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arxiv-665801 | 2410.03747 | Distributed AI Platform for the 6G RAN | <|reference_start|>Distributed AI Platform for the 6G RAN: Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. This article attempts to shed light to these challenges and argues that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. Motivated by this lack of solutions, it proposes a generic distributed AI platform architecture, tailored to the needs of an AI-native RAN and discusses its alignment with ongoing standardization efforts.<|reference_end|> | arxiv | @article{ananthanarayanan2024distributed,
title={Distributed AI Platform for the 6G RAN},
author={Ganesh Ananthanarayanan, Xenofon Foukas, Bozidar Radunovic, Yongguang
Zhang},
journal={arXiv preprint arXiv:2410.03747},
year={2024},
archivePrefix={arXiv},
eprint={2410.03747},
primaryClass={cs.NI cs.AI}
} | ananthanarayanan2024distributed |
arxiv-665802 | 2410.03748 | Khattat: Enhancing Readability and Concept Representation of Semantic Typography | <|reference_start|>Khattat: Enhancing Readability and Concept Representation of Semantic Typography: Designing expressive typography that visually conveys a word's meaning while maintaining readability is a complex task, known as semantic typography. It involves selecting an idea, choosing an appropriate font, and balancing creativity with legibility. We introduce an end-to-end system that automates this process. First, a Large Language Model (LLM) generates imagery ideas for the word, useful for abstract concepts like freedom. Then, the FontCLIP pre-trained model automatically selects a suitable font based on its semantic understanding of font attributes. The system identifies optimal regions of the word for morphing and iteratively transforms them using a pre-trained diffusion model. A key feature is our OCR-based loss function, which enhances readability and enables simultaneous stylization of multiple characters. We compare our method with other baselines, demonstrating great readability enhancement and versatility across multiple languages and writing scripts.<|reference_end|> | arxiv | @article{hussein2024khattat:,
title={Khattat: Enhancing Readability and Concept Representation of Semantic
Typography},
author={Ahmed Hussein, Alaa Elsetohy, Sama Hadhoud, Tameem Bakr, Yasser
Rohaim, and Badr AlKhamissi},
journal={arXiv preprint arXiv:2410.03748},
year={2024},
archivePrefix={arXiv},
eprint={2410.03748},
primaryClass={cs.CL cs.LG}
} | hussein2024khattat: |
arxiv-665803 | 2410.03749 | Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization | <|reference_start|>Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization: This paper presents a machine learning approach to classify countries as peaceful or non-peaceful using linguistic patterns extracted from global media articles. We employ vector embeddings and cosine similarity to develop a supervised classification model that effectively identifies peaceful countries. Additionally, we explore the impact of dataset size on model performance, investigating how shrinking the dataset influences classification accuracy. Our results highlight the challenges and opportunities associated with using large-scale text data for peace studies.<|reference_end|> | arxiv | @article{lian2024machine,
title={Machine Learning Classification of Peaceful Countries: A Comparative
Analysis and Dataset Optimization},
author={K. Lian (1), L. S. Liebovitch (1), M. Wild (1), H. West (1), P. T.
Coleman (1), F. Chen (2), E. Kimani (2), K. Sieck (2) ((1) Columbia
University, (2) Toyota Research Institute)},
journal={arXiv preprint arXiv:2410.03749},
year={2024},
archivePrefix={arXiv},
eprint={2410.03749},
primaryClass={cs.CL cs.LG}
} | lian2024machine |
arxiv-665804 | 2410.03750 | SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models | <|reference_start|>SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models: Large pre-trained models (LPMs), such as large language models, have become ubiquitous and are employed in many applications. These models are often adapted to a desired domain or downstream task through a fine-tuning stage. This paper proposes SQFT, an end-to-end solution for low-precision sparse parameter-efficient fine-tuning of LPMs, allowing for effective model manipulation in resource-constrained environments. Additionally, an innovative strategy enables the merging of sparse weights with low-rank adapters without losing sparsity and accuracy, overcoming the limitations of previous approaches. SQFT also addresses the challenge of having quantized weights and adapters with different numerical precisions, enabling merging in the desired numerical format without sacrificing accuracy. Multiple adaptation scenarios, models, and comprehensive sparsity levels demonstrate the effectiveness of SQFT. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.<|reference_end|> | arxiv | @article{muñoz2024sqft:,
title={SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation
Models},
author={Juan Pablo Mu~noz, Jinjie Yuan, Nilesh Jain},
journal={arXiv preprint arXiv:2410.03750},
year={2024},
archivePrefix={arXiv},
eprint={2410.03750},
primaryClass={cs.LG cs.AI cs.CL}
} | muñoz2024sqft: |
arxiv-665805 | 2410.03751 | Recent Advances in Speech Language Models: A Survey | <|reference_start|>Recent Advances in Speech Language Models: A Survey: Large Language Models (LLMs) have recently garnered significant attention, primarily for their capabilities in text-based interactions. However, natural human interaction often relies on speech, necessitating a shift towards voice-based models. A straightforward approach to achieve this involves a pipeline of ``Automatic Speech Recognition (ASR) + LLM + Text-to-Speech (TTS)", where input speech is transcribed to text, processed by an LLM, and then converted back to speech. Despite being straightforward, this method suffers from inherent limitations, such as information loss during modality conversion and error accumulation across the three stages. To address these issues, Speech Language Models (SpeechLMs) -- end-to-end models that generate speech without converting from text -- have emerged as a promising alternative. This survey paper provides the first comprehensive overview of recent methodologies for constructing SpeechLMs, detailing the key components of their architecture and the various training recipes integral to their development. Additionally, we systematically survey the various capabilities of SpeechLMs, categorize the evaluation metrics for SpeechLMs, and discuss the challenges and future research directions in this rapidly evolving field.<|reference_end|> | arxiv | @article{cui2024recent,
title={Recent Advances in Speech Language Models: A Survey},
author={Wenqian Cui and Dianzhi Yu and Xiaoqi Jiao and Ziqiao Meng and
Guangyan Zhang and Qichao Wang and Yiwen Guo and Irwin King},
journal={arXiv preprint arXiv:2410.03751},
year={2024},
archivePrefix={arXiv},
eprint={2410.03751},
primaryClass={cs.CL cs.SD eess.AS}
} | cui2024recent |
arxiv-665806 | 2410.03752 | Efficient Streaming LLM for Speech Recognition | <|reference_start|>Efficient Streaming LLM for Speech Recognition: Recent works have shown that prompting large language models with audio encodings can unlock speech recognition capabilities. However, existing techniques do not scale efficiently, especially while handling long form streaming audio inputs -- not only do they extrapolate poorly beyond the audio length seen during training, but they are also computationally inefficient due to the quadratic cost of attention. In this work, we introduce SpeechLLM-XL, a linear scaling decoder-only model for streaming speech recognition. We process audios in configurable chunks using limited attention window for reduced computation, and the text tokens for each audio chunk are generated auto-regressively until an EOS is predicted. During training, the transcript is segmented into chunks, using a CTC forced alignment estimated from encoder output. SpeechLLM-XL with 1.28 seconds chunk size achieves 2.7%/6.7% WER on LibriSpeech test clean/other, and it shows no quality degradation on long form utterances 10x longer than the training utterances.<|reference_end|> | arxiv | @article{jia2024efficient,
title={Efficient Streaming LLM for Speech Recognition},
author={Junteng Jia, Gil Keren, Wei Zhou, Egor Lakomkin, Xiaohui Zhang,
Chunyang Wu, Frank Seide, Jay Mahadeokar, Ozlem Kalinli},
journal={arXiv preprint arXiv:2410.03752},
year={2024},
archivePrefix={arXiv},
eprint={2410.03752},
primaryClass={cs.SD cs.AI cs.CL eess.AS}
} | jia2024efficient |
arxiv-665807 | 2410.03753 | A Brief Tutorial on Consensus ADMM for Distributed Optimization with Applications in Robotics | <|reference_start|>A Brief Tutorial on Consensus ADMM for Distributed Optimization with Applications in Robotics: This paper presents a tutorial on the Consensus Alternating Direction Method of Multipliers (Consensus ADMM) for distributed optimization, with a specific focus on applications in multi-robot systems. In this tutorial, we derive the consensus ADMM algorithm, highlighting its connections to the augmented Lagrangian and primal-dual methods. Finally, we apply Consensus ADMM to an example problem for trajectory optimization of a multi-agent system.<|reference_end|> | arxiv | @article{chen2024a,
title={A Brief Tutorial on Consensus ADMM for Distributed Optimization with
Applications in Robotics},
author={Jushan Chen},
journal={arXiv preprint arXiv:2410.03753},
year={2024},
archivePrefix={arXiv},
eprint={2410.03753},
primaryClass={math.OC cs.SY eess.SY}
} | chen2024a |
arxiv-665808 | 2410.03754 | Enhancing Retrieval in QA Systems with Derived Feature Association | <|reference_start|>Enhancing Retrieval in QA Systems with Derived Feature Association: Retrieval augmented generation (RAG) has become the standard in long context question answering (QA) systems. However, typical implementations of RAG rely on a rather naive retrieval mechanism, in which texts whose embeddings are most similar to that of the query are deemed most relevant. This has consequences in subjective QA tasks, where the most relevant text may not directly contain the answer. In this work, we propose a novel extension to RAG systems, which we call Retrieval from AI Derived Documents (RAIDD). RAIDD leverages the full power of the LLM in the retrieval process by deriving inferred features, such as summaries and example questions, from the documents at ingest. We demonstrate that this approach significantly improves the performance of RAG systems on long-context QA tasks.<|reference_end|> | arxiv | @article{shah2024enhancing,
title={Enhancing Retrieval in QA Systems with Derived Feature Association},
author={Keyush Shah and Abhishek Goyal and Isaac Wasserman},
journal={arXiv preprint arXiv:2410.03754},
year={2024},
archivePrefix={arXiv},
eprint={2410.03754},
primaryClass={cs.CL cs.IR}
} | shah2024enhancing |
arxiv-665809 | 2410.03755 | Denoising with a Joint-Embedding Predictive Architecture | <|reference_start|>Denoising with a Joint-Embedding Predictive Architecture: Joint-embedding predictive architectures (JEPAs) have shown substantial promise in self-supervised representation learning, yet their application in generative modeling remains underexplored. Conversely, diffusion models have demonstrated significant efficacy in modeling arbitrary probability distributions. In this paper, we introduce Denoising with a Joint-Embedding Predictive Architecture (D-JEPA), pioneering the integration of JEPA within generative modeling. By recognizing JEPA as a form of masked image modeling, we reinterpret it as a generalized next-token prediction strategy, facilitating data generation in an auto-regressive manner. Furthermore, we incorporate diffusion loss to model the per-token probability distribution, enabling data generation in a continuous space. We also adapt flow matching loss as an alternative to diffusion loss, thereby enhancing the flexibility of D-JEPA. Empirically, with increased GFLOPs, D-JEPA consistently achieves lower FID scores with fewer training epochs, indicating its good scalability. Our base, large, and huge models outperform all previous generative models across all scales on class-conditional ImageNet benchmarks. Beyond image generation, D-JEPA is well-suited for other continuous data modeling, including video and audio.<|reference_end|> | arxiv | @article{chen2024denoising,
title={Denoising with a Joint-Embedding Predictive Architecture},
author={Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua Wu},
journal={arXiv preprint arXiv:2410.03755},
year={2024},
archivePrefix={arXiv},
eprint={2410.03755},
primaryClass={cs.LG cs.CV}
} | chen2024denoising |
arxiv-665810 | 2410.03756 | Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability | <|reference_start|>Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability: Commercial office buildings contribute 17 percent of Carbon Emissions in the US, according to the US Energy Information Administration (EIA), and improving their efficiency will reduce their environmental burden and operating cost. A major contributor of energy consumption in these buildings are the Heating, Ventilation, and Air Conditioning (HVAC) devices. HVAC devices form a complex and interconnected thermodynamic system with the building and outside weather conditions, and current setpoint control policies are not fully optimized for minimizing energy use and carbon emission. Given a suitable training environment, a Reinforcement Learning (RL) agent is able to improve upon these policies, but training such a model, especially in a way that scales to thousands of buildings, presents many practical challenges. Most existing work on applying RL to this important task either makes use of proprietary data, or focuses on expensive and proprietary simulations that may not be grounded in the real world. We present the Smart Buildings Control Suite, the first open source interactive HVAC control dataset extracted from live sensor measurements of devices in real office buildings. The dataset consists of two components: six years of real-world historical data from three buildings, for offline RL, and a lightweight interactive simulator for each of these buildings, calibrated using the historical data, for online and model-based RL. For ease of use, our RL environments are all compatible with the OpenAI gym environment standard. We also demonstrate a novel method of calibrating the simulator, as well as baseline results on training an RL agent on the simulator, predicting real-world data, and training an RL agent directly from data. We believe this benchmark will accelerate progress and collaboration on building optimization and environmental sustainability research.<|reference_end|> | arxiv | @article{goldfeder2024real-world,
title={Real-World Data and Calibrated Simulation Suite for Offline Training of
Reinforcement Learning Agents to Optimize Energy and Emission in Buildings
for Environmental Sustainability},
author={Judah Goldfeder, John Sipple},
journal={arXiv preprint arXiv:2410.03756},
year={2024},
archivePrefix={arXiv},
eprint={2410.03756},
primaryClass={cs.AI cs.CY cs.DC cs.LG cs.SY eess.SY}
} | goldfeder2024real-world |
arxiv-665811 | 2410.03758 | Towards a Deeper Understanding of Transformer for Residential Non-intrusive Load Monitoring | <|reference_start|>Towards a Deeper Understanding of Transformer for Residential Non-intrusive Load Monitoring: Transformer models have demonstrated impressive performance in Non-Intrusive Load Monitoring (NILM) applications in recent years. Despite their success, existing studies have not thoroughly examined the impact of various hyper-parameters on model performance, which is crucial for advancing high-performing transformer models. In this work, a comprehensive series of experiments have been conducted to analyze the influence of these hyper-parameters in the context of residential NILM. This study delves into the effects of the number of hidden dimensions in the attention layer, the number of attention layers, the number of attention heads, and the dropout ratio on transformer performance. Furthermore, the role of the masking ratio has explored in BERT-style transformer training, providing a detailed investigation into its impact on NILM tasks. Based on these experiments, the optimal hyper-parameters have been selected and used them to train a transformer model, which surpasses the performance of existing models. The experimental findings offer valuable insights and guidelines for optimizing transformer architectures, aiming to enhance their effectiveness and efficiency in NILM applications. It is expected that this work will serve as a foundation for future research and development of more robust and capable transformer models for NILM.<|reference_end|> | arxiv | @article{rahman2024towards,
title={Towards a Deeper Understanding of Transformer for Residential
Non-intrusive Load Monitoring},
author={Minhajur Rahman, Yasir Arafat},
journal={arXiv preprint arXiv:2410.03758},
year={2024},
archivePrefix={arXiv},
eprint={2410.03758},
primaryClass={eess.SY cs.AI cs.SY}
} | rahman2024towards |
arxiv-665812 | 2410.03759 | Intelligent CAD 20 | <|reference_start|>Intelligent CAD 20: Integrating modern artificial intelligence (AI) techniques, particularly generative AI, holds the promise of revolutionizing computer-aided design (CAD) tools and the engineering design process. However, the direction of "AI+CAD" remains unclear: how will the current generation of intelligent CAD (ICAD) differ from its predecessor in the 1980s and 1990s, what strategic pathways should researchers and engineers pursue for its implementation, and what potential technical challenges might arise? As an attempt to address these questions, this paper investigates the transformative role of modern AI techniques in advancing CAD towards ICAD. It first analyzes the design process and reconsiders the roles AI techniques can assume in this process, highlighting how they can restructure the path humans, computers, and designs interact with each other. The primary conclusion is that ICAD systems should assume an intensional rather than extensional role in the design process. This offers insights into the evaluation of the previous generation of ICAD (ICAD 1.0) and outlines a prospective framework and trajectory for the next generation of ICAD (ICAD 2.0).<|reference_end|> | arxiv | @article{zou2024intelligent,
title={Intelligent CAD 2.0},
author={Qiang Zou, Yincai Wu, Zhenyu Liu, Weiwei Xu, Shuming Gao},
journal={arXiv preprint arXiv:2410.03759},
year={2024},
archivePrefix={arXiv},
eprint={2410.03759},
primaryClass={cs.HC cs.GR}
} | zou2024intelligent |
arxiv-665813 | 2410.03760 | On the SAGA algorithm with decreasing step | <|reference_start|>On the SAGA algorithm with decreasing step: Stochastic optimization naturally appear in many application areas, including machine learning. Our goal is to go further in the analysis of the Stochastic Average Gradient Accelerated (SAGA) algorithm. To achieve this, we introduce a new $\lambda$-SAGA algorithm which interpolates between the Stochastic Gradient Descent ($\lambda=0$) and the SAGA algorithm ($\lambda=1$). Firstly, we investigate the almost sure convergence of this new algorithm with decreasing step which allows us to avoid the restrictive strong convexity and Lipschitz gradient hypotheses associated to the objective function. Secondly, we establish a central limit theorem for the $\lambda$-SAGA algorithm. Finally, we provide the non-asymptotic $\mathbb{L}^p$ rates of convergence.<|reference_end|> | arxiv | @article{fredes2024on,
title={On the SAGA algorithm with decreasing step},
author={Luis Fredes (IMB), Bernard Bercu (IMB), Em'eric Gbaguidi (IMB)},
journal={arXiv preprint arXiv:2410.03760},
year={2024},
archivePrefix={arXiv},
eprint={2410.03760},
primaryClass={math.OC cs.LG math.PR stat.ML}
} | fredes2024on |
arxiv-665814 | 2410.03761 | HiReview: Hierarchical Taxonomy-Driven Automatic Literature Review Generation | <|reference_start|>HiReview: Hierarchical Taxonomy-Driven Automatic Literature Review Generation: In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven automatic literature review generation. With the exponential growth of academic documents, manual literature reviews have become increasingly labor-intensive and time-consuming, while traditional summarization models struggle to generate comprehensive document reviews effectively. Large language models (LLMs), with their powerful text processing capabilities, offer a potential solution; however, research on incorporating LLMs for automatic document generation remains limited. To address key challenges in large-scale automatic literature review generation (LRG), we propose a two-stage taxonomy-then-generation approach that combines graph-based hierarchical clustering with retrieval-augmented LLMs. First, we retrieve the most relevant sub-community within the citation network, then generate a hierarchical taxonomy tree by clustering papers based on both textual content and citation relationships. In the second stage, an LLM generates coherent and contextually accurate summaries for clusters or topics at each hierarchical level, ensuring comprehensive coverage and logical organization of the literature. Extensive experiments demonstrate that HiReview significantly outperforms state-of-the-art methods, achieving superior hierarchical organization, content relevance, and factual accuracy in automatic literature review generation tasks.<|reference_end|> | arxiv | @article{hu2024hireview:,
title={HiReview: Hierarchical Taxonomy-Driven Automatic Literature Review
Generation},
author={Yuntong Hu, Zhuofeng Li, Zheng Zhang, Chen Ling, Raasikh Kanjiani,
Boxin Zhao, Liang Zhao},
journal={arXiv preprint arXiv:2410.03761},
year={2024},
archivePrefix={arXiv},
eprint={2410.03761},
primaryClass={cs.CL cs.LG}
} | hu2024hireview: |
arxiv-665815 | 2410.03762 | Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | <|reference_start|>Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models: Legal intake, the process of finding out if an applicant is eligible for help from a free legal aid program, takes significant time and resources. In part this is because eligibility criteria are nuanced, open-textured, and require frequent revision as grants start and end. In this paper, we investigate the use of large language models (LLMs) to reduce this burden. We describe a digital intake platform that combines logical rules with LLMs to offer eligibility recommendations, and we evaluate the ability of 8 different LLMs to perform this task. We find promising results for this approach to help close the access to justice gap, with the best model reaching an F1 score of .82, while minimizing false negatives.<|reference_end|> | arxiv | @article{steenhuis2024getting,
title={Getting in the Door: Streamlining Intake in Civil Legal Services with
Large Language Models},
author={Quinten Steenhuis, Hannes Westermann},
journal={arXiv preprint arXiv:2410.03762},
year={2024},
archivePrefix={arXiv},
eprint={2410.03762},
primaryClass={cs.HC cs.AI cs.CL cs.CY}
} | steenhuis2024getting |
arxiv-665816 | 2410.03763 | Electrification of Transportation: A Hybrid Benders/SDDP Algorithm for Optimal Charging Station Trading | <|reference_start|>Electrification of Transportation: A Hybrid Benders/SDDP Algorithm for Optimal Charging Station Trading: This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices.<|reference_end|> | arxiv | @article{sohrabi2024electrification,
title={Electrification of Transportation: A Hybrid Benders/SDDP Algorithm for
Optimal Charging Station Trading},
author={Farnaz Sohrabi, Mohammad Rohaninejad, J'ulius Bemv{s}, Zdenv{e}k
Hanz'alek},
journal={International Journal of Hydrogen Energy, 89 (2024):1060-1074},
year={2024},
doi={10.1016/j.ijhydene.2024.09.345},
archivePrefix={arXiv},
eprint={2410.03763},
primaryClass={math.OC cs.OH}
} | sohrabi2024electrification |
arxiv-665817 | 2410.03764 | Words that Represent Peace | <|reference_start|>Words that Represent Peace: We used data from LexisNexis to determine the words in news media that best classifies countries as higher or lower peace. We found that higher peace news is characterized by themes of finance, daily actitivities, and health and that lower peace news is characterized by themes of politics, government, and legal issues. This work provides a starting point to measure levels of peace and identify the social processes that underly those words.<|reference_end|> | arxiv | @article{prasad2024words,
title={Words that Represent Peace},
author={T. Prasad (1), L. S. Liebovitch (1), M. Wild (1), H. West (1), P. T.
Coleman (1) ((1) Columbia University)},
journal={arXiv preprint arXiv:2410.03764},
year={2024},
archivePrefix={arXiv},
eprint={2410.03764},
primaryClass={cs.CL cs.CY cs.LG}
} | prasad2024words |
arxiv-665818 | 2410.03765 | Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression | <|reference_start|>Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression: Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented as a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis Sharing outperforms state-of-the-art SVD-based compression approaches and parameter sharing techniques, especially under large compression ratios. Code is available at: https://github.com/TUDa-HWAI/Basis_Sharing<|reference_end|> | arxiv | @article{wang2024basis,
title={Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model
Compression},
author={Jingcun Wang, Yu-Guang Chen, Ing-Chao Lin, Bing Li, Grace Li Zhang},
journal={arXiv preprint arXiv:2410.03765},
year={2024},
archivePrefix={arXiv},
eprint={2410.03765},
primaryClass={cs.CL cs.LG}
} | wang2024basis |
arxiv-665819 | 2410.03766 | FutureFill: Fast Generation from Convolutional Sequence Models | <|reference_start|>FutureFill: Fast Generation from Convolutional Sequence Models: We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill: a method for fast generation that applies to any sequence prediction algorithm based on convolutional operators. Our approach reduces the generation time requirement from linear to square root relative to the context length. Additionally, FutureFill requires a prefill cache sized only by the number of tokens generated, which is smaller than the cache requirements for standard convolutional and attention-based models. We validate our theoretical findings with experimental evidence demonstrating correctness and efficiency gains in a synthetic generation task.<|reference_end|> | arxiv | @article{agarwal2024futurefill:,
title={FutureFill: Fast Generation from Convolutional Sequence Models},
author={Naman Agarwal, Xinyi Chen, Evan Dogariu, Vlad Feinberg, Daniel Suo,
Peter Bartlett, Elad Hazan},
journal={arXiv preprint arXiv:2410.03766},
year={2024},
archivePrefix={arXiv},
eprint={2410.03766},
primaryClass={cs.LG cs.AI cs.CL}
} | agarwal2024futurefill: |
arxiv-665820 | 2410.03767 | Reasoning Elicitation in Language Models via Counterfactual Feedback | <|reference_start|>Reasoning Elicitation in Language Models via Counterfactual Feedback: Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive novel metrics that balance accuracy in factual and counterfactual questions, capturing a more complete view of the reasoning abilities of language models than traditional factual-only based metrics. Second, we propose several fine-tuning approaches that aim to elicit better reasoning mechanisms, in the sense of the proposed metrics. Finally, we evaluate the performance of the fine-tuned language models in a variety of realistic scenarios. In particular, we investigate to what extent our fine-tuning approaches systemically achieve better generalization with respect to the base models in several problems that require, among others, inductive and deductive reasoning capabilities.<|reference_end|> | arxiv | @article{hüyük2024reasoning,
title={Reasoning Elicitation in Language Models via Counterfactual Feedback},
author={Alihan H"uy"uk, Xinnuo Xu, Jacqueline Maasch, Aditya V. Nori, Javier
Gonz'alez},
journal={arXiv preprint arXiv:2410.03767},
year={2024},
archivePrefix={arXiv},
eprint={2410.03767},
primaryClass={cs.CL cs.AI cs.LG}
} | hüyük2024reasoning |
arxiv-665821 | 2410.03768 | Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs | <|reference_start|>Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs: The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render collusion practically undetectable. This underscores the need for evaluation frameworks to monitor and mitigate steganographic collusion capabilities. We address a crucial gap in the literature by demonstrating, for the first time, that robust steganographic collusion in LLMs can arise indirectly from optimization pressure. To investigate this problem we design two approaches -- a gradient-based reinforcement learning (GBRL) method and an in-context reinforcement learning (ICRL) method -- for reliably eliciting sophisticated LLM-generated linguistic text steganography. Importantly, we find that emergent steganographic collusion can be robust to both passive steganalytic oversight of model outputs and active mitigation through communication paraphrasing. We contribute a novel model evaluation framework and discuss limitations and future work. Our findings imply that effective risk mitigation from steganographic collusion post-deployment requires innovation in passive and active oversight techniques.<|reference_end|> | arxiv | @article{mathew2024hidden,
title={Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion
in LLMs},
author={Yohan Mathew, Ollie Matthews, Robert McCarthy, Joan Velja, Christian
Schroeder de Witt, Dylan Cope, Nandi Schoots},
journal={arXiv preprint arXiv:2410.03768},
year={2024},
archivePrefix={arXiv},
eprint={2410.03768},
primaryClass={cs.CL cs.CR cs.LG}
} | mathew2024hidden |
arxiv-665822 | 2410.03769 | SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks | <|reference_start|>SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks: Large language models (LLMs) have had a transformative impact on a variety of scientific tasks across disciplines such as biology, chemistry, medicine, and physics. However, ensuring the safety alignment of these models in scientific research remains an underexplored area, with existing benchmarks primarily focus on textual content and overlooking key scientific representations such as molecular, protein, and genomic languages. Moreover, the safety mechanisms of LLMs in scientific tasks are insufficiently studied. To address these limitations, we introduce SciSafeEval, a comprehensive benchmark designed to evaluate the safety alignment of LLMs across a range of scientific tasks. SciSafeEval spans multiple scientific languages - including textual, molecular, protein, and genomic - and covers a wide range of scientific domains. We evaluate LLMs in zero-shot, few-shot and chain-of-thought settings, and introduce a 'jailbreak' enhancement feature that challenges LLMs equipped with safety guardrails, rigorously testing their defenses against malicious intention. Our benchmark surpasses existing safety datasets in both scale and scope, providing a robust platform for assessing the safety and performance of LLMs in scientific contexts. This work aims to facilitate the responsible development and deployment of LLMs, promoting alignment with safety and ethical standards in scientific research.<|reference_end|> | arxiv | @article{li2024scisafeeval:,
title={SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large
Language Models in Scientific Tasks},
author={Tianhao Li, Jingyu Lu, Chuangxin Chu, Tianyu Zeng, Yujia Zheng, Mei
Li, Haotian Huang, Bin Wu, Zuoxian Liu, Kai Ma, Xuejing Yuan, Xingkai Wang,
Keyan Ding, Huajun Chen, Qiang Zhang},
journal={arXiv preprint arXiv:2410.03769},
year={2024},
archivePrefix={arXiv},
eprint={2410.03769},
primaryClass={cs.CL cs.AI cs.CR}
} | li2024scisafeeval: |
arxiv-665823 | 2410.03770 | A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model | <|reference_start|>A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model: Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and other related information that goes beyond medical evidence data (test results) to enhance disease diagnosis. However, this procedure is usually time-consuming and less-efficient, which can be potentially optimized through computer-assisted systems. As such, we propose a diagnostic dialogue system to automate the patient information collection procedure. By exploiting medical history and conversation logic, our conversation agents, particularly the doctor agent, can pose multi-round clinical queries to effectively collect the most relevant disease diagnostic information. Moreover, benefiting from our two-stage recommendation structure, carefully designed ranking criteria, and interactive patient agent, our model is able to overcome the under-exploration and non-flexible challenges in dialogue generation. Our experimental results on a real-world medical conversation dataset show that our model can generate clinical queries that mimic the conversation style of real doctors, with efficient fluency, professionalism, and safety, while effectively collecting relevant disease diagnostic information.<|reference_end|> | arxiv | @article{li2024a,
title={A Two-Stage Proactive Dialogue Generator for Efficient Clinical
Information Collection Using Large Language Model},
author={Xueshen Li, Xinlong Hou, Nirupama Ravi, Ziyi Huang, Yu Gan},
journal={arXiv preprint arXiv:2410.03770},
year={2024},
archivePrefix={arXiv},
eprint={2410.03770},
primaryClass={cs.CL cs.AI}
} | li2024a |
arxiv-665824 | 2410.03771 | SeeSay: An Assistive Device for the Visually Impaired Using Retrieval Augmented Generation | <|reference_start|>SeeSay: An Assistive Device for the Visually Impaired Using Retrieval Augmented Generation: In this paper, we present SeeSay, an assistive device designed for individuals with visual impairments. This system leverages large language models (LLMs) for speech recognition and visual querying. It effectively identifies, records, and responds to the user's environment by providing audio guidance using retrieval-augmented generation (RAG). Our experiments demonstrate the system's capability to recognize its surroundings and respond to queries with audio feedback in diverse settings. We hope that the SeeSay system will facilitate users' comprehension and recollection of their surroundings, thereby enhancing their environmental perception, improving navigational capabilities, and boosting overall independence.<|reference_end|> | arxiv | @article{yu2024seesay:,
title={SeeSay: An Assistive Device for the Visually Impaired Using Retrieval
Augmented Generation},
author={Melody Yu},
journal={arXiv preprint arXiv:2410.03771},
year={2024},
archivePrefix={arXiv},
eprint={2410.03771},
primaryClass={cs.HC cs.SI}
} | yu2024seesay: |
arxiv-665825 | 2410.03772 | Precision Knowledge Editing: Enhancing Safety in Large Language Models | <|reference_start|>Precision Knowledge Editing: Enhancing Safety in Large Language Models: Large language models (LLMs) have demonstrated remarkable capabilities, but they also pose risks related to the generation of toxic or harmful content. This work introduces Precision Knowledge Editing (PKE), an advanced technique that builds upon existing knowledge editing methods to more effectively identify and modify toxic parameter regions within LLMs. By leveraging neuron weight tracking and activation pathway tracing, PKE achieves finer granularity in toxic content management compared to previous methods like Detoxifying Instance Neuron Modification (DINM). Our experiments demonstrate that PKE significantly reduces the attack success rate (ASR) across various models, including Llama2-7b and Llama-3-8b-instruct, while maintaining overall model performance. Additionally, we also compared the performance of some closed-source models (gpt-4-0613 and Claude 3 Sonnet) in our experiments, and found that models adjusted using our method far outperformed the closed-source models in terms of safety. This research contributes to the ongoing efforts to make LLMs safer and more reliable for real-world applications.<|reference_end|> | arxiv | @article{li2024precision,
title={Precision Knowledge Editing: Enhancing Safety in Large Language Models},
author={Xuying Li, Zhuo Li, Yuji Kosuga, Yasuhiro Yoshida, Victor Bian},
journal={arXiv preprint arXiv:2410.03772},
year={2024},
archivePrefix={arXiv},
eprint={2410.03772},
primaryClass={cs.CL cs.AI}
} | li2024precision |
arxiv-665826 | 2410.03774 | Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios | <|reference_start|>Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios: This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information.<|reference_end|> | arxiv | @article{puphal2024human-based,
title={Human-Based Risk Model for Improved Driver Support in Interactive
Driving Scenarios},
author={Tim Puphal, Benedict Flade, Matti Kr"uger, Ryohei Hirano and Akihito
Kimata},
journal={arXiv preprint arXiv:2410.03774},
year={2024},
archivePrefix={arXiv},
eprint={2410.03774},
primaryClass={cs.HC cs.AI}
} | puphal2024human-based |
arxiv-665827 | 2410.03775 | Beyond correlation: The impact of human uncertainty in measuring the effectiveness of automatic evaluation and LLM-as-a-judge | <|reference_start|>Beyond correlation: The impact of human uncertainty in measuring the effectiveness of automatic evaluation and LLM-as-a-judge: The effectiveness of automatic evaluation of generative models is typically measured by comparing it to human evaluation using correlation metrics. However, metrics like Krippendorff's $\alpha$ and Randolph's $\kappa$, originally designed to measure the reliability of human labeling, make assumptions about human behavior and the labeling process. In this paper, we show how *relying on a single aggregate correlation score* can obscure fundamental differences between human behavior and automatic evaluation methods, including LLM-as-a-Judge. Specifically, we demonstrate that when the proportion of samples with variation or uncertainty in human labels (gathered during human evaluation) is relatively high, machine labels (generated by automatic evaluation methods) may superficially appear to have similar or better correlation with the human majority label compared to human-to-human (HH) correlation. This can create the misleading impression that automatic evaluation is accurate enough to approximate the human majority label. However, as the proportion of samples with consistent human labels increases, the correlation between machine labels and human majority labels declines, falling below HH correlation. Based on these findings, we first propose stratifying results by human label uncertainty to provide a more robust analysis of automatic evaluation performance. Second, recognizing that uncertainty and variation are inherent in perception-based human evaluations, such as those involving attitudes or preferences, we introduce a new metric - *binned Jensen-Shannon Divergence for perception* for such scenarios to better measure the effectiveness of automatic evaluations. Third, we present visualization techniques -- *perception charts*, to compare the strengths and limitations of automatic evaluation and to contextualize correlation measures appropriately<|reference_end|> | arxiv | @article{elangovan2024beyond,
title={Beyond correlation: The impact of human uncertainty in measuring the
effectiveness of automatic evaluation and LLM-as-a-judge},
author={Aparna Elangovan, Jongwoo Ko, Lei Xu, Mahsa Elyasi, Ling Liu, Sravan
Bodapati, Dan Roth},
journal={arXiv preprint arXiv:2410.03775},
year={2024},
archivePrefix={arXiv},
eprint={2410.03775},
primaryClass={cs.HC cs.AI}
} | elangovan2024beyond |
arxiv-665828 | 2410.03776 | Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks | <|reference_start|>Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks: We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Parameters, such as the Hurst exponent, are critical in characterizing the long-range dependence, roughness, and self-similarity of stochastic processes. The accurate and fast estimation of these parameters holds significant importance across various scientific disciplines, including finance, physics, and engineering. We harnessed efficient process generators to provide high-quality synthetic training data, enabling the training of scale-invariant 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Our neural models outperform conventional statistical methods, even those augmented with neural networks. The precision, speed, consistency, and robustness of our estimators are demonstrated through experiments involving fractional Brownian motion (fBm), the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process, and the fractional Ornstein-Uhlenbeck (fOU) process. We believe that our work will inspire further research in the field of stochastic process modeling and parameter estimation using deep learning techniques.<|reference_end|> | arxiv | @article{csanády2024parameter,
title={Parameter Estimation of Long Memory Stochastic Processes with Deep
Neural Networks},
author={B'alint Csan'ady, L'or'ant Nagy, D'aniel Boros, Iv'an Ivkovic,
D'avid Kov'acs, Dalma T'oth-Lakits, L'aszl'o M'arkus, Andr'as Luk'acs},
journal={arXiv preprint arXiv:2410.03776},
year={2024},
archivePrefix={arXiv},
eprint={2410.03776},
primaryClass={cs.LG stat.ML}
} | csanády2024parameter |
arxiv-665829 | 2410.03777 | Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling | <|reference_start|>Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling: Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage their complementary advantages. However, existing LLM ensembling methods often overlook model compatibility and struggle with inefficient alignment of probabilities across the entire vocabulary. In this study, we empirically investigate the factors influencing ensemble performance, identifying model performance, vocabulary size, and response style as key determinants, revealing that compatibility among models is essential for effective ensembling. This analysis leads to the development of a simple yet effective model selection strategy that identifies compatible models. Additionally, we introduce the \textsc{Uni}on \textsc{T}op-$k$ \textsc{E}nsembling (\textsc{UniTE}), a novel approach that efficiently combines models by focusing on the union of the top-k tokens from each model, thereby avoiding the need for full vocabulary alignment and reducing computational overhead. Extensive evaluations across multiple benchmarks demonstrate that \textsc{UniTE} significantly enhances performance compared to existing methods, offering a more efficient framework for LLM ensembling.<|reference_end|> | arxiv | @article{yao2024determine-then-ensemble:,
title={Determine-Then-Ensemble: Necessity of Top-k Union for Large Language
Model Ensembling},
author={Yuxuan Yao, Han Wu, Mingyang Liu, Sichun Luo, Xiongwei Han, Jie Liu,
Zhijiang Guo, Linqi Song},
journal={arXiv preprint arXiv:2410.03777},
year={2024},
archivePrefix={arXiv},
eprint={2410.03777},
primaryClass={cs.CL cs.AI}
} | yao2024determine-then-ensemble: |
arxiv-665830 | 2410.03778 | SGW-based Multi-Task Learning in Vision Tasks | <|reference_start|>SGW-based Multi-Task Learning in Vision Tasks: Multi-task-learning(MTL) is a multi-target optimization task. Neural networks try to realize each target using a shared interpretative space within MTL. However, as the scale of datasets expands and the complexity of tasks increases, knowledge sharing becomes increasingly challenging. In this paper, we first re-examine previous cross-attention MTL methods from the perspective of noise. We theoretically analyze this issue and identify it as a flaw in the cross-attention mechanism. To address this issue, we propose an information bottleneck knowledge extraction module (KEM). This module aims to reduce inter-task interference by constraining the flow of information, thereby reducing computational complexity. Furthermore, we have employed neural collapse to stabilize the knowledge-selection process. That is, before input to KEM, we projected the features into ETF space. This mapping makes our method more robust. We implemented and conducted comparative experiments with this method on multiple datasets. The results demonstrate that our approach significantly outperforms existing methods in multi-task learning.<|reference_end|> | arxiv | @article{zhang2024sgw-based,
title={SGW-based Multi-Task Learning in Vision Tasks},
author={Ruiyuan Zhang, Yuyao Chen, Yuchi Huo, Jiaxiang Liu, Dianbing Xi, Jie
Liu, Chao Wu},
journal={ACCV2024},
year={2024},
archivePrefix={arXiv},
eprint={2410.03778},
primaryClass={cs.CV cs.LG}
} | zhang2024sgw-based |
arxiv-665831 | 2410.03779 | Discovering Message Passing Hierarchies for Mesh-Based Physics Simulation | <|reference_start|>Discovering Message Passing Hierarchies for Mesh-Based Physics Simulation: Graph neural networks have emerged as a powerful tool for large-scale mesh-based physics simulation. Existing approaches primarily employ hierarchical, multi-scale message passing to capture long-range dependencies within the graph. However, these graph hierarchies are typically fixed and manually designed, which do not adapt to the evolving dynamics present in complex physical systems. In this paper, we introduce a novel neural network named DHMP, which learns Dynamic Hierarchies for Message Passing networks through a differentiable node selection method. The key component is the anisotropic message passing mechanism, which operates at both intra-level and inter-level interactions. Unlike existing methods, it first supports directionally non-uniform aggregation of dynamic features between adjacent nodes within each graph hierarchy. Second, it determines node selection probabilities for the next hierarchy according to different physical contexts, thereby creating more flexible message shortcuts for learning remote node relations. Our experiments demonstrate the effectiveness of DHMP, achieving 22.7% improvement on average compared to recent fixed-hierarchy message passing networks across five classic physics simulation datasets.<|reference_end|> | arxiv | @article{deng2024discovering,
title={Discovering Message Passing Hierarchies for Mesh-Based Physics
Simulation},
author={Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang Yang},
journal={arXiv preprint arXiv:2410.03779},
year={2024},
archivePrefix={arXiv},
eprint={2410.03779},
primaryClass={cs.LG cs.AI cs.CE}
} | deng2024discovering |
arxiv-665832 | 2410.03780 | Reward-RAG: Enhancing RAG with Reward Driven Supervision | <|reference_start|>Reward-RAG: Enhancing RAG with Reward Driven Supervision: In this paper, we introduce Reward-RAG, a novel approach designed to enhance the Retrieval-Augmented Generation (RAG) model through Reward-Driven Supervision. Unlike previous RAG methodologies, which focus on training language models (LMs) to utilize external knowledge retrieved from external sources, our method adapts retrieval information to specific domains by employing CriticGPT to train a dedicated reward model. This reward model generates synthesized datasets for fine-tuning the RAG encoder, aligning its outputs more closely with human preferences. The versatility of our approach allows it to be effectively applied across various domains through domain-specific fine-tuning. We evaluate Reward-RAG on publicly available benchmarks from multiple domains, comparing it to state-of-the-art methods. Our experimental results demonstrate significant improvements in performance, highlighting the effectiveness of Reward-RAG in improving the relevance and quality of generated responses. These findings underscore the potential of integrating reward models with RAG to achieve superior outcomes in natural language generation tasks.<|reference_end|> | arxiv | @article{nguyen2024reward-rag:,
title={Reward-RAG: Enhancing RAG with Reward Driven Supervision},
author={Thang Nguyen, Peter Chin, Yu-Wing Tai},
journal={arXiv preprint arXiv:2410.03780},
year={2024},
archivePrefix={arXiv},
eprint={2410.03780},
primaryClass={cs.CL cs.LG}
} | nguyen2024reward-rag: |
arxiv-665833 | 2410.03781 | Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure | <|reference_start|>Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure: One-to-one tutoring is one of the most efficient methods of teaching. Following the rise in popularity of Large Language Models (LLMs), there have been efforts to use them to create conversational tutoring systems, which can make the benefits of one-to-one tutoring accessible to everyone. However, current LLMs are primarily trained to be helpful assistants and thus lack crucial pedagogical skills. For example, they often quickly reveal the solution to the student and fail to plan for a richer multi-turn pedagogical interaction. To use LLMs in pedagogical scenarios, they need to be steered towards using effective teaching strategies: a problem we introduce as Pedagogical Steering and believe to be crucial for the efficient use of LLMs as tutors. We address this problem by formalizing a concept of tutoring strategy, and introducing StratL, an algorithm to model a strategy and use prompting to steer the LLM to follow this strategy. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design. To validate our approach in a real-world setting, we run a field study with 17 high school students in Singapore. We quantitatively show that StratL succeeds in steering the LLM to follow a Productive Failure tutoring strategy. We also thoroughly investigate the existence of spillover effects on desirable properties of the LLM, like its ability to generate human-like answers. Based on these results, we highlight the challenges in Pedagogical Steering and suggest opportunities for further improvements. We further encourage follow-up research by releasing a dataset of Productive Failure problems and the code of our prototype and algorithm.<|reference_end|> | arxiv | @article{puech2024towards,
title={Towards the Pedagogical Steering of Large Language Models for Tutoring:
A Case Study with Modeling Productive Failure},
author={Romain Puech, Jakub Macina, Julia Chatain, Mrinmaya Sachan, Manu Kapur},
journal={arXiv preprint arXiv:2410.03781},
year={2024},
archivePrefix={arXiv},
eprint={2410.03781},
primaryClass={cs.HC cs.AI cs.CY cs.MA}
} | puech2024towards |
arxiv-665834 | 2410.03782 | DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation | <|reference_start|>DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation: Adapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model. Although existing weight interpolation methods are simple yet effective, we argue their static nature limits downstream performance while achieving efficiency. In this work, we propose DaWin, a training-free dynamic weight interpolation method that leverages the entropy of individual models over each unlabeled test sample to assess model expertise, and compute per-sample interpolation coefficients dynamically. Unlike previous works that typically rely on additional training to learn such coefficients, our approach requires no training. Then, we propose a mixture modeling approach that greatly reduces inference overhead raised by dynamic interpolation. We validate DaWin on the large-scale visual recognition benchmarks, spanning 14 tasks across robust fine-tuning -- ImageNet and derived five distribution shift benchmarks -- and multi-task learning with eight classification tasks. Results demonstrate that DaWin achieves significant performance gain in considered settings, with minimal computational overhead. We further discuss DaWin's analytic behavior to explain its empirical success.<|reference_end|> | arxiv | @article{oh2024dawin:,
title={DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation},
author={Changdae Oh, Yixuan Li, Kyungwoo Song, Sangdoo Yun, Dongyoon Han},
journal={arXiv preprint arXiv:2410.03782},
year={2024},
archivePrefix={arXiv},
eprint={2410.03782},
primaryClass={cs.LG cs.CV}
} | oh2024dawin: |
arxiv-665835 | 2410.03783 | Improving Neural Optimal Transport via Displacement Interpolation | <|reference_start|>Improving Neural Optimal Transport via Displacement Interpolation: Optimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution. Recently, several approaches have emerged for learning the optimal transport map for a given cost function using neural networks. We refer to these approaches as the OT Map. OT Map provides a powerful tool for diverse machine learning tasks, such as generative modeling and unpaired image-to-image translation. However, existing methods that utilize max-min optimization often experience training instability and sensitivity to hyperparameters. In this paper, we propose a novel method to improve stability and achieve a better approximation of the OT Map by exploiting displacement interpolation, dubbed Displacement Interpolation Optimal Transport Model (DIOTM). We derive the dual formulation of displacement interpolation at specific time $t$ and prove how these dual problems are related across time. This result allows us to utilize the entire trajectory of displacement interpolation in learning the OT Map. Our method improves the training stability and achieves superior results in estimating optimal transport maps. We demonstrate that DIOTM outperforms existing OT-based models on image-to-image translation tasks.<|reference_end|> | arxiv | @article{choi2024improving,
title={Improving Neural Optimal Transport via Displacement Interpolation},
author={Jaemoo Choi, Yongxin Chen, Jaewoong Choi},
journal={arXiv preprint arXiv:2410.03783},
year={2024},
archivePrefix={arXiv},
eprint={2410.03783},
primaryClass={cs.LG cs.CV}
} | choi2024improving |
arxiv-665836 | 2410.03786 | AI-rays: Exploring Bias in the Gaze of AI Through a Multimodal Interactive Installation | <|reference_start|>AI-rays: Exploring Bias in the Gaze of AI Through a Multimodal Interactive Installation: Data surveillance has become more covert and pervasive with AI algorithms, which can result in biased social classifications. Appearance offers intuitive identity signals, but what does it mean to let AI observe and speculate on them? We introduce AI-rays, an interactive installation where AI generates speculative identities from participants' appearance which are expressed through synthesized personal items placed in participants' bags. It uses speculative X-ray visions to contrast reality with AI-generated assumptions, metaphorically highlighting AI's scrutiny and biases. AI-rays promotes discussions on modern surveillance and the future of human-machine reality through a playful, immersive experience exploring AI biases.<|reference_end|> | arxiv | @article{gao2024ai-rays:,
title={AI-rays: Exploring Bias in the Gaze of AI Through a Multimodal
Interactive Installation},
author={Ziyao Gao, Yiwen Zhang, Ling Li, Theodoros Papatheodorou, Wei Zeng},
journal={arXiv preprint arXiv:2410.03786},
year={2024},
doi={10.1145/3680530.3695433},
archivePrefix={arXiv},
eprint={2410.03786},
primaryClass={cs.HC cs.AI cs.CY}
} | gao2024ai-rays: |
arxiv-665837 | 2410.03787 | CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control | <|reference_start|>CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control: In this paper, we introduce CalliffusionV2, a novel system designed to produce natural Chinese calligraphy with flexible multi-modal control. Unlike previous approaches that rely solely on image or text inputs and lack fine-grained control, our system leverages both images to guide generations at fine-grained levels and natural language texts to describe the features of generations. CalliffusionV2 excels at creating a broad range of characters and can quickly learn new styles through a few-shot learning approach. It is also capable of generating non-Chinese characters without prior training. Comprehensive tests confirm that our system produces calligraphy that is both stylistically accurate and recognizable by neural network classifiers and human evaluators.<|reference_end|> | arxiv | @article{liao2024calliffusionv2:,
title={CalliffusionV2: Personalized Natural Calligraphy Generation with
Flexible Multi-modal Control},
author={Qisheng Liao, Liang Li, Yulang Fei, Gus Xia},
journal={arXiv preprint arXiv:2410.03787},
year={2024},
archivePrefix={arXiv},
eprint={2410.03787},
primaryClass={cs.CL cs.AI cs.CV cs.MM}
} | liao2024calliffusionv2: |
arxiv-665838 | 2410.03788 | Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning | <|reference_start|>Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning: Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the reliance on trajectory data, which often fails to capture the semantic interdependencies of activities, and the inherent incompleteness of real-world trajectory data. We have developed a model that reconstructs and learns human mobility patterns by focusing on semantic activity chains. We introduce a semi-supervised iterative transfer learning algorithm to adapt models to diverse geographical contexts and address data scarcity. Our model is validated using comprehensive datasets from the United States, where it effectively reconstructs activity chains and generates high-quality synthetic mobility data, achieving a low Jensen-Shannon Divergence (JSD) value of 0.001, indicating a close similarity between synthetic and real data. Additionally, sparse GPS data from Egypt is used to evaluate the transfer learning algorithm, demonstrating successful adaptation of US mobility patterns to Egyptian contexts, achieving a 64\% of increase in similarity, i.e., a JSD reduction from 0.09 to 0.03. This mobility reconstruction model and the associated transfer learning algorithm show significant potential for global human mobility modeling studies, enabling policymakers and researchers to design more effective and culturally tailored transportation solutions.<|reference_end|> | arxiv | @article{liao2024reconstructing,
title={Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for
Cross-Dataset Transfer Learning},
author={Xishun Liao, Yifan Liu, Chenchen Kuai, Haoxuan Ma, Yueshuai He,
Shangqing Cao, Chris Stanford, Jiaqi Ma},
journal={arXiv preprint arXiv:2410.03788},
year={2024},
archivePrefix={arXiv},
eprint={2410.03788},
primaryClass={cs.LG cs.CL}
} | liao2024reconstructing |
arxiv-665839 | 2410.03790 | Accelerating Deep Learning with Fixed Time Budget | <|reference_start|>Accelerating Deep Learning with Fixed Time Budget: The success of modern deep learning is attributed to two key elements: huge amounts of training data and large model sizes. Where a vast amount of data allows the model to learn more features, the large model architecture boosts the learning capability of the model. However, both these factors result in prolonged training time. In some practical applications such as edge-based learning and federated learning, limited-time budgets necessitate more efficient training methods. This paper proposes an effective technique for training arbitrary deep learning models within fixed time constraints utilizing sample importance and dynamic ranking. The proposed method is extensively evaluated in both classification and regression tasks in computer vision. The results consistently show clear gains achieved by the proposed method in improving the learning performance of various state-of-the-art deep learning models in both regression and classification tasks.<|reference_end|> | arxiv | @article{khan2024accelerating,
title={Accelerating Deep Learning with Fixed Time Budget},
author={Muhammad Asif Khan, Ridha Hamila, and Hamid Menouar},
journal={arXiv preprint arXiv:2410.03790},
year={2024},
archivePrefix={arXiv},
eprint={2410.03790},
primaryClass={cs.LG cs.CV}
} | khan2024accelerating |
arxiv-665840 | 2410.03791 | People are poorly equipped to detect AI-powered voice clones | <|reference_start|>People are poorly equipped to detect AI-powered voice clones: As generative AI continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve in mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot reliably identify short recordings (less than 20 seconds) of AI-generated voices. Specifically, participants mistook the identity of an AI-voice for its real counterpart 80% of the time, and correctly identified a voice as AI-generated only 60% of the time. In all cases, performance is independent of the demographics of the speaker or listener.<|reference_end|> | arxiv | @article{barrington2024people,
title={People are poorly equipped to detect AI-powered voice clones},
author={Sarah Barrington and Hany Farid},
journal={arXiv preprint arXiv:2410.03791},
year={2024},
archivePrefix={arXiv},
eprint={2410.03791},
primaryClass={cs.HC cs.AI cs.CY cs.SD eess.AS}
} | barrington2024people |
arxiv-665841 | 2410.03794 | Repurposing Foundation Model for Generalizable Medical Time Series Classification | <|reference_start|>Repurposing Foundation Model for Generalizable Medical Time Series Classification: Medical time series (MedTS) classification is critical for a wide range of healthcare applications such as Alzheimer's Disease diagnosis. However, its real-world deployment is severely challenged by poor generalizability due to inter- and intra-dataset heterogeneity in MedTS, including variations in channel configurations, time series lengths, and diagnostic tasks. Here, we propose FORMED, a foundation classification model that leverages a pre-trained backbone and tackles these challenges through re-purposing. FORMED integrates the general representation learning enabled by the backbone foundation model and the medical domain knowledge gained on a curated cohort of MedTS datasets. FORMED can adapt seamlessly to unseen MedTS datasets, regardless of the number of channels, sample lengths, or medical tasks. Experimental results show that, without any task-specific adaptation, the repurposed FORMED achieves performance that is competitive with, and often superior to, 11 baseline models trained specifically for each dataset. Furthermore, FORMED can effectively adapt to entirely new, unseen datasets, with lightweight parameter updates, consistently outperforming baselines. Our results highlight FORMED as a versatile and scalable model for a wide range of MedTS classification tasks, positioning it as a strong foundation model for future research in MedTS analysis.<|reference_end|> | arxiv | @article{huang2024repurposing,
title={Repurposing Foundation Model for Generalizable Medical Time Series
Classification},
author={Nan Huang, Haishuai Wang, Zihuai He, Marinka Zitnik, Xiang Zhang},
journal={arXiv preprint arXiv:2410.03794},
year={2024},
archivePrefix={arXiv},
eprint={2410.03794},
primaryClass={cs.LG}
} | huang2024repurposing |
arxiv-665842 | 2410.03795 | Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns | <|reference_start|>Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns: This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications. The book explores the application of classical software engineering patterns, Creational, Structural, Behavioral, and Concurrency Patterns, to optimize the development, maintenance, and scalability of big data analytics systems. Through practical examples and detailed Python implementations, it bridges the gap between traditional object-oriented design patterns and the unique demands of modern data analytics environments. Key design patterns such as Singleton, Factory, Observer, and Strategy are analyzed for their impact on model management, deployment strategies, and team collaboration, providing invaluable insights into the engineering of efficient, reusable, and flexible systems. This volume is an essential resource for developers, researchers, and engineers aiming to enhance their technical expertise in both machine learning and software design.<|reference_end|> | arxiv | @article{chen2024deep,
title={Deep Learning and Machine Learning: Advancing Big Data Analytics and
Management with Design Patterns},
author={Keyu Chen, Ziqian Bi, Tianyang Wang, Yizhu Wen, Pohsun Feng, Qian Niu,
Junyu Liu, Benji Peng, Sen Zhang, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang
Wang, Caitlyn Heqi Yin, Ming Liu},
journal={arXiv preprint arXiv:2410.03795},
year={2024},
archivePrefix={arXiv},
eprint={2410.03795},
primaryClass={cs.SE cs.LG}
} | chen2024deep |
arxiv-665843 | 2410.03796 | Dynamic Evidence Decoupling for Trusted Multi-view Learning | <|reference_start|>Dynamic Evidence Decoupling for Trusted Multi-view Learning: Multi-view learning methods often focus on improving decision accuracy, while neglecting the decision uncertainty, limiting their suitability for safety-critical applications. To mitigate this, researchers propose trusted multi-view learning methods that estimate classification probabilities and uncertainty by learning the class distributions for each instance. However, these methods assume that the data from each view can effectively differentiate all categories, ignoring the semantic vagueness phenomenon in real-world multi-view data. Our findings demonstrate that this phenomenon significantly suppresses the learning of view-specific evidence in existing methods. We propose a Consistent and Complementary-aware trusted Multi-view Learning (CCML) method to solve this problem. We first construct view opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. Next, we dynamically decouple the consistent and complementary evidence. The consistent evidence is derived from the shared portions across all views, while the complementary evidence is obtained by averaging the differing portions across all views. We ensure that the opinion constructed from the consistent evidence strictly aligns with the ground-truth category. For the opinion constructed from the complementary evidence, we allow it for potential vagueness in the evidence. We compare CCML with state-of-the-art baselines on one synthetic and six real-world datasets. The results validate the effectiveness of the dynamic evidence decoupling strategy and show that CCML significantly outperforms baselines on accuracy and reliability. The code is released at https://github.com/Lihong-Liu/CCML.<|reference_end|> | arxiv | @article{liu2024dynamic,
title={Dynamic Evidence Decoupling for Trusted Multi-view Learning},
author={Ying Liu, Lihong Liu, Cai Xu, Xiangyu Song, Ziyu Guan, Wei Zhao},
journal={arXiv preprint arXiv:2410.03796},
year={2024},
archivePrefix={arXiv},
eprint={2410.03796},
primaryClass={cs.LG cs.AI}
} | liu2024dynamic |
arxiv-665844 | 2410.03797 | Searching for Best Practices in Medical Transcription with Large Language Model | <|reference_start|>Searching for Best Practices in Medical Transcription with Large Language Model: The transcription of medical monologues, especially those containing a high density of specialized terminology and delivered with a distinct accent, presents a significant challenge for existing automated systems. This paper introduces a novel approach leveraging a Large Language Model (LLM) to generate highly accurate medical transcripts from audio recordings of doctors' monologues, specifically focusing on Indian accents. Our methodology integrates advanced language modeling techniques to lower the Word Error Rate (WER) and ensure the precise recognition of critical medical terms. Through rigorous testing on a comprehensive dataset of medical recordings, our approach demonstrates substantial improvements in both overall transcription accuracy and the fidelity of key medical terminologies. These results suggest that our proposed system could significantly aid in clinical documentation processes, offering a reliable tool for healthcare providers to streamline their transcription needs while maintaining high standards of accuracy.<|reference_end|> | arxiv | @article{li2024searching,
title={Searching for Best Practices in Medical Transcription with Large
Language Model},
author={Jiafeng Li and Yanda Mu},
journal={arXiv preprint arXiv:2410.03797},
year={2024},
archivePrefix={arXiv},
eprint={2410.03797},
primaryClass={cs.CL}
} | li2024searching |
arxiv-665845 | 2410.03798 | Self-Powered LLM Modality Expansion for Large Speech-Text Models | <|reference_start|>Self-Powered LLM Modality Expansion for Large Speech-Text Models: Large language models (LLMs) exhibit remarkable performance across diverse tasks, indicating their potential for expansion into large speech-text models (LSMs) by integrating speech capabilities. Although unified speech-text pre-training and multimodal data instruction-tuning offer considerable benefits, these methods generally entail significant resource demands and tend to overfit specific tasks. This study aims to refine the use of speech datasets for LSM training by addressing the limitations of vanilla instruction tuning. We explore the instruction-following dynamics within LSMs, identifying a critical issue termed speech anchor bias-a tendency for LSMs to over-rely on speech inputs, mistakenly interpreting the entire speech modality as directives, thereby neglecting textual instructions. To counteract this bias, we introduce a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. Our experiments across a range of speech-based tasks demonstrate that self-powered LSM mitigates speech anchor bias and improves the fusion of speech and text modalities in LSMs. Data, code and scripts are freely available at https://github.com/ytf-philp/Self-powered-LSM.<|reference_end|> | arxiv | @article{yu2024self-powered,
title={Self-Powered LLM Modality Expansion for Large Speech-Text Models},
author={Tengfei Yu, Xuebo Liu, Zhiyi Hou, Liang Ding, Dacheng Tao, Min Zhang},
journal={arXiv preprint arXiv:2410.03798},
year={2024},
archivePrefix={arXiv},
eprint={2410.03798},
primaryClass={cs.CL cs.SD eess.AS}
} | yu2024self-powered |
arxiv-665846 | 2410.03800 | M2AR: A Web-based Modeling Environment for the Augmented Reality Workflow Modeling Language | <|reference_start|>M2AR: A Web-based Modeling Environment for the Augmented Reality Workflow Modeling Language: This paper introduces M2AR, a new web-based, two- and three-dimensional modeling environment that enables the modeling and execution of augmented reality applications without requiring programming knowledge. The platform is based on a 3D JavaScript library and the mixed reality immersive web standard WebXR. For a first demonstration of its feasibility, the previously introduced Augmented Reality Workflow Modeling Language (ARWFML) has been successfully implemented using this environment. The usefulness of the new modeling environment is demonstrated by showing use cases of the ARWFML on M2AR.<|reference_end|> | arxiv | @article{muff2024m2ar:,
title={M2AR: A Web-based Modeling Environment for the Augmented Reality
Workflow Modeling Language},
author={Fabian Muff, Hans-Georg Fill},
journal={arXiv preprint arXiv:2410.03800},
year={2024},
doi={10.1145/3652620.3687779},
archivePrefix={arXiv},
eprint={2410.03800},
primaryClass={cs.HC cs.MM cs.SE}
} | muff2024m2ar: |
arxiv-665847 | 2410.03801 | P1-KAN an effective Kolmogorov Arnold Network for function approximation | <|reference_start|>P1-KAN an effective Kolmogorov Arnold Network for function approximation: A new Kolmogorov-Arnold network (KAN) is proposed to approximate potentially irregular functions in high dimension. We show that it outperforms multilayer perceptrons in terms of accuracy and converges faster. We also compare it with ReLU-KAN, a recently proposed network: it is more time consuming than ReLU-KAN, but more accurate.<|reference_end|> | arxiv | @article{warin2024p1-kan,
title={P1-KAN an effective Kolmogorov Arnold Network for function approximation},
author={Xavier Warin},
journal={arXiv preprint arXiv:2410.03801},
year={2024},
archivePrefix={arXiv},
eprint={2410.03801},
primaryClass={cs.LG cs.NE stat.ML}
} | warin2024p1-kan |
arxiv-665848 | 2410.03802 | Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction | <|reference_start|>Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction: The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, ROMs provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the abdominal aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality.<|reference_end|> | arxiv | @article{d'inverno2024mesh-informed,
title={Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction},
author={Giuseppe Alessio D'Inverno, Saeid Moradizadeh, Sajad Salavatidezfouli,
Pasquale Claudio Africa, Gianluigi Rozza},
journal={arXiv preprint arXiv:2410.03802},
year={2024},
archivePrefix={arXiv},
eprint={2410.03802},
primaryClass={physics.med-ph cs.LG cs.NA math.NA}
} | d'inverno2024mesh-informed |
arxiv-665849 | 2410.03803 | Text-guided Diffusion Model for 3D Molecule Generation | <|reference_start|>Text-guided Diffusion Model for 3D Molecule Generation: The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described in detailed human language. To address this, we propose the text guidance instead, and introduce TextSMOG, a new Text-guided Small Molecule Generation Approach via 3D Diffusion Model which integrates language and diffusion models for text-guided small molecule generation. This method uses textual conditions to guide molecule generation, enhancing both stability and diversity. Experimental results show TextSMOG's proficiency in capturing and utilizing information from textual descriptions, making it a powerful tool for generating 3D molecular structures in response to complex textual customizations.<|reference_end|> | arxiv | @article{luo2024text-guided,
title={Text-guided Diffusion Model for 3D Molecule Generation},
author={Yanchen Luo, Junfeng Fang, Sihang Li, Zhiyuan Liu, Jiancan Wu, An
Zhang, Wenjie Du, Xiang Wang},
journal={arXiv preprint arXiv:2410.03803},
year={2024},
archivePrefix={arXiv},
eprint={2410.03803},
primaryClass={cs.LG cs.AI physics.chem-ph q-bio.BM}
} | luo2024text-guided |
arxiv-665850 | 2410.03804 | Mixture of Attentions For Speculative Decoding | <|reference_start|>Mixture of Attentions For Speculative Decoding: The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy. Speculative decoding (SD) leverages smaller models to efficiently propose future tokens, which are then verified by the LLM in parallel. Small models that utilise activations from the LLM currently achieve the fastest decoding speeds. However, we identify several limitations of SD models including the lack of on-policyness during training and partial observability. To address these shortcomings, we propose a more grounded architecture for small models by introducing a Mixture of Attentions for SD. Our novel architecture can be applied in two scenarios: a conventional single device deployment and a novel client-server deployment where the small model is hosted on a consumer device and the LLM on a server. In a single-device scenario, we demonstrate state-of-the-art speedups improving EAGLE-2 by 9.5% and its acceptance length by 25%. In a client-server setting, our experiments demonstrate: 1) state-of-the-art latencies with minimal calls to the server for different network conditions, and 2) in the event of a complete disconnection, our approach can maintain higher accuracy compared to other SD methods and demonstrates advantages over API calls to LLMs, which would otherwise be unable to continue the generation process.<|reference_end|> | arxiv | @article{zimmer2024mixture,
title={Mixture of Attentions For Speculative Decoding},
author={Matthieu Zimmer, Milan Gritta, Gerasimos Lampouras, Haitham Bou Ammar,
Jun Wang},
journal={arXiv preprint arXiv:2410.03804},
year={2024},
archivePrefix={arXiv},
eprint={2410.03804},
primaryClass={cs.CL cs.AI cs.LG}
} | zimmer2024mixture |
arxiv-665851 | 2410.03805 | Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting | <|reference_start|>Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting: Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis, especially for long-horizon forecasting, showcasing promising results both in performance and running time. In this paper, we introduce Local Attention Mechanism (LAM), an efficient attention mechanism tailored for time series analysis. This mechanism exploits the continuity properties of time series to reduce the number of attention scores computed. We present an algorithm for implementing LAM in tensor algebra that runs in time and memory O(nlogn), significantly improving upon the O(n^2) time and memory complexity of traditional attention mechanisms. We also note the lack of proper datasets to evaluate long-horizon forecast models. Thus, we propose a novel set of datasets to improve the evaluation of models addressing long-horizon forecasting challenges. Our experimental analysis demonstrates that the vanilla transformer architecture magnified with LAM surpasses state-of-the-art models, including the vanilla attention mechanism. These results confirm the effectiveness of our approach and highlight a range of future challenges in long-sequence time series forecasting.<|reference_end|> | arxiv | @article{aguilera-martos2024local,
title={Local Attention Mechanism: Boosting the Transformer Architecture for
Long-Sequence Time Series Forecasting},
author={Ignacio Aguilera-Martos, Andr'es Herrera-Poyatos, Juli'an Luengo,
Francisco Herrera},
journal={arXiv preprint arXiv:2410.03805},
year={2024},
archivePrefix={arXiv},
eprint={2410.03805},
primaryClass={cs.LG}
} | aguilera-martos2024local |
arxiv-665852 | 2410.03806 | Metadata Matters for Time Series: Informative Forecasting with Transformers | <|reference_start|>Metadata Matters for Time Series: Informative Forecasting with Transformers: Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality, endeavoring to capture the intricate variations and dependencies inherent in time series. Beyond numerical time series data, we notice that metadata (e.g.~dataset and variate descriptions) also carries valuable information essential for forecasting, which can be used to identify the application scenario and provide more interpretable knowledge than digit sequences. Inspired by this observation, we propose a Metadata-informed Time Series Transformer (MetaTST), which incorporates multiple levels of context-specific metadata into Transformer forecasting models to enable informative time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates and leverages large language models (LLMs) to encode these texts into metadata tokens as a supplement to classic series tokens, resulting in an informative embedding. Further, a Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information for more accurate forecasting. This design also allows the model to adaptively learn context-specific patterns across various scenarios, which is particularly effective in handling large-scale, diverse-scenario forecasting tasks. Experimentally, MetaTST achieves state-of-the-art compared to advanced time series models and LLM-based methods on widely acknowledged short- and long-term forecasting benchmarks, covering both single-dataset individual and multi-dataset joint training settings.<|reference_end|> | arxiv | @article{dong2024metadata,
title={Metadata Matters for Time Series: Informative Forecasting with
Transformers},
author={Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Jianmin Wang,
Mingsheng Long},
journal={arXiv preprint arXiv:2410.03806},
year={2024},
archivePrefix={arXiv},
eprint={2410.03806},
primaryClass={cs.LG cs.CL}
} | dong2024metadata |
arxiv-665853 | 2410.03807 | On Long-Term Species Coexistence in Five-Species Evolutionary Spatial Cyclic Games with Ablated and Non-Ablated Dominance Networks | <|reference_start|>On Long-Term Species Coexistence in Five-Species Evolutionary Spatial Cyclic Games with Ablated and Non-Ablated Dominance Networks: I present a replication and, to some extent, a refutation of key results published by Zhong, Zhang, Li, Dai, & Yang in their 2022 paper "Species coexistence in spatial cyclic game of five species" (Chaos, Solitons and Fractals, 156: 111806), where ecosystem species coexistence was explored via simulation studies of the evolutionary spatial cyclic game (ESCG) Rock-Paper-Scissors-Lizard-Spock (RPSLS) with certain predator-prey relationships removed from the game's "interaction structure", i.e. with specific arcs ablated in the ESCG's dominance network, and with the ESCG run for 100,000 Monte Carlo Steps (MCS) to identify its asymptotic behaviors. I replicate the results presented by Zhong et al. for interaction structures with one, two, three, and four arcs ablated from the dominance network. I then empirically demonstrate that the dynamics of the RPSLS ESCG have sufficiently long time constants that the true asymptotic outcomes can often only be identified after running the ablated ESCG for 10,000,000MCS or longer, and that the true long-term outcomes can be markedly less diverse than those reported by Zhong et al. as asymptotic. Finally I demonstrate that, when run for sufficiently many MCS, the original unablated RPSLS system exhibits essentially the same asymptotic outcomes as the ablated RPSLS systems, and in this sense the only causal effect of the ablations is to alter the time required for the system to converge to the long-term asymptotic states that the unablated system eventually settles to anyhow.<|reference_end|> | arxiv | @article{cliff2024on,
title={On Long-Term Species Coexistence in Five-Species Evolutionary Spatial
Cyclic Games with Ablated and Non-Ablated Dominance Networks},
author={Dave Cliff},
journal={arXiv preprint arXiv:2410.03807},
year={2024},
archivePrefix={arXiv},
eprint={2410.03807},
primaryClass={q-bio.PE cs.CE}
} | cliff2024on |
arxiv-665854 | 2410.03809 | Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects | <|reference_start|>Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects: This study investigates the effects of radio-opaque artefacts, such as skin markers, breast implants, and pacemakers, on mammography classification models. After manually annotating 22,012 mammograms from the publicly available EMBED dataset, a robust multi-label artefact detector was developed to identify five distinct artefact types (circular and triangular skin markers, breast implants, support devices and spot compression structures). Subsequent experiments on two clinically relevant tasks $-$ breast density assessment and cancer screening $-$ revealed that these artefacts can significantly affect model performance, alter classification thresholds, and distort output distributions. These findings underscore the importance of accurate automatic artefact detection for developing reliable and robust classification models in digital mammography. To facilitate future research our annotations, code, and model predictions are made publicly available.<|reference_end|> | arxiv | @article{schueppert2024radio-opaque,
title={Radio-opaque artefacts in digital mammography: automatic detection and
analysis of downstream effects},
author={Amelia Schueppert, Ben Glocker, M'elanie Roschewitz},
journal={arXiv preprint arXiv:2410.03809},
year={2024},
archivePrefix={arXiv},
eprint={2410.03809},
primaryClass={eess.IV cs.CV}
} | schueppert2024radio-opaque |
arxiv-665855 | 2410.03810 | Can Mamba Always Enjoy the "Free Lunch"? | <|reference_start|>Can Mamba Always Enjoy the "Free Lunch"?: Transformers have been the cornerstone of current Large Language Models (LLMs); however, its linear growth in overhead during inference with respect to sequence length poses challenges for modeling long sequences. In this context, Mamba has gradually attracted attention due to its constant-level size during inference and existing empirical results have shown that it can perform comparably to Transformers in sequence modeling while offering significant savings. However, one may ask that, can Mamba always enjoy the ``free lunch"? In this paper, we focus on analyzing the expressive ability of Mamba from a theoretical standpoint. First, inspired by the connection between Mamba and linear attention, we investigate potential shortcomings of the Mamba when performing the COPY operation. Our results indicate that Mamba with constant size may encounter bottlenecks when handling COPY, while it can achieve perfect performance when the size scales linearly with sequence length. Based on this observation, we analyze Mamba's ability to tackle DP problems when equipped with Chain of Thought (CoT). Our findings suggest that to solve arbitrary DP problems, the total cost of Mamba is comparable to standard and efficient Transformers. However, similar to efficient Transformers, when facing DP problems with favorable properties such as locality, Mamba can provide savings in overhead. Our results contribute to a deeper understanding of Mamba.<|reference_end|> | arxiv | @article{ren2024can,
title={Can Mamba Always Enjoy the "Free Lunch"?},
author={Ruifeng Ren, Zhicong Li and Yong Liu},
journal={arXiv preprint arXiv:2410.03810},
year={2024},
archivePrefix={arXiv},
eprint={2410.03810},
primaryClass={cs.LG cs.AI cs.CL}
} | ren2024can |
arxiv-665856 | 2410.03811 | Enhanced Digital Twin for Human-Centric and Integrated Lighting Asset Management in Public Libraries: From Corrective to Predictive Maintenance | <|reference_start|>Enhanced Digital Twin for Human-Centric and Integrated Lighting Asset Management in Public Libraries: From Corrective to Predictive Maintenance: Lighting asset management in public libraries has traditionally been reactive, focusing on corrective maintenance, addressing issues only when failures occur. Although standards now encourage preventive measures, such as incorporating a maintenance factor, the broader goal of human centric, sustainable lighting systems requires a shift toward predictive maintenance strategies. This study introduces an enhanced digital twin model designed for the proactive management of lighting assets in public libraries. By integrating descriptive, diagnostic, predictive, and prescriptive analytics, the model enables a comprehensive, multilevel view of asset health. The proposed framework supports both preventive and predictive maintenance strategies, allowing for early detection of issues and the timely resolution of potential failures. In addition to the specific application for lighting systems, the design is adaptable for other building assets, providing a scalable solution for integrated asset management in various public spaces.<|reference_end|> | arxiv | @article{lin2024enhanced,
title={Enhanced Digital Twin for Human-Centric and Integrated Lighting Asset
Management in Public Libraries: From Corrective to Predictive Maintenance},
author={Jing Lin, Jingchun Shen},
journal={arXiv preprint arXiv:2410.03811},
year={2024},
archivePrefix={arXiv},
eprint={2410.03811},
primaryClass={cs.HC cs.SY eess.SY}
} | lin2024enhanced |
arxiv-665857 | 2410.03812 | EvenNICER-SLAM: Event-based Neural Implicit Encoding SLAM | <|reference_start|>EvenNICER-SLAM: Event-based Neural Implicit Encoding SLAM: The advancement of dense visual simultaneous localization and mapping (SLAM) has been greatly facilitated by the emergence of neural implicit representations. Neural implicit encoding SLAM, a typical example of which is NICE-SLAM, has recently demonstrated promising results in large-scale indoor scenes. However, these methods typically rely on temporally dense RGB-D image streams as input in order to function properly. When the input source does not support high frame rates or the camera movement is too fast, these methods often experience crashes or significant degradation in tracking and mapping accuracy. In this paper, we propose EvenNICER-SLAM, a novel approach that addresses this issue through the incorporation of event cameras. Event cameras are bio-inspired cameras that respond to intensity changes instead of absolute brightness. Specifically, we integrated an event loss backpropagation stream into the NICE-SLAM pipeline to enhance camera tracking with insufficient RGB-D input. We found through quantitative evaluation that EvenNICER-SLAM, with an inclusion of higher-frequency event image input, significantly outperforms NICE-SLAM with reduced RGB-D input frequency. Our results suggest the potential for event cameras to improve the robustness of dense SLAM systems against fast camera motion in real-world scenarios.<|reference_end|> | arxiv | @article{chen2024evennicer-slam:,
title={EvenNICER-SLAM: Event-based Neural Implicit Encoding SLAM},
author={Shi Chen, Danda Pani Paudel, Luc Van Gool},
journal={arXiv preprint arXiv:2410.03812},
year={2024},
archivePrefix={arXiv},
eprint={2410.03812},
primaryClass={cs.CV}
} | chen2024evennicer-slam: |
arxiv-665858 | 2410.03813 | SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model | <|reference_start|>SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model: Consumer electronics used to follow the miniaturization trend described by Moore's Law. Despite increased processing power in Microcontroller Units (MCUs), MCUs used in the smallest appliances are still not capable of running even moderately big, state-of-the-art artificial neural networks (ANNs) especially in time-sensitive scenarios. In this work, we present a novel method called Scattered Online Inference (SOI) that aims to reduce the computational complexity of ANNs. SOI leverages the continuity and seasonality of time-series data and model predictions, enabling extrapolation for processing speed improvements, particularly in deeper layers. By applying compression, SOI generates more general inner partial states of ANN, allowing skipping full model recalculation at each inference.<|reference_end|> | arxiv | @article{stefański2024soi:,
title={SOI: Scaling Down Computational Complexity by Estimating Partial States
of the Model},
author={Grzegorz Stefa'nski, Pawe{l} Daniluk, Artur Szumaczuk, Jakub Tkaczuk},
journal={arXiv preprint arXiv:2410.03813},
year={2024},
archivePrefix={arXiv},
eprint={2410.03813},
primaryClass={cs.LG cs.SD eess.AS}
} | stefański2024soi: |
arxiv-665859 | 2410.03815 | Sim-to-Real Multirotor Controller Single-shot Learning | <|reference_start|>Sim-to-Real Multirotor Controller Single-shot Learning: This paper demonstrates the sim-to-real capabilities of retrospective cost optimization-based adaptive control for multirotor stabilization and trajectory-tracking problems. First, a continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm is developed. Next, a computationally inexpensive 12-degree-of-freedom model of a multirotor is used to learn the control system in a simulation environment with a single trajectory. Finally, the performance of the learned controller is verified in a complex and realistic multirotor model in simulation and with a physical quadcopter in a waypoint command and a helical trajectory command.<|reference_end|> | arxiv | @article{mirtaba2024sim-to-real,
title={Sim-to-Real Multirotor Controller Single-shot Learning},
author={Mohammad Mirtaba, Parham Oveissi, Ankit Goel},
journal={arXiv preprint arXiv:2410.03815},
year={2024},
archivePrefix={arXiv},
eprint={2410.03815},
primaryClass={eess.SY cs.SY}
} | mirtaba2024sim-to-real |
arxiv-665860 | 2410.03816 | Modeling and Analysis of Spatial and Temporal Land Clutter Statistics in SAR Imaging Based on MSTAR Data | <|reference_start|>Modeling and Analysis of Spatial and Temporal Land Clutter Statistics in SAR Imaging Based on MSTAR Data: The statistical analysis of land clutter for Synthetic Aperture Radar (SAR) imaging has become an increasingly important subject for research and investigation. It is also absolutely necessary for designing robust algorithms capable of performing the task of target detection in the background clutter. Any attempt to extract the energy of the desired targets from the land clutter requires complete knowledge of the statistical properties of the background clutter. In this paper, the spatial as well as the temporal characteristics of the land clutter are studied. Since the data for each image has been collected based on a different aspect angle; therefore, the temporal analysis contains variation in the aspect angle. Consequently, the temporal analysis includes the characteristics of the radar cross section with respect to the aspect angle based on which the data has been collected. In order to perform the statistical analysis, several well-known and relevant distributions, namely, Weibull, Log-normal, Gamma, and Rayleigh are considered as prime candidates to model the land clutter. The goodness-of-fit test is based on the Kullback-Leibler (KL) Divergence metric. The detailed analysis presented in this paper demonstrates that the Weibull distribution is a more accurate fit for the temporal-aspect-angle statistical analysis while the Rayleigh distribution models the spatial characteristics of the background clutter with higher accuracy. Finally, based on the aforementioned statistical analyses and by utilizing the Constant False Alarm Rate (CFAR) algorithm, we perform target detection in land clutter. The overall verification of the analysis is performed by exploiting the Moving and Stationary Target Acquisition and Recognition (MSTAR) data-set, which has been collected in spotlight mode at X-band, and the results are presented.<|reference_end|> | arxiv | @article{hamidi2024modeling,
title={Modeling and Analysis of Spatial and Temporal Land Clutter Statistics in
SAR Imaging Based on MSTAR Data},
author={Shahrokh Hamidi},
journal={arXiv preprint arXiv:2410.03816},
year={2024},
archivePrefix={arXiv},
eprint={2410.03816},
primaryClass={cs.CV eess.SP stat.AP}
} | hamidi2024modeling |
arxiv-665861 | 2410.03817 | A novel TLS-based Fingerprinting approach that combines feature expansion and similarity mapping | <|reference_start|>A novel TLS-based Fingerprinting approach that combines feature expansion and similarity mapping: Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous to both companies and individuals. They can be hosted on variety of technologies and serve an array of content, ranging from Malware, command and control, and complex Phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often involves complex allow or deny list management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprint techniques such as JARM and JA3 are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper is to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity, and to produce a similarity mapping system that enables the tracking and detection of previously unknown malicious domains. This is done by enriching TLS fingerprints with HTTP header data and producing a fine grain similarity visualisation that represented high dimensional data using MinHash and local sensitivity hashing. Influence was taken from the Chemistry domain, where the problem of high dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity mapping technique produced demonstrates definite promise in the arena of early detection of Malware and Phishing domains.<|reference_end|> | arxiv | @article{thomson2024a,
title={A novel TLS-based Fingerprinting approach that combines feature
expansion and similarity mapping},
author={Amanda Thomson, Leandros Maglaras, Naghmeh Moradpoor},
journal={arXiv preprint arXiv:2410.03817},
year={2024},
archivePrefix={arXiv},
eprint={2410.03817},
primaryClass={cs.CR}
} | thomson2024a |
arxiv-665862 | 2410.03818 | Large Language Models can be Strong Self-Detoxifiers | <|reference_start|>Large Language Models can be Strong Self-Detoxifiers: Reducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external reward model (i.e., another language model) or fine-tuning the LLM using self-generated data to influence the outcome. In this paper, we show that LLMs have the capability of self-detoxification without the use of an additional reward model or re-training. We propose \textit{Self-disciplined Autoregressive Sampling (SASA)}, a lightweight controlled decoding algorithm for toxicity reduction of LLMs. SASA leverages the contextual representations from an LLM to learn linear subspaces characterizing toxic v.s. non-toxic output in analytical forms. When auto-completing a response token-by-token, SASA dynamically tracks the margin of the current output to steer the generation away from the toxic subspace, by adjusting the autoregressive sampling strategy. Evaluated on LLMs of different scale and nature, namely Llama-3.1-Instruct (8B), Llama-2 (7B), and GPT2-L models with the RealToxicityPrompts, BOLD, and AttaQ benchmarks, SASA markedly enhances the quality of the generated sentences relative to the original models and attains comparable performance to state-of-the-art detoxification techniques, significantly reducing the toxicity level by only using the LLM's internal representations.<|reference_end|> | arxiv | @article{ko2024large,
title={Large Language Models can be Strong Self-Detoxifiers},
author={Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan,
Georgios Kollias, Subhajit Chaudhury, Tejaswini Pedapati, Luca Daniel},
journal={arXiv preprint arXiv:2410.03818},
year={2024},
archivePrefix={arXiv},
eprint={2410.03818},
primaryClass={cs.LG cs.AI cs.CL}
} | ko2024large |
arxiv-665863 | 2410.03825 | MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion | <|reference_start|>MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion: Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. Our key insight is that by simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. However, this approach presents a significant challenge: the scarcity of suitable training data, namely dynamic, posed videos with depth labels. Despite this, we show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics, even without an explicit motion representation. Based on this, we introduce new optimizations for several downstream video-specific tasks and demonstrate strong performance on video depth and camera pose estimation, outperforming prior work in terms of robustness and efficiency. Moreover, MonST3R shows promising results for primarily feed-forward 4D reconstruction.<|reference_end|> | arxiv | @article{zhang2024monst3r:,
title={MonST3R: A Simple Approach for Estimating Geometry in the Presence of
Motion},
author={Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor
Darrell, Forrester Cole, Deqing Sun, Ming-Hsuan Yang},
journal={arXiv preprint arXiv:2410.03825},
year={2024},
archivePrefix={arXiv},
eprint={2410.03825},
primaryClass={cs.CV}
} | zhang2024monst3r: |
arxiv-665864 | 2410.03829 | Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation | <|reference_start|>Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation: Misinformation, defined as false or inaccurate information, can result in significant societal harm when it is spread with malicious or even innocuous intent. The rapid online information exchange necessitates advanced detection mechanisms to mitigate misinformation-induced harm. Existing research, however, has predominantly focused on assessing veracity, overlooking the legal implications and social consequences of misinformation. In this work, we take a novel angle to consolidate the definition of misinformation detection using legal issues as a measurement of societal ramifications, aiming to bring interdisciplinary efforts to tackle misinformation and its consequence. We introduce a new task: Misinformation with Legal Consequence (MisLC), which leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations. For this task, we advocate a two-step dataset curation approach that utilizes crowd-sourced checkworthiness and expert evaluations of misinformation. We provide insights about the MisLC task through empirical evidence, from the problem definition to experiments and expert involvement. While the latest large language models and retrieval-augmented generation are effective baselines for the task, we find they are still far from replicating expert performance.<|reference_end|> | arxiv | @article{luo2024misinformation,
title={Misinformation with Legal Consequences (MisLC): A New Task Towards
Harnessing Societal Harm of Misinformation},
author={Chu Fei Luo, Radin Shayanfar, Rohan Bhambhoria, Samuel Dahan, Xiaodan
Zhu},
journal={arXiv preprint arXiv:2410.03829},
year={2024},
archivePrefix={arXiv},
eprint={2410.03829},
primaryClass={cs.CL}
} | luo2024misinformation |
arxiv-665865 | 2410.03833 | Why Fine-Tuning Struggles with Forgetting in Machine Unlearning? Theoretical Insights and a Remedial Approach | <|reference_start|>Why Fine-Tuning Struggles with Forgetting in Machine Unlearning? Theoretical Insights and a Remedial Approach: Machine Unlearning has emerged as a significant area of research, focusing on 'removing' specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of the fundamental approaches for approximating unlearning, as they effectively retain model performance. However, it is consistently observed that naive FT methods struggle to forget the targeted data. In this paper, we present the first theoretical analysis of FT methods for machine unlearning within a linear regression framework, providing a deeper exploration of this phenomenon. We investigate two scenarios with distinct features and overlapping features. Our findings reveal that FT models can achieve zero remaining loss yet fail to forget the forgetting data, unlike golden models (trained from scratch without the forgetting data). This analysis reveals that naive FT methods struggle with forgetting because the pretrained model retains information about the forgetting data, and the fine-tuning process has no impact on this retained information. To address this issue, we first propose a theoretical approach to mitigate the retention of forgetting data in the pretrained model. Our analysis shows that removing the forgetting data's influence allows FT models to match the performance of the golden model. Building on this insight, we introduce a discriminative regularization term to practically reduce the unlearning loss gap between the fine-tuned model and the golden model. Our experiments on both synthetic and real-world datasets validate these theoretical insights and demonstrate the effectiveness of the proposed regularization method.<|reference_end|> | arxiv | @article{ding2024why,
title={Why Fine-Tuning Struggles with Forgetting in Machine Unlearning?
Theoretical Insights and a Remedial Approach},
author={Meng Ding, Jinhui Xu, Kaiyi Ji},
journal={arXiv preprint arXiv:2410.03833},
year={2024},
archivePrefix={arXiv},
eprint={2410.03833},
primaryClass={cs.LG stat.ML}
} | ding2024why |
arxiv-665866 | 2410.03834 | GraphRouter: A Graph-based Router for LLM Selections | <|reference_start|>GraphRouter: A Graph-based Router for LLM Selections: The rapidly growing number and variety of Large Language Models (LLMs) present significant challenges in efficiently selecting the appropriate LLM for a given query, especially considering the trade-offs between performance and computational cost. Current LLM selection methods often struggle to generalize across new LLMs and different tasks because of their limited ability to leverage contextual interactions among tasks, queries, and LLMs, as well as their dependence on a transductive learning framework. To address these shortcomings, we introduce a novel inductive graph framework, named as GraphRouter, which fully utilizes the contextual information among tasks, queries, and LLMs to enhance the LLM selection process. GraphRouter constructs a heterogeneous graph comprising task, query, and LLM nodes, with interactions represented as edges, which efficiently captures the contextual information between the query's requirements and the LLM's capabilities. Through an innovative edge prediction mechanism, GraphRouter is able to predict attributes (the effect and cost of LLM response) of potential edges, allowing for optimized recommendations that adapt to both existing and newly introduced LLMs without requiring retraining. Comprehensive experiments across three distinct effect-cost weight scenarios have shown that GraphRouter substantially surpasses existing routers, delivering a minimum performance improvement of 12.3%. In addition, it achieves enhanced generalization across new LLMs settings and supports diverse tasks with at least a 9.5% boost in effect and a significant reduction in computational demands. This work endeavors to apply a graph-based approach for the contextual and adaptive selection of LLMs, offering insights for real-world applications. Our codes for GraphRouter will soon be released at https://github.com/ulab-uiuc/GraphRouter.<|reference_end|> | arxiv | @article{feng2024graphrouter:,
title={GraphRouter: A Graph-based Router for LLM Selections},
author={Tao Feng, Yanzhen Shen, Jiaxuan You},
journal={arXiv preprint arXiv:2410.03834},
year={2024},
archivePrefix={arXiv},
eprint={2410.03834},
primaryClass={cs.AI}
} | feng2024graphrouter: |
arxiv-665867 | 2410.03837 | Learning Code Preference via Synthetic Evolution | <|reference_start|>Learning Code Preference via Synthetic Evolution: Large Language Models (LLMs) have recently demonstrated remarkable coding capabilities. However, assessing code generation based on well-formed properties and aligning it with developer preferences remains challenging. In this paper, we explore two key questions under the new challenge of code preference learning: (i) How do we train models to predict meaningful preferences for code? and (ii) How do human and LLM preferences align with verifiable code properties and developer code tastes? To this end, we propose CodeFavor, a framework for training pairwise code preference models from synthetic evolution data, including code commits and code critiques. To evaluate code preferences, we introduce CodePrefBench, a benchmark comprising 1364 rigorously curated code preference tasks to cover three verifiable properties-correctness, efficiency, and security-along with human preference. Our evaluation shows that CodeFavor holistically improves the accuracy of model-based code preferences by up to 28.8%. Meanwhile, CodeFavor models can match the performance of models with 6-9x more parameters while being 34x more cost-effective. We also rigorously validate the design choices in CodeFavor via a comprehensive set of controlled experiments. Furthermore, we discover the prohibitive costs and limitations of human-based code preference: despite spending 23.4 person-minutes on each task, 15.1-40.3% of tasks remain unsolved. Compared to model-based preference, human preference tends to be more accurate under the objective of code correctness, while being sub-optimal for non-functional objectives.<|reference_end|> | arxiv | @article{liu2024learning,
title={Learning Code Preference via Synthetic Evolution},
author={Jiawei Liu, Thanh Nguyen, Mingyue Shang, Hantian Ding, Xiaopeng Li, Yu
Yu, Varun Kumar, Zijian Wang},
journal={arXiv preprint arXiv:2410.03837},
year={2024},
archivePrefix={arXiv},
eprint={2410.03837},
primaryClass={cs.LG cs.CL cs.SE}
} | liu2024learning |
arxiv-665868 | 2410.03839 | FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation | <|reference_start|>FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation: In ad text generation (ATG), desirable ad text is both faithful and informative. That is, it should be faithful to the input document, while at the same time containing important information that appeals to potential customers. The existing evaluation data, CAMERA (arXiv:2309.12030), is suitable for evaluating informativeness, as it consists of reference ad texts created by ad creators. However, these references often include information unfaithful to the input, which is a notable obstacle in promoting ATG research. In this study, we collaborate with in-house ad creators to refine the CAMERA references and develop an alternative ATG evaluation dataset called FaithCAMERA, in which the faithfulness of references is guaranteed. Using FaithCAMERA, we can evaluate how well existing methods for improving faithfulness can generate informative ad text while maintaining faithfulness. Our experiments show that removing training data that contains unfaithful entities improves the faithfulness and informativeness at the entity level, but decreases both at the sentence level. This result suggests that for future ATG research, it is essential not only to scale the training data but also to ensure their faithfulness. Our dataset will be publicly available.<|reference_end|> | arxiv | @article{kato2024faithcamera:,
title={FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation},
author={Akihiko Kato, Masato Mita, Soichiro Murakami, Ukyo Honda, Sho Hoshino,
Peinan Zhang},
journal={arXiv preprint arXiv:2410.03839},
year={2024},
archivePrefix={arXiv},
eprint={2410.03839},
primaryClass={cs.CL}
} | kato2024faithcamera: |
arxiv-665869 | 2410.03841 | Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System | <|reference_start|>Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System: A lot of effort in recent years have been expended to explain machine learning systems. However, some machine learning methods are inherently explainable, and thus are not completely black box. This enables the developers to make sense of the output without a developing a complex and expensive explainability technique. Besides that, explainability should be tailored to suit the context of the problem. In a recommendation system which relies on collaborative filtering, the recommendation is based on the behaviors of similar users, therefore the explanation should tell which other users are similar to the current user. Similarly, if the recommendation system is based on sequence prediction, the explanation should also tell which input timesteps are the most influential. We demonstrate this philosophy/paradigm in STAN (Spatio-Temporal Attention Network for Next Location Recommendation), a next Point of Interest recommendation system based on collaborative filtering and sequence prediction. We also show that the explanation helps to "debug" the output.<|reference_end|> | arxiv | @article{yunus2024explaining,
title={Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a
Next Point of Interest Recommendation System},
author={Fajrian Yunus and Talel Abdessalem},
journal={arXiv preprint arXiv:2410.03841},
year={2024},
archivePrefix={arXiv},
eprint={2410.03841},
primaryClass={cs.IR cs.AI cs.LG}
} | yunus2024explaining |
arxiv-665870 | 2410.03843 | TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement | <|reference_start|>TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement: Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.<|reference_end|> | arxiv | @article{wang2024trustemg-net:,
title={TrustEMG-Net: Using Representation-Masking Transformer with U-Net for
Surface Electromyography Enhancement},
author={Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao},
journal={arXiv preprint arXiv:2410.03843},
year={2024},
archivePrefix={arXiv},
eprint={2410.03843},
primaryClass={eess.SP cs.LG}
} | wang2024trustemg-net: |
arxiv-665871 | 2410.03844 | Projected Walk on Spheres: A Monte Carlo Closest Point Method for Surface PDEs | <|reference_start|>Projected Walk on Spheres: A Monte Carlo Closest Point Method for Surface PDEs: We present projected walk on spheres (PWoS), a novel pointwise and discretization-free Monte Carlo solver for surface PDEs with Dirichlet boundaries, as a generalization of the walk on spheres method (WoS) [Muller 1956; Sawhney and Crane 2020]. We adapt the recursive relationship of WoS designed for PDEs in volumetric domains to a volumetric neighborhood around the surface, and at the end of each recursion step, we project the sample point on the sphere back to the surface. We motivate this simple modification to WoS with the theory of the closest point extension used in the closest point method. To define the valid volumetric neighborhood domain for PWoS, we develop strategies to estimate the local feature size of the surface and to compute the distance to the Dirichlet boundaries on the surface extended in their normal directions. We also design a mean value filtering method for PWoS to improve the method's efficiency when the surface is represented as a polygonal mesh or a point cloud. Finally, we study the convergence of PWoS and demonstrate its application to graphics tasks, including diffusion curves, geodesic distance computation, and wave propagation animation. We show that our method works with various types of surfaces, including a surface of mixed codimension.<|reference_end|> | arxiv | @article{sugimoto2024projected,
title={Projected Walk on Spheres: A Monte Carlo Closest Point Method for
Surface PDEs},
author={Ryusuke Sugimoto, Nathan King, Toshiya Hachisuka, Christopher Batty},
journal={arXiv preprint arXiv:2410.03844},
year={2024},
doi={10.1145/3680528.3687599},
archivePrefix={arXiv},
eprint={2410.03844},
primaryClass={math.NA cs.GR cs.NA}
} | sugimoto2024projected |
arxiv-665872 | 2410.03845 | ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD | <|reference_start|>ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD: Open-source Electronic Design Automation (EDA) tools are rapidly transforming chip design by addressing key barriers of commercial EDA tools such as complexity, costs, and access. Recent advancements in Large Language Models (LLMs) have further enhanced efficiency in chip design by providing user assistance across a range of tasks like setup, decision-making, and flow automation. This paper introduces ORAssistant, a conversational assistant for OpenROAD, based on Retrieval-Augmented Generation (RAG). ORAssistant aims to improve the user experience for the OpenROAD flow, from RTL-GDSII by providing context-specific responses to common user queries, including installation, command usage, flow setup, and execution, in prose format. Currently, ORAssistant integrates OpenROAD, OpenROAD-flow-scripts, Yosys, OpenSTA, and KLayout. The data model is built from publicly available documentation and GitHub resources. The proposed architecture is scalable, supporting extensions to other open-source tools, operating modes, and LLM models. We use Google Gemini as the base LLM model to build and test ORAssistant. Early evaluation results of the RAG-based model show notable improvements in performance and accuracy compared to non-fine-tuned LLMs.<|reference_end|> | arxiv | @article{kaintura2024orassistant:,
title={ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD},
author={Aviral Kaintura, Palaniappan R, Shui Song Luar, and Indira Iyer
Almeida},
journal={arXiv preprint arXiv:2410.03845},
year={2024},
archivePrefix={arXiv},
eprint={2410.03845},
primaryClass={cs.CL cs.AR}
} | kaintura2024orassistant: |
arxiv-665873 | 2410.03846 | Universal Global State Estimation for Inertial Navigation Systems | <|reference_start|>Universal Global State Estimation for Inertial Navigation Systems: This paper addresses the problem of accurate pose estimation (position, velocity, and orientation) for a rigid body. By utilizing generic exteroceptive measurements in combination with an Inertial Measurement Unit (IMU), we reformulate the vehicle's dynamics and outputs to fit within a linear time-varying (LTV) framework. This transformation enables the application of a linear continuous-time Kalman filter, thereby avoiding the complexities of nonlinear estimators and local Kalman-type filtering methods (e.g., EKF). We perform a complete uniform observability analysis for key benchmark problems (e.g., GPS-INS and Landmark-INS) and derive sufficient conditions for ensuring global uniform exponential stability. Simulations are conducted for two practical applications: stereo-aided inertial navigation systems (INS) with both constant and time-varying gains, as well as GPS-aided INS. The proposed approach notably simplifies observer design for INS.<|reference_end|> | arxiv | @article{benahmed2024universal,
title={Universal Global State Estimation for Inertial Navigation Systems},
author={Sifeddine Benahmed and Soulaimane Berkane},
journal={arXiv preprint arXiv:2410.03846},
year={2024},
archivePrefix={arXiv},
eprint={2410.03846},
primaryClass={eess.SY cs.SY}
} | benahmed2024universal |
arxiv-665874 | 2410.03847 | Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments | <|reference_start|>Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments: In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and performance is degraded. To address this issue, we propose a novel method which infuses the dynamics information into the reward shaping with the theoretical guarantee for the induced optimal policy in the stochastic environments. Incorporating our novel model-enhanced rewards, we present a novel Model-Enhanced AIRL framework, which integrates transition model estimation directly into reward shaping. Furthermore, we provide a comprehensive theoretical analysis of the reward error bound and performance difference bound for our method. The experimental results in MuJoCo benchmarks show that our method can achieve superior performance in stochastic environments and competitive performance in deterministic environments, with significant improvement in sample efficiency, compared to existing baselines.<|reference_end|> | arxiv | @article{zhan2024model-based,
title={Model-Based Reward Shaping for Adversarial Inverse Reinforcement
Learning in Stochastic Environments},
author={Simon Sinong Zhan, Qingyuan Wu, Philip Wang, Yixuan Wang, Ruochen
Jiao, Chao Huang, Qi Zhu},
journal={arXiv preprint arXiv:2410.03847},
year={2024},
archivePrefix={arXiv},
eprint={2410.03847},
primaryClass={cs.LG cs.AI}
} | zhan2024model-based |
arxiv-665875 | 2410.03848 | Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style | <|reference_start|>Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style: Large language models (LLMs), such as GPT series and Llama series have demonstrated strong capabilities in natural language processing, contextual understanding, and text generation. In recent years, researchers are trying to enhance the abilities of LLMs in performing various tasks, and numerous studies have proved that well-designed prompts can significantly improve the performance of LLMs on these tasks. This study compares the language style imitation ability of three different large language models under the guidance of the same zero-shot prompt. It also involves comparing the imitation ability of the same large language model when guided by three different prompts individually. Additionally, by applying a Tree-of-Thoughts (ToT) Prompting method to Llama 3, a conversational AI with the language style of a real person was created. In this study, three evaluation methods were used to evaluate LLMs and prompts. The results show that Llama 3 performs best at imitating language styles, and that the ToT prompting method is the most effective to guide it in imitating language styles. Using a ToT framework, Llama 3 was guided to interact with users in the language style of a specific individual without altering its core parameters, thereby creating a text-based conversational AI that reflects the language style of the individual.<|reference_end|> | arxiv | @article{chen2024using,
title={Using Prompts to Guide Large Language Models in Imitating a Real
Person's Language Style},
author={Ziyang Chen and Stylios Moscholios},
journal={arXiv preprint arXiv:2410.03848},
year={2024},
archivePrefix={arXiv},
eprint={2410.03848},
primaryClass={cs.CL cs.CV}
} | chen2024using |
arxiv-665876 | 2410.03849 | Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood | <|reference_start|>Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood: We study the fundamental problem of sequential probability assignment, also known as online learning with logarithmic loss, with respect to an arbitrary, possibly nonparametric hypothesis class. Our goal is to obtain a complexity measure for the hypothesis class that characterizes the minimax regret and to determine a general, minimax optimal algorithm. Notably, the sequential $\ell_{\infty}$ entropy, extensively studied in the literature (Rakhlin and Sridharan, 2015, Bilodeau et al., 2020, Wu et al., 2023), was shown to not characterize minimax risk in general. Inspired by the seminal work of Shtarkov (1987) and Rakhlin, Sridharan, and Tewari (2010), we introduce a novel complexity measure, the \emph{contextual Shtarkov sum}, corresponding to the Shtarkov sum after projection onto a multiary context tree, and show that the worst case log contextual Shtarkov sum equals the minimax regret. Using the contextual Shtarkov sum, we derive the minimax optimal strategy, dubbed \emph{contextual Normalized Maximum Likelihood} (cNML). Our results hold for sequential experts, beyond binary labels, which are settings rarely considered in prior work. To illustrate the utility of this characterization, we provide a short proof of a new regret upper bound in terms of sequential $\ell_{\infty}$ entropy, unifying and sharpening state-of-the-art bounds by Bilodeau et al. (2020) and Wu et al. (2023).<|reference_end|> | arxiv | @article{liu2024sequential,
title={Sequential Probability Assignment with Contexts: Minimax Regret,
Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood},
author={Ziyi Liu, Idan Attias, Daniel M. Roy},
journal={arXiv preprint arXiv:2410.03849},
year={2024},
archivePrefix={arXiv},
eprint={2410.03849},
primaryClass={cs.LG stat.ML}
} | liu2024sequential |
arxiv-665877 | 2410.03855 | A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research | <|reference_start|>A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research: Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.<|reference_end|> | arxiv | @article{salazar2024a,
title={A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy
of Solutions and Directions for Future Research},
author={Teresa Salazar, Helder Ara'ujo, Alberto Cano, Pedro Henriques Abreu},
journal={arXiv preprint arXiv:2410.03855},
year={2024},
archivePrefix={arXiv},
eprint={2410.03855},
primaryClass={cs.LG cs.AI cs.CY}
} | salazar2024a |
arxiv-665878 | 2410.03856 | Detecting Machine-Generated Long-Form Content with Latent-Space Variables | <|reference_start|>Detecting Machine-Generated Long-Form Content with Latent-Space Variables: The increasing capability of large language models (LLMs) to generate fluent long-form texts is presenting new challenges in distinguishing machine-generated outputs from human-written ones, which is crucial for ensuring authenticity and trustworthiness of expressions. Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts, including different prompting and decoding strategies, and adversarial attacks. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts by training a latent-space model on sequences of events or topics derived from human-written texts. In three different domains, machine-generated texts, which are originally inseparable from human texts on the token level, can be better distinguished with our latent-space model, leading to a 31% improvement over strong baselines such as DetectGPT. Our analysis further reveals that, unlike humans, modern LLMs like GPT-4 generate event triggers and their transitions differently, an inherent disparity that helps our method to robustly detect machine-generated texts.<|reference_end|> | arxiv | @article{tian2024detecting,
title={Detecting Machine-Generated Long-Form Content with Latent-Space
Variables},
author={Yufei Tian, Zeyu Pan, Nanyun Peng},
journal={arXiv preprint arXiv:2410.03856},
year={2024},
archivePrefix={arXiv},
eprint={2410.03856},
primaryClass={cs.CL cs.LG}
} | tian2024detecting |
arxiv-665879 | 2410.03857 | You Know What I'm Saying: Jailbreak Attack via Implicit Reference | <|reference_start|>You Know What I'm Saying: Jailbreak Attack via Implicit Reference: While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, our study reveals that these methods remain inadequate at detecting malicious objectives expressed through context within nested harmless objectives. This study identifies a previously overlooked vulnerability, which we term Attack via Implicit Reference (AIR). AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. This method employs multiple related harmless objectives to generate malicious content without triggering refusal responses, thereby effectively bypassing existing detection techniques.Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on most models, including GPT-4o, Claude-3.5-Sonnet, and Qwen-2-72B. Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method. These findings underscore the urgent need for defense mechanisms capable of understanding and preventing contextual attacks. Furthermore, we introduce a cross-model attack strategy that leverages less secure models to generate malicious contexts, thereby further increasing the ASR when targeting other models.Our code and jailbreak artifacts can be found at https://github.com/Lucas-TY/llm_Implicit_reference.<|reference_end|> | arxiv | @article{wu2024you,
title={You Know What I'm Saying: Jailbreak Attack via Implicit Reference},
author={Tianyu Wu, Lingrui Mei, Ruibin Yuan, Lujun Li, Wei Xue, Yike Guo},
journal={arXiv preprint arXiv:2410.03857},
year={2024},
archivePrefix={arXiv},
eprint={2410.03857},
primaryClass={cs.CL}
} | wu2024you |
arxiv-665880 | 2410.03858 | Unsupervised Prior Learning: Discovering Categorical Pose Priors from Videos | <|reference_start|>Unsupervised Prior Learning: Discovering Categorical Pose Priors from Videos: A prior represents a set of beliefs or assumptions about a system, aiding inference and decision-making. In this work, we introduce the challenge of unsupervised prior learning in pose estimation, where AI models learn pose priors of animate objects from videos in a self-supervised manner. These videos present objects performing various actions, providing crucial information about their keypoints and connectivity. While priors are effective in pose estimation, acquiring them can be difficult. We propose a novel method, named Pose Prior Learner (PPL), to learn general pose priors applicable to any object category. PPL uses a hierarchical memory to store compositional parts of prototypical poses, from which we distill a general pose prior. This prior enhances pose estimation accuracy through template transformation and image reconstruction. PPL learns meaningful pose priors without any additional human annotations or interventions, outperforming competitive baselines on both human and animal pose estimation datasets. Notably, our experimental results reveal the effectiveness of PPL using learnt priors for pose estimation on occluded images. Through iterative inference, PPL leverages priors to refine estimated poses, regressing them to any prototypical poses stored in memory. Our code, model, and data will be publicly available.<|reference_end|> | arxiv | @article{wang2024unsupervised,
title={Unsupervised Prior Learning: Discovering Categorical Pose Priors from
Videos},
author={Ziyu Wang, Shuangpeng Han, Mike Zheng Shou, Mengmi Zhang},
journal={arXiv preprint arXiv:2410.03858},
year={2024},
archivePrefix={arXiv},
eprint={2410.03858},
primaryClass={cs.CV}
} | wang2024unsupervised |
arxiv-665881 | 2410.03859 | SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? | <|reference_start|>SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?: Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as images. This limited coverage motivates our inquiry into how existing systems might perform on unrepresented software engineering domains (e.g., front-end, game development, DevOps), which use different programming languages and paradigms. Therefore, we propose SWE-bench Multimodal (SWE-bench M), to evaluate systems on their ability to fix bugs in visual, user-facing JavaScript software. SWE-bench M features 617 task instances collected from 17 JavaScript libraries used for web interface design, diagramming, data visualization, syntax highlighting, and interactive mapping. Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. Lastly, we show that SWE-agent's flexible language-agnostic features enable it to substantially outperform alternatives on SWE-bench M, resolving 12% of task instances compared to 6% for the next best system.<|reference_end|> | arxiv | @article{yang2024swe-bench,
title={SWE-bench Multimodal: Do AI Systems Generalize to Visual Software
Domains?},
author={John Yang, Carlos E. Jimenez, Alex L. Zhang, Kilian Lieret, Joyce
Yang, Xindi Wu, Ori Press, Niklas Muennighoff, Gabriel Synnaeve, Karthik R.
Narasimhan, Diyi Yang, Sida I. Wang, Ofir Press},
journal={arXiv preprint arXiv:2410.03859},
year={2024},
archivePrefix={arXiv},
eprint={2410.03859},
primaryClass={cs.CL cs.AI cs.SE}
} | yang2024swe-bench |
arxiv-665882 | 2410.03860 | MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty | <|reference_start|>MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty: This paper introduces a Multi-modal Diffusion model for Motion Prediction (MDMP) that integrates and synchronizes skeletal data and textual descriptions of actions to generate refined long-term motion predictions with quantifiable uncertainty. Existing methods for motion forecasting or motion generation rely solely on either prior motions or text prompts, facing limitations with precision or control, particularly over extended durations. The multi-modal nature of our approach enhances the contextual understanding of human motion, while our graph-based transformer framework effectively capture both spatial and temporal motion dynamics. As a result, our model consistently outperforms existing generative techniques in accurately predicting long-term motions. Additionally, by leveraging diffusion models' ability to capture different modes of prediction, we estimate uncertainty, significantly improving spatial awareness in human-robot interactions by incorporating zones of presence with varying confidence levels for each body joint.<|reference_end|> | arxiv | @article{bringer2024mdmp:,
title={MDMP: Multi-modal Diffusion for supervised Motion Predictions with
uncertainty},
author={Leo Bringer, Joey Wilson, Kira Barton, Maani Ghaffari},
journal={arXiv preprint arXiv:2410.03860},
year={2024},
archivePrefix={arXiv},
eprint={2410.03860},
primaryClass={cs.CV}
} | bringer2024mdmp: |
arxiv-665883 | 2410.03861 | Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering | <|reference_start|>Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering: The accurate reconstruction of per-pixel depth for an image is vital for many tasks in computer graphics, computer vision, and robotics. In this paper, we present a novel approach to generate view consistent and detailed depth maps from a number of posed images. We leverage advances in monocular depth estimation, which generate topologically complete, but metrically inaccurate depth maps and refine them in a two-stage optimization process based on a differentiable renderer. Taking the monocular depth map as input, we first scale this map to absolute distances based on structure-from-motion and transform the depths to a triangle surface mesh. We then refine this depth mesh in a local optimization, enforcing photometric and geometric consistency. Our evaluation shows that our method is able to generate dense, detailed, high-quality depth maps, also in challenging indoor scenarios, and outperforms state-of-the-art depth reconstruction approaches. Overview and supplemental material of this project can be found at https://lorafib.github.io/ref_depth/.<|reference_end|> | arxiv | @article{fink2024refinement,
title={Refinement of Monocular Depth Maps via Multi-View Differentiable
Rendering},
author={Laura Fink, Linus Franke, Joachim Keinert, Marc Stamminger},
journal={arXiv preprint arXiv:2410.03861},
year={2024},
archivePrefix={arXiv},
eprint={2410.03861},
primaryClass={cs.CV}
} | fink2024refinement |
arxiv-665884 | 2410.03862 | Improving Mapper's Robustness by Varying Resolution According to Lens-Space Density | <|reference_start|>Improving Mapper's Robustness by Varying Resolution According to Lens-Space Density: We propose an improvement to the Mapper algorithm that removes the assumption of a single resolution scale across semantic space, and improves the robustness of the results under change of parameters. This eases parameter selection, especially for datasets with highly variable local density in the Morse function $f$ used for Mapper. This is achieved by incorporating this density into the choice of cover for Mapper. Furthermore, we prove that for covers with some natural hypotheses, the graph output by Mapper still converges in bottleneck distance to the Reeb graph of the Rips complex of the data, but captures more topological features than when using the usual Mapper cover. Finally, we discuss implementation details, and include the results of computational experiments. We also provide an accompanying reference implementation.<|reference_end|> | arxiv | @article{ruscitti2024improving,
title={Improving Mapper's Robustness by Varying Resolution According to
Lens-Space Density},
author={Kaleb D. Ruscitti and Leland McInnes},
journal={arXiv preprint arXiv:2410.03862},
year={2024},
archivePrefix={arXiv},
eprint={2410.03862},
primaryClass={cs.LG math.AT stat.ML}
} | ruscitti2024improving |
arxiv-665885 | 2410.03863 | Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination | <|reference_start|>Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination: Existing stochastic selection strategies for parent selection in generational GA help build genetic diversity and sustain exploration; however, it ignores the possibility of exploiting knowledge gained by the process to make informed decisions for parent selection, which can often lead to an inefficient search for large, challenging optimization problems. This work proposes a deterministic parent selection strategy for recombination in a generational GA setting called Upper Bound-based Parent Selection (UBS) to solve NP-hard combinatorial optimization problems. Specifically, as part of the UBS strategy, we formulate the parent selection problem using the MAB framework and a modified UCB1 algorithm to manage exploration and exploitation. Further, we provided a unique similarity-based approach for transferring knowledge of the search progress between generations to accelerate the search. To demonstrate the effectiveness of the proposed UBS strategy in comparison to traditional stochastic selection strategies, we conduct experimental studies on two NP-hard combinatorial optimization problems: team orienteering and quadratic assignment. Specifically, we first perform a characterization study to determine the potential of UBS and the best configuration for all the selection strategies involved. Next, we run experiments using these best configurations as part of the comparison study. The results from the characterization studies reveal that UBS, in most cases, favors larger variations among the population between generations. Next, the comparison studies reveal that UBS can effectively search for high-quality solutions faster than traditional stochastic selection strategies on challenging NP-hard combinatorial optimization problems under given experimental conditions.<|reference_end|> | arxiv | @article{sankaran2024rethinking,
title={Rethinking Selection in Generational Genetic Algorithms to Solve
Combinatorial Optimization Problems: An Upper Bound-based Parent Selection
Strategy for Recombination},
author={Prashant Sankaran and Katie McConky},
journal={arXiv preprint arXiv:2410.03863},
year={2024},
archivePrefix={arXiv},
eprint={2410.03863},
primaryClass={cs.NE}
} | sankaran2024rethinking |
arxiv-665886 | 2410.03864 | DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search | <|reference_start|>DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search: Enhancing the capability of large language models (LLMs) in reasoning has gained significant attention in recent years. Previous studies have demonstrated the effectiveness of various prompting strategies in aiding LLMs in reasoning (called "reasoning actions"), such as step-by-step thinking, reflecting before answering, solving with programs, and their combinations. However, these approaches often applied static, predefined reasoning actions uniformly to all questions, without considering the specific characteristics of each question or the capability of the task-solving LLM. In this paper, we propose DOTS, an approach enabling LLMs to reason dynamically via optimal reasoning trajectory search, tailored to the specific characteristics of each question and the inherent capability of the task-solving LLM. Our approach involves three key steps: i) defining atomic reasoning action modules that can be composed into various reasoning action trajectories; ii) searching for the optimal action trajectory for each training question through iterative exploration and evaluation for the specific task-solving LLM; and iii) using the collected optimal trajectories to train an LLM to plan for the reasoning trajectories of unseen questions. In particular, we propose two learning paradigms, i.e., fine-tuning an external LLM as a planner to guide the task-solving LLM, or directly fine-tuning the task-solving LLM with an internalized capability for reasoning actions planning. Our experiments across eight reasoning tasks show that our method consistently outperforms static reasoning techniques and the vanilla instruction tuning approach. Further analysis reveals that our method enables LLMs to adjust their computation based on problem complexity, allocating deeper thinking and reasoning to harder problems.<|reference_end|> | arxiv | @article{yue2024dots:,
title={DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning
Trajectories Search},
author={Murong Yue, Wenlin Yao, Haitao Mi, Dian Yu, Ziyu Yao, Dong Yu},
journal={arXiv preprint arXiv:2410.03864},
year={2024},
archivePrefix={arXiv},
eprint={2410.03864},
primaryClass={cs.AI cs.CL cs.LG}
} | yue2024dots: |
arxiv-665887 | 2410.03866 | A Tool to Facilitate Web-Browsing | <|reference_start|>A Tool to Facilitate Web-Browsing: Search engine results often misalign with users' goals due to opaque algorithms, leading to unhelpful or detrimental information consumption. To address this, we developed a Google Chrome plugin that provides "content labels" for webpages in Google search results, assessing Actionability (guiding actions), Knowledge (enhancing understanding), and Emotion. Using natural language processing and machine learning, the plugin predicts these properties from webpage text based on models trained on participants' ratings, effectively reflecting user perceptions. The implications include enhanced user control over information consumption and promotion of healthier engagement with online content, potentially improving decision-making and well-being.<|reference_end|> | arxiv | @article{kelly2024a,
title={A Tool to Facilitate Web-Browsing},
author={Christopher Kelly, Jonatan Fontanez, Tali Sharot},
journal={arXiv preprint arXiv:2410.03866},
year={2024},
archivePrefix={arXiv},
eprint={2410.03866},
primaryClass={cs.HC}
} | kelly2024a |
arxiv-665888 | 2410.03867 | Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance | <|reference_start|>Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance: In an era dominated by data, the management and utilization of domain-specific language have emerged as critical challenges in various application domains, particularly those with industry-specific requirements. Our work is driven by the need to effectively manage and process large volumes of short text documents inherent in specific application domains. By leveraging domain-specific knowledge and expertise, our approach aims to shape factual data within these domains, thereby facilitating enhanced utilization and understanding by end-users. Central to our methodology is the integration of domain-specific language models with graph-oriented databases, facilitating seamless processing, analysis, and utilization of textual data within targeted domains. Our work underscores the transformative potential of the partnership of domain-specific language models and graph-oriented databases. This cooperation aims to assist researchers and engineers in metric usage, mitigation of latency issues, boosting explainability, enhancing debug and improving overall model performance. Moving forward, we envision our work as a guide AI engineers, providing valuable insights for the implementation of domain-specific language models in conjunction with graph-oriented databases, and additionally provide valuable experience in full-life cycle maintenance of this kind of products.<|reference_end|> | arxiv | @article{di pasquale2024empowering,
title={Empowering Domain-Specific Language Models with Graph-Oriented
Databases: A Paradigm Shift in Performance and Model Maintenance},
author={Ricardo Di Pasquale and Soledad Represa},
journal={arXiv preprint arXiv:2410.03867},
year={2024},
archivePrefix={arXiv},
eprint={2410.03867},
primaryClass={cs.AI cs.DB cs.LG}
} | di pasquale2024empowering |
arxiv-665889 | 2410.03868 | Can Language Models Reason about Individualistic Human Values and Preferences? | <|reference_start|>Can Language Models Reason about Individualistic Human Values and Preferences?: Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics, personalities, communication styles), risking smoothing out or even stereotyping the rich spectrum of individualistic variations. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Specifically, given a sample of an individual's value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs' abilities to reason about individualistic human values with accuracies, only ranging between 55% to 65%. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. We also identify a partiality of LMs in reasoning about global individualistic values, as measured by our proposed Value Inequity Index ({\sigma}INEQUITY). Finally, we train a series of Individualistic Value Reasoners (IndieValueReasoner) using IndieValueCatalog to enhance models' individualistic value reasoning capability, revealing new patterns and dynamics into global human values. We outline future research challenges and opportunities for advancing individualistic alignment.<|reference_end|> | arxiv | @article{jiang2024can,
title={Can Language Models Reason about Individualistic Human Values and
Preferences?},
author={Liwei Jiang and Taylor Sorensen and Sydney Levine and Yejin Choi},
journal={arXiv preprint arXiv:2410.03868},
year={2024},
archivePrefix={arXiv},
eprint={2410.03868},
primaryClass={cs.CL}
} | jiang2024can |
arxiv-665890 | 2410.03869 | Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step | <|reference_start|>Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step: Text-based image generation models, such as Stable Diffusion and DALL-E 3, hold significant potential in content creation and publishing workflows, making them the focus in recent years. Despite their remarkable capability to generate diverse and vivid images, considerable efforts are being made to prevent the generation of harmful content, such as abusive, violent, or pornographic material. To assess the safety of existing models, we introduce a novel jailbreaking method called Chain-of-Jailbreak (CoJ) attack, which compromises image generation models through a step-by-step editing process. Specifically, for malicious queries that cannot bypass the safeguards with a single prompt, we intentionally decompose the query into multiple sub-queries. The image generation models are then prompted to generate and iteratively edit images based on these sub-queries. To evaluate the effectiveness of our CoJ attack method, we constructed a comprehensive dataset, CoJ-Bench, encompassing nine safety scenarios, three types of editing operations, and three editing elements. Experiments on four widely-used image generation services provided by GPT-4V, GPT-4o, Gemini 1.5 and Gemini 1.5 Pro, demonstrate that our CoJ attack method can successfully bypass the safeguards of models for over 60% cases, which significantly outperforms other jailbreaking methods (i.e., 14%). Further, to enhance these models' safety against our CoJ attack method, we also propose an effective prompting-based method, Think Twice Prompting, that can successfully defend over 95% of CoJ attack. We release our dataset and code to facilitate the AI safety research.<|reference_end|> | arxiv | @article{wang2024chain-of-jailbreak,
title={Chain-of-Jailbreak Attack for Image Generation Models via Editing Step
by Step},
author={Wenxuan Wang, Kuiyi Gao, Zihan Jia, Youliang Yuan, Jen-tse Huang,
Qiuzhi Liu, Shuai Wang, Wenxiang Jiao, Zhaopeng Tu},
journal={arXiv preprint arXiv:2410.03869},
year={2024},
archivePrefix={arXiv},
eprint={2410.03869},
primaryClass={cs.CL cs.AI cs.CR cs.CV cs.MM}
} | wang2024chain-of-jailbreak |
arxiv-665891 | 2410.03870 | From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues | <|reference_start|>From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues: Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue datasets, outlining the contrasts between self-anthropomorphic and non-self-anthropomorphic responses in dialogue systems. We show significant differences in these two types of responses and propose transitioning from one type to the other. We also introduce Pix2Persona, a novel dataset aimed at developing ethical and engaging AI systems in various embodiments. This dataset preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropomorphic for each original bot response. Our work not only uncovers a new category of bot responses that were previously under-explored but also lays the groundwork for future studies about dynamically adjusting self-anthropomorphism levels in AI systems to align with ethical standards and user expectations.<|reference_end|> | arxiv | @article{li2024from,
title={From Pixels to Personas: Investigating and Modeling
Self-Anthropomorphism in Human-Robot Dialogues},
author={Yu Li, Devamanyu Hazarika, Di Jin, Julia Hirschberg, Yang Liu},
journal={arXiv preprint arXiv:2410.03870},
year={2024},
archivePrefix={arXiv},
eprint={2410.03870},
primaryClass={cs.CL}
} | li2024from |
arxiv-665892 | 2410.03877 | A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis | <|reference_start|>A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis: The training of classification models for fault diagnosis tasks using geographically dispersed data is a crucial task for original parts manufacturers (OEMs) seeking to provide long-term service contracts (LTSCs) to their customers. Due to privacy and bandwidth constraints, such models must be trained in a federated fashion. Moreover, due to harsh industrial settings the data often suffers from feature and label uncertainty. Therefore, we study the problem of training a distributionally robust (DR) support vector machine (SVM) in a federated fashion over a network comprised of a central server and $G$ clients without sharing data. We consider the setting where the local data of each client $g$ is sampled from a unique true distribution $\mathbb{P}_g$, and the clients can only communicate with the central server. We propose a novel Mixture of Wasserstein Balls (MoWB) ambiguity set that relies on local Wasserstein balls centered at the empirical distribution of the data at each client. We study theoretical aspects of the proposed ambiguity set, deriving its out-of-sample performance guarantees and demonstrating that it naturally allows for the separability of the DR problem. Subsequently, we propose two distributed optimization algorithms for training the global FDR-SVM: i) a subgradient method-based algorithm, and ii) an alternating direction method of multipliers (ADMM)-based algorithm. We derive the optimization problems to be solved by each client and provide closed-form expressions for the computations performed by the central server during each iteration for both algorithms. Finally, we thoroughly examine the performance of the proposed algorithms in a series of numerical experiments utilizing both simulation data and popular real-world datasets.<|reference_end|> | arxiv | @article{ibrahim2024a,
title={A Federated Distributionally Robust Support Vector Machine with Mixture
of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis},
author={Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel, and Weijun Xie},
journal={arXiv preprint arXiv:2410.03877},
year={2024},
archivePrefix={arXiv},
eprint={2410.03877},
primaryClass={cs.LG stat.ML}
} | ibrahim2024a |
arxiv-665893 | 2410.03878 | SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models | <|reference_start|>SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models: Integrating the 3D world into large language models (3D-based LLMs) has been a promising research direction for 3D scene understanding. However, current 3D-based LLMs fall short in situated understanding due to two key limitations: 1) existing 3D datasets are constructed from a global perspective of the 3D scenes and lack situated context. 2) the architectures of existing 3D-based LLMs lack explicit alignment between the spatial representations of 3D scenes and natural language, limiting their performance in tasks requiring precise spatial reasoning. We address these issues by introducing a scalable situated 3D dataset, named Spartun3D, that incorporates various situated spatial reasoning tasks. Furthermore, we propose Spartun3D-LLM, built on an existing 3D-based LLM but integrated with a novel situated spatial alignment module, aiming to enhance the alignment between 3D visual representations and their corresponding textual descriptions. Experimental results demonstrate that both our proposed dataset and alignment module significantly enhance the situated spatial understanding of 3D-based LLMs.<|reference_end|> | arxiv | @article{zhang2024spartun3d:,
title={SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language
Models},
author={Yue Zhang, Zhiyang Xu, Ying Shen, Parisa Kordjamshidi, Lifu Huang},
journal={arXiv preprint arXiv:2410.03878},
year={2024},
archivePrefix={arXiv},
eprint={2410.03878},
primaryClass={cs.CV}
} | zhang2024spartun3d: |
arxiv-665894 | 2410.03879 | SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual Data | <|reference_start|>SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual Data: We present SONIQUE, a model for generating background music tailored to video content. Unlike traditional video-to-music generation approaches, which rely heavily on paired audio-visual datasets, SONIQUE leverages unpaired data, combining royalty-free music and independent video sources. By utilizing large language models (LLMs) for video understanding and converting visual descriptions into musical tags, alongside a U-Net-based conditional diffusion model, SONIQUE enables customizable music generation. Users can control specific aspects of the music, such as instruments, genres, tempo, and melodies, ensuring the generated output fits their creative vision. SONIQUE is open-source, with a demo available online.<|reference_end|> | arxiv | @article{zhang2024sonique:,
title={SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual
Data},
author={Liqian Zhang, Magdalena Fuentes},
journal={arXiv preprint arXiv:2410.03879},
year={2024},
archivePrefix={arXiv},
eprint={2410.03879},
primaryClass={cs.SD cs.MM eess.AS}
} | zhang2024sonique: |
arxiv-665895 | 2410.03882 | JumpStarter: Getting Started on Personal Goals with AI-Powered Context Curation | <|reference_start|>JumpStarter: Getting Started on Personal Goals with AI-Powered Context Curation: Everyone aspires to achieve personal goals. However, getting started is often complex and daunting, especially for large projects. AI has the potential to create plans and help jumpstart progress, but it often lacks sufficient personal context to be useful. We introduce JumpStarter, a system that uses AI-powered context curation to create action plans and draft personalized working solutions. JumpStarter assists users by posing questions to elicit relevant context, breaking down goals into manageable steps, and selecting appropriate context to draft working solutions for each step. A technical evaluation indicates that context curation results in plans and working solutions of higher quality. A user study demonstrates that compared to ChatGPT, JumpStarter significantly reduces users' mental load and increases their efficiency in kickstarting personal projects. We discuss the design implications of AI-powered context curation to facilitate the use of generative AI in complex problem-solving.<|reference_end|> | arxiv | @article{wang2024jumpstarter:,
title={JumpStarter: Getting Started on Personal Goals with AI-Powered Context
Curation},
author={Sitong Wang, Xuanming Zhang, Jenny Ma, Alyssa Hwang, Lydia B. Chilton},
journal={arXiv preprint arXiv:2410.03882},
year={2024},
archivePrefix={arXiv},
eprint={2410.03882},
primaryClass={cs.HC}
} | wang2024jumpstarter: |
arxiv-665896 | 2410.03883 | DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction | <|reference_start|>DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction: Differential privacy (DP) offers a robust framework for safeguarding individual data privacy. To utilize DP in training modern machine learning models, differentially private optimizers have been widely used in recent years. A popular approach to privatize an optimizer is to clip the individual gradients and add sufficiently large noise to the clipped gradient. This approach led to the development of DP optimizers that have comparable performance with their non-private counterparts in fine-tuning tasks or in tasks with a small number of training parameters. However, a significant performance drop is observed when these optimizers are applied to large-scale training. This degradation stems from the substantial noise injection required to maintain DP, which disrupts the optimizer's dynamics. This paper introduces DiSK, a novel framework designed to significantly enhance the performance of DP optimizers. DiSK employs Kalman filtering, a technique drawn from control and signal processing, to effectively denoise privatized gradients and generate progressively refined gradient estimations. To ensure practicality for large-scale training, we simplify the Kalman filtering process, minimizing its memory and computational demands. We establish theoretical privacy-utility trade-off guarantees for DiSK, and demonstrate provable improvements over standard DP optimizers like DPSGD in terms of iteration complexity upper-bound. Extensive experiments across diverse tasks, including vision tasks such as CIFAR-100 and ImageNet-1k and language fine-tuning tasks such as GLUE, E2E, and DART, validate the effectiveness of DiSK. The results showcase its ability to significantly improve the performance of DP optimizers, surpassing state-of-the-art results under the same privacy constraints on several benchmarks.<|reference_end|> | arxiv | @article{zhang2024disk:,
title={DiSK: Differentially Private Optimizer with Simplified Kalman Filter for
Noise Reduction},
author={Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn,
Vahab Mirrokni},
journal={arXiv preprint arXiv:2410.03883},
year={2024},
archivePrefix={arXiv},
eprint={2410.03883},
primaryClass={cs.LG cs.CR stat.ML}
} | zhang2024disk: |
arxiv-665897 | 2410.03884 | KidLM: Advancing Language Models for Children -- Early Insights and Future Directions | <|reference_start|>KidLM: Advancing Language Models for Children -- Early Insights and Future Directions: Recent studies highlight the potential of large language models in creating educational tools for children, yet significant challenges remain in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. In this paper, we explore foundational steps toward the development of child-specific language models, emphasizing the necessity of high-quality pre-training data. We introduce a novel user-centric data collection pipeline that involves gathering and validating a corpus specifically written for and sometimes by children. Additionally, we propose a new training objective, Stratified Masking, which dynamically adjusts masking probabilities based on our domain-specific child language data, enabling models to prioritize vocabulary and concepts more suitable for children. Experimental evaluations demonstrate that our model excels in understanding lower grade-level text, maintains safety by avoiding stereotypes, and captures children's unique preferences. Furthermore, we provide actionable insights for future research and development in child-specific language modeling.<|reference_end|> | arxiv | @article{nayeem2024kidlm:,
title={KidLM: Advancing Language Models for Children -- Early Insights and
Future Directions},
author={Mir Tafseer Nayeem, Davood Rafiei},
journal={arXiv preprint arXiv:2410.03884},
year={2024},
archivePrefix={arXiv},
eprint={2410.03884},
primaryClass={cs.CL cs.AI cs.CY cs.HC}
} | nayeem2024kidlm: |
arxiv-665898 | 2410.03885 | Collaborative Safety-Critical Formation Control with Obstacle Avoidance | <|reference_start|>Collaborative Safety-Critical Formation Control with Obstacle Avoidance: This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previously developed collaborative safety framework to an obstacle avoidance problem for agents with acceleration control inputs. We then incorporate multi-obstacle collision avoidance into the collaborative safety framework. This framework includes a method for computing the maximum capability of agents to satisfy their individual safety requirements. We analyze the convergence rate of our collaborative safety algorithm, and prove the linear-time convergence of cooperating agents to a jointly feasible safe action for all agents under the special case of a tree-structured communication network with a single obstacle for each agent. We illustrate the analytical results via simulation on a mass-spring kinematics-based formation controller and demonstrate the finite-time convergence of the collaborative safety algorithm in the simple proven case, the more general case of a fully-connected system with multiple static obstacles, and with dynamic obstacles.<|reference_end|> | arxiv | @article{butler2024collaborative,
title={Collaborative Safety-Critical Formation Control with Obstacle Avoidance},
author={Brooks A. Butler, Chi Ho Leung, and Philip E. Par'e},
journal={arXiv preprint arXiv:2410.03885},
year={2024},
archivePrefix={arXiv},
eprint={2410.03885},
primaryClass={cs.RO cs.SY eess.SY math.OC}
} | butler2024collaborative |
arxiv-665899 | 2410.03887 | Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates | <|reference_start|>Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates: This paper investigates dual sourcing problems with supply mode dependent failure rates, particularly relevant in managing spare parts for downtime-critical assets. To enhance resilience, businesses increasingly adopt dual sourcing strategies using both conventional and additive manufacturing techniques. This paper explores how these strategies can optimise sourcing by addressing variations in part properties and failure rates. A significant challenge is the distinct failure characteristics of parts produced by these methods, which influence future demand. To tackle this, we propose a new iterative heuristic and several reinforcement learning techniques combined with an endogenous parameterised learning (EPL) approach. This EPL approach - compatible with any learning method - allows a single policy to handle various input parameters for multiple items. In a stylised setting, our best policy achieves an average optimality gap of 0.4%. In a case study within the energy sector, our policies outperform the baseline in 91.1% of instances, yielding average cost savings up to 22.6%.<|reference_end|> | arxiv | @article{akkerman2024solving,
title={Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates},
author={Fabian Akkerman, Nils Knofius, Matthieu van der Heijden, Martijn Mes},
journal={arXiv preprint arXiv:2410.03887},
year={2024},
archivePrefix={arXiv},
eprint={2410.03887},
primaryClass={cs.LG cs.AI}
} | akkerman2024solving |
arxiv-665900 | 2410.03889 | Identification of Anomalous Geospatial Trajectories via Persistent Homology | <|reference_start|>Identification of Anomalous Geospatial Trajectories via Persistent Homology: We present a novel method for analyzing geospatial trajectory data using topological data analysis (TDA) to identify a specific class of anomalies, commonly referred to as crop circles, in AIS data. This approach is the first of its kind to be applied to spatiotemporal data. By embedding $2+1$-dimensional spatiotemporal data into $\mathbb{R}^3$, we utilize persistent homology to detect loops within the trajectories in $\mathbb{R}^2$. Our research reveals that, under normal conditions, trajectory data embedded in $\mathbb{R}^3$ over time do not form loops. Consequently, we can effectively identify anomalies characterized by the presence of loops within the trajectories. This method is robust and capable of detecting loops that are invariant to small perturbations, variations in geometric shape, and local coordinate projections. Additionally, our approach provides a novel perspective on anomaly detection, offering enhanced sensitivity and specificity in identifying atypical patterns in geospatial data. This approach has significant implications for various applications, including maritime navigation, environmental monitoring, and surveillance.<|reference_end|> | arxiv | @article{evans-lee2024identification,
title={Identification of Anomalous Geospatial Trajectories via Persistent
Homology},
author={Kyle Evans-Lee, Kevin Lamb},
journal={arXiv preprint arXiv:2410.03889},
year={2024},
archivePrefix={arXiv},
eprint={2410.03889},
primaryClass={cs.CG}
} | evans-lee2024identification |
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