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arxiv-660201
|
2409.13980
|
Enhancing Advanced Visual Reasoning Ability of Large Language Models
|
<|reference_start|>Enhancing Advanced Visual Reasoning Ability of Large Language Models: Recent advancements in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models' advanced reasoning ability. Traditional Vision-Language Models (VLMs) perform well in visual perception tasks while struggling with complex reasoning scenarios. Conversely, Large Language Models (LLMs) demonstrate robust text reasoning capabilities; however, they lack visual acuity. To bridge this gap, we propose Complex Visual Reasoning Large Language Models (CVR-LLM), capitalizing on VLMs' visual perception proficiency and LLMs' extensive reasoning capability. Unlike recent multimodal large language models (MLLMs) that require a projection layer, our approach transforms images into detailed, context-aware descriptions using an iterative self-refinement loop and leverages LLMs' text knowledge for accurate predictions without extra training. We also introduce a novel multi-modal in-context learning (ICL) methodology to enhance LLMs' contextual understanding and reasoning. Additionally, we introduce Chain-of-Comparison (CoC), a step-by-step comparison technique enabling contrasting various aspects of predictions. Our CVR-LLM presents the first comprehensive study across a wide array of complex visual reasoning tasks and achieves SOTA performance among all.<|reference_end|>
|
arxiv
|
@article{li2024enhancing,
title={Enhancing Advanced Visual Reasoning Ability of Large Language Models},
author={Zhiyuan Li, Dongnan Liu, Chaoyi Zhang, Heng Wang, Tengfei Xue, Weidong
Cai},
journal={arXiv preprint arXiv:2409.13980},
year={2024},
archivePrefix={arXiv},
eprint={2409.13980},
primaryClass={cs.CV cs.AI}
}
|
li2024enhancing
|
arxiv-660202
|
2409.13982
|
CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
|
<|reference_start|>CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise: To ease the difficulty of acquiring annotation labels in 3D data, a common method is using unsupervised and open-vocabulary semantic segmentation, which leverage 2D CLIP semantic knowledge. In this paper, unlike previous research that ignores the ``noise'' raised during feature projection from 2D to 3D, we propose a novel distillation learning framework named CUS3D. In our approach, an object-level denosing projection module is designed to screen out the ``noise'' and ensure more accurate 3D feature. Based on the obtained features, a multimodal distillation learning module is designed to align the 3D feature with CLIP semantic feature space with object-centered constrains to achieve advanced unsupervised semantic segmentation. We conduct comprehensive experiments in both unsupervised and open-vocabulary segmentation, and the results consistently showcase the superiority of our model in achieving advanced unsupervised segmentation results and its effectiveness in open-vocabulary segmentation.<|reference_end|>
|
arxiv
|
@article{yu2024cus3d,
title={CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise},
author={Fuyang Yu, Runze Tian, Zhen Wang, Xiaochuan Wang, Xiaohui Liang},
journal={arXiv preprint arXiv:2409.13982},
year={2024},
doi={10.1109/ICME57554.2024},
archivePrefix={arXiv},
eprint={2409.13982},
primaryClass={cs.CV cs.MM}
}
|
yu2024cus3d
|
arxiv-660203
|
2409.13983
|
Enhanced Semantic Segmentation for Large-Scale and Imbalanced Point Clouds
|
<|reference_start|>Enhanced Semantic Segmentation for Large-Scale and Imbalanced Point Clouds: Semantic segmentation of large-scale point clouds is of significant importance in environment perception and scene understanding. However, point clouds collected from real-world environments are usually imbalanced and small-sized objects are prone to be under-sampled or misclassified due to their low occurrence frequency, thereby reducing the overall accuracy of semantic segmentation. In this study, we propose the Multilateral Cascading Network (MCNet) for large-scale and sample-imbalanced point cloud scenes. To increase the frequency of small-sized objects, we introduce the semantic-weighted sampling module, which incorporates a probability parameter into the collected data group. To facilitate feature learning, we propose a Multilateral Cascading Attention Enhancement (MCAE) module to learn complex local features through multilateral cascading operations and attention mechanisms. To promote feature fusion, we propose a Point Cross Stage Partial (P-CSP) module to combine global and local features, optimizing the integration of valuable feature information across multiple scales. Finally, we introduce the neighborhood voting module to integrate results at the output layer. Our proposed method demonstrates either competitive or superior performance relative to state-of-the-art approaches across three widely recognized benchmark datasets: S3DIS, Toronto3D, and SensatUrban with mIoU scores of 74.0\%, 82.9\% and 64.5\%, respectively. Notably, our work yielded consistent optimal results on the under-sampled semantic categories, thereby demonstrating exceptional performance in the recognition of small-sized objects.<|reference_end|>
|
arxiv
|
@article{gong2024enhanced,
title={Enhanced Semantic Segmentation for Large-Scale and Imbalanced Point
Clouds},
author={Haoran Gong, Haodong Wang, Di Wang},
journal={arXiv preprint arXiv:2409.13983},
year={2024},
archivePrefix={arXiv},
eprint={2409.13983},
primaryClass={cs.CV}
}
|
gong2024enhanced
|
arxiv-660204
|
2409.13984
|
Cycle-Consistency Uncertainty Estimation for Visual Prompting based One-Shot Defect Segmentation
|
<|reference_start|>Cycle-Consistency Uncertainty Estimation for Visual Prompting based One-Shot Defect Segmentation: Industrial defect detection traditionally relies on supervised learning models trained on fixed datasets of known defect types. While effective within a closed set, these models struggle with new, unseen defects, necessitating frequent re-labeling and re-training. Recent advances in visual prompting offer a solution by allowing models to adaptively infer novel categories based on provided visual cues. However, a prevalent issue in these methods is the over-confdence problem, where models can mis-classify unknown objects as known objects with high certainty. To addresssing the fundamental concerns about the adaptability, we propose a solution to estimate uncertainty of the visual prompting process by cycle-consistency. We designed to check whether it can accurately restore the original prompt from its predictions. To quantify this, we measure the mean Intersection over Union (mIoU) between the restored prompt mask and the originally provided prompt mask. Without using complex designs or ensemble methods with multiple networks, our approach achieved a yield rate of 0.9175 in the VISION24 one-shot industrial challenge.<|reference_end|>
|
arxiv
|
@article{kim2024cycle-consistency,
title={Cycle-Consistency Uncertainty Estimation for Visual Prompting based
One-Shot Defect Segmentation},
author={Geonuk Kim},
journal={arXiv preprint arXiv:2409.13984},
year={2024},
archivePrefix={arXiv},
eprint={2409.13984},
primaryClass={cs.CV}
}
|
kim2024cycle-consistency
|
arxiv-660205
|
2409.13985
|
LiDAR-based Quadrotor for Slope Inspection in Dense Vegetation
|
<|reference_start|>LiDAR-based Quadrotor for Slope Inspection in Dense Vegetation: This work presents a LiDAR-based quadrotor system for slope inspection in dense vegetation environments. Cities like Hong Kong are vulnerable to climate hazards, which often result in landslides. To mitigate the landslide risks, the Civil Engineering and Development Department (CEDD) has constructed steel flexible debris-resisting barriers on vulnerable natural catchments to protect residents. However, it is necessary to carry out regular inspections to identify any anomalies, which may affect the proper functioning of the barriers. Traditional manual inspection methods face challenges and high costs due to steep terrain and dense vegetation. Compared to manual inspection, unmanned aerial vehicles (UAVs) equipped with LiDAR sensors and cameras have advantages such as maneuverability in complex terrain, and access to narrow areas and high spots. However, conducting slope inspections using UAVs in dense vegetation poses significant challenges. First, in terms of hardware, the overall design of the UAV must carefully consider its maneuverability in narrow spaces, flight time, and the types of onboard sensors required for effective inspection. Second, regarding software, navigation algorithms need to be designed to enable obstacle avoidance flight in dense vegetation environments. To overcome these challenges, we develop a LiDAR-based quadrotor, accompanied by a comprehensive software system. The goal is to deploy our quadrotor in field environments to achieve efficient slope inspection. To assess the feasibility of our hardware and software system, we conduct functional tests in non-operational scenarios. Subsequently, invited by CEDD, we deploy our quadrotor in six field environments, including five flexible debris-resisting barriers located in dense vegetation and one slope that experienced a landslide. These experiments demonstrated the superiority of our quadrotor in slope inspection.<|reference_end|>
|
arxiv
|
@article{liu2024lidar-based,
title={LiDAR-based Quadrotor for Slope Inspection in Dense Vegetation},
author={Wenyi Liu, Yunfan Ren, Rui Guo, Vickie W. W. Kong, Anthony S. P. Hung,
Fangcheng Zhu, Yixi Cai, Yuying Zou, Fu Zhang},
journal={arXiv preprint arXiv:2409.13985},
year={2024},
archivePrefix={arXiv},
eprint={2409.13985},
primaryClass={cs.RO}
}
|
liu2024lidar-based
|
arxiv-660206
|
2409.13987
|
Holistic and Historical Instance Comparison for Cervical Cell Detection
|
<|reference_start|>Holistic and Historical Instance Comparison for Cervical Cell Detection: Cytology screening from Papanicolaou (Pap) smears is a common and effective tool for the preventive clinical management of cervical cancer, where abnormal cell detection from whole slide images serves as the foundation for reporting cervical cytology. However, cervical cell detection remains challenging due to 1) hazily-defined cell types (e.g., ASC-US) with subtle morphological discrepancies caused by the dynamic cancerization process, i.e., cell class ambiguity, and 2) imbalanced class distributions of clinical data may cause missed detection, especially for minor categories, i.e., cell class imbalance. To this end, we propose a holistic and historical instance comparison approach for cervical cell detection. Specifically, we first develop a holistic instance comparison scheme enforcing both RoI-level and class-level cell discrimination. This coarse-to-fine cell comparison encourages the model to learn foreground-distinguishable and class-wise representations. To emphatically improve the distinguishability of minor classes, we then introduce a historical instance comparison scheme with a confident sample selection-based memory bank, which involves comparing current embeddings with historical embeddings for better cell instance discrimination. Extensive experiments and analysis on two large-scale cytology datasets including 42,592 and 114,513 cervical cells demonstrate the effectiveness of our method. The code is available at https://github.com/hjiangaz/HERO.<|reference_end|>
|
arxiv
|
@article{jiang2024holistic,
title={Holistic and Historical Instance Comparison for Cervical Cell Detection},
author={Hao Jiang, Runsheng Liu, Yanning Zhou, Huangjing Lin, Hao Chen},
journal={arXiv preprint arXiv:2409.13987},
year={2024},
archivePrefix={arXiv},
eprint={2409.13987},
primaryClass={cs.CV}
}
|
jiang2024holistic
|
arxiv-660207
|
2409.13988
|
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation
|
<|reference_start|>GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation: Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of different structures. Current methods often address the challenges of touching, overlapping or crossing instances through individual modeling, while neglecting the intrinsic interrelation between these conditions. In this work, we propose a Gradient Anomaly-aware Biomedical Instance Segmentation approach (GAInS), which leverages instance gradient information to perceive local gradient anomaly regions, thus modeling the spatial relationship between instances and refining local region segmentation. Specifically, GAInS is firstly built on a Gradient Anomaly Mapping Module (GAMM), which encodes the radial fields of instances through window sliding to obtain instance gradient anomaly maps. To efficiently refine boundaries and regions with gradient anomaly attention, we propose an Adaptive Local Refinement Module (ALRM) with a gradient anomaly-aware loss function. Extensive comparisons and ablation experiments in three biomedical scenarios demonstrate that our proposed GAInS outperforms other state-of-the-art (SOTA) instance segmentation methods. The code is available at https://github.com/DeepGAInS/GAInS.<|reference_end|>
|
arxiv
|
@article{liu2024gains:,
title={GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation},
author={Runsheng Liu, Hao Jiang, Yanning Zhou, Huangjing Lin, Liansheng Wang,
Hao Chen},
journal={arXiv preprint arXiv:2409.13988},
year={2024},
archivePrefix={arXiv},
eprint={2409.13988},
primaryClass={cs.CV}
}
|
liu2024gains:
|
arxiv-660208
|
2409.13989
|
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models
|
<|reference_start|>ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models: There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess the performance of LLMs across a spectrum of chemical tasks varying in type and complexity. However, existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks. Specifically, ChemEval identified 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks which are informed by open-source data and the data meticulously crafted by chemical experts, ensuring that the tasks have practical value and can effectively evaluate the capabilities of LLMs. In the experiment, we evaluate 12 mainstream LLMs on ChemEval under zero-shot and few-shot learning contexts, which included carefully selected demonstration examples and carefully designed prompts. The results show that while general LLMs like GPT-4 and Claude-3.5 excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge. Conversely, specialized LLMs exhibit enhanced chemical competencies, albeit with reduced literary comprehension. This suggests that LLMs have significant potential for enhancement when tackling sophisticated tasks in the field of chemistry. We believe our work will facilitate the exploration of their potential to drive progress in chemistry. Our benchmark and analysis will be available at {\color{blue} \url{https://github.com/USTC-StarTeam/ChemEval}}.<|reference_end|>
|
arxiv
|
@article{huang2024chemeval:,
title={ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large
Language Models},
author={Yuqing Huang, Rongyang Zhang, Xuesong He, Xuyang Zhi, Hao Wang, Xin
Li, Feiyang Xu, Deguang Liu, Huadong Liang, Yi Li, Jian Cui, Zimu Liu, Shijin
Wang, Guoping Hu, Guiquan Liu, Qi Liu, Defu Lian, Enhong Chen},
journal={arXiv preprint arXiv:2409.13989},
year={2024},
archivePrefix={arXiv},
eprint={2409.13989},
primaryClass={cs.CL cs.AI cs.LG physics.chem-ph q-bio.BM}
}
|
huang2024chemeval:
|
arxiv-660209
|
2409.13992
|
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
|
<|reference_start|>SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval: Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conventional RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. SMART leverages Determinantal Point Processes (DPPs) to simultaneously model relevance, diversity and conflict, ensuring the selection of potentially high-quality contexts. Experimental results across multiple datasets demonstrate that SMART significantly enhances QA performance and surpasses previous unsupervised context selection methods, showing a promising strategy for RAG.<|reference_end|>
|
arxiv
|
@article{li2024smart-rag:,
title={SMART-RAG: Selection using Determinantal Matrices for Augmented
Retrieval},
author={Jiatao Li and Xinyu Hu and Xiaojun Wan},
journal={arXiv preprint arXiv:2409.13992},
year={2024},
archivePrefix={arXiv},
eprint={2409.13992},
primaryClass={cs.CL}
}
|
li2024smart-rag:
|
arxiv-660210
|
2409.13993
|
Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach
|
<|reference_start|>Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach: Modeling the interaction between traffic agents is a key issue in designing safe and non-conservative maneuvers in autonomous driving. This problem can be challenging when multi-modality and behavioral uncertainties are engaged. Existing methods either fail to plan interactively or consider unimodal behaviors that could lead to catastrophic results. In this paper, we introduce an integrated decision-making and trajectory planning framework based on Bayesian game (i.e., game of incomplete information). Human decisions inherently exhibit discrete characteristics and therefore are modeled as types of players in the game. A general solver based on no-regret learning is introduced to obtain a corresponding Bayesian Coarse Correlated Equilibrium, which captures the interaction between traffic agents in the multimodal context. With the attained equilibrium, decision-making and trajectory planning are performed simultaneously, and the resulting interactive strategy is shown to be optimal over the expectation of rivals' driving intentions. Closed-loop simulations on different traffic scenarios are performed to illustrate the generalizability and the effectiveness of the proposed framework.<|reference_end|>
|
arxiv
|
@article{huang2024integrated,
title={Integrated Decision Making and Trajectory Planning for Autonomous
Driving Under Multimodal Uncertainties: A Bayesian Game Approach},
author={Zhenmin Huang, Tong Li, Shaojie Shen, Jun Ma},
journal={arXiv preprint arXiv:2409.13993},
year={2024},
archivePrefix={arXiv},
eprint={2409.13993},
primaryClass={cs.RO cs.GT}
}
|
huang2024integrated
|
arxiv-660211
|
2409.13994
|
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
|
<|reference_start|>Contrastive Learning for Knowledge-Based Question Generation in Large Language Models: With the rapid development of artificial intelligence technology, especially the increasingly widespread application of question-and-answer systems, high-quality question generation has become a key component in supporting the development of these systems. This article focuses on knowledge-based question generation technology, which aims to enable computers to simulate the human questioning process based on understanding specific texts or knowledge bases. In light of the issues of hallucination and knowledge gaps present in large-scale language models when applied to knowledge-intensive tasks, this paper proposes an enhanced question generation method that incorporates contrastive learning. This method utilizes multiple models to jointly mine domain knowledge and uses contrastive learning to guide the model in reducing noise and hallucinations in generation. Experimental results show that by designing prompts containing contrasting examples, the model's performance in question generation improves considerably, particularly when contrasting instructions and examples are used simultaneously, leading to the highest quality of generated questions and improved accuracy. These results demonstrate that the method proposed in this study, which combines contrasting context and chain-of-thought prompts, can effectively improve both the quality and the practicality of question generation.<|reference_end|>
|
arxiv
|
@article{zhang2024contrastive,
title={Contrastive Learning for Knowledge-Based Question Generation in Large
Language Models},
author={Zhenhong Zhang, Jiajing Chen, Weiyan Shi, Lingjie Yi, Chihang Wang,
Qian Yu},
journal={arXiv preprint arXiv:2409.13994},
year={2024},
archivePrefix={arXiv},
eprint={2409.13994},
primaryClass={cs.CL cs.AI}
}
|
zhang2024contrastive
|
arxiv-660212
|
2409.13997
|
Drift to Remember
|
<|reference_start|>Drift to Remember: Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrophic forgetting. Recent neuroscience research suggests that neural activity in biological systems undergoes representational drift, where neural responses evolve over time, even with consistent inputs and tasks. We hypothesize that representational drift can alleviate catastrophic forgetting in AI during new task acquisition. To test this, we introduce DriftNet, a network designed to constantly explore various local minima in the loss landscape while dynamically retrieving relevant tasks. This approach ensures efficient integration of new information and preserves existing knowledge. Experimental studies in image classification and natural language processing demonstrate that DriftNet outperforms existing models in lifelong learning. Importantly, DriftNet is scalable in handling a sequence of tasks such as sentiment analysis and question answering using large language models (LLMs) with billions of parameters on a single Nvidia A100 GPU. DriftNet efficiently updates LLMs using only new data, avoiding the need for full dataset retraining. Tested on GPT-2 and RoBERTa, DriftNet is a robust, cost-effective solution for lifelong learning in LLMs. This study not only advances AI systems to emulate biological learning, but also provides insights into the adaptive mechanisms of biological neural systems, deepening our understanding of lifelong learning in nature.<|reference_end|>
|
arxiv
|
@article{du2024drift,
title={Drift to Remember},
author={Jin Du, Xinhe Zhang, Hao Shen, Xun Xian, Ganghua Wang, Jiawei Zhang,
Yuhong Yang, Na Li, Jia Liu, Jie Ding},
journal={arXiv preprint arXiv:2409.13997},
year={2024},
archivePrefix={arXiv},
eprint={2409.13997},
primaryClass={cs.AI q-bio.NC}
}
|
du2024drift
|
arxiv-660213
|
2409.13998
|
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration
|
<|reference_start|>Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration: Human intelligence possesses the ability to effectively focus on important environmental components, which enhances perception, learning, reasoning, and decision-making. Inspired by this cognitive mechanism, we introduced a novel concept termed relevance for Human-Robot Collaboration (HRC). Relevance is defined as the importance of the objects based on the applicability and pertinence of the objects for the human objective or other factors. In this paper, we further developed a novel two-loop framework integrating real-time and asynchronous processing to quantify relevance and apply relevance for safer and more efficient HRC. The asynchronous loop leverages the world knowledge from an LLM and quantifies relevance, and the real-time loop executes scene understanding, human intent prediction, and decision-making based on relevance. In decision making, we proposed and developed a human robot task allocation method based on relevance and a novel motion generation and collision avoidance methodology considering the prediction of human trajectory. Simulations and experiments show that our methodology for relevance quantification can accurately and robustly predict the human objective and relevance, with an average accuracy of up to 0.90 for objective prediction and up to 0.96 for relevance prediction. Moreover, our motion generation methodology reduces collision cases by 63.76% and collision frames by 44.74% when compared with a state-of-the-art (SOTA) collision avoidance method. Our framework and methodologies, with relevance, guide the robot on how to best assist humans and generate safer and more efficient actions for HRC.<|reference_end|>
|
arxiv
|
@article{zhang2024relevance-driven,
title={Relevance-driven Decision Making for Safer and More Efficient Human
Robot Collaboration},
author={Xiaotong Zhang, Dingcheng Huang, Kamal Youcef-Toumi},
journal={arXiv preprint arXiv:2409.13998},
year={2024},
archivePrefix={arXiv},
eprint={2409.13998},
primaryClass={cs.RO cs.AI}
}
|
zhang2024relevance-driven
|
arxiv-660214
|
2409.13999
|
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer
|
<|reference_start|>Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer: Parameter-efficient transfer learning (PETL) has shown great potential in adapting a vision transformer (ViT) pre-trained on large-scale datasets to various downstream tasks. Existing studies primarily focus on minimizing the number of learnable parameters. Although these methods are storage-efficient, they allocate excessive computational resources to easy samples, leading to inefficient inference. To address this issue, we introduce an inference-efficient tuning method termed multiple-exit tuning (MET). MET integrates multiple exits into the pre-trained ViT backbone. Since the predictions in ViT are made by a linear classifier, each exit is equipped with a linear prediction head. In inference stage, easy samples will exit at early exits and only hard enough samples will flow to the last exit, thus saving the computational cost for easy samples. MET consists of exit-specific adapters (E-adapters) and graph regularization. E-adapters are designed to extract suitable representations for different exits. To ensure parameter efficiency, all E-adapters share the same down-projection and up-projection matrices. As the performances of linear classifiers are influenced by the relationship among samples, we employ graph regularization to improve the representations fed into the classifiers at early exits. Finally, we conduct extensive experiments to verify the performance of MET. Experimental results show that MET has an obvious advantage over the state-of-the-art methods in terms of both accuracy and inference efficiency.<|reference_end|>
|
arxiv
|
@article{liu2024multiple-exit,
title={Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision
Transformer},
author={Zheng Liu, Jinchao Zhu, Nannan Li, Gao Huang},
journal={arXiv preprint arXiv:2409.13999},
year={2024},
archivePrefix={arXiv},
eprint={2409.13999},
primaryClass={cs.CV}
}
|
liu2024multiple-exit
|
arxiv-660215
|
2409.14000
|
Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature
|
<|reference_start|>Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature: Amidst the swift evolution of social media platforms and e-commerce ecosystems, the domain of opinion mining has surged as a pivotal area of exploration within natural language processing. A specialized segment within this field focuses on extracting nuanced evaluations tied to particular elements within textual contexts. This research advances a composite framework that amalgamates the positional cues of topical descriptors. The proposed system converts syntactic structures into a matrix format, leveraging convolutions and attention mechanisms within a graph to distill salient characteristics. Incorporating the positional relevance of descriptors relative to lexical items enhances the sequential integrity of the input. Trials have substantiated that this integrated graph-centric scheme markedly elevates the efficacy of evaluative categorization, showcasing preeminence.<|reference_end|>
|
arxiv
|
@article{wu2024graph,
title={Graph Neural Network Framework for Sentiment Analysis Using Syntactic
Feature},
author={Linxiao Wu, Yuanshuai Luo, Binrong Zhu, Guiran Liu, Rui Wang, Qian Yu},
journal={arXiv preprint arXiv:2409.14000},
year={2024},
archivePrefix={arXiv},
eprint={2409.14000},
primaryClass={cs.CL cs.AI}
}
|
wu2024graph
|
arxiv-660216
|
2409.14001
|
Boolean Product Graph Neural Networks
|
<|reference_start|>Boolean Product Graph Neural Networks: Graph Neural Networks (GNNs) have recently achieved significant success, with a key operation involving the aggregation of information from neighboring nodes. Substantial researchers have focused on defining neighbors for aggregation, predominantly based on observed adjacency matrices. However, in many scenarios, the explicitly given graphs contain noise, which can be amplified during the messages-passing process. Therefore, many researchers have turned their attention to latent graph inference, specifically learning a parametric graph. To mitigate fluctuations in latent graph structure learning, this paper proposes a novel Boolean product-based graph residual connection in GNNs to link the latent graph and the original graph. It computes the Boolean product between the latent graph and the original graph at each layer to correct the learning process. The Boolean product between two adjacency matrices is equivalent to triangle detection. Accordingly, the proposed Boolean product graph neural networks can be interpreted as discovering triangular cliques from the original and the latent graph. We validate the proposed method in benchmark datasets and demonstrate its ability to enhance the performance and robustness of GNNs.<|reference_end|>
|
arxiv
|
@article{wang2024boolean,
title={Boolean Product Graph Neural Networks},
author={Ziyan Wang, Bin Liu, Ling Xiang},
journal={arXiv preprint arXiv:2409.14001},
year={2024},
archivePrefix={arXiv},
eprint={2409.14001},
primaryClass={cs.LG cs.AI}
}
|
wang2024boolean
|
arxiv-660217
|
2409.14004
|
Superconvergence of the local discontinuous Galerkin method with generalized numerical fluxes for one-dimensional linear time-dependent fourth-order equations
|
<|reference_start|>Superconvergence of the local discontinuous Galerkin method with generalized numerical fluxes for one-dimensional linear time-dependent fourth-order equations: In this paper, we concentrate on the superconvergence of the local discontinuous Galerkin method with generalized numerical fluxes for one-dimensional linear time-dependent fourth-order equations. The adjustable numerical viscosity of the generalized numerical fluxes is beneficial for long time simulations with a slower error growth. By using generalized Gauss--Radau projections and correction functions together with a suitable numerical initial condition, we derive, for polynomials of degree $k$, $(2k+1)$th order superconvergence for the numerical flux and cell averages, $(k+2)$th order superconvergence at generalized Radau points, and $(k+1)$th order for error derivative at generalized Radau points. Moreover, a supercloseness result of order $(k+2)$ is established between the generalized Gauss--Radau projection and the numerical solution. Superconvergence analysis of mixed boundary conditions is also given. Equations with discontinuous initial condition and nonlinear convection term are numerically investigated, illustrating that the conclusions are valid for more general cases.<|reference_end|>
|
arxiv
|
@article{li2024superconvergence,
title={Superconvergence of the local discontinuous Galerkin method with
generalized numerical fluxes for one-dimensional linear time-dependent
fourth-order equations},
author={Linhui Li, Xiong Meng, Boying Wu},
journal={arXiv preprint arXiv:2409.14004},
year={2024},
archivePrefix={arXiv},
eprint={2409.14004},
primaryClass={math.NA cs.NA}
}
|
li2024superconvergence
|
arxiv-660218
|
2409.14005
|
High-order Space-time Flux Reconstruction Methods for Moving Domain Simulation
|
<|reference_start|>High-order Space-time Flux Reconstruction Methods for Moving Domain Simulation: A high-order space-time flux reconstruction (FR) method has been developed to solve conservation laws on moving domains. In the space-time framework, the moving domain simulation is similar to that on a stationary domain, except that the shape of the space-time elements varies with time (and space when a deforming grid is used). The geometric conservation law can be automatically satisfied to the level of the numerical resolution of the space-time schemes when the space-time discretization of the governing partial differential equations can resolve the geometric nonlinearity of curvilinear space-time elements. In this study, a space-time tensor product operation is used to construct the FR formulation, and the Gauss-Legendre quadrature points are used as solution points both in space and time. A dual time stepping method is used to solve the resulting space-time system. As has been proved by Huynh [J Sci Comput 96, 51 (2023)], in the temporal direction, the FR scheme with the Gauss-Legendre solution points is equivalent to the so-called DG-Gauss implicit Runge-Kutta (IRK) scheme when the quadrature rule based on the solution points (i.e. quadrature points used in DG) is sufficiently accurate to integrate the space-time curvilinear elements. Specifically, we show that when linear space-time elements are adopted in moving domain simulations, the temporal FR scheme based on Gauss-Legendre solution points can always guarantee its equivalency to IRK DG-Gauss. The conditions, under which the moving domain simulation with the method of lines are consistent with those using the space-time formulation, are also discussed. The new space-time FR method can achieve arbitrarily high-order spatial and temporal accuracy without numerical constraints on the physical time step in moving domain simulations. The temporal superconvergence property for moving domain simulations have been demonstrated.<|reference_end|>
|
arxiv
|
@article{yu2024high-order,
title={High-order Space-time Flux Reconstruction Methods for Moving Domain
Simulation},
author={Meilin Yu},
journal={arXiv preprint arXiv:2409.14005},
year={2024},
archivePrefix={arXiv},
eprint={2409.14005},
primaryClass={math.NA cs.NA physics.comp-ph}
}
|
yu2024high-order
|
arxiv-660219
|
2409.14008
|
Cyber-Physical Authentication Scheme for Secure V2G Transactions Using Blockchain and Smart Contracts
|
<|reference_start|>Cyber-Physical Authentication Scheme for Secure V2G Transactions Using Blockchain and Smart Contracts: The rapid adoption of electric vehicles (EVs) globally has catalyzed the need for robust cybersecurity measures within vehicle-to-grid (V2G) networks. As these networks are increasingly being integrated into smart charging infrastructures, they also introduce new vulnerabilities that threaten grid stability and user privacy This paper proposes a cyber-physical authentication protocol and trading smart contract tailored to plug and charge (PnC) operations within blockchain-based V2G systems. The protocol leverages advanced cryptographic techniques and blockchain to ensure secure, transparent, and tamper-proof energy transactions between EVs and charging stations. Key contributions include the development of a cyber-physical authentication method, the implementation of a smart contract framework for secure energy trading, and a detailed security and privacy analysis. The proposed protocol effectively mitigates risks such as distributed denial of service (DDoS) attacks, man-in-the-middle (MitM) attacks and replay attacks while preserving user anonymity and data integrity.<|reference_end|>
|
arxiv
|
@article{chen2024cyber-physical,
title={Cyber-Physical Authentication Scheme for Secure V2G Transactions},
author={Yunwang Chen, Yanmin Zhao, Siuming Yiu},
journal={arXiv preprint arXiv:2409.14008},
year={2024},
archivePrefix={arXiv},
eprint={2409.14008},
primaryClass={cs.CR}
}
|
chen2024cyber-physical
|
arxiv-660220
|
2409.14009
|
GPU Accelerated Sparse Cholesky Factorization
|
<|reference_start|>GPU Accelerated Sparse Cholesky Factorization: The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky factorization of the coefficient matrix is performed using a right-looking approach and the resulting triangular factors are used to compute the solution. Sparse Cholesky factorization is compute intensive. In this work we investigate techniques for reducing the factorization time in sparse Cholesky factorization by offloading some of the dense matrix operations on a GPU. We will describe the techniques we have considered. We achieved up to 4x speedup compared to the CPU-only version.<|reference_end|>
|
arxiv
|
@article{karsavuran2024gpu,
title={GPU Accelerated Sparse Cholesky Factorization},
author={M. Ozan Karsavuran, Esmond G. Ng, Barry W. Peyton},
journal={arXiv preprint arXiv:2409.14009},
year={2024},
archivePrefix={arXiv},
eprint={2409.14009},
primaryClass={cs.DC}
}
|
karsavuran2024gpu
|
arxiv-660221
|
2409.14010
|
RRD-Bio: Building An Integrated Research Resource Database for Biomedicine
|
<|reference_start|>RRD-Bio: Building An Integrated Research Resource Database for Biomedicine: Research resources (RRs) such as data, software, and tools are essential pillars of scientific research. The field of biomedicine, a critical scientific discipline, is witnessing a surge in research publications resulting in the accumulation of a substantial number of RRs. However, these resources are dispersed among various biomedical articles and can be challenging to locate and reuse due to their transient nature. In this paper, we report our recent progress in biomedical data curation - building a large research resource database for biomedicine (RRD-Bio), based on a collection of 40 million papers from two large biomedical literature databases, PubMed and PubMed Central. The database contains 2,555,116 RRs, each identified by a location on the Internet (URL) and descriptive information (Context). We made the RRD-Bio database publicly available (\url{https://zenodo.org/records/10526493}) to enhance the visibility of biomedical research resources, the ability to preserve important resources and the reproducibility of biomedical research.<|reference_end|>
|
arxiv
|
@article{zhang2024rrd-bio:,
title={RRD-Bio: Building An Integrated Research Resource Database for
Biomedicine},
author={Li Zhang, Mengting Sun, Chong Jiang, Haihua Chen},
journal={arXiv preprint arXiv:2409.14010},
year={2024},
archivePrefix={arXiv},
eprint={2409.14010},
primaryClass={cs.DL}
}
|
zhang2024rrd-bio:
|
arxiv-660222
|
2409.14011
|
Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
|
<|reference_start|>Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors: Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications. Despite promising results, existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors, e.g., single fixed path compensation. Moreover, these approaches still possess limited generalization ability, particularly when dealing with scenes at a low signal-to-noise ratio (SNR). To overcome the above problems, we introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF). The LPC applies tailored path compensation coefficients to adapt to different objects in the scene, effectively reducing light wave attenuation, especially in distant regions. Meanwhile, the APF learns the precise Gaussian window of the illumination function for the phasor field, dynamically selecting the relevant spectrum band of the transient measurement. Experimental validations demonstrate that our proposed approach, only trained on synthetic data, exhibits the capability to seamlessly generalize across various real-world datasets captured by different imaging systems and characterized by low SNRs.<|reference_end|>
|
arxiv
|
@article{sun2024generalizable,
title={Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors},
author={Shida Sun, Yue Li, Yueyi Zhang, Zhiwei Xiong},
journal={arXiv preprint arXiv:2409.14011},
year={2024},
archivePrefix={arXiv},
eprint={2409.14011},
primaryClass={cs.CV}
}
|
sun2024generalizable
|
arxiv-660223
|
2409.14012
|
Test Time Learning for Time Series Forecasting
|
<|reference_start|>Test Time Learning for Time Series Forecasting: Time-series forecasting has seen significant advancements with the introduction of token prediction mechanisms such as multi-head attention. However, these methods often struggle to achieve the same performance as in language modeling, primarily due to the quadratic computational cost and the complexity of capturing long-range dependencies in time-series data. State-space models (SSMs), such as Mamba, have shown promise in addressing these challenges by offering efficient solutions with linear RNNs capable of modeling long sequences with larger context windows. However, there remains room for improvement in accuracy and scalability. We propose the use of Test-Time Training (TTT) modules in a parallel architecture to enhance performance in long-term time series forecasting. Through extensive experiments on standard benchmark datasets, we demonstrate that TTT modules consistently outperform state-of-the-art models, including the Mamba-based TimeMachine, particularly in scenarios involving extended sequence and prediction lengths. Our results show significant improvements in Mean Squared Error (MSE) and Mean Absolute Error (MAE), especially on larger datasets such as Electricity, Traffic, and Weather, underscoring the effectiveness of TTT in capturing long-range dependencies. Additionally, we explore various convolutional architectures within the TTT framework, showing that even simple configurations like 1D convolution with small filters can achieve competitive results. This work sets a new benchmark for time-series forecasting and lays the groundwork for future research in scalable, high-performance forecasting models.<|reference_end|>
|
arxiv
|
@article{christou2024test,
title={Test Time Learning for Time Series Forecasting},
author={Panayiotis Christou, Shichu Chen, Xupeng Chen, Parijat Dube},
journal={arXiv preprint arXiv:2409.14012},
year={2024},
archivePrefix={arXiv},
eprint={2409.14012},
primaryClass={cs.LG cs.AI}
}
|
christou2024test
|
arxiv-660224
|
2409.14013
|
ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation
|
<|reference_start|>ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation: Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. To tackle these obstacles, we introduce a robust framework aimed at addressing and mitigating these issues effectively. This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs. This framework benefits from a time series-based loss function and oversight from a supervisory network, both of which capture the stepwise conditional distributions of the data effectively. The generator functions within the latent space, while the discriminator offers essential feedback based on the feature space. Moreover, we introduce an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series. Through joint training, our framework consistently outperforms existing benchmarks, generating high-quality time series data across a range of real and synthetic datasets with diverse characteristics.<|reference_end|>
|
arxiv
|
@article{eskandarinasab2024chronogan:,
title={ChronoGAN: Supervised and Embedded Generative Adversarial Networks for
Time Series Generation},
author={MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali
Boubrahimi},
journal={arXiv preprint arXiv:2409.14013},
year={2024},
archivePrefix={arXiv},
eprint={2409.14013},
primaryClass={cs.LG cs.AI}
}
|
eskandarinasab2024chronogan:
|
arxiv-660225
|
2409.14014
|
Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations
|
<|reference_start|>Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations: Molecular conformation generation poses a significant challenge in the field of computational chemistry. Recently, Diffusion Probabilistic Models (DPMs) and Score-Based Generative Models (SGMs) are effectively used due to their capacity for generating accurate conformations far beyond conventional physics-based approaches. However, the discrepancy between training and inference rises a critical problem known as the exposure bias. While this issue has been extensively investigated in DPMs, the existence of exposure bias in SGMs and its effective measurement remain unsolved, which hinders the use of compensation methods for SGMs, including ConfGF and Torsional Diffusion as the representatives. In this work, we first propose a method for measuring exposure bias in SGMs used for molecular conformation generation, which confirms the significant existence of exposure bias in these models and measures its value. We design a new compensation algorithm Input Perturbation (IP), which is adapted from a method originally designed for DPMs only. Experimental results show that by introducing IP, SGM-based molecular conformation models can significantly improve both the accuracy and diversity of the generated conformations. Especially by using the IP-enhanced Torsional Diffusion model, we achieve new state-of-the-art performance on the GEOM-Drugs dataset and are on par on GEOM-QM9. We provide the code publicly at https://github.com/jia-975/torsionalDiff-ip.<|reference_end|>
|
arxiv
|
@article{wang2024mitigating,
title={Mitigating Exposure Bias in Score-Based Generation of Molecular
Conformations},
author={Sijia Wang, Chen Wang, Zhenhao Zhao, Jiqiang Zhang and Weiran Cai},
journal={arXiv preprint arXiv:2409.14014},
year={2024},
archivePrefix={arXiv},
eprint={2409.14014},
primaryClass={cs.LG cs.AI q-bio.BM}
}
|
wang2024mitigating
|
arxiv-660226
|
2409.14015
|
The vertex-pancyclicity of the simplified shuffle-cube and the vertex-bipancyclicity of the balanced shuffle-cube
|
<|reference_start|>The vertex-pancyclicity of the simplified shuffle-cube and the vertex-bipancyclicity of the balanced shuffle-cube: A graph $G$ $=$ $(V,E)$ is vertex-pancyclic if for every vertex $u$ and any integer $l$ ranging from $3$ to $|V|$, $G$ contains a cycle $C$ of length $l$ such that $u$ is on $C$. A bipartite graph $G$ $=$ $(V,E)$ is vertex-bipancyclic if for every vertex $u$ and any even integer $l$ ranging from $4$ to $|V|$, $G$ contains a cycle $C$ of length $l$ such that $u$ is on $C$. The simplified shuffle-cube and the balanced shuffle-cube, which are two variants of the shuffle-cube and are superior to shuffle-cube in terms of vertex-transitivity. In this paper, we show that the $n$-dimensional simplified shuffle-cube is vertex-pancyclic for $n\geqslant 6$, and the $n$-dimensional balanced shuffle-cube is vertex-bipancyclic for $n\geqslant 2$.<|reference_end|>
|
arxiv
|
@article{liu2024the,
title={The vertex-pancyclicity of the simplified shuffle-cube and the
vertex-bipancyclicity of the balanced shuffle-cube},
author={Yasong Liu and Huazhong L"u},
journal={arXiv preprint arXiv:2409.14015},
year={2024},
archivePrefix={arXiv},
eprint={2409.14015},
primaryClass={math.CO cs.DM}
}
|
liu2024the
|
arxiv-660227
|
2409.14016
|
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning
|
<|reference_start|>Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning: Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based dataset of photospheric magnetic field parameters. First, our study employs a novel preprocessing pipeline that includes missing value imputation, normalization, balanced sampling, near decision boundary sample removal, and feature selection to significantly boost prediction accuracy. Second, we integrate contrastive learning with a GRU regression model to develop a novel classifier, termed ContReg, which employs dual learning methodologies, thereby further enhancing prediction performance. To validate the effectiveness of our preprocessing pipeline, we compare and demonstrate the performance gain of each step, and to demonstrate the efficacy of the ContReg classifier, we compare its performance to that of sequence-based deep learning architectures, machine learning models, and findings from previous studies. Our results illustrate exceptional True Skill Statistic (TSS) scores, surpassing previous methods and highlighting the critical role of precise data preprocessing and classifier development in time series-based solar flare prediction.<|reference_end|>
|
arxiv
|
@article{eskandarinasab2024enhancing,
title={Enhancing Multivariate Time Series-based Solar Flare Prediction with
Multifaceted Preprocessing and Contrastive Learning},
author={MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali
Boubrahimi},
journal={arXiv preprint arXiv:2409.14016},
year={2024},
archivePrefix={arXiv},
eprint={2409.14016},
primaryClass={astro-ph.SR cs.AI cs.LG stat.ML}
}
|
eskandarinasab2024enhancing
|
arxiv-660228
|
2409.14017
|
SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inference
|
<|reference_start|>SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inference: Deploying deep neural networks (DNNs) on those resource-constrained edge platforms is hindered by their substantial computation and storage demands. Quantized multi-precision DNNs, denoted as MP-DNNs, offer a promising solution for these limitations but pose challenges for existing RISC-V processors due to complex instructions, suboptimal parallel processing, and inefficient dataflow mapping. To tackle the challenges mentioned above, SPEED, a scalable RISC-V vector (RVV) processor, is proposed to enable efficient MP-DNN inference, incorporating innovations in customized instructions, hardware architecture, and dataflow mapping. Firstly, some dedicated customized RISC-V instructions are introduced based on RVV extensions to reduce the instruction complexity, allowing SPEED to support processing precision ranging from 4-bit to 16-bit with minimized hardware overhead. Secondly, a parameterized multi-precision tensor unit is developed and integrated within the scalable module to enhance parallel processing capability by providing reconfigurable parallelism that matches the computation patterns of diverse MP-DNNs. Finally, a flexible mixed dataflow method is adopted to improve computational and energy efficiency according to the computing patterns of different DNN operators. The synthesis of SPEED is conducted on TSMC 28nm technology. Experimental results show that SPEED achieves a peak throughput of 737.9 GOPS and an energy efficiency of 1383.4 GOPS/W for 4-bit operators. Furthermore, SPEED exhibits superior area efficiency compared to prior RVV processors, with enhancements of 5.9$\sim$26.9$\times$ and 8.2$\sim$18.5$\times$ for 8-bit operator and best integer performance, respectively, which highlights SPEED's significant potential for efficient MP-DNN inference.<|reference_end|>
|
arxiv
|
@article{wang2024speed:,
title={SPEED: A Scalable RISC-V Vector Processor Enabling Efficient
Multi-Precision DNN Inference},
author={Chuanning Wang, Chao Fang, Xiao Wu, Zhongfeng Wang, Jun Lin},
journal={arXiv preprint arXiv:2409.14017},
year={2024},
doi={10.1109/TVLSI.2024.3466224},
archivePrefix={arXiv},
eprint={2409.14017},
primaryClass={cs.AR}
}
|
wang2024speed:
|
arxiv-660229
|
2409.14019
|
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors
|
<|reference_start|>MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors: Accurately reconstructing dense and semantically annotated 3D meshes from monocular images remains a challenging task due to the lack of geometry guidance and imperfect view-dependent 2D priors. Though we have witnessed recent advancements in implicit neural scene representations enabling precise 2D rendering simply from multi-view images, there have been few works addressing 3D scene understanding with monocular priors alone. In this paper, we propose MOSE, a neural field semantic reconstruction approach to lift inferred image-level noisy priors to 3D, producing accurate semantics and geometry in both 3D and 2D space. The key motivation for our method is to leverage generic class-agnostic segment masks as guidance to promote local consistency of rendered semantics during training. With the help of semantics, we further apply a smoothness regularization to texture-less regions for better geometric quality, thus achieving mutual benefits of geometry and semantics. Experiments on the ScanNet dataset show that our MOSE outperforms relevant baselines across all metrics on tasks of 3D semantic segmentation, 2D semantic segmentation and 3D surface reconstruction.<|reference_end|>
|
arxiv
|
@article{du2024mose:,
title={MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors},
author={Zhenhua Du, Binbin Xu, Haoyu Zhang, Kai Huo, Shuaifeng Zhi},
journal={arXiv preprint arXiv:2409.14019},
year={2024},
doi={10.1109/LRA.2024.3466077},
archivePrefix={arXiv},
eprint={2409.14019},
primaryClass={cs.CV cs.AI cs.RO}
}
|
du2024mose:
|
arxiv-660230
|
2409.14020
|
Point Cloud Structural Similarity-based Underwater Sonar Loop Detection
|
<|reference_start|>Point Cloud Structural Similarity-based Underwater Sonar Loop Detection: In order to enable autonomous navigation in underwater environments, a map needs to be created in advance using a Simultaneous Localization and Mapping (SLAM) algorithm that utilizes sensors like a sonar. At this time, loop closure is employed to reduce the pose error accumulated during the SLAM process. In the case of loop detection using a sonar, some previous studies have used a method of projecting the 3D point cloud into 2D, then extracting keypoints and matching them. However, during the 2D projection process, data loss occurs due to image resolution, and in monotonous underwater environments such as rivers or lakes, it is difficult to extract keypoints. Additionally, methods that use neural networks or are based on Bag of Words (BoW) have the disadvantage of requiring additional preprocessing tasks, such as training the model in advance or pre-creating a vocabulary. To address these issues, in this paper, we utilize the point cloud obtained from sonar data without any projection to prevent performance degradation due to data loss. Additionally, by calculating the point-wise structural feature map of the point cloud using mathematical formulas and comparing the similarity between point clouds, we eliminate the need for keypoint extraction and ensure that the algorithm can operate in new environments without additional learning or tasks. To evaluate the method, we validated the performance of the proposed algorithm using the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our proposed method achieves the best loop detection performance in both datasets. Our code is available at https://github.com/donghwijung/point_cloud_structural_similarity_based_underwater_sonar_loop_detection.<|reference_end|>
|
arxiv
|
@article{jung2024point,
title={Point Cloud Structural Similarity-based Underwater Sonar Loop Detection},
author={Donghwi Jung, Andres Pulido, Jane Shin, and Seong-Woo Kim},
journal={arXiv preprint arXiv:2409.14020},
year={2024},
archivePrefix={arXiv},
eprint={2409.14020},
primaryClass={cs.RO}
}
|
jung2024point
|
arxiv-660231
|
2409.14021
|
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance
|
<|reference_start|>BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance: Can we directly visualize what we imagine in our brain together with what we describe? The inherent nature of human perception reveals that, when we think, our body can combine language description and build a vivid picture in our brain. Intuitively, generative models should also hold such versatility. In this paper, we introduce BrainDreamer, a novel end-to-end language-guided generative framework that can mimic human reasoning and generate high-quality images from electroencephalogram (EEG) brain signals. Our method is superior in its capacity to eliminate the noise introduced by non-invasive EEG data acquisition and meanwhile achieve a more precise mapping between the EEG and image modality, thus leading to significantly better-generated images. Specifically, BrainDreamer consists of two key learning stages: 1) modality alignment and 2) image generation. In the alignment stage, we propose a novel mask-based triple contrastive learning strategy to effectively align EEG, text, and image embeddings to learn a unified representation. In the generation stage, we inject the EEG embeddings into the pre-trained Stable Diffusion model by designing a learnable EEG adapter to generate high-quality reasoning-coherent images. Moreover, BrainDreamer can accept textual descriptions (e.g., color, position, etc.) to achieve controllable image generation. Extensive experiments show that our method significantly outperforms prior arts in terms of generating quality and quantitative performance.<|reference_end|>
|
arxiv
|
@article{wang2024braindreamer:,
title={BrainDreamer: Reasoning-Coherent and Controllable Image Generation from
EEG Brain Signals via Language Guidance},
author={Ling Wang, Chen Wu and Lin Wang},
journal={arXiv preprint arXiv:2409.14021},
year={2024},
archivePrefix={arXiv},
eprint={2409.14021},
primaryClass={cs.CV cs.AI}
}
|
wang2024braindreamer:
|
arxiv-660232
|
2409.14023
|
FAMOUS: Flexible Accelerator for the Attention Mechanism of Transformer on UltraScale+ FPGAs
|
<|reference_start|>FAMOUS: Flexible Accelerator for the Attention Mechanism of Transformer on UltraScale+ FPGAs: Transformer neural networks (TNNs) are being applied across a widening range of application domains, including natural language processing (NLP), machine translation, and computer vision (CV). Their popularity is largely attributed to the exceptional performance of their multi-head self-attention blocks when analyzing sequential data and extracting features. To date, there are limited hardware accelerators tailored for this mechanism, which is the first step before designing an accelerator for a complete model. This paper proposes \textit{FAMOUS}, a flexible hardware accelerator for dense multi-head attention (MHA) computation of TNNs on field-programmable gate arrays (FPGAs). It is optimized for high utilization of processing elements and on-chip memories to improve parallelism and reduce latency. An efficient tiling of large matrices has been employed to distribute memory and computing resources across different modules on various FPGA platforms. The design is evaluated on Xilinx Alveo U55C and U200 data center cards containing Ultrascale+ FPGAs. Experimental results are presented that show that it can attain a maximum throughput, number of parallel attention heads, embedding dimension and tile size of 328 (giga operations/second (GOPS)), 8, 768 and 64 respectively on the U55C. Furthermore, it is 3.28$\times$ and 2.6$\times$ faster than the Intel Xeon Gold 5220R CPU and NVIDIA V100 GPU respectively. It is also 1.3$\times$ faster than the fastest state-of-the-art FPGA-based accelerator.<|reference_end|>
|
arxiv
|
@article{kabir2024famous:,
title={FAMOUS: Flexible Accelerator for the Attention Mechanism of Transformer
on UltraScale+ FPGAs},
author={Ehsan Kabir, Md. Arafat Kabir, Austin R.J. Downey, Jason D. Bakos,
David Andrews, Miaoqing Huang},
journal={arXiv preprint arXiv:2409.14023},
year={2024},
archivePrefix={arXiv},
eprint={2409.14023},
primaryClass={cs.AR cs.AI cs.LG}
}
|
kabir2024famous:
|
arxiv-660233
|
2409.14025
|
Convexification for the 3D Problem of Travel Time Tomography
|
<|reference_start|>Convexification for the 3D Problem of Travel Time Tomography: The travel time tomography problem is a coefficient inverse problem for the eikonal equation. This problem has well known applications in seismic. The eikonal equation is considered here in the circular cylinder, where point sources run along its axis and measurements of travel times are conductes on the whole surface of this cylinder. A new version of the globally convergent convexification numerical method for this problem is developed. Results of numerical studies are presented.<|reference_end|>
|
arxiv
|
@article{klibanov2024convexification,
title={Convexification for the 3D Problem of Travel Time Tomography},
author={Michael V. Klibanov, Jingzhi Li, Vladimir G. Romanov, Zhipeng Yang},
journal={arXiv preprint arXiv:2409.14025},
year={2024},
archivePrefix={arXiv},
eprint={2409.14025},
primaryClass={math.NA cs.NA}
}
|
klibanov2024convexification
|
arxiv-660234
|
2409.14026
|
Uncovering Latent Chain of Thought Vectors in Language Models
|
<|reference_start|>Uncovering Latent Chain of Thought Vectors in Language Models: As language models grow more influential and trusted in our society, our ability to reliably steer them toward favorable behaviors becomes increasingly paramount. For this, we investigate the technique of steering vectors: biasing the forward pass of language models using a "steering vector" derived from a specific task. We apply them to steer language models toward performing Chain of Thought (CoT) Reasoning without the need to prompt through natural language. We demonstrate this approach on Llama3 8b and Mistral 7b v0.2, and obtain competitive results compared to CoT-prompted performances on a series of reasoning benchmarks (GSM8k, MMLU, AGI Eval, ARC AI2) and qualitative examples. We find this approach yields consistent steering towards CoT responses and takes less compute than traditional methods of fine-tuning models towards CoT.<|reference_end|>
|
arxiv
|
@article{zhang2024uncovering,
title={Uncovering Latent Chain of Thought Vectors in Language Models},
author={Jason Zhang, Scott Viteri},
journal={arXiv preprint arXiv:2409.14026},
year={2024},
archivePrefix={arXiv},
eprint={2409.14026},
primaryClass={cs.CL cs.AI}
}
|
zhang2024uncovering
|
arxiv-660235
|
2409.14028
|
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
|
<|reference_start|>MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule: Pulmonary nodules are critical indicators for the early diagnosis of lung cancer, making their detection essential for timely treatment. However, traditional CT imaging methods suffered from cumbersome procedures, low detection rates, and poor localization accuracy. The subtle differences between pulmonary nodules and surrounding tissues in complex lung CT images, combined with repeated downsampling in feature extraction networks, often lead to missed or false detections of small nodules. Existing methods such as FPN, with its fixed feature fusion and limited receptive field, struggle to effectively overcome these issues. To address these challenges, our paper proposed three key contributions: Firstly, we proposed MSDet, a multiscale attention and receptive field network for detecting tiny pulmonary nodules. Secondly, we proposed the extended receptive domain (ERD) strategy to capture richer contextual information and reduce false positives caused by nodule occlusion. We also proposed the position channel attention mechanism (PCAM) to optimize feature learning and reduce multiscale detection errors, and designed the tiny object detection block (TODB) to enhance the detection of tiny nodules. Lastly, we conducted thorough experiments on the public LUNA16 dataset, achieving state-of-the-art performance, with an mAP improvement of 8.8% over the previous state-of-the-art method YOLOv8. These advancements significantly boosted detection accuracy and reliability, providing a more effective solution for early lung cancer diagnosis. The code will be available at https://github.com/CaiGuoHui123/MSDet<|reference_end|>
|
arxiv
|
@article{cai2024msdet:,
title={MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary
Nodule},
author={Guohui Cai, Ying Cai, Zeyu Zhang, Daji Ergu, Yuanzhouhan Cao, Binbin
Hu, Zhibin Liao, Yang Zhao},
journal={arXiv preprint arXiv:2409.14028},
year={2024},
archivePrefix={arXiv},
eprint={2409.14028},
primaryClass={eess.IV cs.CV}
}
|
cai2024msdet:
|
arxiv-660236
|
2409.14034
|
Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks
|
<|reference_start|>Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks: Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applications usually have high requirements on the balance between time complexity and accuracy of influential nodes identification. To address the challenges of imperfect node influence measurement and inefficient seed nodes selection mechanism in such class of foregoing techniques, this article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence maximization in complex networks. First, we develop a method for measuring the importance of different nodes in networks based on an original concept of Dual-Scale Community-Hierarchy Information that synthesizes both hierarchy structural information and community structural information of nodes. The community structural information contained in the nodes is measured by a new notion of Hierarchical-Community Entropy. Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection. Hereinto, a low-computational-cost mutual voting mechanism and an updating strategy called Lazy Score Updating Strategy are newly constructed for optimizing the selecting of seed nodes. Third, we develop a balance index to evaluate the performance of different methods in striking the tradeoff between time complexity and the accuracy of influential nodes identification. Finally, we demonstrate the approach performance over ten public datasets. The extensive experiments show that the proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification. Compared with the method with the second highest value of the balance index, our approach can be improved by at most 9.29%.<|reference_end|>
|
arxiv
|
@article{liu2024cost-effective,
title={Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for
Influence Maximization in Complex Networks},
author={Yi Liu, Xiaoan Tang, Witold Pedrycz, Qiang Zhang},
journal={arXiv preprint arXiv:2409.14034},
year={2024},
archivePrefix={arXiv},
eprint={2409.14034},
primaryClass={cs.SI cs.IR}
}
|
liu2024cost-effective
|
arxiv-660237
|
2409.14035
|
Implicit Neural Representations for Speed-of-Sound Estimation in Ultrasound
|
<|reference_start|>Implicit Neural Representations for Speed-of-Sound Estimation in Ultrasound: Accurate estimation of the speed-of-sound (SoS) is important for ultrasound (US) image reconstruction techniques and tissue characterization. Various approaches have been proposed to calculate SoS, ranging from tomography-inspired algorithms like CUTE to convolutional networks, and more recently, physics-informed optimization frameworks based on differentiable beamforming. In this work, we utilize implicit neural representations (INRs) for SoS estimation in US. INRs are a type of neural network architecture that encodes continuous functions, such as images or physical quantities, through the weights of a network. Implicit networks may overcome the current limitations of SoS estimation techniques, which mainly arise from the use of non-adaptable and oversimplified physical models of tissue. Moreover, convolutional networks for SoS estimation, usually trained using simulated data, often fail when applied to real tissues due to out-of-distribution and data-shift issues. In contrast, implicit networks do not require extensive training datasets since each implicit network is optimized for an individual data case. This adaptability makes them suitable for processing US data collected from varied tissues and across different imaging protocols. We evaluated the proposed SoS estimation method based on INRs using data collected from a tissue-mimicking phantom containing four cylindrical inclusions, with SoS values ranging from 1480 m/s to 1600 m/s. The inclusions were immersed in a material with an SoS value of 1540 m/s. In experiments, the proposed method achieved strong performance, clearly demonstrating the usefulness of implicit networks for quantitative US applications.<|reference_end|>
|
arxiv
|
@article{byra2024implicit,
title={Implicit Neural Representations for Speed-of-Sound Estimation in
Ultrasound},
author={Michal Byra, Piotr Jarosik, Piotr Karwat, Ziemowit Klimonda, Marcin
Lewandowski},
journal={arXiv preprint arXiv:2409.14035},
year={2024},
archivePrefix={arXiv},
eprint={2409.14035},
primaryClass={cs.LG physics.med-ph}
}
|
byra2024implicit
|
arxiv-660238
|
2409.14037
|
Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs as Science Communicators
|
<|reference_start|>Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs as Science Communicators: Large Language Models (LLMs) and AI assistants driven by these models are experiencing exponential growth in usage among both expert and amateur users. In this work, we focus on evaluating the reliability of current LLMs as science communicators. Unlike existing benchmarks, our approach emphasizes assessing these models on scientific questionanswering tasks that require a nuanced understanding and awareness of answerability. We introduce a novel dataset, SCiPS-QA, comprising 742 Yes/No queries embedded in complex scientific concepts, along with a benchmarking suite that evaluates LLMs for correctness and consistency across various criteria. We benchmark three proprietary LLMs from the OpenAI GPT family and 13 open-access LLMs from the Meta Llama-2, Llama-3, and Mistral families. While most open-access models significantly underperform compared to GPT-4 Turbo, our experiments identify Llama-3-70B as a strong competitor, often surpassing GPT-4 Turbo in various evaluation aspects. We also find that even the GPT models exhibit a general incompetence in reliably verifying LLM responses. Moreover, we observe an alarming trend where human evaluators are deceived by incorrect responses from GPT-4 Turbo.<|reference_end|>
|
arxiv
|
@article{bajpai2024can,
title={Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs
as Science Communicators},
author={Prasoon Bajpai, Niladri Chatterjee, Subhabrata Dutta, Tanmoy
Chakraborty},
journal={arXiv preprint arXiv:2409.14037},
year={2024},
archivePrefix={arXiv},
eprint={2409.14037},
primaryClass={cs.CL cs.AI}
}
|
bajpai2024can
|
arxiv-660239
|
2409.14038
|
OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
|
<|reference_start|>OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching: Hallucinations of large language models (LLMs) commonly occur in domain-specific downstream tasks, with no exception in ontology matching (OM). The prevalence of using LLMs for OM raises the need for benchmarks to better understand LLM hallucinations. The OAEI-LLM dataset is an extended version of the Ontology Alignment Evaluation Initiative (OAEI) datasets that evaluate LLM-specific hallucinations in OM tasks. We outline the methodology used in dataset construction and schema extension, and provide examples of potential use cases.<|reference_end|>
|
arxiv
|
@article{qiang2024oaei-llm:,
title={OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model
Hallucinations in Ontology Matching},
author={Zhangcheng Qiang, Kerry Taylor, Weiqing Wang, Jing Jiang},
journal={arXiv preprint arXiv:2409.14038},
year={2024},
archivePrefix={arXiv},
eprint={2409.14038},
primaryClass={cs.AI cs.CL cs.IR}
}
|
qiang2024oaei-llm:
|
arxiv-660240
|
2409.14039
|
Towards Lightweight and Privacy-preserving Data Provision in Digital Forensics for Driverless Taxi
|
<|reference_start|>Towards Lightweight and Privacy-preserving Data Provision in Digital Forensics for Driverless Taxi: Data provision, referring to the data upload and data access, is one key phase in vehicular digital forensics. The unique features of Driverless Taxi (DT) bring new issues to this phase: 1) efficient verification of data integrity when diverse Data Providers (DPs) upload data; 2) DP privacy preservation during data upload; and 3) privacy preservation of both data and INvestigator (IN) under complex data ownership when accessing data. To this end, we propose a novel Lightweight and Privacy-preserving Data Provision (LPDP) approach consisting of three mechanisms: 1) the Privacy-friendly Batch Verification Mechanism (PBVm) based on elliptic curve cryptography, 2) Data Access Control Mechanism (DACm) based on ciphertext-policy attribute-based encryption, and 3) Decentralized IN Warrant Issuance Mechanism (DIWIm) based on secret sharing. Privacy preservation of data provision is achieved through: 1) ensuring the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests by PBVm, 2) ensuring data privacy preservation by DACm and DIWIm, and 3) ensuring the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability. Lightweight of data provision is achieved through: 1) ensuring scalable verification of data integrity by PBVm, and 2) ensuring low-overhead warrant update with respect to DIWIm. Security analysis and performance evaluation are conducted to validate the security and performance features of LPDP.<|reference_end|>
|
arxiv
|
@article{gong2024towards,
title={Towards Lightweight and Privacy-preserving Data Provision in Digital
Forensics for Driverless Taxi},
author={Yanwei Gong, Xiaolin Chang, Jelena Miv{s}i'c, Vojislav B.
Miv{s}i'c, Junchao Fan, Kaiwen Wang},
journal={arXiv preprint arXiv:2409.14039},
year={2024},
archivePrefix={arXiv},
eprint={2409.14039},
primaryClass={cs.CR}
}
|
gong2024towards
|
arxiv-660241
|
2409.14040
|
PepINVENT: Generative peptide design beyond the natural amino acids
|
<|reference_start|>PepINVENT: Generative peptide design beyond the natural amino acids: Peptides play a crucial role in the drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the peptide properties from binding affinity, plasma stability to permeability. Incorporating novel NNAAs facilitates the design of more effective peptides with improved properties. The generative models used in the field, have focused on navigating the peptide sequence space. The sequence space is formed by combinations of a predefined set of amino acids. However, there is still a need for a tool to explore the peptide landscape beyond this enumerated space to unlock and effectively incorporate de novo design of new amino acids. To thoroughly explore the theoretical chemical space of the peptides, we present PepINVENT, a novel generative AI-based tool as an extension to the small molecule molecular design platform, REINVENT. PepINVENT navigates the vast space of natural and non-natural amino acids to propose valid, novel, and diverse peptide designs. The generative model can serve as a central tool for peptide-related tasks, as it was not trained on peptides with specific properties or topologies. The prior was trained to understand the granularity of peptides and to design amino acids for filling the masked positions within a peptide. PepINVENT coupled with reinforcement learning enables the goal-oriented design of peptides using its chemistry-informed generative capabilities. This study demonstrates PepINVENT's ability to explore the peptide space with unique and novel designs, and its capacity for property optimization in the context of therapeutically relevant peptides. Our tool can be employed for multi-parameter learning objectives, peptidomimetics, lead optimization, and variety of other tasks within the peptide domain.<|reference_end|>
|
arxiv
|
@article{geylan2024pepinvent:,
title={PepINVENT: Generative peptide design beyond the natural amino acids},
author={G"okc{c}e Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He,
Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola
Engkvist and Leonardo De Maria},
journal={arXiv preprint arXiv:2409.14040},
year={2024},
archivePrefix={arXiv},
eprint={2409.14040},
primaryClass={q-bio.BM cs.AI}
}
|
geylan2024pepinvent:
|
arxiv-660242
|
2409.14043
|
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning
|
<|reference_start|>ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning: Environment Sound Classification has been a well-studied research problem in the field of signal processing and up till now more focus has been laid on fully supervised approaches. Over the last few years, focus has moved towards semi-supervised methods which concentrate on the utilization of unlabeled data, and self-supervised methods which learn the intermediate representation through pretext task or contrastive learning. However, both approaches require a vast amount of unlabelled data to improve performance. In this work, we propose a novel framework called Environmental Sound Classification with Hierarchical Ontology-guided semi-supervised Learning (ECHO) that utilizes label ontology-based hierarchy to learn semantic representation by defining a novel pretext task. In the pretext task, the model tries to predict coarse labels defined by the Large Language Model (LLM) based on ground truth label ontology. The trained model is further fine-tuned in a supervised way to predict the actual task. Our proposed novel semi-supervised framework achieves an accuracy improvement in the range of 1\% to 8\% over baseline systems across three datasets namely UrbanSound8K, ESC-10, and ESC-50.<|reference_end|>
|
arxiv
|
@article{gupta2024echo:,
title={ECHO: Environmental Sound Classification with Hierarchical
Ontology-guided Semi-Supervised Learning},
author={Pranav Gupta, Raunak Sharma, Rashmi Kumari, Sri Krishna Aditya,
Shwetank Choudhary, Sumit Kumar, Kanchana M, Thilagavathy R},
journal={arXiv preprint arXiv:2409.14043},
year={2024},
doi={10.1109/CONECCT62155.2024.10677303},
archivePrefix={arXiv},
eprint={2409.14043},
primaryClass={cs.SD cs.CV eess.AS}
}
|
gupta2024echo:
|
arxiv-660243
|
2409.14047
|
Personalized Route Recommendation Based on User Habits for Vehicle Navigation
|
<|reference_start|>Personalized Route Recommendation Based on User Habits for Vehicle Navigation: Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of research. This paper introduces a personalized route recommendation method based on user historical navigation data. First, we formulate route sorting as a pointwise problem based on a large set of pertinent features. Second, we construct route features and user profiles to establish a comprehensive feature dataset. Furthermore, we propose a Deep-Cross-Recurrent (DCR) learning model aimed at learning route sorting scores and offering customized route recommendations. This approach effectively captures recommended navigation routes and user preferences by integrating DCN-v2 and LSTM. In offline evaluations, our method compared with the minimum ETA (estimated time of arrival), LightGBM, and DCN-v2 indicated 8.72%, 2.19%, and 0.9% reduction in the mean inconsistency rate respectively, demonstrating significant improvements in recommendation accuracy.<|reference_end|>
|
arxiv
|
@article{huang2024personalized,
title={Personalized Route Recommendation Based on User Habits for Vehicle
Navigation},
author={Yinuo Huang, Xin Jin, Miao Fan, Xunwei Yang, Fangliang Jiang},
journal={arXiv preprint arXiv:2409.14047},
year={2024},
archivePrefix={arXiv},
eprint={2409.14047},
primaryClass={cs.RO}
}
|
huang2024personalized
|
arxiv-660244
|
2409.14050
|
The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills
|
<|reference_start|>The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills: OpenAI's ChatGPT (GPT-4 and GPT-4o) and other Large Language Models (LLMs) like Microsoft's Copilot, Google's Gemini 1.5 Pro, and Antrophic's Claude 3.5 Sonnet can be effectively used in various phases of scientific research. Their performance in diverse verbal tasks and reasoning is close to or above the average human level and rapidly increasing, providing those models with a capacity that resembles a relatively high level of theory of mind. The current ability of LLMs to process information about human psychology and communication creates an opportunity for their scientific use in the fields of personality psychology and interpersonal communication skills. This article illustrates the possible uses of GPT-4o and other advanced LLMs for typical tasks in designing self-assessment scales for interpersonal communication skills measurement like the selection and improvement of scale items and evaluation of content validity of scales. The potential for automated item generation and application is illustrated as well. The case study examples are accompanied by prompts for LLMs that can be useful for these purposes. Finally, a summary is provided of the potential benefits of using LLMs in the process of evaluation, design, and improvement of interpersonal communication skills self-assessment scales.<|reference_end|>
|
arxiv
|
@article{bubaš2024the,
title={The use of GPT-4o and Other Large Language Models for the Improvement
and Design of Self-Assessment Scales for Measurement of Interpersonal
Communication Skills},
author={Goran Bubav{s}},
journal={arXiv preprint arXiv:2409.14050},
year={2024},
archivePrefix={arXiv},
eprint={2409.14050},
primaryClass={cs.AI}
}
|
bubaš2024the
|
arxiv-660245
|
2409.14051
|
GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion
|
<|reference_start|>GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion: In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number of agents and debate rounds. However, the escalation in the number of agents and debate rounds can drastically raise the tokens cost of debates, thereby limiting the scalability of the multi-agent debate technique. To better harness the advantages of multi-agent debates in logical reasoning tasks, this paper proposes a method to significantly reduce token cost in multi-agent debates. This approach involves dividing all agents into multiple debate groups, with agents engaging in debates within their respective groups and sharing interim debate results between groups. Comparative experiments across multiple datasets have demonstrated that this method can reduce the total tokens by up to 51.7% during debates and while potentially enhancing accuracy by as much as 25%. Our method significantly enhances the performance and efficiency of interactions in the multi-agent debate.<|reference_end|>
|
arxiv
|
@article{liu2024groupdebate:,
title={GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group
Discussion},
author={Tongxuan Liu, Xingyu Wang, Weizhe Huang, Wenjiang Xu, Yuting Zeng, Lei
Jiang, Hailong Yang, Jing Li},
journal={arXiv preprint arXiv:2409.14051},
year={2024},
archivePrefix={arXiv},
eprint={2409.14051},
primaryClass={cs.CL cs.AI}
}
|
liu2024groupdebate:
|
arxiv-660246
|
2409.14052
|
An average case efficient algorithm for solving two variable linear diophantine equations
|
<|reference_start|>An average case efficient algorithm for solving two variable linear diophantine equations: Solving two variable linear diophantine equations has applications in many cryptographic protocols such as RSA and Elliptic curve cryptography. Extended euclid's algorithm is the most widely used algorithm to solve these equations. We revisit two algorithms to solve two variable linear diophantine equations. For one of them, we do fine-grained analysis of the number of recursive calls and find a periodic function, which represents the number of recursive calls. We find the period and use it to derive an accurate closed form expression for the average number of recursive calls incurred by that algorithm. In the process of this derivation we get an upper bound on the average number of recursive calls, which depends on the intermediate values observed during the execution of algorithm. We propose an iterative version of the algorithm. While implementation of our algorithm, we verify a well known result from number theory about the probability of two random integers being coprime. Due to that result, our algorithm encounters an additional constraint for approximately 40% times. On almost all of these constrained inputs i.e. on nearly 100 % of them the algorithm outperforms two existing algorithms.<|reference_end|>
|
arxiv
|
@article{deora2024an,
title={An average case efficient algorithm for solving two variable linear
diophantine equations},
author={Mayank Deora, Pinakpani Pal},
journal={arXiv preprint arXiv:2409.14052},
year={2024},
archivePrefix={arXiv},
eprint={2409.14052},
primaryClass={cs.CR cs.DS math.NT}
}
|
deora2024an
|
arxiv-660247
|
2409.14055
|
Monitoring Human Dependence On AI Systems With Reliance Drills
|
<|reference_start|>Monitoring Human Dependence On AI Systems With Reliance Drills: AI systems are assisting humans with an increasingly broad range of intellectual tasks. Humans could be over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify real-world instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. We introduce a pipeline that organisations could use to implement these drills. As an example, we explain how this approach could be used to limit over-reliance on AI in a medical setting. We conclude by arguing that reliance drills could become a key tool for ensuring humans remain appropriately involved in AI-assisted decisions.<|reference_end|>
|
arxiv
|
@article{hunter2024monitoring,
title={Monitoring Human Dependence On AI Systems With Reliance Drills},
author={Rosco Hunter, Richard Moulange, Jamie Bernardi, Merlin Stein},
journal={arXiv preprint arXiv:2409.14055},
year={2024},
archivePrefix={arXiv},
eprint={2409.14055},
primaryClass={cs.CY}
}
|
hunter2024monitoring
|
arxiv-660248
|
2409.14057
|
Co-occurrence is not Factual Association in Language Models
|
<|reference_start|>Co-occurrence is not Factual Association in Language Models: Pretrained language models can encode a large amount of knowledge and utilize it for various reasoning tasks, yet they can still struggle to learn novel factual knowledge effectively from finetuning on limited textual demonstrations. In this work, we show that the reason for this deficiency is that language models are biased to learn word co-occurrence statistics instead of true factual associations. We identify the differences between two forms of knowledge representation in language models: knowledge in the form of co-occurrence statistics is encoded in the middle layers of the transformer model and does not generalize well to reasoning scenarios beyond simple question answering, while true factual associations are encoded in the lower layers and can be freely utilized in various reasoning tasks. Based on these observations, we propose two strategies to improve the learning of factual associations in language models. We show that training on text with implicit rather than explicit factual associations can force the model to learn factual associations instead of co-occurrence statistics, significantly improving the generalization of newly learned knowledge. We also propose a simple training method to actively forget the learned co-occurrence statistics, which unblocks and enhances the learning of factual associations when training on plain narrative text. On both synthetic and real-world corpora, the two proposed strategies improve the generalization of the knowledge learned during finetuning to reasoning scenarios such as indirect and multi-hop question answering.<|reference_end|>
|
arxiv
|
@article{zhang2024co-occurrence,
title={Co-occurrence is not Factual Association in Language Models},
author={Xiao Zhang, Miao Li, Ji Wu},
journal={arXiv preprint arXiv:2409.14057},
year={2024},
archivePrefix={arXiv},
eprint={2409.14057},
primaryClass={cs.CL}
}
|
zhang2024co-occurrence
|
arxiv-660249
|
2409.14058
|
Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach
|
<|reference_start|>Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach: Incorporating LLM into cybersecurity operations, a typical real-world high-stakes task, is critical but non-trivial in practice. Using cybersecurity as the study context, we conduct a three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively. Specifically, we deconstruct the deficiencies in user satisfaction within the existing process (Study 1). This inspires us to design, implement, and empirically validate an LLM-supported collaborative vulnerability remediation process through a field study (Study 2). Given LLM's diverse contributions, we further investigate LLM's double-edge roles through the analysis of remediation reports and follow-up interviews (Study 3). In essence, our contribution lies in promoting an efficient LLM-supported collaborative vulnerability remediation process. These first-hand, real-world pieces of evidence suggest that when incorporating LLMs into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLMs' roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset.<|reference_end|>
|
arxiv
|
@article{wang2024practically,
title={Practically implementing an LLM-supported collaborative vulnerability
remediation process: a team-based approach},
author={Xiaoqing Wang, Yuanjing Tian, Keman Huang, Bin Liang},
journal={Computers & Security, 104113 (2024)},
year={2024},
doi={10.1016/j.cose.2024.104113},
archivePrefix={arXiv},
eprint={2409.14058},
primaryClass={cs.CR}
}
|
wang2024practically
|
arxiv-660250
|
2409.14060
|
Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured
|
<|reference_start|>Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured: Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains challenging due to high costs and data availability issues. To overcome these challenges, synthetic data generated through simulations have been employed, although discrepancies between synthetic and real data can degrade model performance. In this study, we introduce a novel framework, soft segmented randomization, designed to reduce domain discrepancy and improve the generalize ability of synthetic aperture radar automatic target recognition models. The soft segmented randomization framework applies a Gaussian mixture model to segment target and clutter regions softly, introducing randomized variations that align the synthetic data's statistical properties more closely with those of real-world data. Experimental results demonstrate that the proposed soft segmented randomization framework significantly enhances model performance on measured synthetic aperture radar data, making it a promising approach for robust automatic target recognition in scenarios with limited or no access to measured data.<|reference_end|>
|
arxiv
|
@article{kim2024soft,
title={Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR
for Synthetic-to-Measured},
author={Minjun Kim, Ohtae Jang, Haekang Song, Heesub Shin, Jaewoo Ok, Minyoung
Back, Jaehyuk Youn, Sungho Kim},
journal={arXiv preprint arXiv:2409.14060},
year={2024},
archivePrefix={arXiv},
eprint={2409.14060},
primaryClass={cs.CV}
}
|
kim2024soft
|
arxiv-660251
|
2409.14063
|
Recovering Global Data Distribution Locally in Federated Learning
|
<|reference_start|>Recovering Global Data Distribution Locally in Federated Learning: Federated Learning (FL) is a distributed machine learning paradigm that enables collaboration among multiple clients to train a shared model without sharing raw data. However, a major challenge in FL is the label imbalance, where clients may exclusively possess certain classes while having numerous minority and missing classes. Previous works focus on optimizing local updates or global aggregation but ignore the underlying imbalanced label distribution across clients. In this paper, we propose a novel approach ReGL to address this challenge, whose key idea is to Recover the Global data distribution Locally. Specifically, each client uses generative models to synthesize images that complement the minority and missing classes, thereby alleviating label imbalance. Moreover, we adaptively fine-tune the image generation process using local real data, which makes the synthetic images align more closely with the global distribution. Importantly, both the generation and fine-tuning processes are conducted at the client-side without leaking data privacy. Through comprehensive experiments on various image classification datasets, we demonstrate the remarkable superiority of our approach over existing state-of-the-art works in fundamentally tackling label imbalance in FL.<|reference_end|>
|
arxiv
|
@article{yao2024recovering,
title={Recovering Global Data Distribution Locally in Federated Learning},
author={Ziyu Yao},
journal={arXiv preprint arXiv:2409.14063},
year={2024},
archivePrefix={arXiv},
eprint={2409.14063},
primaryClass={cs.LG cs.CV}
}
|
yao2024recovering
|
arxiv-660252
|
2409.14064
|
On structure preservation for fully discrete finite difference schemes of stochastic heat equation with L\'evy space-time white noise
|
<|reference_start|>On structure preservation for fully discrete finite difference schemes of stochastic heat equation with L\'evy space-time white noise: This paper investigates the structure preservation and convergence analysis of a class of fully discrete finite difference schemes for the stochastic heat equation driven by L\'evy space-time white noise. The novelty lies in the simultaneous preservation of intrinsic structures for the exact solution, in particular the weak intermittency of moments and the regularity of c\`adl\`ag path in negative fractional Sobolev spaces. The key in the proof is the detailed analysis of technical estimates for discrete Green functions of the numerical solution. This analysis is also crucial in establishing the mean-square convergence of the schemes with orders of almost $\frac12$ in space and almost $\frac14$ in time.<|reference_end|>
|
arxiv
|
@article{chen2024on,
title={On structure preservation for fully discrete finite difference schemes
of stochastic heat equation with L\'evy space-time white noise},
author={Chuchu Chen, Tonghe Dang, Jialin Hong},
journal={arXiv preprint arXiv:2409.14064},
year={2024},
archivePrefix={arXiv},
eprint={2409.14064},
primaryClass={math.NA cs.NA}
}
|
chen2024on
|
arxiv-660253
|
2409.14065
|
Temporally Consistent Factuality Probing for Large Language Models
|
<|reference_start|>Temporally Consistent Factuality Probing for Large Language Models: The prolific use of Large Language Models (LLMs) as an alternate knowledge base requires them to be factually consistent, necessitating both correctness and consistency traits for paraphrased queries. Recently, significant attempts have been made to benchmark datasets and metrics to evaluate LLMs for these traits. However, structural simplicity (subject-relation-object) and contemporary association in their query formulation limit the broader definition of factuality and consistency. In this study, we introduce TeCFaP, a novel Temporally Consistent Factuality Probe task to expand the consistent factuality probe in the temporal dimension. To this end, we propose TEMP-COFAC, a high-quality dataset of prefix-style English query paraphrases. Subsequently, we extend the definitions of existing metrics to represent consistent factuality across temporal dimension. We experiment with a diverse set of LLMs and find most of them performing poorly on TeCFaP. Next, we propose a novel solution CoTSeLF (Consistent-Time-Sensitive Learning Framework) combining multi-task instruction tuning (MT-IT) with consistent-time-sensitive reinforcement learning (CTSRL) to improve temporally consistent factuality in LLMs. Our experiments demonstrate the efficacy of CoTSeLF over several baselines.<|reference_end|>
|
arxiv
|
@article{bajpai2024temporally,
title={Temporally Consistent Factuality Probing for Large Language Models},
author={Ashutosh Bajpai, Aaryan Goyal, Atif Anwer, Tanmoy Chakraborty},
journal={arXiv preprint arXiv:2409.14065},
year={2024},
archivePrefix={arXiv},
eprint={2409.14065},
primaryClass={cs.CL cs.LG}
}
|
bajpai2024temporally
|
arxiv-660254
|
2409.14066
|
KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data
|
<|reference_start|>KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data: Building generalist robotic systems involves effectively endowing robots with the capabilities to handle novel objects in an open-world setting. Inspired by the advances of large pre-trained models, we propose Keypoint Affordance Learning from Imagined Environments (KALIE), which adapts pre-trained Vision Language Models (VLMs) for robotic control in a scalable manner. Instead of directly producing motor commands, KALIE controls the robot by predicting point-based affordance representations based on natural language instructions and visual observations of the scene. The VLM is trained on 2D images with affordances labeled by humans, bypassing the need for training data collected on robotic systems. Through an affordance-aware data synthesis pipeline, KALIE automatically creates massive high-quality training data based on limited example data manually collected by humans. We demonstrate that KALIE can learn to robustly solve new manipulation tasks with unseen objects given only 50 example data points. Compared to baselines using pre-trained VLMs, our approach consistently achieves superior performance.<|reference_end|>
|
arxiv
|
@article{tang2024kalie:,
title={KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation
without Robot Data},
author={Grace Tang, Swetha Rajkumar, Yifei Zhou, Homer Rich Walke, Sergey
Levine, Kuan Fang},
journal={arXiv preprint arXiv:2409.14066},
year={2024},
archivePrefix={arXiv},
eprint={2409.14066},
primaryClass={cs.RO cs.AI cs.LG}
}
|
tang2024kalie:
|
arxiv-660255
|
2409.14067
|
SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality
|
<|reference_start|>SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality: Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Project page: \href{https://zju3dv.github.io/splatloc}{https://zju3dv.github.io/splatloc}.<|reference_end|>
|
arxiv
|
@article{zhai2024splatloc:,
title={SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented
Reality},
author={Hongjia Zhai, Xiyu Zhang, Boming Zhao, Hai Li, Yijia He, Zhaopeng Cui,
Hujun Bao, and Guofeng Zhang},
journal={arXiv preprint arXiv:2409.14067},
year={2024},
archivePrefix={arXiv},
eprint={2409.14067},
primaryClass={cs.CV}
}
|
zhai2024splatloc:
|
arxiv-660256
|
2409.14069
|
Semi-intrusive audio evaluation: Casting non-intrusive assessment as a multi-modal text prediction task
|
<|reference_start|>Semi-intrusive audio evaluation: Casting non-intrusive assessment as a multi-modal text prediction task: Assessment of audio by humans possesses the unique ability to attend to specific sources in a mixture of signals. Mimicking this human ability, we propose a semi-intrusive assessment where we frame the audio assessment task as a text prediction task with audio-text input. To this end we leverage instruction fine-tuning of the multi-modal PENGI model. Our experiments on MOS prediction for speech and music using both real and simulated data show that the proposed method, on average, outperforms baselines that operate on a single task. To justify the model generability, we propose a new semi-intrusive SNR estimator that is able to estimate the SNR of arbitrary signal classes in a mixture of signals with different classes.<|reference_end|>
|
arxiv
|
@article{coldenhoff2024semi-intrusive,
title={Semi-intrusive audio evaluation: Casting non-intrusive assessment as a
multi-modal text prediction task},
author={Jozef Coldenhoff, Milos Cernak},
journal={arXiv preprint arXiv:2409.14069},
year={2024},
archivePrefix={arXiv},
eprint={2409.14069},
primaryClass={eess.AS cs.SD}
}
|
coldenhoff2024semi-intrusive
|
arxiv-660257
|
2409.14070
|
IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning
|
<|reference_start|>IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning: Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detailed boundary information. Secondly, their strategies focus on hard samples for rapid adaptation, leading to forgetting and biased predictions. In this work, we propose IMOST, a continual traversability learning framework composed of two key modules: incremental dynamic memory (IDM) and self-supervised annotation (SSA). By mimicking human memory mechanisms, IDM allocates novel data samples to new clusters according to information expansion criterion. It also updates clusters based on diversity rule, ensuring a representative characterization of new scene. This mechanism enhances scene-aware knowledge diversity while maintaining a compact memory capacity. The SSA module, integrating FastSAM, utilizes point prompts to generate complete annotations in real time which reduces training complexity. Furthermore, IMOST has been successfully deployed on the quadruped robot, with performance evaluated during the online learning process. Experimental results on both public and self-collected datasets demonstrate that our IMOST outperforms current state-of-the-art method, maintains robust recognition capabilities and adaptability across various scenarios. The code is available at https://github.com/SJTU-MKH/OCLTrav.<|reference_end|>
|
arxiv
|
@article{ma2024imost:,
title={IMOST: Incremental Memory Mechanism with Online Self-Supervision for
Continual Traversability Learning},
author={Kehui Ma, Zhen Sun, Chaoran Xiong, Qiumin Zhu, Kewei Wang, Ling Pei},
journal={arXiv preprint arXiv:2409.14070},
year={2024},
doi={10.13140/RG.2.2.33195.86560},
archivePrefix={arXiv},
eprint={2409.14070},
primaryClass={cs.RO}
}
|
ma2024imost:
|
arxiv-660258
|
2409.14071
|
N-Version Assessment and Enhancement of Generative AI
|
<|reference_start|>N-Version Assessment and Enhancement of Generative AI: Generative AI (GAI) holds great potential to improve software engineering productivity, but its untrustworthy outputs, particularly in code synthesis, pose significant challenges. The need for extensive verification and validation (V&V) of GAI-generated artifacts may undermine the potential productivity gains. This paper proposes a way of mitigating these risks by exploiting GAI's ability to generate multiple versions of code and tests to facilitate comparative analysis across versions. Rather than relying on the quality of a single test or code module, this "differential GAI" (D-GAI) approach promotes more reliable quality evaluation through version diversity. We introduce the Large-Scale Software Observatorium (LASSO), a platform that supports D-GAI by executing and analyzing large sets of code versions and tests. We discuss how LASSO enables rigorous evaluation of GAI-generated artifacts and propose its application in both software development and GAI research.<|reference_end|>
|
arxiv
|
@article{kessel2024n-version,
title={N-Version Assessment and Enhancement of Generative AI},
author={Marcus Kessel, Colin Atkinson},
journal={IEEE Software September 2024},
year={2024},
doi={10.1109/MS.2024.3469388},
archivePrefix={arXiv},
eprint={2409.14071},
primaryClass={cs.SE cs.AI}
}
|
kessel2024n-version
|
arxiv-660259
|
2409.14072
|
Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects
|
<|reference_start|>Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects: Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, a high-quality dynamic mesh sequence of the object can be extracted. Experiments demonstrate that our D-2DGS is outstanding in reconstructing high-quality meshes from sparse input. More demos and code are available at https://github.com/hustvl/Dynamic-2DGS.<|reference_end|>
|
arxiv
|
@article{zhang2024dynamic,
title={Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic
objects},
author={Shuai Zhang, Guanjun Wu, Xinggang Wang, Bin Feng, Wenyu Liu},
journal={arXiv preprint arXiv:2409.14072},
year={2024},
archivePrefix={arXiv},
eprint={2409.14072},
primaryClass={cs.CV}
}
|
zhang2024dynamic
|
arxiv-660260
|
2409.14073
|
Digital Advertising in a Post-Cookie World: Charting the Impact of Google's Topics API
|
<|reference_start|>Digital Advertising in a Post-Cookie World: Charting the Impact of Google's Topics API: Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices. This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility. Through simulations based on extensive datasets capturing user behavior and market share data for ad networks, we evaluate metrics such as Ad Placement Eligibility, Low Competition Rate, and solo competitor. The findings reveal a noticeable impact on ad networks, with larger players strengthening their dominance and smaller networks facing challenges securing ad spaces and competing effectively. Moreover, the study explores the potential environmental implications of Google's actions, highlighting the need to carefully consider policy and regulatory measures to ensure fair competition and privacy protection. Overall, this research contributes valuable insights into the evolving dynamics of digital advertising and highlights the importance of balancing privacy with competition and innovation in the online advertising landscape.<|reference_end|>
|
arxiv
|
@article{romero2024digital,
title={Digital Advertising in a Post-Cookie World: Charting the Impact of
Google's Topics API},
author={Jes'us Romero, 'Angel Cuevas, Rub'en Cuevas},
journal={arXiv preprint arXiv:2409.14073},
year={2024},
archivePrefix={arXiv},
eprint={2409.14073},
primaryClass={cs.SI cs.CY cs.NI}
}
|
romero2024digital
|
arxiv-660261
|
2409.14074
|
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder
|
<|reference_start|>MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder: Multilingual automatic speech recognition (ASR) in the medical domain serves as a foundational task for various downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. This technology enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we introduce MultiMed, a collection of small-to-large end-to-end ASR models for the medical domain, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese, together with the corresponding real-world ASR dataset. To our best knowledge, MultiMed stands as the largest and the first multilingual medical ASR dataset, in terms of total duration, number of speakers, diversity of diseases, recording conditions, speaker roles, unique medical terms, accents, and ICD-10 codes. Secondly, we establish the empirical baselines, present the first reproducible study of multilinguality in medical ASR, conduct a layer-wise ablation study for end-to-end ASR training, and provide the first linguistic analysis for multilingual medical ASR. All code, data, and models are available online https://github.com/leduckhai/MultiMed/tree/master/MultiMed<|reference_end|>
|
arxiv
|
@article{le-duc2024multimed:,
title={MultiMed: Multilingual Medical Speech Recognition via Attention Encoder
Decoder},
author={Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo,
Truong-Son Hy},
journal={arXiv preprint arXiv:2409.14074},
year={2024},
archivePrefix={arXiv},
eprint={2409.14074},
primaryClass={cs.CL cs.SD eess.AS}
}
|
le-duc2024multimed:
|
arxiv-660262
|
2409.14076
|
Implicit Test Oracles for Quantum Computing
|
<|reference_start|>Implicit Test Oracles for Quantum Computing: Testing can be key to software quality assurance. Automated verification may increase throughput and reduce human fallibility errors. Test scripts supply inputs, run programs and check their outputs mechanically using test oracles. In software engineering implicit oracles automatically check for universally undesirable behaviour, such as the software under test crashing. We propose 4 properties (probability distributions, fixed qubit width, reversibility and entropy conservation) which all quantum computing must have and suggest they could be implicit test oracles for automatic, random, or fuzz testing of quantum circuits and simulators of quantum programs.<|reference_end|>
|
arxiv
|
@article{langdon2024implicit,
title={Implicit Test Oracles for Quantum Computing},
author={William B. Langdon},
journal={arXiv preprint arXiv:2409.14076},
year={2024},
archivePrefix={arXiv},
eprint={2409.14076},
primaryClass={cs.SE quant-ph}
}
|
langdon2024implicit
|
arxiv-660263
|
2409.14078
|
Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking
|
<|reference_start|>Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking: Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world settings. In recommender systems, the use of synthetic data is somewhat limited; some work has concentrated on building user-item interaction data at large scale. We believe that fairness-aware recommendation research can benefit from simulated data as it allows the study of protected groups and their interactions without depending on sensitive data that needs privacy protection. In this paper, we propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs that can be used to study re-ranking algorithms.<|reference_end|>
|
arxiv
|
@article{stefancova2024data,
title={Data Generation via Latent Factor Simulation for Fairness-aware
Re-ranking},
author={Elena Stefancova, Cassidy All, Joshua Paup, Martin Homola, Nicholas
Mattei, Robin Burke},
journal={arXiv preprint arXiv:2409.14078},
year={2024},
archivePrefix={arXiv},
eprint={2409.14078},
primaryClass={cs.IR}
}
|
stefancova2024data
|
arxiv-660264
|
2409.14082
|
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL
|
<|reference_start|>PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL: Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and commonsense reasoning, SQL solutions have a relatively fixed pattern. This facilitates the investigation of whether LLMs can benefit from categorical thinking, mirroring how humans acquire knowledge through inductive reasoning based on comparable examples. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, consequently enhancing their reasoning abilities across diverse difficulty levels and problem categories. Our experiments reveal that multiple advanced LLMs, when equipped with PTD-SQL, can either surpass or match previous state-of-the-art (SOTA) methods on the Spider and BIRD datasets. Intriguingly, models with varying initial performances have exhibited significant improvements, mainly at the boundary of their capabilities after targeted drilling, suggesting a parallel with human progress. Code is available at https://github.com/lrlbbzl/PTD-SQL.<|reference_end|>
|
arxiv
|
@article{luo2024ptd-sql:,
title={PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL},
author={Ruilin Luo, Liyuan Wang, Binghuai Lin, Zicheng Lin, Yujiu Yang},
journal={arXiv preprint arXiv:2409.14082},
year={2024},
archivePrefix={arXiv},
eprint={2409.14082},
primaryClass={cs.CL cs.AI}
}
|
luo2024ptd-sql:
|
arxiv-660265
|
2409.14083
|
SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information
|
<|reference_start|>SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information: Large Vision-Language Models (LVLMs) have become pivotal at the intersection of computer vision and natural language processing. However, the full potential of LVLMs Retrieval-Augmented Generation (RAG) capabilities remains underutilized. Existing works either focus solely on the text modality or are limited to specific tasks. Moreover, most LVLMs struggle to selectively utilize retrieved information and are sensitive to irrelevant or misleading references. To address these challenges, we propose a self-refinement framework designed to teach LVLMs to Selectively Utilize Retrieved Information (SURf). Specifically, when given questions that are incorrectly answered by the LVLM backbone, we obtain references that help correct the answers (positive references) and those that do not (negative references). We then fine-tune the LVLM backbone using a combination of these positive and negative references. Our experiments across three tasks and seven datasets demonstrate that our framework significantly enhances LVLMs ability to effectively utilize retrieved multimodal references and improves their robustness against irrelevant or misleading information. The source code is available at https://github.com/GasolSun36/SURf.<|reference_end|>
|
arxiv
|
@article{sun2024surf:,
title={SURf: Teaching Large Vision-Language Models to Selectively Utilize
Retrieved Information},
author={Jiashuo Sun, Jihai Zhang, Yucheng Zhou, Zhaochen Su, Xiaoye Qu, Yu
Cheng},
journal={arXiv preprint arXiv:2409.14083},
year={2024},
archivePrefix={arXiv},
eprint={2409.14083},
primaryClass={cs.CV}
}
|
sun2024surf:
|
arxiv-660266
|
2409.14084
|
One-shot World Models Using a Transformer Trained on a Synthetic Prior
|
<|reference_start|>One-shot World Models Using a Transformer Trained on a Synthetic Prior: A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models are typically trained on observations from the real world environment, and they usually do not enable learning policies for other real environments. We propose One-Shot World Model (OSWM), a transformer world model that is learned in an in-context learning fashion from purely synthetic data sampled from a prior distribution. Our prior is composed of multiple randomly initialized neural networks, where each network models the dynamics of each state and reward dimension of a desired target environment. We adopt the supervised learning procedure of Prior-Fitted Networks by masking next-state and reward at random context positions and query OSWM to make probabilistic predictions based on the remaining transition context. During inference time, OSWM is able to quickly adapt to the dynamics of a simple grid world, as well as the CartPole gym and a custom control environment by providing 1k transition steps as context and is then able to successfully train environment-solving agent policies. However, transferring to more complex environments remains a challenge, currently. Despite these limitations, we see this work as an important stepping-stone in the pursuit of learning world models purely from synthetic data.<|reference_end|>
|
arxiv
|
@article{ferreira2024one-shot,
title={One-shot World Models Using a Transformer Trained on a Synthetic Prior},
author={Fabio Ferreira and Moreno Schlageter and Raghu Rajan and Andre
Biedenkapp and Frank Hutter},
journal={arXiv preprint arXiv:2409.14084},
year={2024},
archivePrefix={arXiv},
eprint={2409.14084},
primaryClass={cs.LG cs.AI}
}
|
ferreira2024one-shot
|
arxiv-660267
|
2409.14085
|
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
|
<|reference_start|>Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models: Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The ideal neural audio codec should maintain content, paralinguistics, speaker characteristics, and audio information even at low bitrates. Recently, numerous advanced neural codec models have been proposed. However, codec models are often tested under varying experimental conditions. As a result, we introduce the Codec-SUPERB challenge at SLT 2024, designed to facilitate fair and lightweight comparisons among existing codec models and inspire advancements in the field. This challenge brings together representative speech applications and objective metrics, and carefully selects license-free datasets, sampling them into small sets to reduce evaluation computation costs. This paper presents the challenge's rules, datasets, five participant systems, results, and findings.<|reference_end|>
|
arxiv
|
@article{wu2024codec-superb,
title={Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec
models},
author={Haibin Wu, Xuanjun Chen, Yi-Cheng Lin, Kaiwei Chang, Jiawei Du, Ke-Han
Lu, Alexander H. Liu, Ho-Lam Chung, Yuan-Kuei Wu, Dongchao Yang, Songxiang
Liu, Yi-Chiao Wu, Xu Tan, James Glass, Shinji Watanabe, Hung-yi Lee},
journal={arXiv preprint arXiv:2409.14085},
year={2024},
archivePrefix={arXiv},
eprint={2409.14085},
primaryClass={eess.AS cs.SD}
}
|
wu2024codec-superb
|
arxiv-660268
|
2409.14086
|
AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model
|
<|reference_start|>AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model: There have been several studies on automatically generating piano covers, and recent advancements in deep learning have enabled the creation of more sophisticated covers. However, existing automatic piano cover models still have room for improvement in terms of expressiveness and fidelity to the original. To address these issues, we propose a learning algorithm called AMT-APC, which leverages the capabilities of automatic music transcription models. By utilizing the strengths of well-established automatic music transcription models, we aim to improve the accuracy of piano cover generation. Our experiments demonstrate that the AMT-APC model reproduces original tracks more accurately than any existing models.<|reference_end|>
|
arxiv
|
@article{komiya2024amt-apc:,
title={AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music
Transcription Model},
author={Kazuma Komiya, Yoshihisa Fukuhara},
journal={arXiv preprint arXiv:2409.14086},
year={2024},
archivePrefix={arXiv},
eprint={2409.14086},
primaryClass={cs.SD cs.LG eess.AS}
}
|
komiya2024amt-apc:
|
arxiv-660269
|
2409.14087
|
BRep Boundary and Junction Detection for CAD Reverse Engineering
|
<|reference_start|>BRep Boundary and Junction Detection for CAD Reverse Engineering: In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model, being able to parse all its structural compositions and design steps. In this paper, we propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset. We have carefully annotated the 50K and 45K scans of both the datasets with appropriate topological relations (e.g., next, mate, previous) between the geometrical primitives (i.e., boundaries, junctions, loops, faces) of their BRep data structures. The proposed solution decomposes the Scan-to-CAD problem in Scan-to-BRep ensuring the right step towards feature-based modeling, and therefore, leveraging other existing BRep-to-CAD modeling methods. Our proposed Scan-to-BRep neural network learns to detect BRep boundaries and junctions by minimizing focal-loss and non-maximal suppression (NMS) during training time. Experimental results show that our BRepDetNet with NMS-Loss achieves impressive results.<|reference_end|>
|
arxiv
|
@article{ali2024brep,
title={BRep Boundary and Junction Detection for CAD Reverse Engineering},
author={Sk Aziz Ali and Mohammad Sadil Khan and Didier Stricker},
journal={arXiv preprint arXiv:2409.14087},
year={2024},
doi={10.1109/ICMI60790.2024.10585950},
archivePrefix={arXiv},
eprint={2409.14087},
primaryClass={cs.CV cs.LG cs.MM}
}
|
ali2024brep
|
arxiv-660270
|
2409.14088
|
Intelligent Reflecting Surface-Aided Multiuser Communication: Co-design of Transmit Diversity and Active/Passive Precoding
|
<|reference_start|>Intelligent Reflecting Surface-Aided Multiuser Communication: Co-design of Transmit Diversity and Active/Passive Precoding: Intelligent reflecting surface (IRS) has become a cost-effective solution for constructing a smart and adaptive radio environment. Most previous works on IRS have jointly designed the active and passive precoding based on perfectly or partially known channel state information (CSI). However, in delay-sensitive or high-mobility communications, it is imperative to explore more effective methods for leveraging IRS to enhance communication reliability without the need for any CSI. In this paper, we investigate an innovative IRS-aided multiuser communication system, which integrates an IRS with its aided multi-antenna base station (BS) to simultaneously serve multiple high-mobility users through transmit diversity and multiple low-mobility users through active/passive precoding. In specific, we first reveal that when dynamically tuning the IRS's common phase-shift shared with all reflecting elements, its passive precoding gain to any low-mobility user remains unchanged. Inspired by this property, we utilize the design of common phase-shift at the IRS for achieving transmit diversity to serve high-mobility users, yet without requiring any CSI at the BS. Meanwhile, the active/passive precoding design is incorporated into the IRS-integrated BS to serve low-mobility users (assuming the CSI is known). Then, taking into account the interference among different users, we formulate and solve a joint optimization problem of the IRS's reflect precoding and the BS's transmit precoding, with the aim of minimizing the total transmit power at the BS.<|reference_end|>
|
arxiv
|
@article{zheng2024intelligent,
title={Intelligent Reflecting Surface-Aided Multiuser Communication: Co-design
of Transmit Diversity and Active/Passive Precoding},
author={Beixiong Zheng, Tiantian Ma, Jie Tang, Changsheng You, Shaoe Lin,
Kai-Kit Wong},
journal={arXiv preprint arXiv:2409.14088},
year={2024},
archivePrefix={arXiv},
eprint={2409.14088},
primaryClass={cs.IT eess.SP math.IT}
}
|
zheng2024intelligent
|
arxiv-660271
|
2409.14089
|
Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
|
<|reference_start|>Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data: Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in Medicine and Biology fields is still in its infancy with few examples. In this study, we aimed to assess whether Quantum Kernels (QK) could effectively classify subtypes of Breast Cancer (BC) patients on the basis of molecular characteristics. We performed an heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points, and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the Quantum Processing Unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on the NISQ devices. Our findings suggest that QK methods show promises for application in Precision Oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.<|reference_end|>
|
arxiv
|
@article{repetto2024quantum,
title={Quantum enhanced stratification of Breast Cancer: exploring quantum
expressivity for real omics data},
author={Valeria Repetto, Elia Giuseppe Ceroni, Giuseppe Buonaiuto, Romina
D'Aurizio},
journal={arXiv preprint arXiv:2409.14089},
year={2024},
archivePrefix={arXiv},
eprint={2409.14089},
primaryClass={quant-ph cs.LG}
}
|
repetto2024quantum
|
arxiv-660272
|
2409.14090
|
Window-based Channel Attention for Wavelet-enhanced Learned Image Compression
|
<|reference_start|>Window-based Channel Attention for Wavelet-enhanced Learned Image Compression: Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transformer as basic blocks. However, limited by the shifted window attention, Swin-Transformer-based LIC exhibits a restricted growth of receptive fields, affecting the ability to model large objects for image compression. To address this issue and improve the performance, we incorporate window partition into channel attention for the first time to obtain large receptive fields and capture more global information. Since channel attention hinders local information learning, it is important to extend existing attention mechanisms in Transformer codecs to the space-channel attention to establish multiple receptive fields, being able to capture global correlations with large receptive fields while maintaining detailed characterization of local correlations with small receptive fields. We also incorporate the discrete wavelet transform into our Spatial-Channel Hybrid (SCH) framework for efficient frequency-dependent down-sampling and further enlarging receptive fields. Experiment results demonstrate that our method achieves state-of-the-art performances, reducing BD-rate by 18.54%, 23.98%, 22.33%, and 24.71% on four standard datasets compared to VTM-23.1.<|reference_end|>
|
arxiv
|
@article{xu2024window-based,
title={Window-based Channel Attention for Wavelet-enhanced Learned Image
Compression},
author={Heng Xu, Bowen Hai, Yushun Tang, Zhihai He},
journal={arXiv preprint arXiv:2409.14090},
year={2024},
archivePrefix={arXiv},
eprint={2409.14090},
primaryClass={eess.IV cs.CV}
}
|
xu2024window-based
|
arxiv-660273
|
2409.14091
|
Normalized Narrow Jump To Conclusions: Normalized Narrow Shortcuts for Parameter Efficient Early Exit Transformer Prediction
|
<|reference_start|>Normalized Narrow Jump To Conclusions: Normalized Narrow Shortcuts for Parameter Efficient Early Exit Transformer Prediction: With the size and cost of large transformer-based language models growing, recently, there has been interest in shortcut casting of early transformer hidden-representations to final-representations for cheaper model inference. In particular, shortcutting pre-trained transformers with linear transformations over early layers has been shown to improve precision in early inference. However, for large language models, even this becomes computationally expensive. In this work, we propose Narrow Jump to Conclusions (NJTC) and Normalized Narrow Jump to Conclusions (N-NJTC) - parameter efficient alternatives to standard linear shortcutting that reduces shortcut parameter count by over 97%. We show that N-NJTC reliably outperforms Identity shortcuts at early stages and offers stable precision from all transformer block levels for GPT-2-XL, Phi3-Mini and Llama2-7B transformer models, demonstrating the viability of more parameter efficient short-cutting approaches.<|reference_end|>
|
arxiv
|
@article{seshadri2024normalized,
title={Normalized Narrow Jump To Conclusions: Normalized Narrow Shortcuts for
Parameter Efficient Early Exit Transformer Prediction},
author={Amrit Diggavi Seshadri},
journal={arXiv preprint arXiv:2409.14091},
year={2024},
archivePrefix={arXiv},
eprint={2409.14091},
primaryClass={cs.AI}
}
|
seshadri2024normalized
|
arxiv-660274
|
2409.14092
|
Encryption of Audio Signals Using the Elzaki Transformation and the Lorenz Chaotic System Lorenz Chaotic System
|
<|reference_start|>Encryption of Audio Signals Using the Elzaki Transformation and the Lorenz Chaotic System Lorenz Chaotic System: The preservation of image privacy during storage and transmission is of paramount importance in several areas including healthcare, military, safe communication, and video conferencing. Protecting data privacy demands the use of robust image encryption techniques. Several cryptographic techniques have been particularly designed to ensure the privacy of digital images. This study presents a novel method for encrypting color images utilizing chaos theory and a special transformation. This indicated approach first employs the Lorenz chaos theory to scramble the audio files. Following that, we utilize a technique that involves using the Maclaurin series expansion of hyperbolic functions and the Elzaki transform to encrypt the audio. Subsequently, we decode it by applying the inverse Elzaki transform. The key for the coefficients obtained from the transformation is created using modular arithmetic methods. Comparisons between the techniques are conducted based on a number of performance measures, including entropy analysis, spectrogram plotting, and correlation coefficients. Theoretical analysis and simulation indicate the efficacy of the proposed approach and confirm that this method is suitable for actual audio encryption. Moreover, the security inquiry indicates that an extra layer of security is provided by the provided audio encryption approach<|reference_end|>
|
arxiv
|
@article{kareem2024encryption,
title={Encryption of Audio Signals Using the Elzaki Transformation and the
Lorenz Chaotic System Lorenz Chaotic System},
author={Shadman R. Kareem},
journal={arXiv preprint arXiv:2409.14092},
year={2024},
archivePrefix={arXiv},
eprint={2409.14092},
primaryClass={cs.CR}
}
|
kareem2024encryption
|
arxiv-660275
|
2409.14093
|
Available Transfer Capability Calculation for Wind-Integrated Power Systems Considering Wind Speed Spatiotemporal Correlation and Primal-Dual Interior Point Method
|
<|reference_start|>Available Transfer Capability Calculation for Wind-Integrated Power Systems Considering Wind Speed Spatiotemporal Correlation and Primal-Dual Interior Point Method: This paper explores the intricate effects of wind power integration on the Available Transfer Capability (ATC) of power systems, emphasizing the significance of spatiotemporal correlations in wind speed. We present an innovative optimal power flow model that integrates the Primal-Dual Interior Point Method (PDIPM), ensuring both computational accuracy and efficiency. This research pioneers a systematic analysis of how spatiotemporal wind speed correlations influence wind power output, thereby refining ATC calculations and improving prediction reliability. Furthermore, we assess the impacts of wind farm integration capacity, location, and connection methods on ATC, offering valuable insights for power system planning and market operations.<|reference_end|>
|
arxiv
|
@article{huangpu2024available,
title={Available Transfer Capability Calculation for Wind-Integrated Power
Systems Considering Wind Speed Spatiotemporal Correlation and Primal-Dual
Interior Point Method},
author={Xia-Liang Huangpu},
journal={arXiv preprint arXiv:2409.14093},
year={2024},
archivePrefix={arXiv},
eprint={2409.14093},
primaryClass={eess.SY cs.SY}
}
|
huangpu2024available
|
arxiv-660276
|
2409.14094
|
A Simple Algorithm for Worst-Case Optimal Join and Sampling
|
<|reference_start|>A Simple Algorithm for Worst-Case Optimal Join and Sampling: We present an elementary branch and bound algorithm with a simple analysis of why it achieves worstcase optimality for join queries on classes of databases defined respectively by cardinality or acyclic degree constraints. We then show that if one is given a reasonable way for recursively estimating upper bounds on the number of answers of the join queries, our algorithm can be turned into algorithm for uniformly sampling answers with expected running time $O(UP/OUT)$ where $UP$ is the upper bound, $OUT$ is the actual number of answers and $O(\cdot)$ ignores polylogarithmic factors. Our approach recovers recent results on worstcase optimal join algorithm and sampling in a modular, clean and elementary way.<|reference_end|>
|
arxiv
|
@article{capelli2024a,
title={A Simple Algorithm for Worst-Case Optimal Join and Sampling},
author={Florent Capelli, Oliver Irwin and Sylvain Salvati},
journal={arXiv preprint arXiv:2409.14094},
year={2024},
archivePrefix={arXiv},
eprint={2409.14094},
primaryClass={cs.DB}
}
|
capelli2024a
|
arxiv-660277
|
2409.14095
|
Foundation Models for Amodal Video Instance Segmentation in Automated Driving
|
<|reference_start|>Foundation Models for Amodal Video Instance Segmentation in Automated Driving: In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance segmentation relying on methods trained on entirely labeled video data with techniques borrowed from standard video instance segmentation. Such amodally labeled video data is difficult and expensive to obtain and the resulting methods suffer from a trade-off between instance segmentation and tracking performance. To largely solve this issue, we propose to study the application of foundation models for this task. More precisely, we exploit the extensive knowledge of the Segment Anything Model (SAM), while fine-tuning it to the amodal instance segmentation task. Given an initial video instance segmentation, we sample points from the visible masks to prompt our amodal SAM. We use a point memory to store those points. If a previously observed instance is not predicted in a following frame, we retrieve its most recent points from the point memory and use a point tracking method to follow those points to the current frame, together with the corresponding last amodal instance mask. This way, while basing our method on an amodal instance segmentation, we nevertheless obtain video-level amodal instance segmentation results. Our resulting S-AModal method achieves state-of-the-art results in amodal video instance segmentation while resolving the need for amodal video-based labels. Code for S-AModal is available at https://github.com/ifnspaml/S-AModal.<|reference_end|>
|
arxiv
|
@article{breitenstein2024foundation,
title={Foundation Models for Amodal Video Instance Segmentation in Automated
Driving},
author={Jasmin Breitenstein, Franz J"unger, Andreas B"ar, Tim Fingscheidt},
journal={arXiv preprint arXiv:2409.14095},
year={2024},
archivePrefix={arXiv},
eprint={2409.14095},
primaryClass={cs.CV}
}
|
breitenstein2024foundation
|
arxiv-660278
|
2409.14096
|
VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and Language-Guided Experience Replay
|
<|reference_start|>VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and Language-Guided Experience Replay: In this paper, we propose VLM-Vac, a novel framework designed to enhance the autonomy of smart robot vacuum cleaners. Our approach integrates the zero-shot object detection capabilities of a Vision-Language Model (VLM) with a Knowledge Distillation (KD) strategy. By leveraging the VLM, the robot can categorize objects into actionable classes -- either to avoid or to suck -- across diverse backgrounds. However, frequently querying the VLM is computationally expensive and impractical for real-world deployment. To address this issue, we implement a KD process that gradually transfers the essential knowledge of the VLM to a smaller, more efficient model. Our real-world experiments demonstrate that this smaller model progressively learns from the VLM and requires significantly fewer queries over time. Additionally, we tackle the challenge of continual learning in dynamic home environments by exploiting a novel experience replay method based on language-guided sampling. Our results show that this approach is not only energy-efficient but also surpasses conventional vision-based clustering methods, particularly in detecting small objects across diverse backgrounds.<|reference_end|>
|
arxiv
|
@article{mirjalili2024vlm-vac:,
title={VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and
Language-Guided Experience Replay},
author={Reihaneh Mirjalili, Michael Krawez, Florian Walter and Wolfram Burgard},
journal={arXiv preprint arXiv:2409.14096},
year={2024},
archivePrefix={arXiv},
eprint={2409.14096},
primaryClass={cs.RO}
}
|
mirjalili2024vlm-vac:
|
arxiv-660279
|
2409.14097
|
Probing Context Localization of Polysemous Words in Pre-trained Language Model Sub-Layers
|
<|reference_start|>Probing Context Localization of Polysemous Words in Pre-trained Language Model Sub-Layers: In the era of high performing Large Language Models, researchers have widely acknowledged that contextual word representations are one of the key drivers in achieving top performances in downstream tasks. In this work, we investigate the degree of contextualization encoded in the fine-grained sub-layer representations of a Pre-trained Language Model (PLM) by empirical experiments using linear probes. Unlike previous work, we are particularly interested in identifying the strength of contextualization across PLM sub-layer representations (i.e. Self-Attention, Feed-Forward Activation and Output sub-layers). To identify the main contributions of sub-layers to contextualisation, we first extract the sub-layer representations of polysemous words in minimally different sentence pairs, and compare how these representations change through the forward pass of the PLM network. Second, by probing on a sense identification classification task, we try to empirically localize the strength of contextualization information encoded in these sub-layer representations. With these probing experiments, we also try to gain a better understanding of the influence of context length and context richness on the degree of contextualization. Our main conclusion is cautionary: BERT demonstrates a high degree of contextualization in the top sub-layers if the word in question is in a specific position in the sentence with a shorter context window, but this does not systematically generalize across different word positions and context sizes.<|reference_end|>
|
arxiv
|
@article{vijayakumar2024probing,
title={Probing Context Localization of Polysemous Words in Pre-trained Language
Model Sub-Layers},
author={Soniya Vijayakumar, Josef van Genabith and Simon Ostermann},
journal={arXiv preprint arXiv:2409.14097},
year={2024},
archivePrefix={arXiv},
eprint={2409.14097},
primaryClass={cs.CL}
}
|
vijayakumar2024probing
|
arxiv-660280
|
2409.14101
|
PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture
|
<|reference_start|>PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture: The data scarcity problem is a crucial factor that hampers the model performance of IMU-based human motion capture. However, effective data augmentation for IMU-based motion capture is challenging, since it has to capture the physical relations and constraints of the human body, while maintaining the data distribution and quality. We propose PoseAugment, a novel pipeline incorporating VAE-based pose generation and physical optimization. Given a pose sequence, the VAE module generates infinite poses with both high fidelity and diversity, while keeping the data distribution. The physical module optimizes poses to satisfy physical constraints with minimal motion restrictions. High-quality IMU data are then synthesized from the augmented poses for training motion capture models. Experiments show that PoseAugment outperforms previous data augmentation and pose generation methods in terms of motion capture accuracy, revealing a strong potential of our method to alleviate the data collection burden for IMU-based motion capture and related tasks driven by human poses.<|reference_end|>
|
arxiv
|
@article{li2024poseaugment:,
title={PoseAugment: Generative Human Pose Data Augmentation with Physical
Plausibility for IMU-based Motion Capture},
author={Zhuojun Li, Chun Yu, Chen Liang, Yuanchun Shi},
journal={arXiv preprint arXiv:2409.14101},
year={2024},
archivePrefix={arXiv},
eprint={2409.14101},
primaryClass={cs.CV cs.HC}
}
|
li2024poseaugment:
|
arxiv-660281
|
2409.14103
|
ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events
|
<|reference_start|>ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events: Recent years have witnessed tremendous progress in the 3D reconstruction of dynamic humans from a monocular video with the advent of neural rendering techniques. This task has a wide range of applications, including the creation of virtual characters for virtual reality (VR) environments. However, it is still challenging to reconstruct clear humans when the monocular video is affected by motion blur, particularly caused by rapid human motion (e.g., running, dancing), as often occurs in the wild. This leads to distinct inconsistency of shape and appearance for the rendered 3D humans, especially in the blurry regions with rapid motion, e.g., hands and legs. In this paper, we propose ExFMan, the first neural rendering framework that unveils the possibility of rendering high-quality humans in rapid motion with a hybrid frame-based RGB and bio-inspired event camera. The ``out-of-the-box'' insight is to leverage the high temporal information of event data in a complementary manner and adaptively reweight the effect of losses for both RGB frames and events in the local regions, according to the velocity of the rendered human. This significantly mitigates the inconsistency associated with motion blur in the RGB frames. Specifically, we first formulate a velocity field of the 3D body in the canonical space and render it to image space to identify the body parts with motion blur. We then propose two novel losses, i.e., velocity-aware photometric loss and velocity-relative event loss, to optimize the neural human for both modalities under the guidance of the estimated velocity. In addition, we incorporate novel pose regularization and alpha losses to facilitate continuous pose and clear boundary. Extensive experiments on synthetic and real-world datasets demonstrate that ExFMan can reconstruct sharper and higher quality humans.<|reference_end|>
|
arxiv
|
@article{chen2024exfman:,
title={ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames
and Events},
author={Kanghao Chen, Zeyu Wang and Lin Wang},
journal={arXiv preprint arXiv:2409.14103},
year={2024},
archivePrefix={arXiv},
eprint={2409.14103},
primaryClass={cs.CV}
}
|
chen2024exfman:
|
arxiv-660282
|
2409.14104
|
IPF-HMGNN: A novel integrative prediction framework for metro passenger flow
|
<|reference_start|>IPF-HMGNN: A novel integrative prediction framework for metro passenger flow: The operation and management of the metro system in urban areas rely on accurate predictions of future passenger flow. While using all the available information can potentially improve on the accuracy of the flow prediction, there has been little attention to the hierarchical relationship between the type of tickets collected from the passengers entering/exiting a station and its resulting passenger flow. To this end, we propose a novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN). The proposed framework consists of three components: initial prediction, task judgment and hierarchical coordination modules. Using the Wuxi, China metro network as an example, we study two prediction approaches (i) traditional prediction approach where the model directly predicts passenger flow at the station, and (ii) hierarchical prediction approach where the prediction of ticket type and station passenger flow are performed simultaneously considering the hierarchical constraints (i.e., the sum of predicted passenger flow per ticket type equals the predicted station aggregated passenger flow). Experimental results indicate that in the traditional prediction approach, our IPF-HMGNN can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model by 49.56% and 53.88%, respectively. In the hierarchical prediction approach, IPF-HMGNN can achieve a maximum reduction of 35.32% in MAE and 36.18% in RMSE, while satisfying the hierarchical constraint.<|reference_end|>
|
arxiv
|
@article{lu2024ipf-hmgnn:,
title={IPF-HMGNN: A novel integrative prediction framework for metro passenger
flow},
author={Wenbo Lu, Yong Zhang, Hai L.Vu, Jinhua Xu, Peikun Li},
journal={arXiv preprint arXiv:2409.14104},
year={2024},
archivePrefix={arXiv},
eprint={2409.14104},
primaryClass={cs.CY}
}
|
lu2024ipf-hmgnn:
|
arxiv-660283
|
2409.14105
|
ESDS: AI-Powered Early Stunting Detection and Monitoring System using Edited Radius-SMOTE Algorithm
|
<|reference_start|>ESDS: AI-Powered Early Stunting Detection and Monitoring System using Edited Radius-SMOTE Algorithm: Stunting detection is a significant issue in Indonesian healthcare, causing lower cognitive function, lower productivity, a weakened immunity, delayed neuro-development, and degenerative diseases. In regions with a high prevalence of stunting and limited welfare resources, identifying children in need of treatment is critical. The diagnostic process often raises challenges, such as the lack of experience in medical workers, incompatible anthropometric equipment, and inefficient medical bureaucracy. To counteract the issues, the use of load cell sensor and ultrasonic sensor can provide suitable anthropometric equipment and streamline the medical bureaucracy for stunting detection. This paper also employs machine learning for stunting detection based on sensor readings. The experiment results show that the sensitivity of the load cell sensor and the ultrasonic sensor is 0.9919 and 0.9986, respectively. Also, the machine learning test results have three classification classes, which are normal, stunted, and stunting with an accuracy rate of 98\%.<|reference_end|>
|
arxiv
|
@article{pramana2024esds:,
title={ESDS: AI-Powered Early Stunting Detection and Monitoring System using
Edited Radius-SMOTE Algorithm},
author={A.A. Gde Yogi Pramana, Haidar Muhammad Zidan, Muhammad Fazil Maulana,
Oskar Natan},
journal={arXiv preprint arXiv:2409.14105},
year={2024},
archivePrefix={arXiv},
eprint={2409.14105},
primaryClass={cs.LG eess.SP}
}
|
pramana2024esds:
|
arxiv-660284
|
2409.14106
|
FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training
|
<|reference_start|>FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training: Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance molecular representation learning. However, they focus on the whole molecular graph and neglect frequently occurring subgraphs, known as motifs,which are essential for determining molecular properties. Without such fine-grained knowledge, these models struggle to generalize to unseen molecules and tasks that require motif-level insights. To bridge this gap, we propose FineMolTex, a novel Fine-grained Molecular graph-Text pre-training framework to jointly learn coarse-grained molecule-level knowledge and fine-grained motif-level knowledge. Specifically, FineMolTex consists of two pre-training tasks: a contrastive alignment task for coarse-grained matching and a masked multi-modal modeling task for fine-grained matching. In particular, the latter predicts the labels of masked motifs and words, leveraging insights from each other, thereby enabling FineMolTex to understand the fine-grained matching between motifs and words. Finally, we conduct extensive experiments across three downstream tasks, achieving up to 230% improvement in the text-based molecule editing task. Additionally, our case studies reveal that FineMolTex successfully captures fine-grained knowledge, potentially offering valuable insights for drug discovery and catalyst design.<|reference_end|>
|
arxiv
|
@article{li2024finemoltex:,
title={FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training},
author={Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi},
journal={arXiv preprint arXiv:2409.14106},
year={2024},
archivePrefix={arXiv},
eprint={2409.14106},
primaryClass={cs.AI}
}
|
li2024finemoltex:
|
arxiv-660285
|
2409.14107
|
Routing in Sparsely-gated Language Models responds to Context
|
<|reference_start|>Routing in Sparsely-gated Language Models responds to Context: Language Models (LMs) recently incorporate mixture-of-experts layers consisting of a router and a collection of experts to scale up their parameter count given a fixed computational budget. Building on previous efforts indicating that token-expert assignments are predominantly influenced by token identities and positions, we trace routing decisions of similarity-annotated text pairs to evaluate the context sensitivity of learned token-expert assignments. We observe that routing in encoder layers mainly depends on (semantic) associations, but contextual cues provide an additional layer of refinement. Conversely, routing in decoder layers is more variable and markedly less sensitive to context.<|reference_end|>
|
arxiv
|
@article{arnold2024routing,
title={Routing in Sparsely-gated Language Models responds to Context},
author={Stefan Arnold, Marian Fietta, Dilara Yesilbas},
journal={arXiv preprint arXiv:2409.14107},
year={2024},
archivePrefix={arXiv},
eprint={2409.14107},
primaryClass={cs.CL}
}
|
arnold2024routing
|
arxiv-660286
|
2409.14109
|
Vision-Language Models Assisted Unsupervised Video Anomaly Detection
|
<|reference_start|>Vision-Language Models Assisted Unsupervised Video Anomaly Detection: Video anomaly detection is a subject of great interest across industrial and academic domains due to its crucial role in computer vision applications. However, the inherent unpredictability of anomalies and the scarcity of anomaly samples present significant challenges for unsupervised learning methods. To overcome the limitations of unsupervised learning, which stem from a lack of comprehensive prior knowledge about anomalies, we propose VLAVAD (Video-Language Models Assisted Anomaly Detection). Our method employs a cross-modal pre-trained model that leverages the inferential capabilities of large language models (LLMs) in conjunction with a Selective-Prompt Adapter (SPA) for selecting semantic space. Additionally, we introduce a Sequence State Space Module (S3M) that detects temporal inconsistencies in semantic features. By mapping high-dimensional visual features to low-dimensional semantic ones, our method significantly enhance the interpretability of unsupervised anomaly detection. Our proposed approach effectively tackles the challenge of detecting elusive anomalies that are hard to discern over periods, achieving SOTA on the challenging ShanghaiTech dataset.<|reference_end|>
|
arxiv
|
@article{jiang2024vision-language,
title={Vision-Language Models Assisted Unsupervised Video Anomaly Detection},
author={Yalong Jiang, Liquan Mao},
journal={arXiv preprint arXiv:2409.14109},
year={2024},
archivePrefix={arXiv},
eprint={2409.14109},
primaryClass={cs.CV}
}
|
jiang2024vision-language
|
arxiv-660287
|
2409.14111
|
Data Management in the Noisy Intermediate-Scale Quantum Era
|
<|reference_start|>Data Management in the Noisy Intermediate-Scale Quantum Era: Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the Noisy Intermediate-Scale Quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.<|reference_end|>
|
arxiv
|
@article{hai2024data,
title={Data Management in the Noisy Intermediate-Scale Quantum Era},
author={Rihan Hai, Shih-Han Hung, Tim Coopmans, Floris Geerts},
journal={arXiv preprint arXiv:2409.14111},
year={2024},
archivePrefix={arXiv},
eprint={2409.14111},
primaryClass={quant-ph cs.DB}
}
|
hai2024data
|
arxiv-660288
|
2409.14113
|
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
|
<|reference_start|>Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning: To accelerate Magnetic Resonance (MR) imaging procedures, Multi-Contrast MR Reconstruction (MCMR) has become a prevalent trend that utilizes an easily obtainable modality as an auxiliary to support high-quality reconstruction of the target modality with under-sampled k-space measurements. The exploration of global dependency and complementary information across different modalities is essential for MCMR. However, existing methods either struggle to capture global dependency due to the limited receptive field or suffer from quadratic computational complexity. To tackle this dilemma, we propose a novel Frequency and Spatial Mutual Learning Network (FSMNet), which efficiently explores global dependencies across different modalities. Specifically, the features for each modality are extracted by the Frequency-Spatial Feature Extraction (FSFE) module, featuring a frequency branch and a spatial branch. Benefiting from the global property of the Fourier transform, the frequency branch can efficiently capture global dependency with an image-size receptive field, while the spatial branch can extract local features. To exploit complementary information from the auxiliary modality, we propose a Cross-Modal Selective fusion (CMS-fusion) module that selectively incorporate the frequency and spatial features from the auxiliary modality to enhance the corresponding branch of the target modality. To further integrate the enhanced global features from the frequency branch and the enhanced local features from the spatial branch, we develop a Frequency-Spatial fusion (FS-fusion) module, resulting in a comprehensive feature representation for the target modality. Extensive experiments on the BraTS and fastMRI datasets demonstrate that the proposed FSMNet achieves state-of-the-art performance for the MCMR task with different acceleration factors. The code is available at: https://github.com/qic999/FSMNet.<|reference_end|>
|
arxiv
|
@article{chen2024accelerated,
title={Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial
Mutual Learning},
author={Qi Chen, Xiaohan Xing, Zhen Chen, Zhiwei Xiong},
journal={arXiv preprint arXiv:2409.14113},
year={2024},
archivePrefix={arXiv},
eprint={2409.14113},
primaryClass={eess.IV cs.CV}
}
|
chen2024accelerated
|
arxiv-660289
|
2409.14115
|
Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control
|
<|reference_start|>Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control: Aerial grasping, particularly soft aerial grasping, holds significant promise for drone delivery and harvesting tasks. However, controlling UAV dynamics during aerial grasping presents considerable challenges. The increased mass during payload grasping adversely affects thrust prediction, while unpredictable environmental disturbances further complicate control efforts. In this study, our objective aims to enhance the control of the Soft Aerial Vehicle (SAV) during aerial grasping by incorporating a disturbance observer into a Nonlinear Model Predictive Control (NMPC) SAV controller. By integrating the disturbance observer into the NMPC SAV controller, we aim to compensate for dynamic model idealization and uncertainties arising from additional payloads and unpredictable disturbances. Our approach combines a disturbance observer-based NMPC with the SAV controller, effectively minimizing tracking errors and enabling precise aerial grasping along all three axes. The proposed SAV equipped with Disturbance Observer-based Nonlinear Model Predictive Control (DOMPC) demonstrates remarkable capabilities in handling both static and non-static payloads, leading to the successful grasping of various objects. Notably, our SAV achieves an impressive payload-to-weight ratio, surpassing previous investigations in the domain of soft grasping. Using the proposed soft aerial vehicle weighing 1.002 kg, we achieve a maximum payload of 337 g by grasping.<|reference_end|>
|
arxiv
|
@article{cheung2024aerial,
title={Aerial Grasping with Soft Aerial Vehicle Using Disturbance
Observer-Based Model Predictive Control},
author={Hiu Ching Cheung, Bailun Jiang, Yang Hu, Henry K. Chu, Chih-Yung Wen,
Ching-Wei Chang},
journal={arXiv preprint arXiv:2409.14115},
year={2024},
archivePrefix={arXiv},
eprint={2409.14115},
primaryClass={cs.RO}
}
|
cheung2024aerial
|
arxiv-660290
|
2409.14119
|
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm
|
<|reference_start|>Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm: Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language models. However, its reliance on fewer trainable parameters poses security risks, such as task-agnostic backdoors. Despite their severe impact on a wide range of tasks, there is no practical defense solution available that effectively counters task-agnostic backdoors within the context of PEFT. In this study, we introduce Obliviate, a PEFT-integrable backdoor defense. We develop two techniques aimed at amplifying benign neurons within PEFT layers and penalizing the influence of trigger tokens. Our evaluations across three major PEFT architectures show that our method can significantly reduce the attack success rate of the state-of-the-art task-agnostic backdoors (83.6%$\downarrow$). Furthermore, our method exhibits robust defense capabilities against both task-specific backdoors and adaptive attacks. Source code will be obtained at https://github.com/obliviateARR/Obliviate.<|reference_end|>
|
arxiv
|
@article{kim2024obliviate:,
title={Obliviate: Neutralizing Task-agnostic Backdoors within the
Parameter-efficient Fine-tuning Paradigm},
author={Jaehan Kim, Minkyoo Song, Seung Ho Na, Seungwon Shin},
journal={arXiv preprint arXiv:2409.14119},
year={2024},
archivePrefix={arXiv},
eprint={2409.14119},
primaryClass={cs.CL cs.AI cs.CR cs.LG}
}
|
kim2024obliviate:
|
arxiv-660291
|
2409.14121
|
CONGRA: Benchmarking Automatic Conflict Resolution
|
<|reference_start|>CONGRA: Benchmarking Automatic Conflict Resolution: Resolving conflicts from merging different software versions is a challenging task. To reduce the overhead of manual merging, researchers develop various program analysis-based tools which only solve specific types of conflicts and have a limited scope of application. With the development of language models, researchers treat conflict code as text, which theoretically allows for addressing almost all types of conflicts. However, the absence of effective conflict difficulty grading methods hinders a comprehensive evaluation of large language models (LLMs), making it difficult to gain a deeper understanding of their limitations. Furthermore, there is a notable lack of large-scale open benchmarks for evaluating the performance of LLMs in automatic conflict resolution. To address these issues, we introduce ConGra, a CONflict-GRAded benchmarking scheme designed to evaluate the performance of software merging tools under varying complexity conflict scenarios. We propose a novel approach to classify conflicts based on code operations and use it to build a large-scale evaluation dataset based on 44,948 conflicts from 34 real-world projects. We evaluate state-of-the-art LLMs on conflict resolution tasks using this dataset. By employing the dataset, we assess the performance of multiple state-of-the-art LLMs and code LLMs, ultimately uncovering two counterintuitive yet insightful phenomena. ConGra will be released at https://github.com/HKU-System-Security-Lab/ConGra.<|reference_end|>
|
arxiv
|
@article{zhang2024congra:,
title={CONGRA: Benchmarking Automatic Conflict Resolution},
author={Qingyu Zhang, Liangcai Su, Kai Ye, Chenxiong Qian},
journal={arXiv preprint arXiv:2409.14121},
year={2024},
archivePrefix={arXiv},
eprint={2409.14121},
primaryClass={cs.SE cs.LG}
}
|
zhang2024congra:
|
arxiv-660292
|
2409.14122
|
Efficient and Effective Model Extraction
|
<|reference_start|>Efficient and Effective Model Extraction: Model extraction aims to create a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor to further attacks, posing a significant threat to the MLaaS ecosystem. However, recent studies have shown that model extraction is highly inefficient, particularly when the target task distribution is unavailable. In such cases, even substantially increasing the attack budget fails to produce a sufficiently similar replica, reducing the adversary's motivation to pursue extraction attacks. In this paper, we revisit the elementary design choices throughout the extraction lifecycle. We propose an embarrassingly simple yet dramatically effective algorithm, Efficient and Effective Model Extraction (E3), focusing on both query preparation and training routine. E3 achieves superior generalization compared to state-of-the-art methods while minimizing computational costs. For instance, with only 0.005 times the query budget and less than 0.2 times the runtime, E3 outperforms classical generative model based data-free model extraction by an absolute accuracy improvement of over 50% on CIFAR-10. Our findings underscore the persistent threat posed by model extraction and suggest that it could serve as a valuable benchmarking algorithm for future security evaluations.<|reference_end|>
|
arxiv
|
@article{zhu2024efficient,
title={Efficient and Effective Model Extraction},
author={Hongyu Zhu, Wentao Hu, Sichu Liang, Fangqi Li, Wenwen Wang, Shilin
Wang},
journal={arXiv preprint arXiv:2409.14122},
year={2024},
archivePrefix={arXiv},
eprint={2409.14122},
primaryClass={cs.CR cs.LG}
}
|
zhu2024efficient
|
arxiv-660293
|
2409.14123
|
A General Framework of the Consistency for Large Neural Networks
|
<|reference_start|>A General Framework of the Consistency for Large Neural Networks: Neural networks have shown remarkable success, especially in overparameterized or "large" models. Despite increasing empirical evidence and intuitive understanding, a formal mathematical justification for the behavior of such models, particularly regarding overfitting, remains incomplete. In this paper, we propose a general regularization framework to study the Mean Integrated Squared Error (MISE) of neural networks. This framework includes many commonly used neural networks and penalties, such as ReLu and Sigmoid activations and $L^1$, $L^2$ penalties. Based on our frameworks, we find the MISE curve has two possible shapes, namely the shape of double descents and monotone decreasing. The latter phenomenon is new in literature and the causes of these two phenomena are also studied in theory. These studies challenge conventional statistical modeling frameworks and broadens recent findings on the double descent phenomenon in neural networks.<|reference_end|>
|
arxiv
|
@article{zhan2024a,
title={A General Framework of the Consistency for Large Neural Networks},
author={Haoran Zhan, Yingcun Xia},
journal={arXiv preprint arXiv:2409.14123},
year={2024},
archivePrefix={arXiv},
eprint={2409.14123},
primaryClass={stat.ML cs.LG math.ST stat.TH}
}
|
zhan2024a
|
arxiv-660294
|
2409.14128
|
Present and Future Generalization of Synthetic Image Detectors
|
<|reference_start|>Present and Future Generalization of Synthetic Image Detectors: The continued release of new and better image generation models increases the demand for synthetic image detectors. In such a dynamic field, detectors need to be able to generalize widely and be robust to uncontrolled alterations. The present work is motivated by this setting, when looking at the role of time, image transformations and data sources, for detector generalization. In these experiments, none of the evaluated detectors is found universal, but results indicate an ensemble could be. Experiments on data collected in the wild show this task to be more challenging than the one defined by large-scale datasets, pointing to a gap between experimentation and actual practice. Finally, we observe a race equilibrium effect, where better generators lead to better detectors, and vice versa. We hypothesize this pushes the field towards a perpetually close race between generators and detectors.<|reference_end|>
|
arxiv
|
@article{bernabeu-perez2024present,
title={Present and Future Generalization of Synthetic Image Detectors},
author={Pablo Bernabeu-Perez, Enrique Lopez-Cuena, Dario Garcia-Gasulla},
journal={arXiv preprint arXiv:2409.14128},
year={2024},
archivePrefix={arXiv},
eprint={2409.14128},
primaryClass={cs.CV cs.AI cs.LG}
}
|
bernabeu-perez2024present
|
arxiv-660295
|
2409.14131
|
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models
|
<|reference_start|>Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models: In this study, for the first time, we extensively investigate whether music foundation models (MFMs) or speech foundation models (SFMs) work better for singing voice deepfake detection (SVDD), which has recently attracted attention in the research community. For this, we perform a comprehensive comparative study of state-of-the-art (SOTA) MFMs (MERT variants and music2vec) and SFMs (pre-trained for general speech representation learning as well as speaker recognition). We show that speaker recognition SFM representations perform the best amongst all the foundation models (FMs), and this performance can be attributed to its higher efficacy in capturing the pitch, tone, intensity, etc, characteristics present in singing voices. To our end, we also explore the fusion of FMs for exploiting their complementary behavior for improved SVDD, and we propose a novel framework, FIONA for the same. With FIONA, through the synchronization of x-vector (speaker recognition SFM) and MERT-v1-330M (MFM), we report the best performance with the lowest Equal Error Rate (EER) of 13.74 %, beating all the individual FMs as well as baseline FM fusions and achieving SOTA results.<|reference_end|>
|
arxiv
|
@article{phukan2024are,
title={Are Music Foundation Models Better at Singing Voice Deepfake Detection?
Far-Better Fuse them with Speech Foundation Models},
author={Orchid Chetia Phukan, Sarthak Jain, Swarup Ranjan Behera, Arun Balaji
Buduru, Rajesh Sharma and S.R Mahadeva Prasanna},
journal={arXiv preprint arXiv:2409.14131},
year={2024},
archivePrefix={arXiv},
eprint={2409.14131},
primaryClass={eess.AS cs.LG cs.SD}
}
|
phukan2024are
|
arxiv-660296
|
2409.14137
|
Triangulating on Possible Futures: Conducting User Studies on Several Futures Instead of Only One
|
<|reference_start|>Triangulating on Possible Futures: Conducting User Studies on Several Futures Instead of Only One: Plausible findings about futures are inherently difficult to obtain as they require critical, well-informed speculations backed with data. HCI addresses this challenge with user studies where futuristic prototypes and other props concretise possible futures for participants. By observing participants' actions, researchers can "time-travel" to the future and see it alive, in action. However, a single study may yield particularised findings, inherent to study's intricacies, and lack wider plausibility. We suggest that triangulation of possible futures helps researchers disentangle particularities from findings that have wider plausibility. We explored this approach by arranging two studies on different futures of AI-augmented knowledge work. Some findings emerged in both studies while others were particular to only one or the other. This enabled us both to cross-validate their plausibility and gain deeper insights. We discuss how triangulation of possible futures makes HCI studies more future-proof and provides means to more critically anticipate possible futures.<|reference_end|>
|
arxiv
|
@article{salovaara2024triangulating,
title={Triangulating on Possible Futures: Conducting User Studies on Several
Futures Instead of Only One},
author={Antti Salovaara and Leevi Vahvelainen},
journal={arXiv preprint arXiv:2409.14137},
year={2024},
archivePrefix={arXiv},
eprint={2409.14137},
primaryClass={cs.HC}
}
|
salovaara2024triangulating
|
arxiv-660297
|
2409.14141
|
A Feature Generator for Few-Shot Learning
|
<|reference_start|>A Feature Generator for Few-Shot Learning: Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach outperforming the baseline model by 10% in 1-shot and around 5% in 5-shot approaches. Additionally, both visual-only and visual + textual generators have also been tested in this paper.<|reference_end|>
|
arxiv
|
@article{kanagalingam2024a,
title={A Feature Generator for Few-Shot Learning},
author={Heethanjan Kanagalingam, Thenukan Pathmanathan, Navaneethan
Ketheeswaran, Mokeeshan Vathanakumar, Mohamed Afham, Ranga Rodrigo},
journal={arXiv preprint arXiv:2409.14141},
year={2024},
archivePrefix={arXiv},
eprint={2409.14141},
primaryClass={cs.CV}
}
|
kanagalingam2024a
|
arxiv-660298
|
2409.14144
|
Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis
|
<|reference_start|>Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis: We find arithmetic ability resides within a limited number of attention heads, with each head specializing in distinct operations. To delve into the reason, we introduce the Comparative Neuron Analysis (CNA) method, which identifies an internal logic chain consisting of four distinct stages from input to prediction: feature enhancing with shallow FFN neurons, feature transferring by shallow attention layers, feature predicting by arithmetic heads, and prediction enhancing among deep FFN neurons. Moreover, we identify the human-interpretable FFN neurons within both feature-enhancing and feature-predicting stages. These findings lead us to investigate the mechanism of LoRA, revealing that it enhances prediction probabilities by amplifying the coefficient scores of FFN neurons related to predictions. Finally, we apply our method in model pruning for arithmetic tasks and model editing for reducing gender bias. Code is on https://github.com/zepingyu0512/arithmetic-mechanism.<|reference_end|>
|
arxiv
|
@article{yu2024interpreting,
title={Interpreting Arithmetic Mechanism in Large Language Models through
Comparative Neuron Analysis},
author={Zeping Yu, Sophia Ananiadou},
journal={arXiv preprint arXiv:2409.14144},
year={2024},
archivePrefix={arXiv},
eprint={2409.14144},
primaryClass={cs.CL}
}
|
yu2024interpreting
|
arxiv-660299
|
2409.14148
|
A New Upper Bound for Distributed Hypothesis Testing Using the Auxiliary Receiver Approach
|
<|reference_start|>A New Upper Bound for Distributed Hypothesis Testing Using the Auxiliary Receiver Approach: This paper employs the add-and-subtract technique of the auxiliary receiver approach to establish a new upper bound for the distributed hypothesis testing problem. This new bound has fewer assumptions than the upper bound proposed by Rahman and Wagner, is at least as tight as the bound by Rahman and Wagner, and outperforms it in specific scenarios, particularly in the Gaussian setting.<|reference_end|>
|
arxiv
|
@article{wen2024a,
title={A New Upper Bound for Distributed Hypothesis Testing Using the Auxiliary
Receiver Approach},
author={Zhenduo Wen and Amin Gohari},
journal={arXiv preprint arXiv:2409.14148},
year={2024},
archivePrefix={arXiv},
eprint={2409.14148},
primaryClass={cs.IT math.IT}
}
|
wen2024a
|
arxiv-660300
|
2409.14149
|
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
|
<|reference_start|>JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation: We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion models: a Latent Image Diffusion Model (LIDM) trained on images and a Latent Video Diffusion Model (LVDM) trained on video data. Our method combines these models in the reverse diffusion process, where the LIDM enhances image quality and the LVDM ensures temporal consistency. This unique combination allows us to effectively handle the complex spatio-temporal dynamics in video generation. Our results demonstrate quantitative and qualitative improvements in producing realistic and coherent videos.<|reference_end|>
|
arxiv
|
@article{reynaud2024jvid:,
title={JVID: Joint Video-Image Diffusion for Visual-Quality and
Temporal-Consistency in Video Generation},
author={Hadrien Reynaud and Matthew Baugh and Mischa Dombrowski and Sarah
Cechnicka and Qingjie Meng and Bernhard Kainz},
journal={arXiv preprint arXiv:2409.14149},
year={2024},
archivePrefix={arXiv},
eprint={2409.14149},
primaryClass={cs.CV}
}
|
reynaud2024jvid:
|
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