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arxiv-666601 | 2410.05186 | State Estimation of Marine Vessels Affected by Waves by Unmanned Aerial Vehicles | <|reference_start|>State Estimation of Marine Vessels Affected by Waves by Unmanned Aerial Vehicles: A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.<|reference_end|> | arxiv | @article{novák2024state,
title={State Estimation of Marine Vessels Affected by Waves by Unmanned Aerial
Vehicles},
author={Filip Nov'ak, Tom'av{s} B'av{c}a, Ondv{r}ej Proch'azka, Martin
Saska},
journal={arXiv preprint arXiv:2410.05186},
year={2024},
archivePrefix={arXiv},
eprint={2410.05186},
primaryClass={cs.RO}
} | novák2024state |
arxiv-666602 | 2410.05188 | Matrix-weighted networks for modeling multidimensional dynamics | <|reference_start|>Matrix-weighted networks for modeling multidimensional dynamics: Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a novel, general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to non-trivial steady states that generalize the notions of communities and structural balance in traditional networks.<|reference_end|> | arxiv | @article{tian2024matrix-weighted,
title={Matrix-weighted networks for modeling multidimensional dynamics},
author={Yu Tian, Sadamori Kojaku, Hiroki Sayama, Renaud Lambiotte},
journal={arXiv preprint arXiv:2410.05188},
year={2024},
archivePrefix={arXiv},
eprint={2410.05188},
primaryClass={cs.SI cs.LG math-ph math.MP physics.soc-ph}
} | tian2024matrix-weighted |
arxiv-666603 | 2410.05191 | LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation | <|reference_start|>LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation: Building on the advancements of Large Language Models (LLMs) and Vision Language Models (VLMs), recent research has introduced Vision-Language-Action (VLA) models as an integrated solution for robotic manipulation tasks. These models take camera images and natural language task instructions as input and directly generate control actions for robots to perform specified tasks, greatly improving both decision-making capabilities and interaction with human users. However, the data-driven nature of VLA models, combined with their lack of interpretability, makes the assurance of their effectiveness and robustness a challenging task. This highlights the need for a reliable testing and evaluation platform. For this purpose, in this work, we propose LADEV, a comprehensive and efficient platform specifically designed for evaluating VLA models. We first present a language-driven approach that automatically generates simulation environments from natural language inputs, mitigating the need for manual adjustments and significantly improving testing efficiency. Then, to further assess the influence of language input on the VLA models, we implement a paraphrase mechanism that produces diverse natural language task instructions for testing. Finally, to expedite the evaluation process, we introduce a batch-style method for conducting large-scale testing of VLA models. Using LADEV, we conducted experiments on several state-of-the-art VLA models, demonstrating its effectiveness as a tool for evaluating these models. Our results showed that LADEV not only enhances testing efficiency but also establishes a solid baseline for evaluating VLA models, paving the way for the development of more intelligent and advanced robotic systems.<|reference_end|> | arxiv | @article{wang2024ladev:,
title={LADEV: A Language-Driven Testing and Evaluation Platform for
Vision-Language-Action Models in Robotic Manipulation},
author={Zhijie Wang, Zhehua Zhou, Jiayang Song, Yuheng Huang, Zhan Shu, Lei Ma},
journal={arXiv preprint arXiv:2410.05191},
year={2024},
archivePrefix={arXiv},
eprint={2410.05191},
primaryClass={cs.RO cs.AI}
} | wang2024ladev: |
arxiv-666604 | 2410.05192 | Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective | <|reference_start|>Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective: Training language models currently requires pre-determining a fixed compute budget because the typical cosine learning rate schedule depends on the total number of steps. In contrast, the Warmup-Stable-Decay (WSD) schedule uses a constant learning rate to produce a main branch of iterates that can in principle continue indefinitely without a pre-specified compute budget. Then, given any compute budget, one can branch out from the main branch at a proper at any time with a rapidly decaying learning rate to produce a strong model. Empirically, WSD generates a non-traditional loss curve: the loss remains elevated during the stable phase but sharply declines during the decay phase. Towards explaining this phenomenon, we conjecture that pretraining loss exhibits a river valley landscape, which resembles a deep valley with a river at its bottom. Under this assumption, we show that during the stable phase, the iterate undergoes large oscillations due to the high learning rate, yet it progresses swiftly along the river. During the decay phase, the rapidly dropping learning rate minimizes the iterate's oscillations, moving it closer to the river and revealing true optimization progress. Therefore, the sustained high learning rate phase and fast decaying phase are responsible for progress in the river and the mountain directions respectively, and are both critical. Our analysis predicts phenomenons consistent with empirical observations and shows that this landscape can emerge from pretraining on a simple bi-gram dataset. Inspired by the theory, we introduce WSD-S, a variant of WSD that reuses previous checkpoints' decay phases and keeps only one main branch, where we resume from a decayed checkpoint. WSD-S empirically outperforms WSD and Cyclic-Cosine in obtaining multiple language model checkpoints across various compute budgets in a single run for parameters scaling from 0.1B to 1.2B.<|reference_end|> | arxiv | @article{wen2024understanding,
title={Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss
Landscape Perspective},
author={Kaiyue Wen, Zhiyuan Li, Jason Wang, David Hall, Percy Liang, Tengyu Ma},
journal={arXiv preprint arXiv:2410.05192},
year={2024},
archivePrefix={arXiv},
eprint={2410.05192},
primaryClass={cs.LG cs.CL stat.ML}
} | wen2024understanding |
arxiv-666605 | 2410.05193 | RevisEval: Improving LLM-as-a-Judge via Response-Adapted References | <|reference_start|>RevisEval: Improving LLM-as-a-Judge via Response-Adapted References: With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing the text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a-Judge and human evaluation. One important reason is the lack of guided oracles in the evaluation process. Motivated by the role of reference pervasively used in classic text evaluation, we introduce RevisEval, a novel text generation evaluation paradigm via the response-adapted references. RevisEval is driven by the key observation that an ideal reference should maintain the necessary relevance to the response to be evaluated. Specifically, RevisEval leverages the text revision capabilities of large language models (LLMs) to adaptively revise the response, then treat the revised text as the reference (response-adapted reference) for the subsequent evaluation. Extensive experiments demonstrate that RevisEval outperforms traditional reference-free and reference-based evaluation paradigms that use LLM-as-a-Judge across NLG tasks and open-ended instruction-following tasks. More importantly, our response-adapted references can further boost the classical text metrics, e.g., BLEU and BERTScore, compared to traditional references and even rival the LLM-as-a-Judge. A detailed analysis is also conducted to confirm RevisEval's effectiveness in bias reduction, the impact of inference cost, and reference relevance.<|reference_end|> | arxiv | @article{zhang2024reviseval:,
title={RevisEval: Improving LLM-as-a-Judge via Response-Adapted References},
author={Qiyuan Zhang,Yufei Wang,Tiezheng YU,Yuxin Jiang,Chuhan Wu,Liangyou
Li,Yasheng Wang,Xin Jiang,Lifeng Shang,Ruiming Tang,Fuyuan Lyu,Chen Ma},
journal={arXiv preprint arXiv:2410.05193},
year={2024},
archivePrefix={arXiv},
eprint={2410.05193},
primaryClass={cs.CL}
} | zhang2024reviseval: |
arxiv-666606 | 2410.05199 | Counterexample to Babai's lonely colour conjecture | <|reference_start|>Counterexample to Babai's lonely colour conjecture: Motivated by colouring minimal Cayley graphs, in 1978, Babai conjectured that no-lonely-colour graphs have bounded chromatic number. We disprove this in a strong sense by constructing graphs of arbitrarily large girth and chromatic number that have a proper edge-colouring in which each cycle contains no colour exactly once.<|reference_end|> | arxiv | @article{davies2024counterexample,
title={Counterexample to Babai's lonely colour conjecture},
author={James Davies and Meike Hatzel and Liana Yepremyan},
journal={arXiv preprint arXiv:2410.05199},
year={2024},
archivePrefix={arXiv},
eprint={2410.05199},
primaryClass={math.CO cs.DM math.GR}
} | davies2024counterexample |
arxiv-666607 | 2410.05203 | Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality | <|reference_start|>Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality: The Fr\'echet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel. Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average.<|reference_end|> | arxiv | @article{luo2024beyond,
title={Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality},
author={Ge Ya Luo, Gian Mario Favero, Zhi Hao Luo, Alexia Jolicoeur-Martineau,
Christopher Pal},
journal={arXiv preprint arXiv:2410.05203},
year={2024},
archivePrefix={arXiv},
eprint={2410.05203},
primaryClass={cs.CV cs.AI cs.LG}
} | luo2024beyond |
arxiv-666608 | 2410.05206 | Studying and Mitigating Biases in Sign Language Understanding Models | <|reference_start|>Studying and Mitigating Biases in Sign Language Understanding Models: Ensuring that the benefits of sign language technologies are distributed equitably among all community members is crucial. Thus, it is important to address potential biases and inequities that may arise from the design or use of these resources. Crowd-sourced sign language datasets, such as the ASL Citizen dataset, are great resources for improving accessibility and preserving linguistic diversity, but they must be used thoughtfully to avoid reinforcing existing biases. In this work, we utilize the rich information about participant demographics and lexical features present in the ASL Citizen dataset to study and document the biases that may result from models trained on crowd-sourced sign datasets. Further, we apply several bias mitigation techniques during model training, and find that these techniques reduce performance disparities without decreasing accuracy. With the publication of this work, we release the demographic information about the participants in the ASL Citizen dataset to encourage future bias mitigation work in this space.<|reference_end|> | arxiv | @article{atwell2024studying,
title={Studying and Mitigating Biases in Sign Language Understanding Models},
author={Katherine Atwell, Danielle Bragg, and Malihe Alikhani},
journal={arXiv preprint arXiv:2410.05206},
year={2024},
archivePrefix={arXiv},
eprint={2410.05206},
primaryClass={cs.CL cs.CV}
} | atwell2024studying |
arxiv-666609 | 2410.05210 | Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality | <|reference_start|>Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality: In this paper, we propose a new method to enhance compositional understanding in pre-trained vision and language models (VLMs) without sacrificing performance in zero-shot multi-modal tasks. Traditional fine-tuning approaches often improve compositional reasoning at the cost of degrading multi-modal capabilities, primarily due to the use of global hard negative (HN) loss, which contrasts global representations of images and texts. This global HN loss pushes HN texts that are highly similar to the original ones, damaging the model's multi-modal representations. To overcome this limitation, we propose Fine-grained Selective Calibrated CLIP (FSC-CLIP), which integrates local hard negative loss and selective calibrated regularization. These innovations provide fine-grained negative supervision while preserving the model's representational integrity. Our extensive evaluations across diverse benchmarks for both compositionality and multi-modal tasks show that FSC-CLIP not only achieves compositionality on par with state-of-the-art models but also retains strong multi-modal capabilities. Code is available at: https://github.com/ytaek-oh/fsc-clip.<|reference_end|> | arxiv | @article{oh2024preserving,
title={Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving
Vision-Linguistic Compositionality},
author={Youngtaek Oh, Jae Won Cho, Dong-Jin Kim, In So Kweon, Junmo Kim},
journal={arXiv preprint arXiv:2410.05210},
year={2024},
archivePrefix={arXiv},
eprint={2410.05210},
primaryClass={cs.CV cs.AI cs.CL}
} | oh2024preserving |
arxiv-666610 | 2410.05217 | Organizing Unstructured Image Collections using Natural Language | <|reference_start|>Organizing Unstructured Image Collections using Natural Language: Organizing unstructured visual data into semantic clusters is a key challenge in computer vision. Traditional deep clustering (DC) approaches focus on a single partition of data, while multiple clustering (MC) methods address this limitation by uncovering distinct clustering solutions. The rise of large language models (LLMs) and multimodal LLMs (MLLMs) has enhanced MC by allowing users to define clustering criteria in natural language. However, manually specifying criteria for large datasets is impractical. In this work, we introduce the task Semantic Multiple Clustering (SMC) that aims to automatically discover clustering criteria from large image collections, uncovering interpretable substructures without requiring human input. Our framework, Text Driven Semantic Multiple Clustering (TeDeSC), uses text as a proxy to concurrently reason over large image collections, discover partitioning criteria, expressed in natural language, and reveal semantic substructures. To evaluate TeDeSC, we introduce the COCO-4c and Food-4c benchmarks, each containing four grouping criteria and ground-truth annotations. We apply TeDeSC to various applications, such as discovering biases and analyzing social media image popularity, demonstrating its utility as a tool for automatically organizing image collections and revealing novel insights.<|reference_end|> | arxiv | @article{liu2024organizing,
title={Organizing Unstructured Image Collections using Natural Language},
author={Mingxuan Liu, Zhun Zhong, Jun Li, Gianni Franchi, Subhankar Roy, Elisa
Ricci},
journal={arXiv preprint arXiv:2410.05217},
year={2024},
archivePrefix={arXiv},
eprint={2410.05217},
primaryClass={cs.CV}
} | liu2024organizing |
arxiv-666611 | 2410.05218 | Density estimation with LLMs: a geometric investigation of in-context learning trajectories | <|reference_start|>Density estimation with LLMs: a geometric investigation of in-context learning trajectories: Large language models (LLMs) demonstrate remarkable emergent abilities to perform in-context learning across various tasks, including time series forecasting. This work investigates LLMs' ability to estimate probability density functions (PDFs) from data observed in-context; such density estimation (DE) is a fundamental task underlying many probabilistic modeling problems. We leverage the Intensive Principal Component Analysis (InPCA) to visualize and analyze the in-context learning dynamics of LLaMA-2 models. Our main finding is that these LLMs all follow similar learning trajectories in a low-dimensional InPCA space, which are distinct from those of traditional density estimation methods like histograms and Gaussian kernel density estimation (KDE). We interpret the LLaMA in-context DE process as a KDE with an adaptive kernel width and shape. This custom kernel model captures a significant portion of LLaMA's behavior despite having only two parameters. We further speculate on why LLaMA's kernel width and shape differs from classical algorithms, providing insights into the mechanism of in-context probabilistic reasoning in LLMs.<|reference_end|> | arxiv | @article{liu2024density,
title={Density estimation with LLMs: a geometric investigation of in-context
learning trajectories},
author={Toni J.B. Liu, Nicolas Boull'e, Rapha"el Sarfati, Christopher J.
Earls},
journal={arXiv preprint arXiv:2410.05218},
year={2024},
archivePrefix={arXiv},
eprint={2410.05218},
primaryClass={cs.LG cs.CL stat.ML}
} | liu2024density |
arxiv-666612 | 2410.05222 | Precise Model Benchmarking with Only a Few Observations | <|reference_start|>Precise Model Benchmarking with Only a Few Observations: How can we precisely estimate a large language model's (LLM) accuracy on questions belonging to a specific topic within a larger question-answering dataset? The standard direct estimator, which averages the model's accuracy on the questions in each subgroup, may exhibit high variance for subgroups (topics) with small sample sizes. Synthetic regression modeling, which leverages the model's accuracy on questions about other topics, may yield biased estimates that are too unreliable for large subgroups. We prescribe a simple yet effective solution: an empirical Bayes (EB) estimator that balances direct and regression estimates for each subgroup separately, improving the precision of subgroup-level estimates of model performance. Our experiments on multiple datasets show that this approach consistently provides more precise estimates of the LLM performance compared to the direct and regression approaches, achieving substantial reductions in the mean squared error. Confidence intervals for EB estimates also have near-nominal coverage and are narrower compared to those for the direct estimator. Additional experiments on tabular and vision data validate the benefits of this EB approach.<|reference_end|> | arxiv | @article{fogliato2024precise,
title={Precise Model Benchmarking with Only a Few Observations},
author={Riccardo Fogliato, Pratik Patil, Nil-Jana Akpinar, Mathew Monfort},
journal={arXiv preprint arXiv:2410.05222},
year={2024},
archivePrefix={arXiv},
eprint={2410.05222},
primaryClass={cs.LG cs.CL cs.CV stat.AP}
} | fogliato2024precise |
arxiv-666613 | 2410.05224 | Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates | <|reference_start|>Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates: Fine-tuning large language models (LLMs) on instruction datasets is a common way to improve their generative capabilities. However, instruction datasets can be expensive and time-consuming to manually curate, and while LLM-generated data is less labor-intensive, it may violate user privacy agreements or terms of service of LLM providers. Therefore, we seek a way of constructing instruction datasets with samples that are not generated by humans or LLMs but still improve LLM generative capabilities. In this work, we introduce Cookbook, a framework that programmatically generates training data consisting of simple patterns over random tokens, resulting in a scalable, cost-effective approach that avoids legal and privacy issues. First, Cookbook uses a template -- a data generating Python function -- to produce training data that encourages the model to learn an explicit pattern-based rule that corresponds to a desired task. We find that fine-tuning on Cookbook-generated data is able to improve performance on its corresponding task by up to 52.7 accuracy points. Second, since instruction datasets improve performance on multiple downstream tasks simultaneously, Cookbook algorithmically learns how to mix data from various templates to optimize performance on multiple tasks. On the standard multi-task GPT4ALL evaluation suite, Mistral-7B fine-tuned using a Cookbook-generated dataset attains the best accuracy on average compared to other 7B parameter instruction-tuned models and is the best performing model on 3 out of 8 tasks. Finally, we analyze when and why Cookbook improves performance and present a metric that allows us to verify that the improvement is largely explained by the model's generations adhering better to template rules.<|reference_end|> | arxiv | @article{narayan2024cookbook:,
title={Cookbook: A framework for improving LLM generative abilities via
programmatic data generating templates},
author={Avanika Narayan, Mayee F. Chen, Kush Bhatia, Christopher R'e},
journal={arXiv preprint arXiv:2410.05224},
year={2024},
archivePrefix={arXiv},
eprint={2410.05224},
primaryClass={cs.CL cs.LG}
} | narayan2024cookbook: |
arxiv-666614 | 2410.05225 | ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control | <|reference_start|>ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control: We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, \emph{${\epsilon}{t}$-greedy}, which generates exploratory options for exploring less-visited states. We prove that search using $\epsilon t$-greedy has polynomial sample complexity under mild MDP assumptions. To more efficiently use the information provided by rewarded transitions, we develop a new dual experience replay buffer framework, \emph{GDRB}, and implement \emph{longest n-step returns}. The resulting algorithm, \emph{ETGL-DDPG}, integrates all three techniques: \bm{$\epsilon t$}-greedy, \textbf{G}DRB, and \textbf{L}ongest $n$-step, into DDPG. We evaluate ETGL-DDPG on standard benchmarks and demonstrate that it outperforms DDPG, as well as other state-of-the-art methods, across all tested sparse-reward continuous environments. Ablation studies further highlight how each strategy individually enhances the performance of DDPG in this setting.<|reference_end|> | arxiv | @article{futuhi2024etgl-ddpg:,
title={ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse
Reward Continuous Control},
author={Ehsan Futuhi, Shayan Karimi, Chao Gao, Martin M"uller},
journal={arXiv preprint arXiv:2410.05225},
year={2024},
archivePrefix={arXiv},
eprint={2410.05225},
primaryClass={cs.LG cs.RO stat.ML}
} | futuhi2024etgl-ddpg: |
arxiv-666615 | 2410.05227 | The Dawn of Video Generation: Preliminary Explorations with SORA-like Models | <|reference_start|>The Dawn of Video Generation: Preliminary Explorations with SORA-like Models: High-quality video generation, encompassing text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) generation, holds considerable significance in content creation to benefit anyone express their inherent creativity in new ways and world simulation to modeling and understanding the world. Models like SORA have advanced generating videos with higher resolution, more natural motion, better vision-language alignment, and increased controllability, particularly for long video sequences. These improvements have been driven by the evolution of model architectures, shifting from UNet to more scalable and parameter-rich DiT models, along with large-scale data expansion and refined training strategies. However, despite the emergence of DiT-based closed-source and open-source models, a comprehensive investigation into their capabilities and limitations remains lacking. Furthermore, the rapid development has made it challenging for recent benchmarks to fully cover SORA-like models and recognize their significant advancements. Additionally, evaluation metrics often fail to align with human preferences.<|reference_end|> | arxiv | @article{zeng2024the,
title={The Dawn of Video Generation: Preliminary Explorations with SORA-like
Models},
author={Ailing Zeng, Yuhang Yang, Weidong Chen, Wei Liu},
journal={arXiv preprint arXiv:2410.05227},
year={2024},
archivePrefix={arXiv},
eprint={2410.05227},
primaryClass={cs.CV}
} | zeng2024the |
arxiv-666616 | 2410.05229 | GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models | <|reference_start|>GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models: Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of models.Our findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning.<|reference_end|> | arxiv | @article{mirzadeh2024gsm-symbolic:,
title={GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in
Large Language Models},
author={Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy
Bengio, Mehrdad Farajtabar},
journal={arXiv preprint arXiv:2410.05229},
year={2024},
archivePrefix={arXiv},
eprint={2410.05229},
primaryClass={cs.LG cs.AI}
} | mirzadeh2024gsm-symbolic: |
arxiv-666617 | 2410.05232 | SymmetryLens: A new candidate paradigm for unsupervised symmetry learning via locality and equivariance | <|reference_start|>SymmetryLens: A new candidate paradigm for unsupervised symmetry learning via locality and equivariance: We develop a new, unsupervised symmetry learning method that starts with raw data, and gives the minimal (discrete) generator of an underlying Lie group of symmetries, together with a symmetry equivariant representation of the data. The method is able to learn the pixel translation operator from a dataset with only an approximate translation symmetry, and can learn quite different types of symmetries which are not apparent to the naked eye, equally well. The method is based on the formulation of an information-theoretic loss function that measures both the degree to which the dataset is symmetric under a given candidate symmetry, and also, the degree of locality of the samples in the dataset with respect to this symmetry. We demonstrate that this coupling between symmetry and locality, together with a special optimization technique developed for entropy estimation, results in a highly stable system that gives reproducible results. The symmetry actions we consider are group representations, however, we believe the approach has the potential to be generalized to more general, nonlinear actions of non-commutative Lie groups.<|reference_end|> | arxiv | @article{efe2024symmetrylens:,
title={SymmetryLens: A new candidate paradigm for unsupervised symmetry
learning via locality and equivariance},
author={Onur Efe, Arkadas Ozakin},
journal={arXiv preprint arXiv:2410.05232},
year={2024},
archivePrefix={arXiv},
eprint={2410.05232},
primaryClass={cs.LG}
} | efe2024symmetrylens: |
arxiv-666618 | 2410.05233 | SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning | <|reference_start|>SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning: We introduce a novel anchor-free contrastive learning (AFCL) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a semi-metric discriminative loss function that simultaneously optimizes two key objectives: reducing the distance and orthogonality between embeddings of similar inputs while maximizing these metrics for dissimilar inputs, facilitating more fine-grained contrastive learning. The AFCL method, powered by SimO loss, creates a fiber bundle topological structure in the embedding space, forming class-specific, internally cohesive yet orthogonal neighborhoods. We validate the efficacy of our method on the CIFAR-10 dataset, providing visualizations that demonstrate the impact of SimO loss on the embedding space. Our results illustrate the formation of distinct, orthogonal class neighborhoods, showcasing the method's ability to create well-structured embeddings that balance class separation with intra-class variability. This work opens new avenues for understanding and leveraging the geometric properties of learned representations in various machine learning tasks.<|reference_end|> | arxiv | @article{bouhsine2024simo,
title={SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised
Contrastive Learning},
author={Taha Bouhsine, Imad El Aaroussi, Atik Faysal, Wang Huaxia},
journal={arXiv preprint arXiv:2410.05233},
year={2024},
archivePrefix={arXiv},
eprint={2410.05233},
primaryClass={cs.LG cs.AI cs.CV}
} | bouhsine2024simo |
arxiv-666619 | 2410.05234 | DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration | <|reference_start|>DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration: Deformable image registration aims to precisely align medical images from different modalities or times. Traditional deep learning methods, while effective, often lack interpretability, real-time observability and adjustment capacity during registration inference. Denoising diffusion models present an alternative by reformulating registration as iterative image denoising. However, existing diffusion registration approaches do not fully harness capabilities, neglecting the critical sampling phase that enables continuous observability during the inference. Hence, we introduce DiffuseReg, an innovative diffusion-based method that denoises deformation fields instead of images for improved transparency. We also propose a novel denoising network upon Swin Transformer, which better integrates moving and fixed images with diffusion time step throughout the denoising process. Furthermore, we enhance control over the denoising registration process with a novel similarity consistency regularization. Experiments on ACDC datasets demonstrate DiffuseReg outperforms existing diffusion registration methods by 1.32 in Dice score. The sampling process in DiffuseReg enables real-time output observability and adjustment unmatched by previous deep models.<|reference_end|> | arxiv | @article{zhuo2024diffusereg:,
title={DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields
in Unsupervised Deformable Image Registration},
author={Yongtai Zhuo, Yiqing Shen},
journal={arXiv preprint arXiv:2410.05234},
year={2024},
archivePrefix={arXiv},
eprint={2410.05234},
primaryClass={cs.CV}
} | zhuo2024diffusereg: |
arxiv-666620 | 2410.05235 | CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures | <|reference_start|>CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures: Explaining Artificial Intelligence (AI) decisions is a major challenge nowadays in AI, in particular when applied to sensitive scenarios like medicine and law. However, the need to explain the rationale behind decisions is a main issue also for human-based deliberation as it is important to justify \textit{why} a certain decision has been taken. Resident medical doctors for instance are required not only to provide a (possibly correct) diagnosis, but also to explain how they reached a certain conclusion. Developing new tools to aid residents to train their explanation skills is therefore a central objective of AI in education. In this paper, we follow this direction, and we present, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors. These explanations have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support), resulting in the Multilingual CasiMedicos-Arg dataset which consists of 558 clinical cases in four languages (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations. We conclude by showing how competitive baselines perform over this challenging dataset for the argument mining task.<|reference_end|> | arxiv | @article{sviridova2024casimedicos-arg:,
title={CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with
Explanatory Argumentative Structures},
author={Ekaterina Sviridova, Anar Yeginbergen, Ainara Estarrona, Elena Cabrio,
Serena Villata, Rodrigo Agerri},
journal={EMNLP 2024},
year={2024},
archivePrefix={arXiv},
eprint={2410.05235},
primaryClass={cs.CL cs.AI}
} | sviridova2024casimedicos-arg: |
arxiv-666621 | 2410.05239 | TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models | <|reference_start|>TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models: Vision-Language Models (VLMs) have shown impressive performance in vision tasks, but adapting them to new domains often requires expensive fine-tuning. Prompt tuning techniques, including textual, visual, and multimodal prompting, offer efficient alternatives by leveraging learnable prompts. However, their application to Vision-Language Segmentation Models (VLSMs) and evaluation under significant domain shifts remain unexplored. This work presents an open-source benchmarking framework, TuneVLSeg, to integrate various unimodal and multimodal prompt tuning techniques into VLSMs, making prompt tuning usable for downstream segmentation datasets with any number of classes. TuneVLSeg includes $6$ prompt tuning strategies on various prompt depths used in $2$ VLSMs totaling of $8$ different combinations. We test various prompt tuning on $8$ diverse medical datasets, including $3$ radiology datasets (breast tumor, echocardiograph, chest X-ray pathologies) and $5$ non-radiology datasets (polyp, ulcer, skin cancer), and two natural domain segmentation datasets. Our study found that textual prompt tuning struggles under significant domain shifts, from natural-domain images to medical data. Furthermore, visual prompt tuning, with fewer hyperparameters than multimodal prompt tuning, often achieves performance competitive to multimodal approaches, making it a valuable first attempt. Our work advances the understanding and applicability of different prompt-tuning techniques for robust domain-specific segmentation. The source code is available at https://github.com/naamiinepal/tunevlseg.<|reference_end|> | arxiv | @article{adhikari2024tunevlseg:,
title={TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation
Models},
author={Rabin Adhikari, Safal Thapaliya, Manish Dhakal, Bishesh Khanal},
journal={arXiv preprint arXiv:2410.05239},
year={2024},
archivePrefix={arXiv},
eprint={2410.05239},
primaryClass={cs.CV cs.CL}
} | adhikari2024tunevlseg: |
arxiv-666622 | 2410.05240 | Vizing's Theorem in Near-Linear Time | <|reference_start|>Vizing's Theorem in Near-Linear Time: Vizing's theorem states that any $n$-vertex $m$-edge graph of maximum degree $\Delta$ can be \emph{edge colored} using at most $\Delta + 1$ different colors [Vizing, 1964]. Vizing's original proof is algorithmic and shows that such an edge coloring can be found in $O(mn)$ time. This was subsequently improved to $\tilde O(m\sqrt{n})$ time, independently by [Arjomandi, 1982] and by [Gabow et al., 1985]. Very recently, independently and concurrently, using randomization, this runtime bound was further improved to $\tilde{O}(n^2)$ by [Assadi, 2024] and $\tilde O(mn^{1/3})$ by [Bhattacharya, Carmon, Costa, Solomon and Zhang, 2024] (and subsequently to $\tilde O(mn^{1/4})$ time by [Bhattacharya, Costa, Solomon and Zhang, 2024]). We present an algorithm that computes a $(\Delta+1)$-edge coloring in $\tilde O(m)$ time -- in fact, even $O(m\log{\Delta})$ time -- with high probability, \emph{giving a near-optimal algorithm for this fundamental problem}.<|reference_end|> | arxiv | @article{assadi2024vizing's,
title={Vizing's Theorem in Near-Linear Time},
author={Sepehr Assadi, Soheil Behnezhad, Sayan Bhattacharya, Mart'in Costa,
Shay Solomon, Tianyi Zhang},
journal={arXiv preprint arXiv:2410.05240},
year={2024},
archivePrefix={arXiv},
eprint={2410.05240},
primaryClass={cs.DS}
} | assadi2024vizing's |
arxiv-666623 | 2410.05243 | Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents | <|reference_start|>Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents: Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robustness of their grounding capability. Current GUI agents predominantly utilize text-based representations such as HTML or accessibility trees, which, despite their utility, often introduce noise, incompleteness, and increased computational overhead. In this paper, we advocate a human-like embodiment for GUI agents that perceive the environment entirely visually and directly take pixel-level operations on the GUI. The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms. We show that a simple recipe, which includes web-based synthetic data and slight adaptation of the LLaVA architecture, is surprisingly effective for training such visual grounding models. We collect the largest dataset for GUI visual grounding so far, containing 10M GUI elements and their referring expressions over 1.3M screenshots, and use it to train UGround, a strong universal visual grounding model for GUI agents. Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms existing visual grounding models for GUI agents, by up to 20% absolute, and 2) agents with UGround outperform state-of-the-art agents, despite the fact that existing agents use additional text-based input while ours only uses visual perception. These results provide strong support for the feasibility and promises of GUI agents that navigate the digital world as humans do.<|reference_end|> | arxiv | @article{gou2024navigating,
title={Navigating the Digital World as Humans Do: Universal Visual Grounding
for GUI Agents},
author={Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan Xie, Cheng Chang, Yiheng
Shu, Huan Sun, Yu Su},
journal={arXiv preprint arXiv:2410.05243},
year={2024},
archivePrefix={arXiv},
eprint={2410.05243},
primaryClass={cs.AI cs.CL cs.CV}
} | gou2024navigating |
arxiv-666624 | 2410.05247 | Accelerated alternating minimization algorithm for low-rank approximations in the Chebyshev norm | <|reference_start|>Accelerated alternating minimization algorithm for low-rank approximations in the Chebyshev norm: Nowadays, low-rank approximations of matrices are an important component of many methods in science and engineering. Traditionally, low-rank approximations are considered in unitary invariant norms, however, recently element-wise approximations have also received significant attention in the literature. In this paper, we propose an accelerated alternating minimization algorithm for solving the problem of low-rank approximation of matrices in the Chebyshev norm. Through the numerical evaluation we demonstrate the effectiveness of the proposed procedure for large-scale problems. We also theoretically investigate the alternating minimization method and introduce the notion of a $2$-way alternance of rank $r$. We show that the presence of a $2$-way alternance of rank $r$ is the necessary condition of the optimal low-rank approximation in the Chebyshev norm and that all limit points of the alternating minimization method satisfy this condition.<|reference_end|> | arxiv | @article{morozov2024accelerated,
title={Accelerated alternating minimization algorithm for low-rank
approximations in the Chebyshev norm},
author={Stanislav Morozov, Dmitry Zheltkov, Alexander Osinsky},
journal={arXiv preprint arXiv:2410.05247},
year={2024},
archivePrefix={arXiv},
eprint={2410.05247},
primaryClass={math.NA cs.NA}
} | morozov2024accelerated |
arxiv-666625 | 2410.05248 | SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe | <|reference_start|>SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe: To induce desired behaviors in large language models (LLMs) for interaction-driven tasks, the instruction-tuning stage typically trains LLMs on instruction-response pairs using the next-token prediction (NTP) loss. Previous work aiming to improve instruction-tuning performance often emphasizes the need for higher-quality supervised fine-tuning (SFT) datasets, which typically involves expensive data filtering with proprietary LLMs or labor-intensive data generation by human annotators. However, these approaches do not fully leverage the datasets' intrinsic properties, resulting in high computational and labor costs, thereby limiting scalability and performance gains. In this paper, we propose SFTMix, a novel recipe that elevates instruction-tuning performance beyond the conventional NTP paradigm, without the need for well-curated datasets. Observing that LLMs exhibit uneven confidence across the semantic representation space, we argue that examples with different confidence levels should play distinct roles during the instruction-tuning process. Based on this insight, SFTMix leverages training dynamics to identify examples with varying confidence levels, then applies a Mixup-based regularization to mitigate overfitting on confident examples while propagating supervision signals to improve learning on relatively unconfident ones. This approach enables SFTMix to significantly outperform NTP across a wide range of instruction-following and healthcare domain-specific SFT tasks, demonstrating its adaptability to diverse LLM families and scalability to datasets of any size. Comprehensive ablation studies further verify the robustness of SFTMix's design choices, underscoring its versatility in consistently enhancing performance across different LLMs and datasets in broader natural language processing applications.<|reference_end|> | arxiv | @article{xiao2024sftmix:,
title={SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe},
author={Yuxin Xiao, Shujian Zhang, Wenxuan Zhou, Marzyeh Ghassemi, Sanqiang
Zhao},
journal={arXiv preprint arXiv:2410.05248},
year={2024},
archivePrefix={arXiv},
eprint={2410.05248},
primaryClass={cs.CL cs.AI cs.LG}
} | xiao2024sftmix: |
arxiv-666626 | 2410.05249 | LoTLIP: Improving Language-Image Pre-training for Long Text Understanding | <|reference_start|>LoTLIP: Improving Language-Image Pre-training for Long Text Understanding: Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key reason causing such an issue is that the training images are usually paired with short captions, leaving certain tokens easily overshadowed by salient tokens. Towards this problem, our initial attempt is to relabel the data with long captions, however, directly learning with which may lead to performance degradation in understanding short text (e.g., in the image classification task). Then, after incorporating corner tokens to aggregate diverse textual information, we manage to help the model catch up to its original level of short text understanding yet greatly enhance its capability of long text understanding. We further look into whether the model can continuously benefit from longer captions and notice a clear trade-off between the performance and the efficiency. Finally, we validate the effectiveness of our approach using a self-constructed large-scale dataset, which consists of 100M long caption oriented text-image pairs. It is noteworthy that, on the task of long-text image retrieval, we beat the competitor using long captions with 11.1% improvement (i.e., from 72.62% to 83.72%). We will release the code, the model, and the new dataset to facilitate the reproducibility and further research. The project page is available at https://wuw2019.github.io/lot-lip.<|reference_end|> | arxiv | @article{wu2024lotlip:,
title={LoTLIP: Improving Language-Image Pre-training for Long Text
Understanding},
author={Wei Wu, Kecheng Zheng, Shuailei Ma, Fan Lu, Yuxin Guo, Yifei Zhang,
Wei Chen, Qingpei Guo, Yujun Shen, Zheng-Jun Zha},
journal={arXiv preprint arXiv:2410.05249},
year={2024},
archivePrefix={arXiv},
eprint={2410.05249},
primaryClass={cs.CV}
} | wu2024lotlip: |
arxiv-666627 | 2410.05251 | Block MedCare: Advancing healthcare through blockchain integration | <|reference_start|>Block MedCare: Advancing healthcare through blockchain integration: In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings...<|reference_end|> | arxiv | @article{simonoski2024block,
title={Block MedCare: Advancing healthcare through blockchain integration},
author={Oliver Simonoski and Dijana Capeska Bogatinoska},
journal={(IJCI) Vol.13, No.5, October 2024},
year={2024},
doi={10.5121/ijci.2024.130505},
archivePrefix={arXiv},
eprint={2410.05251},
primaryClass={cs.SE cs.CR}
} | simonoski2024block |
arxiv-666628 | 2410.05252 | Causal Micro-Narratives | <|reference_start|>Causal Micro-Narratives: We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject. The approach requires only a subject-specific ontology of causes and effects, and we demonstrate it with an application to inflation narratives. Using a human-annotated dataset spanning historical and contemporary US news articles for training, we evaluate several large language models (LLMs) on this multi-label classification task. The best-performing model--a fine-tuned Llama 3.1 8B--achieves F1 scores of 0.87 on narrative detection and 0.71 on narrative classification. Comprehensive error analysis reveals challenges arising from linguistic ambiguity and highlights how model errors often mirror human annotator disagreements. This research establishes a framework for extracting causal micro-narratives from real-world data, with wide-ranging applications to social science research.<|reference_end|> | arxiv | @article{heddaya2024causal,
title={Causal Micro-Narratives},
author={Mourad Heddaya, Qingcheng Zeng, Chenhao Tan, Rob Voigt, Alexander
Zentefis},
journal={Proceedings of the The 6th Workshop on Narrative Understanding at
EMNLP 2024, pages 67-84, Miami, Florida, USA. Association for Computational
Linguistics},
year={2024},
archivePrefix={arXiv},
eprint={2410.05252},
primaryClass={cs.CL cs.AI cs.IR cs.LG}
} | heddaya2024causal |
arxiv-666629 | 2410.05253 | Multicontinuum splitting scheme for multiscale flow problems | <|reference_start|>Multicontinuum splitting scheme for multiscale flow problems: In this paper, we propose multicontinuum splitting schemes for multiscale problems, focusing on a parabolic equation with a high-contrast coefficient. Using the framework of multicontinuum homogenization, we introduce spatially smooth macroscopic variables and decompose the multicontinuum solution space into two components to effectively separate the dynamics at different speeds (or the effects of contrast in high-contrast cases). By treating the component containing fast dynamics (or dependent on the contrast) implicitly and the component containing slow dynamics (or independent of the contrast) explicitly, we construct partially explicit time discretization schemes, which can reduce computational cost. The derived stability conditions are contrast-independent, provided the continua are chosen appropriately. Additionally, we discuss possible methods to obtain an optimized decomposition of the solution space, which relaxes the stability conditions while enhancing computational efficiency. A Rayleigh quotient problem in tensor form is formulated, and simplifications are achieved under certain assumptions. Finally, we present numerical results for various coefficient fields and different continua to validate our proposed approach. It can be observed that the multicontinuum splitting schemes enjoy high accuracy and efficiency.<|reference_end|> | arxiv | @article{efendiev2024multicontinuum,
title={Multicontinuum splitting scheme for multiscale flow problems},
author={Yalchin Efendiev, Wing Tat Leung, Buzheng Shan, Min Wang},
journal={arXiv preprint arXiv:2410.05253},
year={2024},
archivePrefix={arXiv},
eprint={2410.05253},
primaryClass={math.NA cs.NA}
} | efendiev2024multicontinuum |
arxiv-666630 | 2410.05254 | GLEE: A Unified Framework and Benchmark for Language-based Economic Environments | <|reference_start|>GLEE: A Unified Framework and Benchmark for Language-based Economic Environments: Large Language Models (LLMs) show significant potential in economic and strategic interactions, where communication via natural language is often prevalent. This raises key questions: Do LLMs behave rationally? Can they mimic human behavior? Do they tend to reach an efficient and fair outcome? What is the role of natural language in the strategic interaction? How do characteristics of the economic environment influence these dynamics? These questions become crucial concerning the economic and societal implications of integrating LLM-based agents into real-world data-driven systems, such as online retail platforms and recommender systems. While the ML community has been exploring the potential of LLMs in such multi-agent setups, varying assumptions, design choices and evaluation criteria across studies make it difficult to draw robust and meaningful conclusions. To address this, we introduce a benchmark for standardizing research on two-player, sequential, language-based games. Inspired by the economic literature, we define three base families of games with consistent parameterization, degrees of freedom and economic measures to evaluate agents' performance (self-gain), as well as the game outcome (efficiency and fairness). We develop an open-source framework for interaction simulation and analysis, and utilize it to collect a dataset of LLM vs. LLM interactions across numerous game configurations and an additional dataset of human vs. LLM interactions. Through extensive experimentation, we demonstrate how our framework and dataset can be used to: (i) compare the behavior of LLM-based agents to human players in various economic contexts; (ii) evaluate agents in both individual and collective performance measures; and (iii) quantify the effect of the economic characteristics of the environments on the behavior of agents.<|reference_end|> | arxiv | @article{shapira2024glee:,
title={GLEE: A Unified Framework and Benchmark for Language-based Economic
Environments},
author={Eilam Shapira, Omer Madmon, Itamar Reinman, Samuel Joseph Amouyal, Roi
Reichart, Moshe Tennenholtz},
journal={arXiv preprint arXiv:2410.05254},
year={2024},
archivePrefix={arXiv},
eprint={2410.05254},
primaryClass={cs.CL cs.AI cs.CY cs.GT cs.LG}
} | shapira2024glee: |
arxiv-666631 | 2410.05255 | SePPO: Semi-Policy Preference Optimization for Diffusion Alignment | <|reference_start|>SePPO: Semi-Policy Preference Optimization for Diffusion Alignment: Reinforcement learning from human feedback (RLHF) methods are emerging as a way to fine-tune diffusion models (DMs) for visual generation. However, commonly used on-policy strategies are limited by the generalization capability of the reward model, while off-policy approaches require large amounts of difficult-to-obtain paired human-annotated data, particularly in visual generation tasks. To address the limitations of both on- and off-policy RLHF, we propose a preference optimization method that aligns DMs with preferences without relying on reward models or paired human-annotated data. Specifically, we introduce a Semi-Policy Preference Optimization (SePPO) method. SePPO leverages previous checkpoints as reference models while using them to generate on-policy reference samples, which replace "losing images" in preference pairs. This approach allows us to optimize using only off-policy "winning images." Furthermore, we design a strategy for reference model selection that expands the exploration in the policy space. Notably, we do not simply treat reference samples as negative examples for learning. Instead, we design an anchor-based criterion to assess whether the reference samples are likely to be winning or losing images, allowing the model to selectively learn from the generated reference samples. This approach mitigates performance degradation caused by the uncertainty in reference sample quality. We validate SePPO across both text-to-image and text-to-video benchmarks. SePPO surpasses all previous approaches on the text-to-image benchmarks and also demonstrates outstanding performance on the text-to-video benchmarks. Code will be released in https://github.com/DwanZhang-AI/SePPO.<|reference_end|> | arxiv | @article{zhang2024seppo:,
title={SePPO: Semi-Policy Preference Optimization for Diffusion Alignment},
author={Daoan Zhang, Guangchen Lan, Dong-Jun Han, Wenlin Yao, Xiaoman Pan,
Hongming Zhang, Mingxiao Li, Pengcheng Chen, Yu Dong, Christopher Brinton,
Jiebo Luo},
journal={arXiv preprint arXiv:2410.05255},
year={2024},
archivePrefix={arXiv},
eprint={2410.05255},
primaryClass={cs.CV cs.LG}
} | zhang2024seppo: |
arxiv-666632 | 2410.05256 | Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions | <|reference_start|>Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions: Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive sensors. We formulate the methodology by combining recent advances in state estimation theory with the use of robust cost functions in the measurement update. We tested our methodology on quadruped robots through experiments and public datasets, showing that we can obtain a pose drift up to 40% lower in trajectories covering a distance of over 450m, in comparison with a state-of-the-art Invariant Extended Kalman filter.<|reference_end|> | arxiv | @article{santana2024proprioceptive,
title={Proprioceptive State Estimation for Quadruped Robots using Invariant
Kalman Filtering and Scale-Variant Robust Cost Functions},
author={Hilton Marques Souza Santana, Jo~ao Carlos Virgolino Soares, Ylenia
Nistic`o, Marco Antonio Meggiolaro, Claudio Semini},
journal={arXiv preprint arXiv:2410.05256},
year={2024},
archivePrefix={arXiv},
eprint={2410.05256},
primaryClass={cs.RO}
} | santana2024proprioceptive |
arxiv-666633 | 2410.05258 | Differential Transformer | <|reference_start|>Differential Transformer: Transformer tends to overallocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling noise. Specifically, the differential attention mechanism calculates attention scores as the difference between two separate softmax attention maps. The subtraction cancels noise, promoting the emergence of sparse attention patterns. Experimental results on language modeling show that Diff Transformer outperforms Transformer in various settings of scaling up model size and training tokens. More intriguingly, it offers notable advantages in practical applications, such as long-context modeling, key information retrieval, hallucination mitigation, in-context learning, and reduction of activation outliers. By being less distracted by irrelevant context, Diff Transformer can mitigate hallucination in question answering and text summarization. For in-context learning, Diff Transformer not only enhances accuracy but is also more robust to order permutation, which was considered as a chronic robustness issue. The results position Diff Transformer as a highly effective and promising architecture to advance large language models.<|reference_end|> | arxiv | @article{ye2024differential,
title={Differential Transformer},
author={Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu
Wei},
journal={arXiv preprint arXiv:2410.05258},
year={2024},
archivePrefix={arXiv},
eprint={2410.05258},
primaryClass={cs.CL cs.LG}
} | ye2024differential |
arxiv-666634 | 2410.05259 | GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting | <|reference_start|>GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting: Diffusion-based 2D virtual try-on (VTON) techniques have recently demonstrated strong performance, while the development of 3D VTON has largely lagged behind. Despite recent advances in text-guided 3D scene editing, integrating 2D VTON into these pipelines to achieve vivid 3D VTON remains challenging. The reasons are twofold. First, text prompts cannot provide sufficient details in describing clothing. Second, 2D VTON results generated from different viewpoints of the same 3D scene lack coherence and spatial relationships, hence frequently leading to appearance inconsistencies and geometric distortions. To resolve these problems, we introduce an image-prompted 3D VTON method (dubbed GS-VTON) which, by leveraging 3D Gaussian Splatting (3DGS) as the 3D representation, enables the transfer of pre-trained knowledge from 2D VTON models to 3D while improving cross-view consistency. (1) Specifically, we propose a personalized diffusion model that utilizes low-rank adaptation (LoRA) fine-tuning to incorporate personalized information into pre-trained 2D VTON models. To achieve effective LoRA training, we introduce a reference-driven image editing approach that enables the simultaneous editing of multi-view images while ensuring consistency. (2) Furthermore, we propose a persona-aware 3DGS editing framework to facilitate effective editing while maintaining consistent cross-view appearance and high-quality 3D geometry. (3) Additionally, we have established a new 3D VTON benchmark, 3D-VTONBench, which facilitates comprehensive qualitative and quantitative 3D VTON evaluations. Through extensive experiments and comparative analyses with existing methods, the proposed \OM has demonstrated superior fidelity and advanced editing capabilities, affirming its effectiveness for 3D VTON.<|reference_end|> | arxiv | @article{cao2024gs-vton:,
title={GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting},
author={Yukang Cao, Masoud Hadi, Liang Pan, Ziwei Liu},
journal={arXiv preprint arXiv:2410.05259},
year={2024},
archivePrefix={arXiv},
eprint={2410.05259},
primaryClass={cs.CV}
} | cao2024gs-vton: |
arxiv-666635 | 2410.05260 | DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control | <|reference_start|>DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control: Text-conditioned human motion generation, which allows for user interaction through natural language, has become increasingly popular. Existing methods typically generate short, isolated motions based on a single input sentence. However, human motions are continuous and can extend over long periods, carrying rich semantics. Creating long, complex motions that precisely respond to streams of text descriptions, particularly in an online and real-time setting, remains a significant challenge. Furthermore, incorporating spatial constraints into text-conditioned motion generation presents additional challenges, as it requires aligning the motion semantics specified by text descriptions with geometric information, such as goal locations and 3D scene geometry. To address these limitations, we propose DART, a Diffusion-based Autoregressive motion primitive model for Real-time Text-driven motion control. Our model, DART, effectively learns a compact motion primitive space jointly conditioned on motion history and text inputs using latent diffusion models. By autoregressively generating motion primitives based on the preceding history and current text input, DART enables real-time, sequential motion generation driven by natural language descriptions. Additionally, the learned motion primitive space allows for precise spatial motion control, which we formulate either as a latent noise optimization problem or as a Markov decision process addressed through reinforcement learning. We present effective algorithms for both approaches, demonstrating our model's versatility and superior performance in various motion synthesis tasks. Experiments show our method outperforms existing baselines in motion realism, efficiency, and controllability. Video results are available on the project page: https://zkf1997.github.io/DART/.<|reference_end|> | arxiv | @article{zhao2024dart:,
title={DART: A Diffusion-Based Autoregressive Motion Model for Real-Time
Text-Driven Motion Control},
author={Kaifeng Zhao, Gen Li, Siyu Tang},
journal={arXiv preprint arXiv:2410.05260},
year={2024},
archivePrefix={arXiv},
eprint={2410.05260},
primaryClass={cs.CV cs.GR}
} | zhao2024dart: |
arxiv-666636 | 2410.05261 | TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokens | <|reference_start|>TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokens: Reading dense text and locating objects within images are fundamental abilities for Large Vision-Language Models (LVLMs) tasked with advanced jobs. Previous LVLMs, including superior proprietary models like GPT-4o, have struggled to excel in both tasks simultaneously. Moreover, previous LVLMs with fine-grained perception cost thousands of tokens per image, making them resource-intensive. We present TextHawk2, a bilingual LVLM featuring efficient fine-grained perception and demonstrating cutting-edge performance across general-purpose, OCR, and grounding tasks with 16 times fewer image tokens. Critical improvements include: (1) Token Compression: Building on the efficient architecture of its predecessor, TextHawk2 significantly reduces the number of tokens per image by 16 times, facilitating training and deployment of the TextHawk series with minimal resources. (2) Visual Encoder Reinforcement: We enhance the visual encoder through LVLM co-training, unlocking its potential for previously unseen tasks like Chinese OCR and grounding. (3) Data Diversity: We maintain a comparable scale of 100 million samples while diversifying the sources of pre-training data. We assess TextHawk2 across multiple benchmarks, where it consistently delivers superior performance and outperforms closed-source models of similar scale, such as achieving 78.4% accuracy on OCRBench, 81.4% accuracy on ChartQA, 89.6% ANLS on DocVQA, and 88.1% [email protected] on RefCOCOg-test.<|reference_end|> | arxiv | @article{yu2024texthawk2:,
title={TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and
Grounding with 16x Fewer Tokens},
author={Ya-Qi Yu, Minghui Liao, Jiwen Zhang, Jihao Wu},
journal={arXiv preprint arXiv:2410.05261},
year={2024},
archivePrefix={arXiv},
eprint={2410.05261},
primaryClass={cs.CV cs.AI}
} | yu2024texthawk2: |
arxiv-666637 | 2410.05262 | TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles | <|reference_start|>TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles: As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, making it challenging to assess model performance in dynamic interactions with users. Moreover, these benchmarks often depend on specific background knowledge, complicating the measurement of a model's logical reasoning capabilities. Other dynamic evaluation methods based on strong models or manual efforts may introduce biases and incur high costs and time demands, hindering large-scale application. To address these issues, we propose TurtleBench. TurtleBench collects real user guesses from our online Turtle Soup Puzzle platform that we developed. This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations. TurtleBench includes 1,532 user guesses along with the correctness of guesses after annotation. Using this dataset, we thoroughly evaluated nine of the most advanced LLMs available today. Notably, the OpenAI o1 series models did not achieve leading results in these evaluations. We propose several hypotheses for further research, such as "the latent reasoning of o1 utilizes trivial Chain-of-Thought (CoT) techniques" and "increasing CoT length not only provides reasoning benefits but also incurs noise costs."<|reference_end|> | arxiv | @article{yu2024turtlebench:,
title={TurtleBench: Evaluating Top Language Models via Real-World Yes/No
Puzzles},
author={Qingchen Yu,Shichao Song,Ke Fang,Yunfeng Shi,Zifan Zheng,Hanyu
Wang,Simin Niu,Zhiyu Li},
journal={arXiv preprint arXiv:2410.05262},
year={2024},
archivePrefix={arXiv},
eprint={2410.05262},
primaryClass={cs.CL}
} | yu2024turtlebench: |
arxiv-666638 | 2410.05263 | Regression Conformal Prediction under Bias | <|reference_start|>Regression Conformal Prediction under Bias: Uncertainty quantification is crucial to account for the imperfect predictions of machine learning algorithms for high-impact applications. Conformal prediction (CP) is a powerful framework for uncertainty quantification that generates calibrated prediction intervals with valid coverage. In this work, we study how CP intervals are affected by bias - the systematic deviation of a prediction from ground truth values - a phenomenon prevalent in many real-world applications. We investigate the influence of bias on interval lengths of two different types of adjustments -- symmetric adjustments, the conventional method where both sides of the interval are adjusted equally, and asymmetric adjustments, a more flexible method where the interval can be adjusted unequally in positive or negative directions. We present theoretical and empirical analyses characterizing how symmetric and asymmetric adjustments impact the "tightness" of CP intervals for regression tasks. Specifically for absolute residual and quantile-based non-conformity scores, we prove: 1) the upper bound of symmetrically adjusted interval lengths increases by $2|b|$ where $b$ is a globally applied scalar value representing bias, 2) asymmetrically adjusted interval lengths are not affected by bias, and 3) conditions when asymmetrically adjusted interval lengths are guaranteed to be smaller than symmetric ones. Our analyses suggest that even if predictions exhibit significant drift from ground truth values, asymmetrically adjusted intervals are still able to maintain the same tightness and validity of intervals as if the drift had never happened, while symmetric ones significantly inflate the lengths. We demonstrate our theoretical results with two real-world prediction tasks: sparse-view computed tomography (CT) reconstruction and time-series weather forecasting. Our work paves the way for more bias-robust machine learning systems.<|reference_end|> | arxiv | @article{cheung2024regression,
title={Regression Conformal Prediction under Bias},
author={Matt Y. Cheung, Tucker J. Netherton, Laurence E. Court, Ashok
Veeraraghavan, Guha Balakrishnan},
journal={arXiv preprint arXiv:2410.05263},
year={2024},
archivePrefix={arXiv},
eprint={2410.05263},
primaryClass={stat.ML cs.AI cs.LG math.ST stat.ME stat.TH}
} | cheung2024regression |
arxiv-666639 | 2410.05265 | PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs | <|reference_start|>PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs: Quantization is essential for deploying Large Language Models (LLMs) by enhancing memory efficiency and inference speed. Existing methods for activation quantization mainly address channel-wise outliers, often neglecting token-wise outliers, leading to reliance on costly per-token dynamic quantization. To address this, we introduce PrefixQuant, a novel technique that isolates outlier tokens offline without re-training. Specifically, PrefixQuant identifies high-frequency outlier tokens and prefixes them in the KV cache, preventing the generation of outlier tokens during inference and simplifying quantization. To our knowledge, PrefixQuant is the first to enable efficient per-tensor static quantization to outperform expensive per-token dynamic quantization. For instance, in W4A4KV4 (4- bit weight, 4-bit activation, and 4-bit KV cache) Llama-3-8B, PrefixQuant with per-tensor static quantization achieves a 7.43 WikiText2 perplexity and 71.08% average accuracy on 5 common-sense reasoning tasks, outperforming previous per-token dynamic quantization methods like QuaRot with 0.98 perplexity improvement and +5.98 points accuracy. Additionally, the inference speed of W4A4 quantized models using PrefixQuant is 1.60x to 2.81x faster than FP16 models and exceeds QuaRot models by 1.2x to 1.3x. Our code is available at \url{https://github.com/ChenMnZ/PrefixQuant}.<|reference_end|> | arxiv | @article{chen2024prefixquant:,
title={PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers
in LLMs},
author={Mengzhao Chen, Yi Liu, Jiahao Wang, Yi Bin, Wenqi Shao, Ping Luo},
journal={arXiv preprint arXiv:2410.05265},
year={2024},
archivePrefix={arXiv},
eprint={2410.05265},
primaryClass={cs.LG cs.CL}
} | chen2024prefixquant: |
arxiv-666640 | 2410.05266 | Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers | <|reference_start|>Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers: Advances in large-scale artificial neural networks have facilitated novel insights into the functional topology of the brain. Here, we leverage this approach to study how semantic categories are organized in the human visual cortex. To overcome the challenge presented by the co-occurrence of multiple categories in natural images, we introduce BrainSAIL (Semantic Attribution and Image Localization), a method for isolating specific neurally-activating visual concepts in images. BrainSAIL exploits semantically consistent, dense spatial features from pre-trained vision models, building upon their demonstrated ability to robustly predict neural activity. This method derives clean, spatially dense embeddings without requiring any additional training, and employs a novel denoising process that leverages the semantic consistency of images under random augmentations. By unifying the space of whole-image embeddings and dense visual features and then applying voxel-wise encoding models to these features, we enable the identification of specific subregions of each image which drive selectivity patterns in different areas of the higher visual cortex. We validate BrainSAIL on cortical regions with known category selectivity, demonstrating its ability to accurately localize and disentangle selectivity to diverse visual concepts. Next, we demonstrate BrainSAIL's ability to characterize high-level visual selectivity to scene properties and low-level visual features such as depth, luminance, and saturation, providing insights into the encoding of complex visual information. Finally, we use BrainSAIL to directly compare the feature selectivity of different brain encoding models across different regions of interest in visual cortex. Our innovative method paves the way for significant advances in mapping and decomposing high-level visual representations in the human brain.<|reference_end|> | arxiv | @article{luo2024brain,
title={Brain Mapping with Dense Features: Grounding Cortical Semantic
Selectivity in Natural Images With Vision Transformers},
author={Andrew F. Luo, Jacob Yeung, Rushikesh Zawar, Shaurya Dewan, Margaret
M. Henderson, Leila Wehbe, Michael J. Tarr},
journal={arXiv preprint arXiv:2410.05266},
year={2024},
archivePrefix={arXiv},
eprint={2410.05266},
primaryClass={cs.CV q-bio.NC}
} | luo2024brain |
arxiv-666641 | 2410.05267 | Grounding Partially-Defined Events in Multimodal Data | <|reference_start|>Grounding Partially-Defined Events in Multimodal Data: How are we able to learn about complex current events just from short snippets of video? While natural language enables straightforward ways to represent under-specified, partially observable events, visual data does not facilitate analogous methods and, consequently, introduces unique challenges in event understanding. With the growing prevalence of vision-capable AI agents, these systems must be able to model events from collections of unstructured video data. To tackle robust event modeling in multimodal settings, we introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a corresponding benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. We propose a collection of LLM-driven approaches to the task of multimodal event analysis, and evaluate them on MultiVENT-G. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.<|reference_end|> | arxiv | @article{sanders2024grounding,
title={Grounding Partially-Defined Events in Multimodal Data},
author={Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander
Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme},
journal={arXiv preprint arXiv:2410.05267},
year={2024},
archivePrefix={arXiv},
eprint={2410.05267},
primaryClass={cs.CL cs.CV}
} | sanders2024grounding |
arxiv-666642 | 2410.05269 | Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models | <|reference_start|>Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models: Data is a crucial element in large language model (LLM) alignment. Recent studies have explored using LLMs for efficient data collection. However, LLM-generated data often suffers from quality issues, with underrepresented or absent aspects and low-quality datapoints. To address these problems, we propose Data Advisor, an enhanced LLM-based method for generating data that takes into account the characteristics of the desired dataset. Starting from a set of pre-defined principles in hand, Data Advisor monitors the status of the generated data, identifies weaknesses in the current dataset, and advises the next iteration of data generation accordingly. Data Advisor can be easily integrated into existing data generation methods to enhance data quality and coverage. Experiments on safety alignment of three representative LLMs (i.e., Mistral, Llama2, and Falcon) demonstrate the effectiveness of Data Advisor in enhancing model safety against various fine-grained safety issues without sacrificing model utility.<|reference_end|> | arxiv | @article{wang2024data,
title={Data Advisor: Dynamic Data Curation for Safety Alignment of Large
Language Models},
author={Fei Wang, Ninareh Mehrabi, Palash Goyal, Rahul Gupta, Kai-Wei Chang,
Aram Galstyan},
journal={arXiv preprint arXiv:2410.05269},
year={2024},
archivePrefix={arXiv},
eprint={2410.05269},
primaryClass={cs.CL cs.AI cs.LG}
} | wang2024data |
arxiv-666643 | 2410.05270 | Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia | <|reference_start|>Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia: We consider the problem of adapting a contrastively pretrained vision-language model like CLIP (Radford et al., 2021) for few-shot classification. The existing literature addresses this problem by learning a linear classifier of the frozen visual features, optimizing word embeddings, or learning external feature adapters. This paper introduces an alternative way for CLIP adaptation without adding 'external' parameters to optimize. We find that simply fine-tuning the last projection matrix of the vision encoder leads to strong performance compared to the existing baselines. Furthermore, we show that regularizing training with the distance between the fine-tuned and pretrained matrices adds reliability for adapting CLIP through this layer. Perhaps surprisingly, this approach, coined ProLIP, yields performances on par or better than state of the art on 11 few-shot classification benchmarks, few-shot domain generalization, cross-dataset transfer and test-time adaptation. Code will be made available at https://github.com/astra-vision/ProLIP .<|reference_end|> | arxiv | @article{fahes2024fine-tuning,
title={Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia},
author={Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick P'erez, Raoul de
Charette},
journal={arXiv preprint arXiv:2410.05270},
year={2024},
archivePrefix={arXiv},
eprint={2410.05270},
primaryClass={cs.CV}
} | fahes2024fine-tuning |
arxiv-666644 | 2410.05272 | DVS: Blood cancer detection using novel CNN-based ensemble approach | <|reference_start|>DVS: Blood cancer detection using novel CNN-based ensemble approach: Blood cancer can only be diagnosed properly if it is detected early. Each year, more than 1.24 million new cases of blood cancer are reported worldwide. There are about 6,000 cancers worldwide due to this disease. The importance of cancer detection and classification has prompted researchers to evaluate Deep Convolutional Neural Networks for the purpose of classifying blood cancers. The objective of this research is to conduct an in-depth investigation of the efficacy and suitability of modern Convolutional Neural Network (CNN) architectures for the detection and classification of blood malignancies. The study focuses on investigating the potential of Deep Convolutional Neural Networks (D-CNNs), comprising not only the foundational CNN models but also those improved through transfer learning methods and incorporated into ensemble strategies, to detect diverse forms of blood cancer with a high degree of accuracy. This paper provides a comprehensive investigation into five deep learning architectures derived from CNNs. These models, namely VGG19, ResNet152v2, SEresNet152, ResNet101, and DenseNet201, integrate ensemble learning techniques with transfer learning strategies. A comparison of DenseNet201 (98.08%), VGG19 (96.94%), and SEresNet152 (90.93%) shows that DVS outperforms CNN. With transfer learning, DenseNet201 had 95.00% accuracy, VGG19 had 72.29%, and SEresNet152 had 94.16%. In the study, the ensemble DVS model achieved 98.76% accuracy. Based on our study, the ensemble DVS model is the best for detecting and classifying blood cancers.<|reference_end|> | arxiv | @article{ahad2024dvs:,
title={DVS: Blood cancer detection using novel CNN-based ensemble approach},
author={Md Taimur Ahad, Israt Jahan Payel, Bo Song, Yan Li},
journal={arXiv preprint arXiv:2410.05272},
year={2024},
archivePrefix={arXiv},
eprint={2410.05272},
primaryClass={eess.IV cs.CV}
} | ahad2024dvs: |
arxiv-666645 | 2410.05273 | HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers | <|reference_start|>HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers: Large Vision-Language-Action (VLA) models, leveraging powerful pre trained Vision-Language Models (VLMs) backends, have shown promise in robotic control due to their impressive generalization ability. However, the success comes at a cost. Their reliance on VLM backends with billions of parameters leads to high computational costs and inference latency, limiting the testing scenarios to mainly quasi-static tasks and hindering performance in dynamic tasks requiring rapid interactions. To address these limitations, this paper proposes HiRT, a Hierarchical Robot Transformer framework that enables flexible frequency and performance trade-off. HiRT keeps VLMs running at low frequencies to capture temporarily invariant features while enabling real-time interaction through a high-frequency vision-based policy guided by the slowly updated features. Experiment results in both simulation and real-world settings demonstrate significant improvements over baseline methods. Empirically, in static tasks, we double the control frequency and achieve comparable success rates. Additionally, on novel real-world dynamic ma nipulation tasks which are challenging for previous VLA models, HiRT improves the success rate from 48% to 75%.<|reference_end|> | arxiv | @article{zhang2024hirt:,
title={HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers},
author={Jianke Zhang, Yanjiang Guo, Xiaoyu Chen, Yen-Jen Wang, Yucheng Hu,
Chengming Shi, Jianyu Chen},
journal={CoRL2024},
year={2024},
archivePrefix={arXiv},
eprint={2410.05273},
primaryClass={cs.CV cs.AI cs.RO}
} | zhang2024hirt: |
arxiv-666646 | 2410.05274 | Scale-Invariant Object Detection by Adaptive Convolution with Unified Global-Local Context | <|reference_start|>Scale-Invariant Object Detection by Adaptive Convolution with Unified Global-Local Context: Dense features are important for detecting minute objects in images. Unfortunately, despite the remarkable efficacy of the CNN models in multi-scale object detection, CNN models often fail to detect smaller objects in images due to the loss of dense features during the pooling process. Atrous convolution addresses this issue by applying sparse kernels. However, sparse kernels often can lose the multi-scale detection efficacy of the CNN model. In this paper, we propose an object detection model using a Switchable (adaptive) Atrous Convolutional Network (SAC-Net) based on the efficientDet model. A fixed atrous rate limits the performance of the CNN models in the convolutional layers. To overcome this limitation, we introduce a switchable mechanism that allows for dynamically adjusting the atrous rate during the forward pass. The proposed SAC-Net encapsulates the benefits of both low-level and high-level features to achieve improved performance on multi-scale object detection tasks, without losing the dense features. Further, we apply a depth-wise switchable atrous rate to the proposed network, to improve the scale-invariant features. Finally, we apply global context on the proposed model. Our extensive experiments on benchmark datasets demonstrate that the proposed SAC-Net outperforms the state-of-the-art models by a significant margin in terms of accuracy.<|reference_end|> | arxiv | @article{singh2024scale-invariant,
title={Scale-Invariant Object Detection by Adaptive Convolution with Unified
Global-Local Context},
author={Amrita Singh, and Snehasis Mukherjee},
journal={arXiv preprint arXiv:2410.05274},
year={2024},
archivePrefix={arXiv},
eprint={2410.05274},
primaryClass={cs.CV cs.AI}
} | singh2024scale-invariant |
arxiv-666647 | 2410.05275 | Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks | <|reference_start|>Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks: Assessing the degree of similarity of code fragments is crucial for ensuring software quality, but it remains challenging due to the need to capture the deeper semantic aspects of code. Traditional syntactic methods often fail to identify these connections. Recent advancements have addressed this challenge, though they frequently sacrifice interpretability. To improve this, we present an approach aiming to improve the transparency of the similarity assessment by using GraphCodeBERT, which enables the identification of semantic relationships between code fragments. This approach identifies similar code fragments and clarifies the reasons behind that identification, helping developers better understand and trust the results. The source code for our implementation is available at https://www.github.com/jorge-martinez-gil/graphcodebert-interpretability.<|reference_end|> | arxiv | @article{martinez-gil2024augmenting,
title={Augmenting the Interpretability of GraphCodeBERT for Code Similarity
Tasks},
author={Jorge Martinez-Gil},
journal={arXiv preprint arXiv:2410.05275},
year={2024},
archivePrefix={arXiv},
eprint={2410.05275},
primaryClass={cs.IR}
} | martinez-gil2024augmenting |
arxiv-666648 | 2410.05278 | Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure | <|reference_start|>Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure: Antibody-drug conjugates (ADCs) have emerged as a promising class of targeted cancer therapeutics, but the design and optimization of their cytotoxic payloads remain challenging. This study introduces DumplingGNN, a novel hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. By integrating Message Passing Neural Networks (MPNN), Graph Attention Networks (GAT), and GraphSAGE layers, DumplingGNN effectively captures multi-scale molecular features and leverages both 2D topological and 3D structural information. We evaluate DumplingGNN on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors, as well as on multiple public benchmarks from MoleculeNet. DumplingGNN achieves state-of-the-art performance across several datasets, including BBBP (96.4\% ROC-AUC), ToxCast (78.2\% ROC-AUC), and PCBA (88.87\% ROC-AUC). On our specialized ADC payload dataset, it demonstrates exceptional accuracy (91.48\%), sensitivity (95.08\%), and specificity (97.54\%). Ablation studies confirm the synergistic effects of the hybrid architecture and the critical role of 3D structural information in enhancing predictive accuracy. The model's strong interpretability, enabled by attention mechanisms, provides valuable insights into structure-activity relationships. DumplingGNN represents a significant advancement in molecular property prediction, with particular promise for accelerating the design and optimization of ADC payloads in targeted cancer therapy development.<|reference_end|> | arxiv | @article{xu2024dumpling,
title={Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction
Based on Chemical Structure},
author={Shengjie Xu, Lingxi Xie},
journal={arXiv preprint arXiv:2410.05278},
year={2024},
archivePrefix={arXiv},
eprint={2410.05278},
primaryClass={q-bio.BM cs.AI cs.LG cs.NE}
} | xu2024dumpling |
arxiv-666649 | 2410.05281 | Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials | <|reference_start|>Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials: Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.<|reference_end|> | arxiv | @article{wang2024micrometer:,
title={Micrometer: Micromechanics Transformer for Predicting Mechanical
Responses of Heterogeneous Materials},
author={Sifan Wang, Tong-Rui Liu, Shyam Sankaran, Paris Perdikaris},
journal={arXiv preprint arXiv:2410.05281},
year={2024},
archivePrefix={arXiv},
eprint={2410.05281},
primaryClass={cs.CE cond-mat.mtrl-sci cs.LG physics.comp-ph}
} | wang2024micrometer: |
arxiv-666650 | 2410.05284 | Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery | <|reference_start|>Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery: Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.<|reference_end|> | arxiv | @article{chang2024psychometrics,
title={Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of
Artificial Mental Imagery},
author={Ching-Chun Chang, Kai Gao, Shuying Xu, Anastasia Kordoni, Christopher
Leckie, Isao Echizen},
journal={arXiv preprint arXiv:2410.05284},
year={2024},
archivePrefix={arXiv},
eprint={2410.05284},
primaryClass={cs.CR cs.AI cs.CV cs.LG}
} | chang2024psychometrics |
arxiv-666651 | 2410.05287 | Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language | <|reference_start|>Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language: Hate speech has grown into a pervasive phenomenon, intensifying during times of crisis, elections, and social unrest. Multiple approaches have been developed to detect hate speech using artificial intelligence, but a generalized model is yet unaccomplished. The challenge for hate speech detection as text classification is the cost of obtaining high-quality training data. This study focuses on detecting bilingual hate speech in YouTube comments and measuring the impact of using additional data from other platforms in the performance of the classification model. We examine the value of additional training datasets from cross-platforms for improving the performance of classification models. We also included factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. Our findings show that adding more similar datasets based on content similarity, hate words, and definitions improves the performance of classification models. The best performance was obtained by combining datasets from YouTube comments, Twitter, and Gab with an F1-score of 0.74 and 0.68 for English and German YouTube comments.<|reference_end|> | arxiv | @article{shahi2024hate,
title={Hate Speech Detection Using Cross-Platform Social Media Data In English
and German Language},
author={Gautam Kishore Shahi and Tim A. Majchrzak},
journal={arXiv preprint arXiv:2410.05287},
year={2024},
archivePrefix={arXiv},
eprint={2410.05287},
primaryClass={cs.CL cs.AI cs.SI}
} | shahi2024hate |
arxiv-666652 | 2410.05289 | MARS: A neurosymbolic approach for interpretable drug discovery | <|reference_start|>MARS: A neurosymbolic approach for interpretable drug discovery: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretability is broadly defined, there are no clear guidelines for assessing the biological plausibility of model interpretations. To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowledge graph (KG), MoA-net. We then develop the MoA Retrieval System (MARS), a NeSy approach for drug discovery which leverages logical rules with learned rule weights. Using this interpretable feature alongside domain knowledge, we find that MARS and other NeSy approaches on KGs are susceptible to reasoning shortcuts, in which the prediction of true labels is driven by "degree-bias" rather than the domain-based rules. Subsequently, we demonstrate ways to identify and mitigate this. Thereafter, MARS achieves performance on par with current state-of-the-art models while producing model interpretations aligned with known MoAs.<|reference_end|> | arxiv | @article{delong2024mars:,
title={MARS: A neurosymbolic approach for interpretable drug discovery},
author={Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi, Jacques D. Fleuriot,
Daniel Domingo-Fern'andez},
journal={arXiv preprint arXiv:2410.05289},
year={2024},
archivePrefix={arXiv},
eprint={2410.05289},
primaryClass={cs.AI cs.LG cs.LO}
} | delong2024mars: |
arxiv-666653 | 2410.05290 | Curve Segment Neighborhood-based Vector Field Exploration | <|reference_start|>Curve Segment Neighborhood-based Vector Field Exploration: Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets.<|reference_end|> | arxiv | @article{phan2024curve,
title={Curve Segment Neighborhood-based Vector Field Exploration},
author={Nguyen Phan, Guoning Chen},
journal={arXiv preprint arXiv:2410.05290},
year={2024},
archivePrefix={arXiv},
eprint={2410.05290},
primaryClass={cs.SI cs.GR}
} | phan2024curve |
arxiv-666654 | 2410.05292 | CaLMFlow: Volterra Flow Matching using Causal Language Models | <|reference_start|>CaLMFlow: Volterra Flow Matching using Causal Language Models: We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling and continuous generative modeling. Our method implements tokenization across space and time, thereby solving a VIE over these domains. This approach enables efficient handling of high-dimensional data and outperforms ODE solver-dependent methods like conditional flow matching (CFM). We demonstrate CaLMFlow's effectiveness on synthetic and real-world data, including single-cell perturbation response prediction, showcasing its ability to incorporate textual context and generalize to unseen conditions. Our results highlight LLM-driven flow matching as a promising paradigm in generative modeling, offering improved scalability, flexibility, and context-awareness.<|reference_end|> | arxiv | @article{he2024calmflow:,
title={CaLMFlow: Volterra Flow Matching using Causal Language Models},
author={Sizhuang He, Daniel Levine, Ivan Vrkic, Marco Francesco Bressana,
David Zhang, Syed Asad Rizvi, Yangtian Zhang, Emanuele Zappala and David van
Dijk},
journal={arXiv preprint arXiv:2410.05292},
year={2024},
archivePrefix={arXiv},
eprint={2410.05292},
primaryClass={cs.LG cs.AI q-bio.QM}
} | he2024calmflow: |
arxiv-666655 | 2410.05295 | AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs | <|reference_start|>AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs: In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human intervention or predefined scopes (e.g., specified candidate strategies), and use them for red-teaming. As a result, AutoDAN-Turbo can significantly outperform baseline methods, achieving a 74.3% higher average attack success rate on public benchmarks. Notably, AutoDAN-Turbo achieves an 88.5 attack success rate on GPT-4-1106-turbo. In addition, AutoDAN-Turbo is a unified framework that can incorporate existing human-designed jailbreak strategies in a plug-and-play manner. By integrating human-designed strategies, AutoDAN-Turbo can even achieve a higher attack success rate of 93.4 on GPT-4-1106-turbo.<|reference_end|> | arxiv | @article{liu2024autodan-turbo:,
title={AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to
Jailbreak LLMs},
author={Xiaogeng Liu, Peiran Li, Edward Suh, Yevgeniy Vorobeychik, Zhuoqing
Mao, Somesh Jha, Patrick McDaniel, Huan Sun, Bo Li, Chaowei Xiao},
journal={arXiv preprint arXiv:2410.05295},
year={2024},
archivePrefix={arXiv},
eprint={2410.05295},
primaryClass={cs.CR cs.AI cs.LG}
} | liu2024autodan-turbo: |
arxiv-666656 | 2410.05297 | Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk Classifications | <|reference_start|>Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk Classifications: Cyber risk classifications are widely used in the modeling of cyber event distributions, yet their effectiveness in out of sample forecasting performance remains underexplored. In this paper, we analyse the most commonly used classifications and argue in favour of switching the attention from goodness-of-fit and in-sample predictive performance, to focusing on the out-of sample forecasting performance. We use a rolling window analysis, to compare cyber risk distribution forecasts via threshold weighted scoring functions. Our results indicate that business motivated cyber risk classifications appear to be too restrictive and not flexible enough to capture the heterogeneity of cyber risk events. We investigate how dynamic and impact-based cyber risk classifiers seem to be better suited in forecasting future cyber risk losses than the other considered classifications. These findings suggest that cyber risk types provide limited forecasting ability concerning cyber event severity distribution, and cyber insurance ratemakers should utilize cyber risk types only when modeling the cyber event frequency distribution. Our study offers valuable insights for decision-makers and policymakers alike, contributing to the advancement of scientific knowledge in the field of cyber risk management.<|reference_end|> | arxiv | @article{malavasi2024cyber,
title={Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk
Classifications},
author={Matteo Malavasi (1), Gareth W. Peters (2), Stefan Treuck (3), Pavel V.
Shevchenko (3), Jiwook Jang (3), Georgy Sofronov (4) ((1) School of Risk and
Actuarial Studies, UNSW Business School, University of New South Wales,
Australia, (2) Department of Statistics and Applied Probability, University
of California Santa Barbara, USA, (3) Department of Actuarial Studies and
Business Analytics, Macquarie University, Australia, (4) School of
Mathematical and Physical Sciences, Macquarie University, Australia)},
journal={arXiv preprint arXiv:2410.05297},
year={2024},
archivePrefix={arXiv},
eprint={2410.05297},
primaryClass={cs.CR q-fin.RM q-fin.ST}
} | malavasi2024cyber |
arxiv-666657 | 2410.05298 | How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension | <|reference_start|>How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension: Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studies have shown that LLMs exhibit a preliminary ability to understand graph structures and node features. However, the potential of LLMs in graph pattern mining remains largely unexplored. This is a key component in fields such as computational chemistry, biology, and social network analysis. To bridge this gap, this work introduces a comprehensive benchmark to assess LLMs' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Additionally, our benchmark tests the LLMs' capacity to autonomously discover graph patterns from data. The benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models. Our experimental framework is designed for easy expansion to accommodate new models and datasets. Our findings reveal that: (1) LLMs have preliminary abilities to understand graph patterns, with O1-mini outperforming in the majority of tasks; (2) Formatting input data to align with the knowledge acquired during pretraining can enhance performance; (3) The strategies employed by LLMs may differ from those used in conventional algorithms.<|reference_end|> | arxiv | @article{dai2024how,
title={How Do Large Language Models Understand Graph Patterns? A Benchmark for
Graph Pattern Comprehension},
author={Xinnan Dai, Haohao Qu, Yifen Shen, Bohang Zhang, Qihao Wen, Wenqi Fan,
Dongsheng Li, Jiliang Tang, Caihua Shan},
journal={arXiv preprint arXiv:2410.05298},
year={2024},
archivePrefix={arXiv},
eprint={2410.05298},
primaryClass={cs.LG cs.AI}
} | dai2024how |
arxiv-666658 | 2410.05300 | Research on short-term load forecasting model based on VMD and IPSO-ELM | <|reference_start|>Research on short-term load forecasting model based on VMD and IPSO-ELM: To enhance the accuracy of power load forecasting in wind farms, this study introduces an advanced combined forecasting method that integrates Variational Mode Decomposition (VMD) with an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the Extreme Learning Machine (ELM). Initially, the VMD algorithm is employed to perform high-precision modal decomposition of the original power load data, which is then categorized into high-frequency and low-frequency sequences based on mutual information entropy theory. Subsequently, this research profoundly modifies the traditional multiverse optimizer by incorporating Tent chaos mapping, exponential travel distance rate, and an elite reverse learning mechanism, developing the IPSO-ELM prediction model. This model independently predicts the high and low-frequency sequences and reconstructs the data to achieve the final forecasting results. Simulation results indicate that the proposed method significantly improves prediction accuracy and convergence speed compared to traditional ELM, PSO-ELM, and PSO-ELM methods.<|reference_end|> | arxiv | @article{xie2024research,
title={Research on short-term load forecasting model based on VMD and IPSO-ELM},
author={Qiang Xie},
journal={arXiv preprint arXiv:2410.05300},
year={2024},
archivePrefix={arXiv},
eprint={2410.05300},
primaryClass={cs.LG cs.NE}
} | xie2024research |
arxiv-666659 | 2410.05301 | Diffusion-based Unsupervised Audio-visual Speech Enhancement | <|reference_start|>Diffusion-based Unsupervised Audio-visual Speech Enhancement: This paper proposes a new unsupervised audiovisual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matrix factorization (NMF) noise model. First, the diffusion model is pre-trained on clean speech conditioned on corresponding video data to simulate the speech generative distribution. This pre-trained model is then paired with the NMF-based noise model to iteratively estimate clean speech. Specifically, a diffusion-based posterior sampling approach is implemented within the reverse diffusion process, where after each iteration, a speech estimate is obtained and used to update the noise parameters. Experimental results confirm that the proposed AVSE approach not only outperforms its audio-only counterpart but also generalizes better than a recent supervisedgenerative AVSE method. Additionally, the new inference algorithm offers a better balance between inference speed and performance compared to the previous diffusion-based method.<|reference_end|> | arxiv | @article{ayilo2024diffusion-based,
title={Diffusion-based Unsupervised Audio-visual Speech Enhancement},
author={Jean-Eudes Ayilo (MULTISPEECH), Mostafa Sadeghi (MULTISPEECH), Romain
Serizel (MULTISPEECH), Xavier Alameda-Pineda (ROBOTLEARN)},
journal={arXiv preprint arXiv:2410.05301},
year={2024},
archivePrefix={arXiv},
eprint={2410.05301},
primaryClass={cs.SD cs.AI cs.CV cs.LG eess.AS eess.SP}
} | ayilo2024diffusion-based |
arxiv-666660 | 2410.05302 | Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification | <|reference_start|>Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification: The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation. Building upon such success, we first propose a simple (yet novel) method to fine-tune a ProtoNet on the (labeled) support set of the test episode of a C-way-K-shot test episode (without using the query set which is only used for evaluation). We then propose an algorithmic framework that combines ProtoNet with optimization-based FSL algorithms (MAML and Meta-Curvature) to work with such a fine-tuning method. Since optimization-based algorithms endow the target learner model with the ability to fast adaption to only a few samples, we utilize ProtoNet as the target model to enhance its fine-tuning performance with the help of a specifically designed episodic fine-tuning strategy. The experimental results confirm that our proposed models, MAML-Proto and MC-Proto, combined with our unique fine-tuning method, outperform regular ProtoNet by a large margin in few-shot audio classification tasks on the ESC-50 and Speech Commands v2 datasets. We note that although we have only applied our model to the audio domain, it is a general method and can be easily extended to other domains.<|reference_end|> | arxiv | @article{zhuang2024episodic,
title={Episodic fine-tuning prototypical networks for optimization-based
few-shot learning: Application to audio classification},
author={Xuanyu Zhuang (LTCI, IP Paris, S2A, IDS), Geoffroy Peeters (LTCI, IP
Paris, S2A, IDS), Ga"el Richard (S2A, IDS, LTCI, IP Paris)},
journal={2024 IEEE International Workshop on Machine Learning for Signal
Processing (MLSP 2024), Sep 2024, London (UK), United Kingdom},
year={2024},
archivePrefix={arXiv},
eprint={2410.05302},
primaryClass={eess.AS cs.LG cs.MM cs.SD eess.SP}
} | zhuang2024episodic |
arxiv-666661 | 2410.05303 | Integrating problem structuring methods with formal design theory: collective water management policy design in Tunisia | <|reference_start|>Integrating problem structuring methods with formal design theory: collective water management policy design in Tunisia: Groundwater management, especially in regions like Tunisia, is challenging due to diverse stakeholder interests and the dry structure of climate, which is extremely challenging for the sustainability of water resources. This paper proposes an innovative approach to policy design by merging Problem Structuring Methods (PSMs) and the Policy-Knowledge, Concepts, Proposals (P-KCP) methodology. Utilizing cognitive maps and value trees, the study aims to generate new collective groundwater management practices. Bridging decision theory and design theory, the study addresses the gap in new alternative generation and highlights the P-KCP's role in innovative policy design. Integrating PSMs and C-K theory, the framework expands policy alternatives and advocates for participatory approaches. It emphasizes adaptability across contexts, provides replicable process descriptions, and encourages the creation of unconventional policy solutions. Ultimately, this comprehensive framework offers a practical guide for policy innovation and collaboration.<|reference_end|> | arxiv | @article{tosunlu2024integrating,
title={Integrating problem structuring methods with formal design theory:
collective water management policy design in Tunisia},
author={Berkay Tosunlu, Joseph H.A. Guillaume, Alexis Tsouki`as, Emeline
Hassenforder, Samia Chrii, Houssem Braiki, Irene Pluchinotta},
journal={arXiv preprint arXiv:2410.05303},
year={2024},
archivePrefix={arXiv},
eprint={2410.05303},
primaryClass={physics.soc-ph cs.CY}
} | tosunlu2024integrating |
arxiv-666662 | 2410.05304 | Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs | <|reference_start|>Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs: This paper presents an approach to developing assurance cases for adversarial robustness and regulatory compliance in large language models (LLMs). Focusing on both natural and code language tasks, we explore the vulnerabilities these models face, including adversarial attacks based on jailbreaking, heuristics, and randomization. We propose a layered framework incorporating guardrails at various stages of LLM deployment, aimed at mitigating these attacks and ensuring compliance with the EU AI Act. Our approach includes a meta-layer for dynamic risk management and reasoning, crucial for addressing the evolving nature of LLM vulnerabilities. We illustrate our method with two exemplary assurance cases, highlighting how different contexts demand tailored strategies to ensure robust and compliant AI systems.<|reference_end|> | arxiv | @article{momcilovic2024developing,
title={Developing Assurance Cases for Adversarial Robustness and Regulatory
Compliance in LLMs},
author={Tomas Bueno Momcilovic, Dian Balta, Beat Buesser, Giulio Zizzo, Mark
Purcell},
journal={arXiv preprint arXiv:2410.05304},
year={2024},
archivePrefix={arXiv},
eprint={2410.05304},
primaryClass={cs.CR cs.AI cs.SE}
} | momcilovic2024developing |
arxiv-666663 | 2410.05305 | Output Scouting: Auditing Large Language Models for Catastrophic Responses | <|reference_start|>Output Scouting: Auditing Large Language Models for Catastrophic Responses: Recent high profile incidents in which the use of Large Language Models (LLMs) resulted in significant harm to individuals have brought about a growing interest in AI safety. One reason LLM safety issues occur is that models often have at least some non-zero probability of producing harmful outputs. In this work, we explore the following scenario: imagine an AI safety auditor is searching for catastrophic responses from an LLM (e.g. a "yes" responses to "can I fire an employee for being pregnant?"), and is able to query the model a limited number times (e.g. 1000 times). What is a strategy for querying the model that would efficiently find those failure responses? To this end, we propose output scouting: an approach that aims to generate semantically fluent outputs to a given prompt matching any target probability distribution. We then run experiments using two LLMs and find numerous examples of catastrophic responses. We conclude with a discussion that includes advice for practitioners who are looking to implement LLM auditing for catastrophic responses. We also release an open-source toolkit (https://github.com/joaopfonseca/outputscouting) that implements our auditing framework using the Hugging Face transformers library.<|reference_end|> | arxiv | @article{bell2024output,
title={Output Scouting: Auditing Large Language Models for Catastrophic
Responses},
author={Andrew Bell and Joao Fonseca},
journal={arXiv preprint arXiv:2410.05305},
year={2024},
archivePrefix={arXiv},
eprint={2410.05305},
primaryClass={cs.CL cs.AI}
} | bell2024output |
arxiv-666664 | 2410.05306 | Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs | <|reference_start|>Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs: Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union's Artificial Intelligence Act seeks to enforce AI robustness in certain contexts, but faces implementation challenges due to the lack of standards, complexity of LLMs and emerging security vulnerabilities. Our research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness. This approach aims to ensure that LLMs adhere to regulatory standards and are equipped to counter potential threats.<|reference_end|> | arxiv | @article{momcilovic2024towards,
title={Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs},
author={Tomas Bueno Momcilovic, Beat Buesser, Giulio Zizzo, Mark Purcell, Dian
Balta},
journal={arXiv preprint arXiv:2410.05306},
year={2024},
archivePrefix={arXiv},
eprint={2410.05306},
primaryClass={cs.CR cs.AI}
} | momcilovic2024towards |
arxiv-666665 | 2410.05307 | Topology-Informed Machine Learning for Efficient Prediction of Solid Oxide Fuel Cell Electrode Polarization | <|reference_start|>Topology-Informed Machine Learning for Efficient Prediction of Solid Oxide Fuel Cell Electrode Polarization: Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming electrode microstructure into a format compatible with artificial neural networks. Input data may range from a comprehensive digital material representation of the electrode to a selected set of microstructural parameters. The chosen representation significantly influences the performance and results of the network. Here, we show a novel approach utilizing persistence representation derived from computational topology. Using 500 microstructures and current-voltage characteristics obtained with 3D first-principles simulations, we have prepared an artificial neural network model that can replicate current-voltage characteristics of unseen microstructures based on their persistent image representation. The artificial neural network can accurately predict the polarization curve of solid oxide fuel cell electrodes. The presented method incorporates complex microstructural information from the digital material representation while requiring substantially less computational resources (preprocessing and prediction time approximately 1 min) compared to our high-fidelity simulations (simulation time approximately 1 hour) to obtain a single current-potential characteristic for one microstructure.<|reference_end|> | arxiv | @article{szemer2024topology-informed,
title={Topology-Informed Machine Learning for Efficient Prediction of Solid
Oxide Fuel Cell Electrode Polarization},
author={Maksym Szemer, Szymon Buchaniec, Tomasz Prokop, Grzegorz Brus},
journal={arXiv preprint arXiv:2410.05307},
year={2024},
archivePrefix={arXiv},
eprint={2410.05307},
primaryClass={cond-mat.mtrl-sci cs.LG}
} | szemer2024topology-informed |
arxiv-666666 | 2410.05308 | Comparative Survey of Cyber-Threat and Attack Trends and Prediction of Future Cyber-Attack Patterns | <|reference_start|>Comparative Survey of Cyber-Threat and Attack Trends and Prediction of Future Cyber-Attack Patterns: This paper presents a comparative survey of cyberthreat and attack trends starting from 2010 till date Cyber security breaches are constantly on the rise with huge uncertainty and risks The trend is causing rife globally because of its consequences to national security and economy With diverse interests and motivations for various categories of threats and attacks we carried out a comparative survey and analysis of security breaches to unravel the patterns and predict what will shape future security challenges The diversity of attacks and growing state actors involvement without any sort of regulation is making cyber weapons attractive to the states States are leveraging the anonymity and attribution flaws to hit hard on perceived adversaries thereby complicating the cyber security equation<|reference_end|> | arxiv | @article{chinanu2024comparative,
title={Comparative Survey of Cyber-Threat and Attack Trends and Prediction of
Future Cyber-Attack Patterns},
author={Uwazie Emmanuel Chinanu and Oluyemi Amujo},
journal={Intl Journal of Innovative Research in Computer and Communication
Engineering, 6, 4, (2018)},
year={2024},
doi={10.6084/m9.figshare.17134661},
archivePrefix={arXiv},
eprint={2410.05308},
primaryClass={cs.CR cs.CY}
} | chinanu2024comparative |
arxiv-666667 | 2410.05309 | ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models through Reinforcement Learning | <|reference_start|>ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models through Reinforcement Learning: With the advance of generative AI, the text-to-image (T2I) model has the ability to generate various contents. However, the generated contents cannot be fully controlled. There is a potential risk that T2I model can generate unsafe images with uncomfortable contents. In our work, we focus on eliminating the NSFW (not safe for work) content generation from T2I model while maintaining the high quality of generated images by fine-tuning the pre-trained diffusion model via reinforcement learning by optimizing the well-designed content-safe reward function. The proposed method leverages a customized reward function consisting of the CLIP (Contrastive Language-Image Pre-training) and nudity rewards to prune the nudity contents that adhere to the pret-rained model and keep the corresponding semantic meaning on the safe side. In this way, the T2I model is robust to unsafe adversarial prompts since unsafe visual representations are mitigated from latent space. Extensive experiments conducted on different datasets demonstrate the effectiveness of the proposed method in alleviating unsafe content generation while preserving the high-fidelity of benign images as well as images generated by unsafe prompts. We compare with five existing state-of-the-art (SOTA) methods and achieve competitive performance on sexual content removal and image quality retention. In terms of robustness, our method outperforms counterparts under the SOTA black-box attacking model. Furthermore, our constructed method can be a benchmark for anti-NSFW generation with semantically-relevant safe alignment.<|reference_end|> | arxiv | @article{han2024shielddiff:,
title={ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models
through Reinforcement Learning},
author={Dong Han, Salaheldin Mohamed, Yong Li},
journal={arXiv preprint arXiv:2410.05309},
year={2024},
archivePrefix={arXiv},
eprint={2410.05309},
primaryClass={cs.CV}
} | han2024shielddiff: |
arxiv-666668 | 2410.05310 | An Approach To Enhance IoT Security In 6G Networks Through Explainable AI | <|reference_start|>An Approach To Enhance IoT Security In 6G Networks Through Explainable AI: Wireless communication has evolved significantly, with 6G offering groundbreaking capabilities, particularly for IoT. However, the integration of IoT into 6G presents new security challenges, expanding the attack surface due to vulnerabilities introduced by advanced technologies such as open RAN, terahertz (THz) communication, IRS, massive MIMO, and AI. Emerging threats like AI exploitation, virtualization risks, and evolving attacks, including data manipulation and signal interference, further complicate security efforts. As 6G standards are set to be finalized by 2030, work continues to align security measures with technological advances. However, substantial gaps remain in frameworks designed to secure integrated IoT and 6G systems. Our research addresses these challenges by utilizing tree-based machine learning algorithms to manage complex datasets and evaluate feature importance. We apply data balancing techniques to ensure fair attack representation and use SHAP and LIME to improve model transparency. By aligning feature importance with XAI methods and cross-validating for consistency, we boost model accuracy and enhance IoT security within the 6G ecosystem.<|reference_end|> | arxiv | @article{kaur2024an,
title={An Approach To Enhance IoT Security In 6G Networks Through Explainable
AI},
author={Navneet Kaur, Lav Gupta},
journal={arXiv preprint arXiv:2410.05310},
year={2024},
archivePrefix={arXiv},
eprint={2410.05310},
primaryClass={cs.CR cs.AI}
} | kaur2024an |
arxiv-666669 | 2410.05311 | ConceptLens: from Pixels to Understanding | <|reference_start|>ConceptLens: from Pixels to Understanding: ConceptLens is an innovative tool designed to illuminate the intricate workings of deep neural networks (DNNs) by visualizing hidden neuron activations. By integrating deep learning with symbolic methods, ConceptLens offers users a unique way to understand what triggers neuron activations and how they respond to various stimuli. The tool uses error-margin analysis to provide insights into the confidence levels of neuron activations, thereby enhancing the interpretability of DNNs. This paper presents an overview of ConceptLens, its implementation, and its application in real-time visualization of neuron activations and error margins through bar charts.<|reference_end|> | arxiv | @article{dalal2024conceptlens:,
title={ConceptLens: from Pixels to Understanding},
author={Abhilekha Dalal, Pascal Hitzler},
journal={arXiv preprint arXiv:2410.05311},
year={2024},
archivePrefix={arXiv},
eprint={2410.05311},
primaryClass={cs.LG cs.AI}
} | dalal2024conceptlens: |
arxiv-666670 | 2410.05312 | An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning | <|reference_start|>An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning: Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately $95.60\%$ in the network slicing architecture and $99.99\%$ for the deployed slice -- intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.<|reference_end|> | arxiv | @article{moreira2024an,
title={An Intelligent Native Network Slicing Security Architecture Empowered by
Federated Learning},
author={Rodrigo Moreira, Rodolfo S. Villaca, Moises R. N. Ribeiro, Joberto S.
B. Martins, Joao Henrique Correa, Tereza C. Carvalho, Flavio de Oliveira
Silva},
journal={Future Generation Computer Systems (FGCS); ISSN:0167-739X; 2024},
year={2024},
doi={10.1016/j.future.2024.107537},
archivePrefix={arXiv},
eprint={2410.05312},
primaryClass={cs.CR cs.AI cs.ET cs.LG cs.NI}
} | moreira2024an |
arxiv-666671 | 2410.05315 | PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms | <|reference_start|>PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms: Deploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy concerns or impractical due to network connection. Recent advancements (MLC, 2023a; Gerganov, 2023) have facilitated the local deployment of LLMs. However, local deployment also presents challenges, particularly in balancing quality (generative performance), latency, and throughput within the hardware constraints of mobile devices. In this paper, we introduce our lightweight, all-in-one automated benchmarking framework that allows users to evaluate LLMs on mobile devices. We provide a comprehensive benchmark of various popular LLMs with different quantization configurations (both weights and activations) across multiple mobile platforms with varying hardware capabilities. Unlike traditional benchmarks that assess full-scale models on high-end GPU clusters, we focus on evaluating resource efficiency (memory and power consumption) and harmful output for compressed models on mobile devices. Our key observations include i) differences in energy efficiency and throughput across mobile platforms; ii) the impact of quantization on memory usage, GPU execution time, and power consumption; and iii) accuracy and performance degradation of quantized models compared to their non-quantized counterparts; and iv) the frequency of hallucinations and toxic content generated by compressed LLMs on mobile devices.<|reference_end|> | arxiv | @article{li2024palmbench:,
title={PalmBench: A Comprehensive Benchmark of Compressed Large Language Models
on Mobile Platforms},
author={Yilong Li, Jingyu Liu, Hao Zhang, M Badri Narayanan, Utkarsh Sharma,
Shuai Zhang, Pan Hu, Yijing Zeng, Jayaram Raghuram, Suman Banerjee},
journal={arXiv preprint arXiv:2410.05315},
year={2024},
archivePrefix={arXiv},
eprint={2410.05315},
primaryClass={cs.LG cs.AI}
} | li2024palmbench: |
arxiv-666672 | 2410.05317 | Accelerating Diffusion Transformers with Token-wise Feature Caching | <|reference_start|>Accelerating Diffusion Transformers with Token-wise Feature Caching: Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching methods have been introduced to accelerate diffusion transformers by caching the features in previous timesteps and reusing them in the following timesteps. However, previous caching methods ignore that different tokens exhibit different sensitivities to feature caching, and feature caching on some tokens may lead to 10$\times$ more destruction to the overall generation quality compared with other tokens. In this paper, we introduce token-wise feature caching, allowing us to adaptively select the most suitable tokens for caching, and further enable us to apply different caching ratios to neural layers in different types and depths. Extensive experiments on PixArt-$\alpha$, OpenSora, and DiT demonstrate our effectiveness in both image and video generation with no requirements for training. For instance, 2.36$\times$ and 1.93$\times$ acceleration are achieved on OpenSora and PixArt-$\alpha$ with almost no drop in generation quality.<|reference_end|> | arxiv | @article{zou2024accelerating,
title={Accelerating Diffusion Transformers with Token-wise Feature Caching},
author={Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, Linfeng Zhang},
journal={arXiv preprint arXiv:2410.05317},
year={2024},
archivePrefix={arXiv},
eprint={2410.05317},
primaryClass={cs.LG cs.AI cs.CV}
} | zou2024accelerating |
arxiv-666673 | 2410.05318 | Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification | <|reference_start|>Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification: Despite significant advancements in the general capability of large language models (LLMs), they continue to struggle with consistent and accurate reasoning, especially in complex tasks such as mathematical and code reasoning. One key limitation is that LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors, which hampers their ability to reliably verify and rank outputs. To address this, we scale up the inference-time computation by generating multiple reasoning paths and employing verifiers to assess and rank the generated outputs by correctness. To facilitate this, we introduce a comprehensive dataset consisting of correct and incorrect solutions for math and code tasks, generated by multiple LLMs. This diverse set of solutions enables verifiers to more effectively distinguish and rank correct answers from erroneous outputs. The training methods for building verifiers were selected based on an extensive comparison of existing approaches. Moreover, to leverage the unique strengths of different reasoning strategies, we propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification. CoT provides a clear, step-by-step reasoning process that enhances interpretability, while PoT, being executable, offers a precise and error-sensitive validation mechanism. By taking both of their strengths, our approach significantly improves the accuracy and reliability of reasoning verification. Our verifiers, Math-Rev and Code-Rev, demonstrate substantial performance gains to existing LLMs, achieving state-of-the-art results on benchmarks such as GSM8k and MATH and even outperforming GPT-4o with Qwen-72B-Instruct as the reasoner.<|reference_end|> | arxiv | @article{liang2024improving,
title={Improving LLM Reasoning through Scaling Inference Computation with
Collaborative Verification},
author={Zhenwen Liang, Ye Liu, Tong Niu, Xiangliang Zhang, Yingbo Zhou, Semih
Yavuz},
journal={arXiv preprint arXiv:2410.05318},
year={2024},
archivePrefix={arXiv},
eprint={2410.05318},
primaryClass={cs.LG cs.AI}
} | liang2024improving |
arxiv-666674 | 2410.05320 | The OCON model: an old but gold solution for distributable supervised classification | <|reference_start|>The OCON model: an old but gold solution for distributable supervised classification: This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recognition research field. Through pseudo-Neural Architecture Search and Hyper-Parameters Tuning experiments conducted with an informed grid-search methodology, we achieve classification accuracy comparable to nowadays complex architectures (90.0 - 93.7%). Despite its simplicity, our model prioritizes generalization of language context and distributed applicability, supported by relevant statistical and performance metrics. The experiments code is openly available at our GitHub.<|reference_end|> | arxiv | @article{giacomelli2024the,
title={The OCON model: an old but gold solution for distributable supervised
classification},
author={Stefano Giacomelli, Marco Giordano, Claudia Rinaldi},
journal={arXiv preprint arXiv:2410.05320},
year={2024},
archivePrefix={arXiv},
eprint={2410.05320},
primaryClass={eess.AS cs.AI cs.CL cs.DB cs.LG cs.SD}
} | giacomelli2024the |
arxiv-666675 | 2410.05322 | Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models | <|reference_start|>Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models: Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational resources, leading to high costs and large environmental impacts. Moreover, video models currently offer limited control of the output motion. This paper introduces a novel approach to video generation by augmenting image diffusion models to create sequential animation frames while maintaining fine detail. These techniques can be applied to existing image models without training any video parameters (zero-shot) by altering the input noise in a latent diffusion model. Two complementary methods are presented. Noise crystallization ensures consistency but is limited to large movements due to reduced latent embedding sizes. Liquid noise trades consistency for greater flexibility without resolution limitations. The core concepts also allow other applications such as relighting, seamless upscaling, and improved video style transfer. Furthermore, an exploration of the VAE embedding used for latent diffusion models is performed, resulting in interesting theoretical insights such as a method for human-interpretable latent spaces.<|reference_end|> | arxiv | @article{khan2024noise,
title={Noise Crystallization and Liquid Noise: Zero-shot Video Generation using
Image Diffusion Models},
author={Muhammad Haaris Khan, Hadrien Reynaud, Bernhard Kainz},
journal={arXiv preprint arXiv:2410.05322},
year={2024},
archivePrefix={arXiv},
eprint={2410.05322},
primaryClass={cs.CV}
} | khan2024noise |
arxiv-666676 | 2410.05323 | From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction | <|reference_start|>From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction: With the rapid development of various sensing devices, spatiotemporal data is becoming increasingly important nowadays. However, due to sensing costs and privacy concerns, the collected data is often incomplete and coarse-grained, limiting its application to specific tasks. To address this, we propose a new task called spatiotemporal data reconstruction, which aims to infer complete and fine-grained data from sparse and coarse-grained observations. To achieve this, we introduce a two-stage data inference framework, DiffRecon, grounded in the Denoising Diffusion Probabilistic Model (DDPM). In the first stage, we present Diffusion-C, a diffusion model augmented by ST-PointFormer, a powerful encoder designed to leverage the spatial correlations between sparse data points. Following this, the second stage introduces Diffusion-F, which incorporates the proposed T-PatternNet to capture the temporal pattern within sequential data. Together, these two stages form an end-to-end framework capable of inferring complete, fine-grained data from incomplete and coarse-grained observations. We conducted experiments on multiple real-world datasets to demonstrate the superiority of our method.<|reference_end|> | arxiv | @article{sun2024from,
title={From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage
Framework for Spatiotemporal Data Reconstruction},
author={Ziyu Sun, Haoyang Su, En Wang, Funing Yang, Yongjian Yang, Wenbin Liu},
journal={arXiv preprint arXiv:2410.05323},
year={2024},
archivePrefix={arXiv},
eprint={2410.05323},
primaryClass={cs.LG cs.AI}
} | sun2024from |
arxiv-666677 | 2410.05325 | Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification | <|reference_start|>Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification: Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study addresses these challenges and evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN) for classifying 31 cancer types and normal tissues. To address the high-dimensionality of multi-omics data, we employed LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection, leading to the creation of LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOTGN models. Graph structures for the networks were constructed using gene correlation matrices and protein-protein interaction networks for multi-omics integration of messenger-RNA, micro-RNA, and DNA methylation data. Such data integration enables the networks to dynamically focus on important relationships between biological entities, improving both model performance and interpretability. Among the models, LASSO-MOGAT with a correlation-based graph structure achieved state-of-the-art accuracy (95.9%) and outperformed the LASSO-MOGCN and LASSO-MOTGN models in terms of precision, recall, and F1-score. Our findings demonstrate that integrating multi-omics data in graph-based architectures enhances cancer classification performance by uncovering distinct molecular patterns that contribute to a better understanding of cancer biology and potential biomarkers for disease progression.<|reference_end|> | arxiv | @article{alharbi2024comparative,
title={Comparative Analysis of Multi-Omics Integration Using Advanced Graph
Neural Networks for Cancer Classification},
author={Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir,
Mohanad Mohammed},
journal={arXiv preprint arXiv:2410.05325},
year={2024},
archivePrefix={arXiv},
eprint={2410.05325},
primaryClass={q-bio.GN cs.LG}
} | alharbi2024comparative |
arxiv-666678 | 2410.05326 | Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers | <|reference_start|>Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers: Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development.<|reference_end|> | arxiv | @article{sours2024early-cycle,
title={Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life
Predictions Across Manufacturers},
author={Tyler Sours (1), Shivang Agarwal (1), Marc Cormier (2), Jordan
Crivelli-Decker (1), Steffen Ridderbusch (1), Stephen L. Glazier (2), Connor
P. Aiken (2), Aayush R. Singh (1), Ang Xiao (1), Omar Allam (1)},
journal={arXiv preprint arXiv:2410.05326},
year={2024},
archivePrefix={arXiv},
eprint={2410.05326},
primaryClass={cs.LG cond-mat.mtrl-sci}
} | sours2024early-cycle |
arxiv-666679 | 2410.05328 | Reward Learning From Preference With Ties | <|reference_start|>Reward Learning From Preference With Ties: Reward learning plays a pivotal role in Reinforcement Learning from Human Feedback (RLHF), ensuring the alignment of language models. The Bradley-Terry (BT) model stands as the prevalent choice for capturing human preferences from datasets containing pairs of chosen and rejected responses. In preference modeling, the focus is not on absolute values but rather on the reward difference between chosen and rejected responses, referred to as preference strength. Thus, precise evaluation of preference strength holds paramount importance in preference modeling. However, an easily overlooked factor significantly affecting preference strength measurement is that human attitudes towards two responses may not solely indicate a preference for one over the other and ties are also a common occurrence. To address this, we propose the adoption of the generalized Bradley-Terry model -- the Bradley-Terry model with ties (BTT) -- to accommodate tied preferences, thus leveraging additional information. We prove that even with the access to the true distributions of prompt and response, disregarding ties can lead to a notable bias in preference strength measurement. Comprehensive experiments further validate the advantages of incorporating ties in preference modeling. Notably, fine-tuning with BTT significantly outperforms fine-tuning with BT on synthetic preference datasets with ties, labeled by state-of-the-art open-source LLMs.<|reference_end|> | arxiv | @article{liu2024reward,
title={Reward Learning From Preference With Ties},
author={Jinsong Liu, Dongdong Ge, Ruihao Zhu},
journal={arXiv preprint arXiv:2410.05328},
year={2024},
archivePrefix={arXiv},
eprint={2410.05328},
primaryClass={cs.LG cs.AI}
} | liu2024reward |
arxiv-666680 | 2410.05329 | El Nino Southern Oscillation and Atlantic Multidecadal Oscillation Impact on Hurricanes North Atlantic Basin | <|reference_start|>El Nino Southern Oscillation and Atlantic Multidecadal Oscillation Impact on Hurricanes North Atlantic Basin: Tropical cyclones (TCs), including hurricanes and typhoons, cause significant property damage and result in fatalities, making it crucial to understand the factors driving extreme TCs. The El Nino Southern Oscillation (ENSO) influences TC formation through tropospheric vorticity, wind shear, and atmospheric circulations. Apart from atmospheric changes, oceans influence activity through sea surface temperatures (SSTs) and deep ocean heat content. These Atlantic SSTs determine the Atlantic Multidecadal Oscillation (AMO), which indicates SST variability in the Atlantic. This research focuses on ENSO, AMO, and SSTs impact on the strength and frequency of TCs in the North Atlantic Basin. AMO and SST anomalies are increasing at an alarming rate, but it remains unclear how their dynamics will influence future TC behavior. I used observational cyclone track data from 1950 to 2023, the Oceanic Nino Index (ONI), and NOAAs Extended Reconstructed SST V5 (ERSST). I found that Increasing SSTs over the past decade indicate stronger TCs, while warm phase AMO periods correspond with higher TC frequency. Meanwhile, a greater frequency of landfalling TCs can be attributed to La Nina or ENSO-neutral, with El Nino decreasing the frequency of landfalling TCs. Such relationships suggest that as the seasonal predictability of ENSO and SSTs improve, seasonal TC forecasts may improve.<|reference_end|> | arxiv | @article{basineni2024el,
title={El Nino Southern Oscillation and Atlantic Multidecadal Oscillation
Impact on Hurricanes North Atlantic Basin},
author={Suchit Basineni},
journal={arXiv preprint arXiv:2410.05329},
year={2024},
archivePrefix={arXiv},
eprint={2410.05329},
primaryClass={physics.ao-ph cs.LG}
} | basineni2024el |
arxiv-666681 | 2410.05330 | Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs | <|reference_start|>Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs: The research investigates how the application of a machine-learning random forest model improves the accuracy and precision of a Delphi model. The context of the research is Azerbaijani SMEs and the data for the study has been obtained from a financial institution which had gathered it from the enterprises (as there is no public data on local SMEs, it was not practical to verify the data independently). The research used accuracy, precision, recall and F-1 scores for both models to compare them and run the algorithms in Python. The findings showed that accuracy, precision, recall and F- 1 all improve considerably (from 0.69 to 0.83, from 0.65 to 0.81, from 0.56 to 0.77 and from 0.58 to 0.79, respectively). The implications are that by applying AI models in credit risk modeling, financial institutions can improve the accuracy of identifying potential defaulters which would reduce their credit risk. In addition, an unfair rejection of credit access for SMEs would also go down having a significant contribution to an economic growth in the economy. Finally, such ethical issues as transparency of algorithms and biases in historical data should be taken on board while making decisions based on AI algorithms in order to reduce mechanical dependence on algorithms that cannot be justified in practice.<|reference_end|> | arxiv | @article{karimova2024application,
title={Application of AI in Credit Risk Scoring for Small Business Loans: A
case study on how AI-based random forest model improves a Delphi model
outcome in the case of Azerbaijani SMEs},
author={Nigar Karimova},
journal={arXiv preprint arXiv:2410.05330},
year={2024},
archivePrefix={arXiv},
eprint={2410.05330},
primaryClass={q-fin.RM cs.LG}
} | karimova2024application |
arxiv-666682 | 2410.05331 | Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion | <|reference_start|>Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion: Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over 4x increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.<|reference_end|> | arxiv | @article{wang2024taylor,
title={Taylor Unswift: Secured Weight Release for Large Language Models via
Taylor Expansion},
author={Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi
Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia
Hu},
journal={arXiv preprint arXiv:2410.05331},
year={2024},
archivePrefix={arXiv},
eprint={2410.05331},
primaryClass={cs.CR cs.AI cs.CL cs.LG}
} | wang2024taylor |
arxiv-666683 | 2410.05332 | VPI-Mlogs: A web-based machine learning solution for applications in petrophysics | <|reference_start|>VPI-Mlogs: A web-based machine learning solution for applications in petrophysics: Machine learning is an important part of the data science field. In petrophysics, machine learning algorithms and applications have been widely approached. In this context, Vietnam Petroleum Institute (VPI) has researched and deployed several effective prediction models, namely missing log prediction, fracture zone and fracture density forecast, etc. As one of our solutions, VPI-MLogs is a web-based deployment platform which integrates data preprocessing, exploratory data analysis, visualisation and model execution. Using the most popular data analysis programming language, Python, this approach gives users a powerful tool to deal with the petrophysical logs section. The solution helps to narrow the gap between common knowledge and petrophysics insights. This article will focus on the web-based application which integrates many solutions to grasp petrophysical data.<|reference_end|> | arxiv | @article{nguyen2024vpi-mlogs:,
title={VPI-Mlogs: A web-based machine learning solution for applications in
petrophysics},
author={Anh Tuan Nguyen},
journal={arXiv preprint arXiv:2410.05332},
year={2024},
doi={10.47800/PVJ.2022.10-06},
archivePrefix={arXiv},
eprint={2410.05332},
primaryClass={cs.LG}
} | nguyen2024vpi-mlogs: |
arxiv-666684 | 2410.05333 | A Global Cybersecurity Standardization Framework for Healthcare Informatics | <|reference_start|>A Global Cybersecurity Standardization Framework for Healthcare Informatics: Healthcare has witnessed an increased digitalization in the post-COVID world. Technologies such as the medical internet of things and wearable devices are generating a plethora of data available on the cloud anytime from anywhere. This data can be analyzed using advanced artificial intelligence techniques for diagnosis, prognosis, or even treatment of disease. This advancement comes with a major risk to protecting and securing protected health information (PHI). The prevailing regulations for preserving PHI are neither comprehensive nor easy to implement. The study first identifies twenty activities crucial for privacy and security, then categorizes them into five homogeneous categories namely: $\complement_1$ (Policy and Compliance Management), $\complement_2$ (Employee Training and Awareness), $\complement_3$ (Data Protection and Privacy Control), $\complement_4$ (Monitoring and Response), and $\complement_5$ (Technology and Infrastructure Security) and prioritizes these categories to provide a framework for the implementation of privacy and security in a wise manner. The framework utilized the Delphi Method to identify activities, criteria for categorization, and prioritization. Categorization is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and prioritization is performed using a Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS). The outcomes conclude that $\complement_3$ activities should be given first preference in implementation and followed by $\complement_1$ and $\complement_2$ activities. Finally, $\complement_4$ and $\complement_5$ should be implemented. The prioritized view of identified clustered healthcare activities related to security and privacy, are useful for healthcare policymakers and healthcare informatics professionals.<|reference_end|> | arxiv | @article{gupta2024a,
title={A Global Cybersecurity Standardization Framework for Healthcare
Informatics},
author={Kishu Gupta, Vinaytosh Mishra, Aaisha Makkar},
journal={IEEE Journal of Biomedical and Health Informatics (2024)},
year={2024},
doi={10.1109/JBHI.2024.3467179},
archivePrefix={arXiv},
eprint={2410.05333},
primaryClass={cs.CR cs.LG}
} | gupta2024a |
arxiv-666685 | 2410.05334 | TA3: Testing Against Adversarial Attacks on Machine Learning Models | <|reference_start|>TA3: Testing Against Adversarial Attacks on Machine Learning Models: Adversarial attacks are major threats to the deployment of machine learning (ML) models in many applications. Testing ML models against such attacks is becoming an essential step for evaluating and improving ML models. In this paper, we report the design and development of an interactive system for aiding the workflow of Testing Against Adversarial Attacks (TA3). In particular, with TA3, human-in-the-loop (HITL) enables human-steered attack simulation and visualization-assisted attack impact evaluation. While the current version of TA3 focuses on testing decision tree models against adversarial attacks based on the One Pixel Attack Method, it demonstrates the importance of HITL in ML testing and the potential application of HITL to the ML testing workflows for other types of ML models and other types of adversarial attacks.<|reference_end|> | arxiv | @article{jin2024ta3:,
title={TA3: Testing Against Adversarial Attacks on Machine Learning Models},
author={Yuanzhe Jin, Min Chen},
journal={arXiv preprint arXiv:2410.05334},
year={2024},
archivePrefix={arXiv},
eprint={2410.05334},
primaryClass={cs.CR cs.LG}
} | jin2024ta3: |
arxiv-666686 | 2410.05336 | GreenLight-Gym: A Reinforcement Learning Benchmark Environment for Greenhouse Crop Production Control | <|reference_start|>GreenLight-Gym: A Reinforcement Learning Benchmark Environment for Greenhouse Crop Production Control: Controlling greenhouse crop production systems is a complex task due to uncertain and non-linear dynamics between crops, indoor and outdoor climate, and economics. The declining number of skilled growers necessitates the development of autonomous greenhouse control systems. Reinforcement Learning (RL) is a promising approach that can learn a control policy to automate greenhouse management. RL optimises a control policy through interactions with a model of the greenhouse while guided by an economic-based reward function. However, its application to real-world systems is limited due to discrepancies between models and real-world dynamics. Moreover, RL controllers may struggle to maintain state constraints while optimising the primary objective, especially when models inadequately capture the adverse effects of constraint violations on crop growth. Also, the generalisation to novel states, for example, due to unseen weather trajectories, is underexplored in RL-based greenhouse control. This work addresses these challenges through three key contributions. First, we present GreenLight-Gym, the first open-source environment designed for training and evaluating RL algorithms on the state-of-the-art greenhouse model GreenLight. GreenLight-Gym enables the community to benchmark RL-based control methodologies. Second, we compare two reward-shaping approaches, using either a multiplicative or additive penalty, to enforce state boundaries. The additive penalty achieves more stable training while better adhering to state constraints, while the multiplicative penalty yields marginally higher profits. Finally, we evaluate RL performance on a disjoint training and testing weather dataset, demonstrating improved generalisation to unseen conditions. Our environment and experiment scripts are open-sourced, facilitating innovative research on learning-based greenhouse control.<|reference_end|> | arxiv | @article{van laatum2024greenlight-gym:,
title={GreenLight-Gym: A Reinforcement Learning Benchmark Environment for
Greenhouse Crop Production Control},
author={Bart van Laatum, Eldert J. van Henten, Sjoerd Boersma},
journal={arXiv preprint arXiv:2410.05336},
year={2024},
archivePrefix={arXiv},
eprint={2410.05336},
primaryClass={eess.SY cs.LG cs.SY math.OC}
} | van laatum2024greenlight-gym: |
arxiv-666687 | 2410.05338 | Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach | <|reference_start|>Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach: Recent advances in Deep Neural Networks (DNNs) have demonstrated outstanding performance across various domains. However, their large size is a challenge for deployment on resource-constrained devices such as mobile, edge, and IoT platforms. To overcome this, a distributed inference setup can be used where a small-sized DNN (initial few layers) can be deployed on mobile, a bigger version on the edge, and the full-fledged, on the cloud. A sample that has low complexity (easy) could be then inferred on mobile, that has moderate complexity (medium) on edge, and higher complexity (hard) on the cloud. As the complexity of each sample is not known beforehand, the following question arises in distributed inference: how to decide complexity so that it is processed by enough layers of DNNs. We develop a novel approach named DIMEE that utilizes Early Exit (EE) strategies developed to minimize inference latency in DNNs. DIMEE aims to improve the accuracy, taking into account the offloading cost from mobile to edge/cloud. Experimental validation on GLUE datasets, encompassing various NLP tasks, shows that our method significantly reduces the inference cost (> 43%) while maintaining a minimal drop in accuracy (< 0.3%) compared to the case where all the inference is made in cloud.<|reference_end|> | arxiv | @article{bajpai2024distributed,
title={Distributed Inference on Mobile Edge and Cloud: An Early Exit based
Clustering Approach},
author={Divya Jyoti Bajpai and Manjesh Kumar Hanawal},
journal={arXiv preprint arXiv:2410.05338},
year={2024},
archivePrefix={arXiv},
eprint={2410.05338},
primaryClass={cs.LG cs.AI cs.DC}
} | bajpai2024distributed |
arxiv-666688 | 2410.05339 | Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024) | <|reference_start|>Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024): Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities, particularly as they grow in size and are trained on more data. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning. The goal of this workshop is to create a platform for researchers from different disciplines and/or AI perspectives, to explore approaches and techniques with the aim to reconcile reasoning between language models using transformers and using logic-based representations. The specific objectives include analyzing the reasoning abilities of language models measured alongside KR methods, injecting KR-style reasoning abilities into language models (including by neuro-symbolic means), and formalizing the kind of reasoning language models carry out. This exploration aims to uncover how language models can effectively integrate and leverage knowledge and reasoning with it, thus improving their application and utility in areas where precision and reliability are a key requirement.<|reference_end|> | arxiv | @article{satoh2024proceedings,
title={Proceedings of the First International Workshop on Next-Generation
Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024)},
author={Ken Satoh, Ha-Thanh Nguyen, Francesca Toni, Randy Goebel, Kostas
Stathis},
journal={arXiv preprint arXiv:2410.05339},
year={2024},
archivePrefix={arXiv},
eprint={2410.05339},
primaryClass={cs.AI}
} | satoh2024proceedings |
arxiv-666689 | 2410.05340 | Generating CAD Code with Vision-Language Models for 3D Designs | <|reference_start|>Generating CAD Code with Vision-Language Models for 3D Designs: Generative AI has transformed the fields of Design and Manufacturing by providing efficient and automated methods for generating and modifying 3D objects. One approach involves using Large Language Models (LLMs) to generate Computer- Aided Design (CAD) scripting code, which can then be executed to render a 3D object; however, the resulting 3D object may not meet the specified requirements. Testing the correctness of CAD generated code is challenging due to the complexity and structure of 3D objects (e.g., shapes, surfaces, and dimensions) that are not feasible in code. In this paper, we introduce CADCodeVerify, a novel approach to iteratively verify and improve 3D objects generated from CAD code. Our approach works by producing ameliorative feedback by prompting a Vision-Language Model (VLM) to generate and answer a set of validation questions to verify the generated object and prompt the VLM to correct deviations. To evaluate CADCodeVerify, we introduce, CADPrompt, the first benchmark for CAD code generation, consisting of 200 natural language prompts paired with expert-annotated scripting code for 3D objects to benchmark progress. Our findings show that CADCodeVerify improves VLM performance by providing visual feedback, enhancing the structure of the 3D objects, and increasing the success rate of the compiled program. When applied to GPT-4, CADCodeVerify achieved a 7.30% reduction in Point Cloud distance and a 5.0% improvement in success rate compared to prior work<|reference_end|> | arxiv | @article{alrashedy2024generating,
title={Generating CAD Code with Vision-Language Models for 3D Designs},
author={Kamel Alrashedy, Pradyumna Tambwekar, Zulfiqar Zaidi, Megan
Langwasser, Wei Xu, Matthew Gombolay},
journal={arXiv preprint arXiv:2410.05340},
year={2024},
archivePrefix={arXiv},
eprint={2410.05340},
primaryClass={cs.LG}
} | alrashedy2024generating |
arxiv-666690 | 2410.05341 | NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping | <|reference_start|>NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping: Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the complex projection from neural activity to fMRI hemodynamic responses and the spatial ambiguity of EEG pose substantial challenges both in modeling and interpretability. Relatively few studies to date have developed approaches for EEG-fMRI translation, and although they have made significant strides, the inference of fMRI signals in a given study has been limited to a small set of brain areas and to a single condition (i.e., either resting-state or a specific task). The capability to predict fMRI signals in other brain areas, as well as to generalize across conditions, remain critical gaps in the field. To tackle these challenges, we introduce a novel and generalizable framework: NeuroBOLT, i.e., Neuro-to-BOLD Transformer, which leverages multi-dimensional representation learning from temporal, spatial, and spectral domains to translate raw EEG data to the corresponding fMRI activity signals across the brain. Our experiments demonstrate that NeuroBOLT effectively reconstructs resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions, achieving state-of-the-art accuracy and significantly advancing the integration of these two modalities.<|reference_end|> | arxiv | @article{li2024neurobolt:,
title={NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional
Feature Mapping},
author={Yamin Li, Ange Lou, Ziyuan Xu, Shengchao Zhang, Shiyu Wang, Dario J.
Englot, Soheil Kolouri, Daniel Moyer, Roza G. Bayrak, Catie Chang},
journal={arXiv preprint arXiv:2410.05341},
year={2024},
archivePrefix={arXiv},
eprint={2410.05341},
primaryClass={eess.IV cs.AI cs.LG}
} | li2024neurobolt: |
arxiv-666691 | 2410.05342 | Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders | <|reference_start|>Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders: The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insufficient supervision. To address these issues at some extend, we propose a multi-stage graph learning framework which incorporates 1) pretrain stage : self-supervised graph learning on insufficient supervision of the fmri data 2) fine-tune stage : supervised graph learning for brain disorder diagnosis. Experiment results on three datasets, Autism Brain Imaging Data Exchange ABIDE I, ABIDE II and ADHD with AAL1,demonstrating the superiority and generalizability of the proposed framework compared to the state of art of models.(ranging from 0.7330 to 0.9321,0.7209 to 0.9021,0.6338 to 0.6699)<|reference_end|> | arxiv | @article{gao2024multi-stage,
title={Multi-Stage Graph Learning for fMRI Analysis to Diagnose
Neuro-Developmental Disorders},
author={Wenjing Gao, Yuanyuan Yang, Jianrui Wei, Xuntao Yin, Xinhan Di},
journal={arXiv preprint arXiv:2410.05342},
year={2024},
archivePrefix={arXiv},
eprint={2410.05342},
primaryClass={q-bio.NC cs.CV eess.IV}
} | gao2024multi-stage |
arxiv-666692 | 2410.05343 | EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos with Procedural Texts | <|reference_start|>EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos with Procedural Texts: Mistake action detection from egocentric videos is crucial for developing intelligent archives that detect workers' errors and provide feedback. Previous studies have been limited to specific domains, focused on detecting mistakes from videos without procedural texts, and analyzed whether actions are mistakes. To address these limitations, in this paper, we propose the EgoOops dataset, which includes egocentric videos, procedural texts, and three types of annotations: video-text alignment, mistake labels, and descriptions for mistakes. EgoOops covers five procedural domains and includes 50 egocentric videos. The video-text alignment allows the model to detect mistakes based on both videos and procedural texts. The mistake labels and descriptions enable detailed analysis of real-world mistakes. Based on EgoOops, we tackle two tasks: video-text alignment and mistake detection. For video-text alignment, we enhance the recent StepFormer model with an additional loss for fine-tuning. Based on the alignment results, we propose a multi-modal classifier to predict mistake labels. In our experiments, the proposed methods achieve higher performance than the baselines. In addition, our ablation study demonstrates the effectiveness of combining videos and texts. We will release the dataset and codes upon publication.<|reference_end|> | arxiv | @article{haneji2024egooops:,
title={EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos
with Procedural Texts},
author={Yuto Haneji, Taichi Nishimura, Hirotaka Kameko, Keisuke Shirai, Tomoya
Yoshida, Keiya Kajimura, Koki Yamamoto, Taiyu Cui, Tomohiro Nishimoto,
Shinsuke Mori},
journal={arXiv preprint arXiv:2410.05343},
year={2024},
archivePrefix={arXiv},
eprint={2410.05343},
primaryClass={cs.CV cs.AI cs.CL}
} | haneji2024egooops: |
arxiv-666693 | 2410.05345 | Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation | <|reference_start|>Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation: Classifiers trained with Empirical Risk Minimization (ERM) tend to rely on attributes that have high spurious correlation with the target. This can degrade the performance on underrepresented (or 'minority') groups that lack these attributes, posing significant challenges for both out-of-distribution generalization and fairness objectives. Many studies aim to enhance robustness to spurious correlation, but they sometimes depend on group annotations for training. Additionally, a common limitation in previous research is the reliance on group-annotated validation datasets for model selection. This constrains their applicability in situations where the nature of the spurious correlation is not known, or when group labels for certain spurious attributes are not available. To enhance model robustness with minimal group annotation assumptions, we propose Environment-based Validation and Loss-based Sampling (EVaLS). It uses the losses from an ERM-trained model to construct a balanced dataset of high-loss and low-loss samples, mitigating group imbalance in data. This significantly enhances robustness to group shifts when equipped with a simple post-training last layer retraining. By using environment inference methods to create diverse environments with correlation shifts, EVaLS can potentially eliminate the need for group annotation in validation data. In this context, the worst environment accuracy acts as a reliable surrogate throughout the retraining process for tuning hyperparameters and finding a model that performs well across diverse group shifts. EVaLS effectively achieves group robustness, showing that group annotation is not necessary even for validation. It is a fast, straightforward, and effective approach that reaches near-optimal worst group accuracy without needing group annotations, marking a new chapter in the robustness of trained models against spurious correlation.<|reference_end|> | arxiv | @article{ghaznavi2024trained,
title={Trained Models Tell Us How to Make Them Robust to Spurious Correlation
without Group Annotation},
author={Mahdi Ghaznavi, Hesam Asadollahzadeh, Fahimeh Hosseini Noohdani,
Soroush Vafaie Tabar, Hosein Hasani, Taha Akbari Alvanagh, Mohammad Hossein
Rohban, Mahdieh Soleymani Baghshah},
journal={arXiv preprint arXiv:2410.05345},
year={2024},
archivePrefix={arXiv},
eprint={2410.05345},
primaryClass={cs.LG cs.AI cs.CV}
} | ghaznavi2024trained |
arxiv-666694 | 2410.05346 | AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models | <|reference_start|>AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models: Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios. However, recent studies have revealed that VLMs are vulnerable to image-based adversarial attacks, particularly targeted adversarial images that manipulate the model to generate harmful content specified by the adversary. Current attack methods rely on predefined target labels to create targeted adversarial attacks, which limits their scalability and applicability for large-scale robustness evaluations. In this paper, we propose AnyAttack, a self-supervised framework that generates targeted adversarial images for VLMs without label supervision, allowing any image to serve as a target for the attack. To address the limitation of existing methods that require label supervision, we introduce a contrastive loss that trains a generator on a large-scale unlabeled image dataset, LAION-400M dataset, for generating targeted adversarial noise. This large-scale pre-training endows our method with powerful transferability across a wide range of VLMs. Extensive experiments on five mainstream open-source VLMs (CLIP, BLIP, BLIP2, InstructBLIP, and MiniGPT-4) across three multimodal tasks (image-text retrieval, multimodal classification, and image captioning) demonstrate the effectiveness of our attack. Additionally, we successfully transfer AnyAttack to multiple commercial VLMs, including Google's Gemini, Claude's Sonnet, and Microsoft's Copilot. These results reveal an unprecedented risk to VLMs, highlighting the need for effective countermeasures.<|reference_end|> | arxiv | @article{zhang2024anyattack:,
title={AnyAttack: Towards Large-scale Self-supervised Generation of Targeted
Adversarial Examples for Vision-Language Models},
author={Jiaming Zhang, Junhong Ye, Xingjun Ma, Yige Li, Yunfan Yang, Jitao
Sang, Dit-Yan Yeung},
journal={arXiv preprint arXiv:2410.05346},
year={2024},
archivePrefix={arXiv},
eprint={2410.05346},
primaryClass={cs.LG cs.AI}
} | zhang2024anyattack: |
arxiv-666695 | 2410.05347 | ResTNet: Defense against Adversarial Policies via Transformer in Computer Go | <|reference_start|>ResTNet: Defense against Adversarial Policies via Transformer in Computer Go: Although AlphaZero has achieved superhuman levels in Go, recent research has highlighted its vulnerability in particular situations requiring a more comprehensive understanding of the entire board. To address this challenge, this paper introduces ResTNet, a network that interleaves residual networks and Transformer. Our empirical experiments demonstrate several advantages of using ResTNet. First, it not only improves playing strength but also enhances the ability of global information. Second, it defends against an adversary Go program, called cyclic-adversary, tailor-made for attacking AlphaZero algorithms, significantly reducing the average probability of being attacked rate from 70.44% to 23.91%. Third, it improves the accuracy from 59.15% to 80.01% in correctly recognizing ladder patterns, which are one of the challenging patterns for Go AIs. Finally, ResTNet offers a potential explanation of the decision-making process and can also be applied to other games like Hex. To the best of our knowledge, ResTNet is the first to integrate residual networks and Transformer in the context of AlphaZero for board games, suggesting a promising direction for enhancing AlphaZero's global understanding.<|reference_end|> | arxiv | @article{wu2024restnet:,
title={ResTNet: Defense against Adversarial Policies via Transformer in
Computer Go},
author={Tai-Lin Wu, Ti-Rong Wu, Chung-Chin Shih, Yan-Ru Ju, I-Chen Wu},
journal={arXiv preprint arXiv:2410.05347},
year={2024},
archivePrefix={arXiv},
eprint={2410.05347},
primaryClass={cs.LG cs.AI}
} | wu2024restnet: |
arxiv-666696 | 2410.05349 | SoK: Towards Security and Safety of Edge AI | <|reference_start|>SoK: Towards Security and Safety of Edge AI: Advanced AI applications have become increasingly available to a broad audience, e.g., as centrally managed large language models (LLMs). Such centralization is both a risk and a performance bottleneck - Edge AI promises to be a solution to these problems. However, its decentralized approach raises additional challenges regarding security and safety. In this paper, we argue that both of these aspects are critical for Edge AI, and even more so, their integration. Concretely, we survey security and safety threats, summarize existing countermeasures, and collect open challenges as a call for more research in this area.<|reference_end|> | arxiv | @article{wingarz2024sok:,
title={SoK: Towards Security and Safety of Edge AI},
author={Tatjana Wingarz, Anne Lauscher, Janick Edinger, Dominik Kaaser, Stefan
Schulte, Mathias Fischer},
journal={arXiv preprint arXiv:2410.05349},
year={2024},
archivePrefix={arXiv},
eprint={2410.05349},
primaryClass={cs.CR cs.AI}
} | wingarz2024sok: |
arxiv-666697 | 2410.05350 | GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV | <|reference_start|>GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV: Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine learning. We trained a gated recurrent unit with decay mechanisms, called GRU-D, for a binary classification between elderly - and young patients. We extracted time series for 5 vital signs from MIMIC-IV as model inputs. GRU-D was evaluated with means of 0.780 AUROC and 0.810 AUPRC on bootstrapped data. Interpreting trained model parameters, we found differences in blood pressure missingness and respiratory rate missingness as important predictors learned by parameterized hidden gated units. We successfully showed how GRU-D can be used to reveal patterns in temporal missingness building the basis of novel research directions.<|reference_end|> | arxiv | @article{giesa2024gru-d,
title={GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV},
author={Niklas Giesa, Mert Akg"ul, Sebastian Daniel Boie, Felix Balzer},
journal={arXiv preprint arXiv:2410.05350},
year={2024},
archivePrefix={arXiv},
eprint={2410.05350},
primaryClass={cs.LG stat.ML}
} | giesa2024gru-d |
arxiv-666698 | 2410.05351 | Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models | <|reference_start|>Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models: This paper investigates the use of a pre-trained language model and siamese network to discern sibling relationships between text-based cybersecurity vulnerability data. The ultimate purpose of the approach presented in this paper is towards the construction of hierarchical attack models based on a set of text descriptions characterising potential/observed vulnerabilities in a given system. Due to the nature of the data, and the uncertainty sensitive environment in which the problem is presented, a practically oriented soft computing approach is necessary. Therefore, a key focus of this work is to investigate practical questions surrounding the reliability of predicted links towards the construction of such models, to which end conceptual and practical challenges and solutions associated with the proposed approach are outlined, such as dataset complexity and stability of predictions. Accordingly, the contributions of this paper focus on producing neural networks using a pre-trained language model for predicting sibling relationships between cybersecurity vulnerabilities, then outlining how to apply this capability towards the generation of hierarchical attack models. In addition, two data sampling mechanisms for tackling data complexity, and a consensus mechanism for reducing the amount of false positive predictions are outlined. Each of these approaches is compared and contrasted using empirical results from three sets of cybersecurity data to determine their effectiveness.<|reference_end|> | arxiv | @article{sowka2024towards,
title={Towards the generation of hierarchical attack models from cybersecurity
vulnerabilities using language models},
author={Kacper Sowka, Vasile Palade, Xiaorui Jiang, Hesam Jadidbonab},
journal={arXiv preprint arXiv:2410.05351},
year={2024},
archivePrefix={arXiv},
eprint={2410.05351},
primaryClass={cs.CR cs.AI cs.LG}
} | sowka2024towards |
arxiv-666699 | 2410.05352 | Recent Advances of Multimodal Continual Learning: A Comprehensive Survey | <|reference_start|>Recent Advances of Multimodal Continual Learning: A Comprehensive Survey: Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As machine learning models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary challenge of MMCL is that it goes beyond a simple stacking of unimodal CL methods, as such straightforward approaches often yield unsatisfactory performance. In this work, we present the first comprehensive survey on MMCL. We provide essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods. We categorize existing MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting their key innovations. Additionally, to prompt further research in this field, we summarize open MMCL datasets and benchmarks, and discuss several promising future directions for investigation and development. We have also created a GitHub repository for indexing relevant MMCL papers and open resources available at https://github.com/LucyDYu/Awesome-Multimodal-Continual-Learning.<|reference_end|> | arxiv | @article{yu2024recent,
title={Recent Advances of Multimodal Continual Learning: A Comprehensive Survey},
author={Dianzhi Yu, Xinni Zhang, Yankai Chen, Aiwei Liu, Yifei Zhang, Philip
S. Yu, Irwin King},
journal={arXiv preprint arXiv:2410.05352},
year={2024},
archivePrefix={arXiv},
eprint={2410.05352},
primaryClass={cs.LG cs.AI}
} | yu2024recent |
arxiv-666700 | 2410.05353 | Towards a Categorical Foundation of Deep Learning: A Survey | <|reference_start|>Towards a Categorical Foundation of Deep Learning: A Survey: The unprecedented pace of machine learning research has lead to incredible advances, but also poses hard challenges. At present, the field lacks strong theoretical underpinnings, and many important achievements stem from ad hoc design choices which are hard to justify in principle and whose effectiveness often goes unexplained. Research debt is increasing and many papers are found not to be reproducible. This thesis is a survey that covers some recent work attempting to study machine learning categorically. Category theory is a branch of abstract mathematics that has found successful applications in many fields, both inside and outside mathematics. Acting as a lingua franca of mathematics and science, category theory might be able to give a unifying structure to the field of machine learning. This could solve some of the aforementioned problems. In this work, we mainly focus on the application of category theory to deep learning. Namely, we discuss the use of categorical optics to model gradient-based learning, the use of categorical algebras and integral transforms to link classical computer science to neural networks, the use of functors to link different layers of abstraction and preserve structure, and, finally, the use of string diagrams to provide detailed representations of neural network architectures.<|reference_end|> | arxiv | @article{crescenzi2024towards,
title={Towards a Categorical Foundation of Deep Learning: A Survey},
author={Francesco Riccardo Crescenzi},
journal={arXiv preprint arXiv:2410.05353},
year={2024},
archivePrefix={arXiv},
eprint={2410.05353},
primaryClass={cs.LG cs.AI math.CT}
} | crescenzi2024towards |
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