RTL using both BERT and ALBERT as the backbone of the meta model on three commonsense reasoning benchmark datasets. Experimental results demonstrate that Meta-RTL substantially outperforms strong baselines and previous task selection strategies and achieves larger improvements on extremely low-resource settings.<|reference_end|>
|
arxiv
|
@article{fu2024meta-rtl:,
title={Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource
Commonsense Reasoning},
author={Yu Fu, Jie He, Yifan Yang, Qun Liu, Deyi Xiong},
journal={arXiv preprint arXiv:2409.19075},
year={2024},
archivePrefix={arXiv},
eprint={2409.19075},
primaryClass={cs.CL cs.AI}
}
|
fu2024meta-rtl:
|
arxiv-662963
|
2409.19077
|
Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based Point Cloud Neural Networks
|
<|reference_start|>Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based Point Cloud Neural Networks: The 3D point cloud perception has emerged as a fundamental role for a wide range of applications. In particular, with the rapid development of neural networks, the voxel-based networks attract great attention due to their excellent performance. Various accelerator designs have been proposed to improve the hardware performance of voxel-based networks, especially to speed up the map search process. However, several challenges still exist including: (1) massive off-chip data access volume caused by map search operations, notably for high resolution and dense distribution cases, (2) frequent data movement for data-intensive convolution operations, (3) imbalanced workload caused by irregular sparsity of point data. To address the above challenges, we propose Voxel-CIM, an efficient Compute-in-Memory based accelerator for voxel-based neural network processing. To reduce off-chip memory access for map search, a depth-encoding-based output major search approach is introduced to maximize data reuse, achieving stable $O(N)$-level data access volume in various situations. Voxel-CIM also employs the in-memory computing paradigm and designs innovative weight mapping strategies to efficiently process Sparse 3D convolutions and 2D convolutions. Implemented on 22 nm technology and evaluated on representative benchmarks, the Voxel-CIM achieves averagely 4.5~7.0$\times$ higher energy efficiency (10.8 TOPS/w), and 2.4~5.4$\times$ speed up in detection task and 1.2~8.1$\times$ speed up in segmentation task compared to the state-of-the-art point cloud accelerators and powerful GPUs.<|reference_end|>
|
arxiv
|
@article{lin2024voxel-cim:,
title={Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based
Point Cloud Neural Networks},
author={Xipeng Lin, Shanshi Huang, Hongwu Jiang},
journal={arXiv preprint arXiv:2409.19077},
year={2024},
archivePrefix={arXiv},
eprint={2409.19077},
primaryClass={cs.AR}
}
|
lin2024voxel-cim:
|
arxiv-662964
|
2409.19078
|
Differential privacy for protecting patient data in speech disorder detection using deep learning
|
<|reference_start|>Differential privacy for protecting patient data in speech disorder detection using deep learning: Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensitive data raises critical privacy concerns. Although differential privacy (DP) has been explored in the medical imaging domain, its application in pathological speech analysis remains largely unexplored despite the equally critical privacy concerns. This study is the first to investigate DP's impact on pathological speech data, focusing on the trade-offs between privacy, diagnostic accuracy, and fairness. Using a large, real-world dataset of 200 hours of recordings from 2,839 German-speaking participants, we observed a maximum accuracy reduction of 3.85% when training with DP with a privacy budget, denoted by {\epsilon}, of 7.51. To generalize our findings, we validated our approach on a smaller dataset of Spanish-speaking Parkinson's disease patients, demonstrating that careful pretraining on large-scale task-specific datasets can maintain or even improve model accuracy under DP constraints. We also conducted a comprehensive fairness analysis, revealing that reasonable privacy levels (2<{\epsilon}<10) do not introduce significant gender bias, though age-related disparities may require further attention. Our results suggest that DP can effectively balance privacy and utility in speech disorder detection, but also highlight the unique challenges in the speech domain, particularly regarding the privacy-fairness trade-off. This provides a foundation for future work to refine DP methodologies and address fairness across diverse patient groups in real-world deployments.<|reference_end|>
|
arxiv
|
@article{arasteh2024differential,
title={Differential privacy for protecting patient data in speech disorder
detection using deep learning},
author={Soroosh Tayebi Arasteh, Mahshad Lotfinia, Paula Andrea Perez-Toro,
Tomas Arias-Vergara, Juan Rafael Orozco-Arroyave, Maria Schuster, Andreas
Maier, Seung Hee Yang},
journal={arXiv preprint arXiv:2409.19078},
year={2024},
archivePrefix={arXiv},
eprint={2409.19078},
primaryClass={cs.LG cs.AI cs.CR cs.SD eess.AS}
}
|
arasteh2024differential
|
arxiv-662965
|
2409.19079
|
Improved formulation for long-duration storage in capacity expansion models using representative periods
|
<|reference_start|>Improved formulation for long-duration storage in capacity expansion models using representative periods: With the increasing complexity and size of capacity expansion models, temporal aggregation has emerged as a common method to improve computational tractability. However, this approach inherently complicates the inclusion of long-duration storage (LDS) systems, whose operation involves the entire time horizon connecting all time steps. This work presents a detailed investigation of LDS modelling with temporal aggregation. A novel compact formulation is proposed to reduce the number of constraints while effectively tracking the storage content and enforcing limits on the state of charge throughout the entire time horizon. The developed method is compared with two leading state-of-the-art formulations. All three methods are implemented in the Dolphyn capacity expansion model and tested on a case study for the continental United States, considering different configurations in terms of spatial resolutions and representative periods. The performance is assessed with both the commercial solver Gurobi and the open-source solver HiGHS. Results show that the developed compact formulation consistently outperforms the other methods in terms of both runtime (30%-70% faster than other methods) and memory usage (1%-9% lower than other methods).<|reference_end|>
|
arxiv
|
@article{parolin2024improved,
title={Improved formulation for long-duration storage in capacity expansion
models using representative periods},
author={Federico Parolin, Paolo Colbertaldo, Ruaridh Macdonald},
journal={arXiv preprint arXiv:2409.19079},
year={2024},
archivePrefix={arXiv},
eprint={2409.19079},
primaryClass={eess.SY cs.SY math.OC}
}
|
parolin2024improved
|
arxiv-662966
|
2409.19087
|
Mechanism Design with Delegated Bidding
|
<|reference_start|>Mechanism Design with Delegated Bidding: We consider the problem of a designer who wants to allocate resources to representatives, that then distribute the resources they receive among the individuals they represent. Motivated by the way Feeding America, one of the largest U.S. charities, allocates donations to food banks, which then further distribute the donations to food-insecure individuals, we focus on mechanisms that use artificial currencies. We compare auctions through the lens of the Price of Anarchy, with respect to three canonical welfare objectives: utilitarian social welfare (sum of individuals' utilities), Nash social welfare (product of individuals' utilities), and egalitarian social welfare (minimum of individuals' utilities). We prove strong lower bounds on the Price of Anarchy of all auctions that allocate each item to the highest bidder, subject to a mild technical constraint; this includes Feeding America's current auction, the First-Price auction. In sharp contrast, our main result shows that adapting the classic Trading Post mechanism of Shapley and Shubik to this setting, and coupled with Feeding America's choice of budget rule (each representative gets an amount of artificial currency equal to the number of individuals it represents), achieves a small Price of Anarchy for all generalized $p$-mean objectives simultaneously. Our bound on the Price of Anarchy of the Trading Post mechanism depends on $\ell$: the product of the rank and the ``incoherence'' of the underlying valuation matrix, which together capture a notion of how ``spread out'' the values of a matrix are. This notion has been extremely influential in the matrix completion literature, and, to the best of our knowledge, has never been used in auction theory prior to our work. Perhaps surprisingly, we prove that the dependence on $\ell$ is necessary: the Price of Anarchy of the Trading Post mechanism is $\Omega(\sqrt{\ell})$.<|reference_end|>
|
arxiv
|
@article{aggarwal2024mechanism,
title={Mechanism Design with Delegated Bidding},
author={Gagan Aggarwal, Marios Mertzanidis, Alexandros Psomas, Di Wang},
journal={arXiv preprint arXiv:2409.19087},
year={2024},
archivePrefix={arXiv},
eprint={2409.19087},
primaryClass={cs.GT}
}
|
aggarwal2024mechanism
|
arxiv-662967
|
2409.19090
|
Calibrating microscopic traffic models with macroscopic data
|
<|reference_start|>Calibrating microscopic traffic models with macroscopic data: Traffic microsimulation is a crucial tool that uses microscopic traffic models, such as car-following and lane-change models, to simulate the trajectories of individual agents. This digital platform allows for the assessment of the impact of emerging technologies on transportation system performance. While these microscopic models are based on mathematical structures, their parameters must be fitted to real-world data through a process called model calibration. Despite extensive studies on calibration, the focus has predominantly been on fitting microscopic data, such as trajectories, rather than evaluating how well the models reproduce macroscopic traffic patterns, such as congestion, bottlenecks, and traffic waves. In this work, we address this gap by calibrating microscopic traffic flow models using macroscopic (aggregated) data, which is more readily accessible. We designed a SUMO-in-the-loop calibration framework with the goal of replicating observed macroscopic traffic features. To assess calibration accuracy, we developed a set of performance measures that evaluate the models' ability to replicate traffic states across the entire spatiotemporal domain and other qualitative characteristics of traffic flow. The calibration method was applied to both a synthetic scenario and a real-world scenario on a segment of Interstate 24, to demonstrate its effectiveness in reproducing observed traffic patterns.<|reference_end|>
|
arxiv
|
@article{wang2024calibrating,
title={Calibrating microscopic traffic models with macroscopic data},
author={Yanbing Wang, Felipe de Souza, Dominik Karbowski},
journal={arXiv preprint arXiv:2409.19090},
year={2024},
archivePrefix={arXiv},
eprint={2409.19090},
primaryClass={stat.AP cs.SY eess.SY}
}
|
wang2024calibrating
|
arxiv-662968
|
2409.19091
|
System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective
|
<|reference_start|>System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective: Large Language Model-based systems (LLM systems) are information and query processing systems that use LLMs to plan operations from natural-language prompts and feed the output of each successive step into the LLM to plan the next. This structure results in powerful tools that can process complex information from diverse sources but raises critical security concerns. Malicious information from any source may be processed by the LLM and can compromise the query processing, resulting in nearly arbitrary misbehavior. To tackle this problem, we present a system-level defense based on the principles of information flow control that we call an f-secure LLM system. An f-secure LLM system disaggregates the components of an LLM system into a context-aware pipeline with dynamically generated structured executable plans, and a security monitor filters out untrusted input into the planning process. This structure prevents compromise while maximizing flexibility. We provide formal models for both existing LLM systems and our f-secure LLM system, allowing analysis of critical security guarantees. We further evaluate case studies and benchmarks showing that f-secure LLM systems provide robust security while preserving functionality and efficiency. Our code is released at https://github.com/fzwark/Secure_LLM_System.<|reference_end|>
|
arxiv
|
@article{wu2024system-level,
title={System-Level Defense against Indirect Prompt Injection Attacks: An
Information Flow Control Perspective},
author={Fangzhou Wu, Ethan Cecchetti, Chaowei Xiao},
journal={arXiv preprint arXiv:2409.19091},
year={2024},
archivePrefix={arXiv},
eprint={2409.19091},
primaryClass={cs.CR}
}
|
wu2024system-level
|
arxiv-662969
|
2409.19092
|
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
|
<|reference_start|>Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups: We study the problems of differentially private federated online prediction from experts against both stochastic adversaries and oblivious adversaries. We aim to minimize the average regret on $m$ clients working in parallel over time horizon $T$ with explicit differential privacy (DP) guarantees. With stochastic adversaries, we propose a Fed-DP-OPE-Stoch algorithm that achieves $\sqrt{m}$-fold speed-up of the per-client regret compared to the single-player counterparts under both pure DP and approximate DP constraints, while maintaining logarithmic communication costs. With oblivious adversaries, we establish non-trivial lower bounds indicating that collaboration among clients does not lead to regret speed-up with general oblivious adversaries. We then consider a special case of the oblivious adversaries setting, where there exists a low-loss expert. We design a new algorithm Fed-SVT and show that it achieves an $m$-fold regret speed-up under both pure DP and approximate DP constraints over the single-player counterparts. Our lower bound indicates that Fed-SVT is nearly optimal up to logarithmic factors. Experiments demonstrate the effectiveness of our proposed algorithms. To the best of our knowledge, this is the first work examining the differentially private online prediction from experts in the federated setting.<|reference_end|>
|
arxiv
|
@article{gao2024federated,
title={Federated Online Prediction from Experts with Differential Privacy:
Separations and Regret Speed-ups},
author={Fengyu Gao, Ruiquan Huang, Jing Yang},
journal={arXiv preprint arXiv:2409.19092},
year={2024},
archivePrefix={arXiv},
eprint={2409.19092},
primaryClass={cs.LG cs.CR stat.ML}
}
|
gao2024federated
|
arxiv-662970
|
2409.19096
|
Enhancing Robustness of Graph Neural Networks through p-Laplacian
|
<|reference_start|>Enhancing Robustness of Graph Neural Networks through p-Laplacian: With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLapGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method.<|reference_end|>
|
arxiv
|
@article{sirohi2024enhancing,
title={Enhancing Robustness of Graph Neural Networks through p-Laplacian},
author={Anuj Kumar Sirohi, Subhanu Halder, Kabir Kumar, Sandeep Kumar},
journal={arXiv preprint arXiv:2409.19096},
year={2024},
archivePrefix={arXiv},
eprint={2409.19096},
primaryClass={cs.LG stat.ML}
}
|
sirohi2024enhancing
|
arxiv-662971
|
2409.19097
|
Implementing LLMs in industrial process modeling: Addressing Categorical Variables
|
<|reference_start|>Implementing LLMs in industrial process modeling: Addressing Categorical Variables: Important variables of processes are, in many occasions, categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we use Large Language Models (LLMs) to derive embeddings of such inputs that represent their actual meaning, or reflect the ``distances" between categories, i.e. how similar or dissimilar they are. This is a marked difference from the current standard practice of using binary, or one-hot encoding to replace categorical variables with sequences of ones and zeros. Combined with dimensionality reduction techniques, either linear such as Principal Components Analysis (PCA), or nonlinear such as Uniform Manifold Approximation and Projection (UMAP), the proposed approach leads to a \textit{meaningful}, low-dimensional feature space. The significance of obtaining meaningful embeddings is illustrated in the context of an industrial coating process for cutting tools that includes both numerical and categorical inputs. The proposed approach enables feature importance which is a marked improvement compared to the current state-of-the-art (SotA) in the encoding of categorical variables.<|reference_end|>
|
arxiv
|
@article{koronaki2024implementing,
title={Implementing LLMs in industrial process modeling: Addressing Categorical
Variables},
author={Eleni D. Koronaki, Geremy Loachamin Suntaxi, Paris Papavasileiou,
Dimitrios G. Giovanis, Martin Kathrein, Andreas G. Boudouvis, St'ephane P.
A. Bordas},
journal={arXiv preprint arXiv:2409.19097},
year={2024},
archivePrefix={arXiv},
eprint={2409.19097},
primaryClass={cs.LG stat.ML}
}
|
koronaki2024implementing
|
arxiv-662972
|
2409.19100
|
Outlining the Borders for LLM Applications in Patient Education: Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer Patient Education
|
<|reference_start|>Outlining the Borders for LLM Applications in Patient Education: Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer Patient Education: Cancer patients often struggle to transition swiftly to treatment due to limited institutional resources, lack of sophisticated professional guidance, and low health literacy. The emergence of Large Language Models (LLMs) offers new opportunities for such patients to access the wealth of existing patient education materials. The current paper presents the development process for an LLM-based chatbot focused on prostate cancer education, including needs assessment, co-design, and usability studies. The resulting application, MedEduChat, integrates with patients' electronic health record data and features a closed-domain, semi-structured, patient-centered approach to address real-world needs. This paper contributes to the growing field of patient-LLM interaction by demonstrating the potential of LLM-based chatbots to enhance prostate cancer patient education and by offering co-design guidelines for future LLM-based healthcare downstream applications.<|reference_end|>
|
arxiv
|
@article{hao2024outlining,
title={Outlining the Borders for LLM Applications in Patient Education:
Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer
Patient Education},
author={Yuexing Hao, Jason Holmes, Mark Waddle, Nathan Yu, Kirstin Vickers,
Heather Preston, Drew Margolin, Corinna E. L"ockenhoff, Aditya Vashistha,
Marzyeh Ghassemi, Saleh Kalantari, Wei Liu},
journal={arXiv preprint arXiv:2409.19100},
year={2024},
archivePrefix={arXiv},
eprint={2409.19100},
primaryClass={cs.HC}
}
|
hao2024outlining
|
arxiv-662973
|
2409.19104
|
Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure
|
<|reference_start|>Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure: The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.<|reference_end|>
|
arxiv
|
@article{chakraborti2024responsible,
title={Responsible AI in Open Ecosystems: Reconciling Innovation with Risk
Assessment and Disclosure},
author={Mahasweta Chakraborti, Bert Joseph Prestoza, Nicholas Vincent, Seth
Frey},
journal={arXiv preprint arXiv:2409.19104},
year={2024},
archivePrefix={arXiv},
eprint={2409.19104},
primaryClass={cs.HC cs.AI cs.CY cs.ET cs.SE}
}
|
chakraborti2024responsible
|
arxiv-662974
|
2409.19107
|
Measuring Software Development Waste in Open-Source Software Projects
|
<|reference_start|>Measuring Software Development Waste in Open-Source Software Projects: Software Development Waste (SDW) is defined as any resource-consuming activity that does not add value to the client or the organization developing the software. SDW impacts the overall efficiency and productivity of a software project as the scale and size of the project grows. Although engineering leaders usually put in effort to minimize waste, the lack of definitive measures to track and manage SDW is a cause of concern. To address this gap, we propose five measures, namely Stale Forks, Project Diversification Index, PR Rejection Rate, Backlog Inversion Index, and Feature Fulfillment Rate to potentially identify unused artifacts, building the wrong feature/product, mismanagement of backlog types of SDW. We apply these measures on ten open-source projects and share our observations to apply them in practice for managing SDW.<|reference_end|>
|
arxiv
|
@article{varanasi2024measuring,
title={Measuring Software Development Waste in Open-Source Software Projects},
author={Dhiraj SM Varanasi, Divij D, Sai Anirudh Karre, Y Raghu Reddy},
journal={arXiv preprint arXiv:2409.19107},
year={2024},
doi={10.1109/SEAA64295.2024.00050},
archivePrefix={arXiv},
eprint={2409.19107},
primaryClass={cs.SE}
}
|
varanasi2024measuring
|
arxiv-662975
|
2409.19109
|
Trust, But Verify, Operator-Reported Geolocation
|
<|reference_start|>Trust, But Verify, Operator-Reported Geolocation: Geolocation plays a critical role in understanding the Internet. In this work, we provide an in-depth analysis of operator-misreported geolocation. Using a bandwidth-efficient methodology, we find in May 2024 that only a small percentage (1.5%) of vantage points in the largest community-vantage point collection, RIPE Atlas, do not respond from their operator-reported geolocation. However, misreported geolocations disproportionately affect areas with limited coverage and cause entire countries to be left with no vantage points. Furthermore, the problem is escalating: within the past five years, the number of probes reporting the wrong location has increased ten-fold. To increase the accuracy of future methodologies and studies that rely upon operator-reported geolocation, we open source our methodology and release a continually updated dataset of RIPE Atlas vantage points that misreport geolocation.<|reference_end|>
|
arxiv
|
@article{izhikevich2024trust,,
title={Trust, But Verify, Operator-Reported Geolocation},
author={Katherine Izhikevich, Ben Du, Sumanth Rao, Alisha Ukani, Liz
Izhikevich},
journal={arXiv preprint arXiv:2409.19109},
year={2024},
archivePrefix={arXiv},
eprint={2409.19109},
primaryClass={cs.NI}
}
|
izhikevich2024trust,
|
arxiv-662976
|
2409.19110
|
S-RRT*-based Obstacle Avoidance Autonomous Motion Planner for Continuum-rigid Manipulator
|
<|reference_start|>S-RRT*-based Obstacle Avoidance Autonomous Motion Planner for Continuum-rigid Manipulator: Continuum robots are compact and flexible, making them suitable for use in the industries and in medical surgeries. Rapidly-exploring random trees (RRT) are a highly efficient path planning method, and its variant, S-RRT, can generate smooth feasible paths for the end-effector. By combining RRT with inverse instantaneous kinematics (IIK), complete motion planning for the continuum arm can be achieved. Due to the high degrees of freedom of continuum arms, the null space in IIK can be utilized for obstacle avoidance. In this work, we propose a novel approach that uses the S-RRT* algorithm to create paths for the continuum-rigid manipulator. By employing IIK and null space techniques, continuous joint configurations are generated that not only track the path but also enable obstacle avoidance. Simulation results demonstrate that our method effectively handles motion planning and obstacle avoidance while generating high-quality end-effector paths in complex environments. Furthermore, compared to similar IIK methods, our approach exhibits superior computation time.<|reference_end|>
|
arxiv
|
@article{li2024s-rrt*-based,
title={S-RRT*-based Obstacle Avoidance Autonomous Motion Planner for
Continuum-rigid Manipulator},
author={Yulin Li, Tetsuro Miyazaki, Yoshiki Yamamoto and Kenji Kawashima},
journal={arXiv preprint arXiv:2409.19110},
year={2024},
archivePrefix={arXiv},
eprint={2409.19110},
primaryClass={cs.RO}
}
|
li2024s-rrt*-based
|
arxiv-662977
|
2409.19111
|
Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis
|
<|reference_start|>Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis: Text-to-image (T2I) models have significantly advanced the development of artificial intelligence, enabling the generation of high-quality images in diverse contexts based on specific text prompts. However, existing T2I-based methods often struggle to accurately reproduce the appearance of individuals from a reference image and to create novel representations of those individuals in various settings. To address this, we leverage the pre-trained UNet from Stable Diffusion to incorporate the target face image directly into the generation process. Our approach diverges from prior methods that depend on fixed encoders or static face embeddings, which often fail to bridge encoding gaps. Instead, we capitalize on UNet's sophisticated encoding capabilities to process reference images across multiple scales. By innovatively altering the cross-attention layers of the UNet, we effectively fuse individual identities into the generative process. This strategic integration of facial features across various scales not only enhances the robustness and consistency of the generated images but also facilitates efficient multi-reference and multi-identity generation. Our method sets a new benchmark in identity-preserving image generation, delivering state-of-the-art results in similarity metrics while maintaining prompt alignment.<|reference_end|>
|
arxiv
|
@article{mohamed2024fusion,
title={Fusion is all you need: Face Fusion for Customized Identity-Preserving
Image Synthesis},
author={Salaheldin Mohamed, Dong Han, Yong Li},
journal={arXiv preprint arXiv:2409.19111},
year={2024},
archivePrefix={arXiv},
eprint={2409.19111},
primaryClass={cs.CV}
}
|
mohamed2024fusion
|
arxiv-662978
|
2409.19115
|
Identifying Key Genes in Cancer Networks Using Persistent Homology
|
<|reference_start|>Identifying Key Genes in Cancer Networks Using Persistent Homology: Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ($\beta_2$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.<|reference_end|>
|
arxiv
|
@article{ramos2024identifying,
title={Identifying Key Genes in Cancer Networks Using Persistent Homology},
author={Rodrigo Henrique Ramos, Yago Augusto Bardelotte, Cynthia de Oliveira
Lage Ferreira, Adenilso Simao},
journal={arXiv preprint arXiv:2409.19115},
year={2024},
archivePrefix={arXiv},
eprint={2409.19115},
primaryClass={q-bio.MN cs.OH}
}
|
ramos2024identifying
|
arxiv-662979
|
2409.19117
|
Range-aware Positional Encoding via High-order Pretraining: Theory and Practice
|
<|reference_start|>Range-aware Positional Encoding via High-order Pretraining: Theory and Practice: Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domains, neglecting the inherent connections within networks. This limits their ability to transfer knowledge to various supervised tasks. In this work, we propose a novel pre-training strategy on graphs that focuses on modeling their multi-resolution structural information, allowing us to capture global information of the whole graph while preserving local structures around its nodes. We extend the work of Wave}let Positional Encoding (WavePE) from (Ngo et al., 2023) by pretraining a High-Order Permutation-Equivariant Autoencoder (HOPE-WavePE) to reconstruct node connectivities from their multi-resolution wavelet signals. Unlike existing positional encodings, our method is designed to become sensitivity to the input graph size in downstream tasks, which efficiently capture global structure on graphs. Since our approach relies solely on the graph structure, it is also domain-agnostic and adaptable to datasets from various domains, therefore paving the wave for developing general graph structure encoders and graph foundation models. We theoretically demonstrate that there exists a parametrization of such architecture that it can predict the output adjacency up to arbitrarily low error. We also evaluate HOPE-WavePE on graph-level prediction tasks of different areas and show its superiority compared to other methods.<|reference_end|>
|
arxiv
|
@article{nguyen2024range-aware,
title={Range-aware Positional Encoding via High-order Pretraining: Theory and
Practice},
author={Viet Anh Nguyen, Nhat Khang Ngo, Truong Son Hy},
journal={arXiv preprint arXiv:2409.19117},
year={2024},
archivePrefix={arXiv},
eprint={2409.19117},
primaryClass={cs.LG eess.SP}
}
|
nguyen2024range-aware
|
arxiv-662980
|
2409.19119
|
Exascale Simulations of Fusion and Fission Systems
|
<|reference_start|>Exascale Simulations of Fusion and Fission Systems: We discuss pioneering heat and fluid flow simulations of fusion and fission energy systems with NekRS on exascale computing facilities, including Frontier and Aurora. The Argonne-based code, NekRS, is a highly-performant open-source code for the simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on rapidly convergent high-order spectral element discretizations that feature minimal numerical dissipation and dispersion. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Frontier, Nek5000/RS has achieved an unprecedented milestone in breaching over 1 trillion degrees of freedom with the spectral element methods for the simulation of the CHIMERA fusion technology testing platform. We also demonstrate for the first time the use of high-order overset grids at scale.<|reference_end|>
|
arxiv
|
@article{min2024exascale,
title={Exascale Simulations of Fusion and Fission Systems},
author={Misun Min, Yu-Hsiang Lan, Paul Fischer, Elia Merzari, Tri Nguyen,
Haomin Yuan, Patrick Shriwise, Stefan Kerkemeier, Andrew Davis, Aleksandr
Dubas, Rupert Eardly, Rob Akers, Thilina Rathnayake, Tim Warburton},
journal={arXiv preprint arXiv:2409.19119},
year={2024},
archivePrefix={arXiv},
eprint={2409.19119},
primaryClass={cs.CE}
}
|
min2024exascale
|
arxiv-662981
|
2409.19120
|
Secure Multiparty Generative AI
|
<|reference_start|>Secure Multiparty Generative AI: As usage of generative AI tools skyrockets, the amount of sensitive information being exposed to these models and centralized model providers is alarming. For example, confidential source code from Samsung suffered a data leak as the text prompt to ChatGPT encountered data leakage. An increasing number of companies are restricting the use of LLMs (Apple, Verizon, JPMorgan Chase, etc.) due to data leakage or confidentiality issues. Also, an increasing number of centralized generative model providers are restricting, filtering, aligning, or censoring what can be used. Midjourney and RunwayML, two of the major image generation platforms, restrict the prompts to their system via prompt filtering. Certain political figures are restricted from image generation, as well as words associated with women's health care, rights, and abortion. In our research, we present a secure and private methodology for generative artificial intelligence that does not expose sensitive data or models to third-party AI providers. Our work modifies the key building block of modern generative AI algorithms, e.g. the transformer, and introduces confidential and verifiable multiparty computations in a decentralized network to maintain the 1) privacy of the user input and obfuscation to the output of the model, and 2) introduce privacy to the model itself. Additionally, the sharding process reduces the computational burden on any one node, enabling the distribution of resources of large generative AI processes across multiple, smaller nodes. We show that as long as there exists one honest node in the decentralized computation, security is maintained. We also show that the inference process will still succeed if only a majority of the nodes in the computation are successful. Thus, our method offers both secure and verifiable computation in a decentralized network.<|reference_end|>
|
arxiv
|
@article{shrestha2024secure,
title={Secure Multiparty Generative AI},
author={Manil Shrestha, Yashodha Ravichandran, Edward Kim},
journal={arXiv preprint arXiv:2409.19120},
year={2024},
archivePrefix={arXiv},
eprint={2409.19120},
primaryClass={cs.CR cs.AI}
}
|
shrestha2024secure
|
arxiv-662982
|
2409.19121
|
Towards Energy- and Cost-Efficient 6G Networks
|
<|reference_start|>Towards Energy- and Cost-Efficient 6G Networks: As the world enters the journey toward the 6th generation (6G) of wireless technology, the promises of ultra-high data rates, unprecedented low latency, and a massive surge in connected devices require crucial exploration of network energy saving (NES) solutions to minimize the carbon footprint and overall energy usage of future cellular networks. On the other hand, network-controlled repeaters (NCRs) have been introduced by 3rd generation partnership project (3GPP) as a cost-effective solution to improve network coverage. However, their impact on network power consumption and energy efficiency has not been thoroughly investigated. This paper studies NES schemes for next-generation 6G networks aided by NCRs and proposes optimal NES strategies aiming at maximizing the overall energy efficiency of the network. Repeaters are shown to allow for power savings at next-generation nodeB (gNB), and offer higher overall energy efficiency (EE) and spectral efficiency (SE), thus providing an energy-efficient and cost-efficient alternative to increase the performance of future 6G networks<|reference_end|>
|
arxiv
|
@article{azzino2024towards,
title={Towards Energy- and Cost-Efficient 6G Networks},
author={Tommy Azzino and Aria HasanzadeZonuzy and Jianghong Luo and Navid
Abedini and Tao Luo},
journal={arXiv preprint arXiv:2409.19121},
year={2024},
archivePrefix={arXiv},
eprint={2409.19121},
primaryClass={cs.NI cs.SY eess.SY}
}
|
azzino2024towards
|
arxiv-662983
|
2409.19125
|
TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems
|
<|reference_start|>TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems: Control Flow Attestation (CFA) offers a means to detect control flow hijacking attacks on remote devices, enabling verification of their runtime trustworthiness. CFA generates a trace (CFLog) containing the destination of all branching instructions executed. This allows a remote Verifier (Vrf) to inspect the execution control flow on a potentially compromised Prover (Prv) before trusting that a value/action was correctly produced/performed by Prv. However, while CFA can be used to detect runtime compromises, it cannot guarantee the eventual delivery of the execution evidence (CFLog) to Vrf. In turn, a compromised Prv may refuse to send CFLog to Vrf, preventing its analysis to determine the exploit's root cause and appropriate remediation actions. In this work, we propose TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems. TRACES guarantees reliable delivery of periodic runtime reports even when Prv is compromised. This enables secure runtime auditing in addition to best-effort delivery of evidence in CFA. TRACES also supports a guaranteed remediation phase, triggered upon compromise detection to ensure that identified runtime vulnerabilities can be reliably patched. To the best of our knowledge, TRACES is the first system to provide this functionality on commodity devices (i.e., without requiring custom hardware modifications). To that end, TRACES leverages support from the ARM TrustZone-M Trusted Execution Environment (TEE). To assess practicality, we implement and evaluate a fully functional (open-source) prototype of TRACES atop the commodity ARM Cortex-M33 micro-controller unit.<|reference_end|>
|
arxiv
|
@article{caulfield2024traces:,
title={TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems},
author={Adam Caulfield, Antonio Joia Neto, Norrathep Rattanavipanon, Ivan De
Oliveira Nunes},
journal={arXiv preprint arXiv:2409.19125},
year={2024},
archivePrefix={arXiv},
eprint={2409.19125},
primaryClass={cs.CR}
}
|
caulfield2024traces:
|
arxiv-662984
|
2409.19128
|
Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
|
<|reference_start|>Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models: Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work, we investigate efficient diffusion training from the perspective of dataset pruning. Inspired by the principles of data-efficient training for generative models such as generative adversarial networks (GANs), we first extend the data selection scheme used in GANs to DM training, where data features are encoded by a surrogate model, and a score criterion is then applied to select the coreset. To further improve the generation performance, we employ a class-wise reweighting approach, which derives class weights through distributionally robust optimization (DRO) over a pre-trained reference DM. For a pixel-wise DM (DDPM) on CIFAR-10, experiments demonstrate the superiority of our methodology over existing approaches and its effectiveness in image synthesis comparable to that of the original full-data model while achieving the speed-up between 2.34 times and 8.32 times. Additionally, our method could be generalized to latent DMs (LDMs), e.g., Masked Diffusion Transformer (MDT) and Stable Diffusion (SD), and achieves competitive generation capability on ImageNet. Code is available here (https://github.com/Yeez-lee/Data-Selection-and-Reweighting-for-Diffusion-Models).<|reference_end|>
|
arxiv
|
@article{li2024pruning,
title={Pruning then Reweighting: Towards Data-Efficient Training of Diffusion
Models},
author={Yize Li, Yihua Zhang, Sijia Liu and Xue Lin},
journal={arXiv preprint arXiv:2409.19128},
year={2024},
archivePrefix={arXiv},
eprint={2409.19128},
primaryClass={cs.CV}
}
|
li2024pruning
|
arxiv-662985
|
2409.19130
|
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion
|
<|reference_start|>Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion: Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities. To address this, we propose a Multi-modal Cross-domain Self-supervised Pre-training Model (MCSP), a novel approach that leverages self-supervised learning to synergize multi-modal information across spatial, temporal, and spectral domains. Our model employs cross-domain self-supervised loss that bridges domain differences by implementing domain-specific data augmentation and contrastive loss, enhancing feature discrimination. Furthermore, MCSP introduces cross-modal self-supervised loss to capitalize on the complementary information of fMRI and EEG, facilitating knowledge distillation within domains and maximizing cross-modal feature convergence. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms to fully harness multimodal neuroimaging data. Through comprehensive experiments, we have demonstrated the superior performance and generalizability of our model on multiple classification tasks. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features, which enriches the existing landscape of neuroimaging research, particularly within the context of mental disorder studies.<|reference_end|>
|
arxiv
|
@article{wei2024multi-modal,
title={Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG
Fusion},
author={Xinxu Wei, Kanhao Zhao, Yong Jiao, Nancy B. Carlisle, Hua Xie, Gregory
A. Fonzo, Yu Zhang},
journal={arXiv preprint arXiv:2409.19130},
year={2024},
archivePrefix={arXiv},
eprint={2409.19130},
primaryClass={eess.IV cs.AI cs.LG}
}
|
wei2024multi-modal
|
arxiv-662986
|
2409.19131
|
Signal Temporal Logic Planning with Time-Varying Robustness
|
<|reference_start|>Signal Temporal Logic Planning with Time-Varying Robustness: This letter aims to generate a continuous-time trajectory consisting of piecewise B\'ezier curves that satisfy signal temporal logic (STL) specifications with piecewise time-varying robustness. Our time-varying robustness is less conservative than the real-valued robustness, which enables more effective tracking in practical applications. Specifically, our continuous-time trajectories account for dynamic feasibility, leading to smaller tracking errors and ensuring that the STL specifications can be met by the tracking trajectory. Comparative experiments demonstrate the efficiency and effectiveness of the proposed approach.<|reference_end|>
|
arxiv
|
@article{yuan2024signal,
title={Signal Temporal Logic Planning with Time-Varying Robustness},
author={Yating Yuan, Thanin Quartz, Jun Liu},
journal={arXiv preprint arXiv:2409.19131},
year={2024},
archivePrefix={arXiv},
eprint={2409.19131},
primaryClass={cs.RO}
}
|
yuan2024signal
|
arxiv-662987
|
2409.19132
|
From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation
|
<|reference_start|>From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation: Video encompasses both visual and auditory data, creating a perceptually rich experience where these two modalities complement each other. As such, videos are a valuable type of media for the investigation of the interplay between audio and visual elements. Previous studies of audio-visual modalities primarily focused on either audio-visual representation learning or generative modeling of a modality conditioned on the other, creating a disconnect between these two branches. A unified framework that learns representation and generates modalities has not been developed yet. In this work, we introduce a novel framework called Vision to Audio and Beyond (VAB) to bridge the gap between audio-visual representation learning and vision-to-audio generation. The key approach of VAB is that rather than working with raw video frames and audio data, VAB performs representation learning and generative modeling within latent spaces. In particular, VAB uses a pre-trained audio tokenizer and an image encoder to obtain audio tokens and visual features, respectively. It then performs the pre-training task of visual-conditioned masked audio token prediction. This training strategy enables the model to engage in contextual learning and simultaneous video-to-audio generation. After the pre-training phase, VAB employs the iterative-decoding approach to rapidly generate audio tokens conditioned on visual features. Since VAB is a unified model, its backbone can be fine-tuned for various audio-visual downstream tasks. Our experiments showcase the efficiency of VAB in producing high-quality audio from video, and its capability to acquire semantic audio-visual features, leading to competitive results in audio-visual retrieval and classification.<|reference_end|>
|
arxiv
|
@article{su2024from,
title={From Vision to Audio and Beyond: A Unified Model for Audio-Visual
Representation and Generation},
author={Kun Su and Xiulong Liu and Eli Shlizerman},
journal={arXiv preprint arXiv:2409.19132},
year={2024},
archivePrefix={arXiv},
eprint={2409.19132},
primaryClass={cs.MM cs.CV cs.LG cs.SD eess.AS}
}
|
su2024from
|
arxiv-662988
|
2409.19134
|
Confidential Prompting: Protecting User Prompts from Cloud LLM Providers
|
<|reference_start|>Confidential Prompting: Protecting User Prompts from Cloud LLM Providers: Our work tackles the challenge of securing user inputs in cloud-based large language model (LLM) services while ensuring output consistency, model confidentiality, and compute efficiency. We introduce Secure Multi-party Decoding (SMD), which leverages confidential computing to confine user prompts to a trusted execution environment, namely a confidential virtual machine (CVM), while allowing service providers to generate tokens efficiently. We also introduce a novel cryptographic method, Prompt Obfuscation (PO), to ensure robustness against reconstruction attacks on SMD. We demonstrate that our approach preserves both prompt confidentiality and LLM serving efficiency. Our solution can enable privacy-preserving cloud LLM services that handle sensitive prompts, such as clinical records, financial data, and personal information.<|reference_end|>
|
arxiv
|
@article{gim2024confidential,
title={Confidential Prompting: Protecting User Prompts from Cloud LLM Providers},
author={In Gim, Caihua Li, Lin Zhong},
journal={arXiv preprint arXiv:2409.19134},
year={2024},
archivePrefix={arXiv},
eprint={2409.19134},
primaryClass={cs.CR cs.CL}
}
|
gim2024confidential
|
arxiv-662989
|
2409.19135
|
Chebyshev Feature Neural Network for Accurate Function Approximation
|
<|reference_start|>Chebyshev Feature Neural Network for Accurate Function Approximation: We present a new Deep Neural Network (DNN) architecture capable of approximating functions up to machine accuracy. Termed Chebyshev Feature Neural Network (CFNN), the new structure employs Chebyshev functions with learnable frequencies as the first hidden layer, followed by the standard fully connected hidden layers. The learnable frequencies of the Chebyshev layer are initialized with exponential distributions to cover a wide range of frequencies. Combined with a multi-stage training strategy, we demonstrate that this CFNN structure can achieve machine accuracy during training. A comprehensive set of numerical examples for dimensions up to $20$ are provided to demonstrate the effectiveness and scalability of the method.<|reference_end|>
|
arxiv
|
@article{xu2024chebyshev,
title={Chebyshev Feature Neural Network for Accurate Function Approximation},
author={Zhongshu Xu, Yuan Chen, Dongbin Xiu},
journal={arXiv preprint arXiv:2409.19135},
year={2024},
archivePrefix={arXiv},
eprint={2409.19135},
primaryClass={cs.LG cs.NA cs.NE math.NA stat.ML}
}
|
xu2024chebyshev
|
arxiv-662990
|
2409.19136
|
Kinematic Detection of Anomalies in Human Trajectory Data
|
<|reference_start|>Kinematic Detection of Anomalies in Human Trajectory Data: Historically, much of the research in understanding, modeling, and mining human trajectory data has focused on where an individual stays. Thus, the focus of existing research has been on where a user goes. On the other hand, the study of how a user moves between locations has great potential for new research opportunities. Kinematic features describe how an individual moves between locations and can be used for tasks such as identification of individuals or anomaly detection. Unfortunately, data availability and quality challenges make kinematic trajectory mining difficult. In this paper, we leverage the Geolife dataset of human trajectories to investigate the viability of using kinematic features to identify individuals and detect anomalies. We show that humans have an individual "kinematic profile" which can be used as a strong signal to identify individual humans. We experimentally show that, for the two use-cases of individual identification and anomaly detection, simple kinematic features fed to standard classification and anomaly detection algorithms significantly improve results.<|reference_end|>
|
arxiv
|
@article{kennedy2024kinematic,
title={Kinematic Detection of Anomalies in Human Trajectory Data},
author={Lance Kennedy and Andreas Z"ufle},
journal={arXiv preprint arXiv:2409.19136},
year={2024},
archivePrefix={arXiv},
eprint={2409.19136},
primaryClass={cs.LG cs.AI}
}
|
kennedy2024kinematic
|
arxiv-662991
|
2409.19138
|
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
|
<|reference_start|>Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks: Inferring the exact parameters of a neural network with only query access is an NP-Hard problem, with few practical existing algorithms. Solutions would have major implications for security, verification, interpretability, and understanding biological networks. The key challenges are the massive parameter space, and complex non-linear relationships between neurons. We resolve these challenges using two insights. First, we observe that almost all networks used in practice are produced by random initialization and first order optimization, an inductive bias that drastically reduces the practical parameter space. Second, we present a novel query generation algorithm that produces maximally informative samples, letting us untangle the non-linear relationships efficiently. We demonstrate reconstruction of a hidden network containing over 1.5 million parameters, and of one 7 layers deep, the largest and deepest reconstructions to date, with max parameter difference less than 0.0001, and illustrate robustness and scalability across a variety of architectures, datasets, and training procedures.<|reference_end|>
|
arxiv
|
@article{goldfeder2024sequencing,
title={Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction
of Black-Box Neural Networks},
author={Judah Goldfeder, Quinten Roets, Gabe Guo, John Wright, Hod Lipson},
journal={arXiv preprint arXiv:2409.19138},
year={2024},
archivePrefix={arXiv},
eprint={2409.19138},
primaryClass={cs.LG cs.AI cs.CR cs.IT cs.NE math.IT}
}
|
goldfeder2024sequencing
|
arxiv-662992
|
2409.19139
|
Gaze-informed Signatures of Trust and Collaboration in Human-Autonomy Teams
|
<|reference_start|>Gaze-informed Signatures of Trust and Collaboration in Human-Autonomy Teams: In the evolving landscape of human-autonomy teaming (HAT), fostering effective collaboration and trust between human and autonomous agents is increasingly important. To explore this, we used the game Overcooked AI to create dynamic teaming scenarios featuring varying agent behaviors (clumsy, rigid, adaptive) and environmental complexities (low, medium, high). Our objectives were to assess the performance of adaptive AI agents designed with hierarchical reinforcement learning for better teamwork and measure eye tracking signals related to changes in trust and collaboration. The results indicate that the adaptive agent was more effective in managing teaming and creating an equitable task distribution across environments compared to the other agents. Working with the adaptive agent resulted in better coordination, reduced collisions, more balanced task contributions, and higher trust ratings. Reduced gaze allocation, across all agents, was associated with higher trust levels, while blink count, scan path length, agent revisits and trust were predictive of the humans contribution to the team. Notably, fixation revisits on the agent increased with environmental complexity and decreased with agent versatility, offering a unique metric for measuring teammate performance monitoring. These findings underscore the importance of designing autonomous teammates that not only excel in task performance but also enhance teamwork by being more predictable and reducing the cognitive load on human team members. Additionally, this study highlights the potential of eye-tracking as an unobtrusive measure for evaluating and improving human-autonomy teams, suggesting eye gaze could be used by agents to dynamically adapt their behaviors.<|reference_end|>
|
arxiv
|
@article{ries2024gaze-informed,
title={Gaze-informed Signatures of Trust and Collaboration in Human-Autonomy
Teams},
author={Anthony J. Ries, St'ephane Aroca-Ouellette, Alessandro Roncone, and
Ewart J. de Visser},
journal={arXiv preprint arXiv:2409.19139},
year={2024},
archivePrefix={arXiv},
eprint={2409.19139},
primaryClass={cs.HC}
}
|
ries2024gaze-informed
|
arxiv-662993
|
2409.19140
|
Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
|
<|reference_start|>Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems: Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws into the training of ESNs, Physics-Informed ESNs (PI-ESNs) were proposed initially to model chaotic dynamic systems without external inputs. They require less data for training since Ordinary Differential Equations (ODEs) of the considered system help to regularize the ESN. In this work, the PI-ESN is extended with external inputs to model controllable nonlinear dynamic systems. Additionally, an existing self-adaptive balancing loss method is employed to balance the contributions of the residual regression term and the physics-informed loss term in the total loss function. The experiments with two nonlinear systems modeled by ODEs, the Van der Pol oscillator and the four-tank system, and with one differential-algebraic (DAE) system, an electric submersible pump, revealed that the proposed PI-ESN outperforms the conventional ESN, especially in scenarios with limited data availability, showing that PI-ESNs can regularize an ESN model with external inputs previously trained on just a few datapoints, reducing its overfitting and improving its generalization error (up to 92% relative reduction in the test error). Further experiments demonstrated that the proposed PI-ESN is robust to parametric uncertainties in the ODE equations and that model predictive control using PI-ESN outperforms the one using plain ESN, particularly when training data is scarce.<|reference_end|>
|
arxiv
|
@article{camponogara2024physics-informed,
title={Physics-Informed Echo State Networks for Modeling Controllable Dynamical
Systems},
author={Eric Mochiutti Eric Aislan Antonelo Eduardo Camponogara},
journal={arXiv preprint arXiv:2409.19140},
year={2024},
archivePrefix={arXiv},
eprint={2409.19140},
primaryClass={cs.LG cs.NE math.DS}
}
|
camponogara2024physics-informed
|
arxiv-662994
|
2409.19142
|
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation
|
<|reference_start|>TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation: Sequential recommendation tasks, which aim to predict the next item a user will interact with, typically rely on models trained solely on historical data. However, in real-world scenarios, user behavior can fluctuate in the long interaction sequences, and training data may be limited to model this dynamics. To address this, Test-Time Training (TTT) offers a novel approach by using self-supervised learning during inference to dynamically update model parameters. This allows the model to adapt to new user interactions in real-time, leading to more accurate recommendations. In this paper, we propose TTT4Rec, a sequential recommendation framework that integrates TTT to better capture dynamic user behavior. By continuously updating model parameters during inference, TTT4Rec is particularly effective in scenarios where user interaction sequences are long, training data is limited, or user behavior is highly variable. We evaluate TTT4Rec on three widely-used recommendation datasets, demonstrating that it achieves performance on par with or exceeding state-of-the-art models. The codes are available at https://github.com/ZhaoqiZachYang/TTT4Rec.<|reference_end|>
|
arxiv
|
@article{yang2024ttt4rec:,
title={TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential
Recommendation},
author={Zhaoqi Yang, Yanan Wang, and Yong Ge},
journal={arXiv preprint arXiv:2409.19142},
year={2024},
archivePrefix={arXiv},
eprint={2409.19142},
primaryClass={cs.IR cs.AI}
}
|
yang2024ttt4rec:
|
arxiv-662995
|
2409.19143
|
Diverse Code Query Learning for Speech-Driven Facial Animation
|
<|reference_start|>Diverse Code Query Learning for Speech-Driven Facial Animation: Speech-driven facial animation aims to synthesize lip-synchronized 3D talking faces following the given speech signal. Prior methods to this task mostly focus on pursuing realism with deterministic systems, yet characterizing the potentially stochastic nature of facial motions has been to date rarely studied. While generative modeling approaches can easily handle the one-to-many mapping by repeatedly drawing samples, ensuring a diverse mode coverage of plausible facial motions on small-scale datasets remains challenging and less explored. In this paper, we propose predicting multiple samples conditioned on the same audio signal and then explicitly encouraging sample diversity to address diverse facial animation synthesis. Our core insight is to guide our model to explore the expressive facial latent space with a diversity-promoting loss such that the desired latent codes for diversification can be ideally identified. To this end, building upon the rich facial prior learned with vector-quantized variational auto-encoding mechanism, our model temporally queries multiple stochastic codes which can be flexibly decoded into a diverse yet plausible set of speech-faithful facial motions. To further allow for control over different facial parts during generation, the proposed model is designed to predict different facial portions of interest in a sequential manner, and compose them to eventually form full-face motions. Our paradigm realizes both diverse and controllable facial animation synthesis in a unified formulation. We experimentally demonstrate that our method yields state-of-the-art performance both quantitatively and qualitatively, especially regarding sample diversity.<|reference_end|>
|
arxiv
|
@article{gu2024diverse,
title={Diverse Code Query Learning for Speech-Driven Facial Animation},
author={Chunzhi Gu, Shigeru Kuriyama, Katsuya Hotta},
journal={arXiv preprint arXiv:2409.19143},
year={2024},
archivePrefix={arXiv},
eprint={2409.19143},
primaryClass={cs.CV}
}
|
gu2024diverse
|
arxiv-662996
|
2409.19146
|
Bound Tightening Network for Robust Crowd Counting
|
<|reference_start|>Bound Tightening Network for Robust Crowd Counting: Crowd Counting is a fundamental topic, aiming to estimate the number of individuals in the crowded images or videos fed from surveillance cameras. Recent works focus on improving counting accuracy, while ignoring the certified robustness of counting models. In this paper, we propose a novel Bound Tightening Network (BTN) for Robust Crowd Counting. It consists of three parts: base model, smooth regularization module and certify bound module. The core idea is to propagate the interval bound through the base model (certify bound module) and utilize the layer weights (smooth regularization module) to guide the network learning. Experiments on different benchmark datasets for counting demonstrate the effectiveness and efficiency of BTN.<|reference_end|>
|
arxiv
|
@article{wu2024bound,
title={Bound Tightening Network for Robust Crowd Counting},
author={Qiming Wu},
journal={arXiv preprint arXiv:2409.19146},
year={2024},
archivePrefix={arXiv},
eprint={2409.19146},
primaryClass={cs.CV cs.AI}
}
|
wu2024bound
|
arxiv-662997
|
2409.19148
|
Uncovering Differences in Persuasive Language in Russian versus English Wikipedia
|
<|reference_start|>Uncovering Differences in Persuasive Language in Russian versus English Wikipedia: We study how differences in persuasive language across Wikipedia articles, written in either English and Russian, can uncover each culture's distinct perspective on different subjects. We develop a large language model (LLM) powered system to identify instances of persuasive language in multilingual texts. Instead of directly prompting LLMs to detect persuasion, which is subjective and difficult, we propose to reframe the task to instead ask high-level questions (HLQs) which capture different persuasive aspects. Importantly, these HLQs are authored by LLMs themselves. LLMs over-generate a large set of HLQs, which are subsequently filtered to a small set aligned with human labels for the original task. We then apply our approach to a large-scale, bilingual dataset of Wikipedia articles (88K total), using a two-stage identify-then-extract prompting strategy to find instances of persuasion. We quantify the amount of persuasion per article, and explore the differences in persuasion through several experiments on the paired articles. Notably, we generate rankings of articles by persuasion in both languages. These rankings match our intuitions on the culturally-salient subjects; Russian Wikipedia highlights subjects on Ukraine, while English Wikipedia highlights the Middle East. Grouping subjects into larger topics, we find politically-related events contain more persuasion than others. We further demonstrate that HLQs obtain similar performance when posed in either English or Russian. Our methodology enables cross-lingual, cross-cultural understanding at scale, and we release our code, prompts, and data.<|reference_end|>
|
arxiv
|
@article{li2024uncovering,
title={Uncovering Differences in Persuasive Language in Russian versus English
Wikipedia},
author={Bryan Li, Aleksey Panasyuk, Chris Callison-Burch},
journal={arXiv preprint arXiv:2409.19148},
year={2024},
archivePrefix={arXiv},
eprint={2409.19148},
primaryClass={cs.CL}
}
|
li2024uncovering
|
arxiv-662998
|
2409.19149
|
Multimodal Pragmatic Jailbreak on Text-to-image Models
|
<|reference_start|>Multimodal Pragmatic Jailbreak on Text-to-image Models: Diffusion models have recently achieved remarkable advancements in terms of image quality and fidelity to textual prompts. Concurrently, the safety of such generative models has become an area of growing concern. This work introduces a novel type of jailbreak, which triggers T2I models to generate the image with visual text, where the image and the text, although considered to be safe in isolation, combine to form unsafe content. To systematically explore this phenomenon, we propose a dataset to evaluate the current diffusion-based text-to-image (T2I) models under such jailbreak. We benchmark nine representative T2I models, including two close-source commercial models. Experimental results reveal a concerning tendency to produce unsafe content: all tested models suffer from such type of jailbreak, with rates of unsafe generation ranging from 8\% to 74\%. In real-world scenarios, various filters such as keyword blocklists, customized prompt filters, and NSFW image filters, are commonly employed to mitigate these risks. We evaluate the effectiveness of such filters against our jailbreak and found that, while current classifiers may be effective for single modality detection, they fail to work against our jailbreak. Our work provides a foundation for further development towards more secure and reliable T2I models.<|reference_end|>
|
arxiv
|
@article{liu2024multimodal,
title={Multimodal Pragmatic Jailbreak on Text-to-image Models},
author={Tong Liu, Zhixin Lai, Gengyuan Zhang, Philip Torr, Vera Demberg,
Volker Tresp, Jindong Gu},
journal={arXiv preprint arXiv:2409.19149},
year={2024},
archivePrefix={arXiv},
eprint={2409.19149},
primaryClass={cs.CV cs.AI cs.CR cs.LG}
}
|
liu2024multimodal
|
arxiv-662999
|
2409.19150
|
On the Power of Decision Trees in Auto-Regressive Language Modeling
|
<|reference_start|>On the Power of Decision Trees in Auto-Regressive Language Modeling: Originally proposed for handling time series data, Auto-regressive Decision Trees (ARDTs) have not yet been explored for language modeling. This paper delves into both the theoretical and practical applications of ARDTs in this new context. We theoretically demonstrate that ARDTs can compute complex functions, such as simulating automata, Turing machines, and sparse circuits, by leveraging "chain-of-thought" computations. Our analysis provides bounds on the size, depth, and computational efficiency of ARDTs, highlighting their surprising computational power. Empirically, we train ARDTs on simple language generation tasks, showing that they can learn to generate coherent and grammatically correct text on par with a smaller Transformer model. Additionally, we show that ARDTs can be used on top of transformer representations to solve complex reasoning tasks. This research reveals the unique computational abilities of ARDTs, aiming to broaden the architectural diversity in language model development.<|reference_end|>
|
arxiv
|
@article{gan2024on,
title={On the Power of Decision Trees in Auto-Regressive Language Modeling},
author={Yulu Gan, Tomer Galanti, Tomaso Poggio, Eran Malach},
journal={arXiv preprint arXiv:2409.19150},
year={2024},
archivePrefix={arXiv},
eprint={2409.19150},
primaryClass={cs.CL}
}
|
gan2024on
|
arxiv-663000
|
2409.19151
|
Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?
|
<|reference_start|>Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?: Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from One Book (Tanzer et al., 2024) suggests prompting long-context LLMs with one grammar book enables English-Kalamang translation, an unseen XLR language - a noteworthy case of linguistic knowledge helping an NLP task. We investigate whether the book's grammatical explanations or its parallel examples are most effective for learning XLR translation, finding almost all improvement stems from the parallel examples. Further, we find similar results for Nepali, a seen low-resource language, and achieve performance comparable to an LLM with a grammar book by simply fine-tuning an encoder-decoder translation model. We then investigate where grammar books help by testing two linguistic tasks, grammaticality judgment and gloss prediction, and we explore what kind of grammatical knowledge helps by introducing a typological feature prompt that achieves leading results on these more relevant tasks. We thus emphasise the importance of task-appropriate data for XLR languages: parallel examples for translation, and grammatical data for linguistic tasks. As we find no evidence that long-context LLMs can make effective use of grammatical explanations for XLR translation, we suggest data collection for multilingual XLR tasks such as translation is best focused on parallel data over linguistic description.<|reference_end|>
|
arxiv
|
@article{aycock2024can,
title={Can LLMs Really Learn to Translate a Low-Resource Language from One
Grammar Book?},
author={Seth Aycock, David Stap, Di Wu, Christof Monz, Khalil Sima'an},
journal={arXiv preprint arXiv:2409.19151},
year={2024},
archivePrefix={arXiv},
eprint={2409.19151},
primaryClass={cs.CL}
}
|
aycock2024can
|
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