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arxiv-663701
|
2409.20500
|
FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot Video Editing
|
<|reference_start|>FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot Video Editing: Text-to-video diffusion models have made remarkable advancements. Driven by their ability to generate temporally coherent videos, research on zero-shot video editing using these fundamental models has expanded rapidly. To enhance editing quality, structural controls are frequently employed in video editing. Among these techniques, cross-attention mask control stands out for its effectiveness and efficiency. However, when cross-attention masks are naively applied to video editing, they can introduce artifacts such as blurring and flickering. Our experiments uncover a critical factor overlooked in previous video editing research: cross-attention masks are not consistently clear but vary with model structure and denoising timestep. To address this issue, we propose the metric Mask Matching Cost (MMC) that quantifies this variability and propose FreeMask, a method for selecting optimal masks tailored to specific video editing tasks. Using MMC-selected masks, we further improve the masked fusion mechanism within comprehensive attention features, e.g., temp, cross, and self-attention modules. Our approach can be seamlessly integrated into existing zero-shot video editing frameworks with better performance, requiring no control assistance or parameter fine-tuning but enabling adaptive decoupling of unedited semantic layouts with mask precision control. Extensive experiments demonstrate that FreeMask achieves superior semantic fidelity, temporal consistency, and editing quality compared to state-of-the-art methods.<|reference_end|>
|
arxiv
|
@article{cai2024freemask:,
title={FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot
Video Editing},
author={Lingling Cai, Kang Zhao, Hangjie Yuan, Yingya Zhang, Shiwei Zhang,
Kejie Huang},
journal={arXiv preprint arXiv:2409.20500},
year={2024},
archivePrefix={arXiv},
eprint={2409.20500},
primaryClass={cs.CV cs.MM}
}
|
cai2024freemask:
|
arxiv-663702
|
2409.20501
|
Packet Aggregation May Harm Batched Network Coding
|
<|reference_start|>Packet Aggregation May Harm Batched Network Coding: Batched network coding (BNC) is a solution to multi-hop transmission on networks with packet loss. To be compatible with the existing infrastructure, BNC is usually implemented over UDP. A single error bit will probably result in discarding the packet. UDP-Lite is a variant of UDP that supports partial checksums. As long as the data covered by the checksum is correct, damaged payload will be delivered. With UDP-Lite, we can cope with other techniques such as payload aggregation of BNC packets to reduce the protocol overhead, and forward error correction to combat against bit errors. Unlike traditional transmissions, BNC has a loss resilience feature and there are dependencies between BNC packets. In this paper, we conduct a preliminary investigation on BNC over UDP-Lite. We show that aggregating as much as we can is not always the best strategy, and a hop-by-hop distributed efficiency optimization approach may lead to a worse throughput compared with the scheme without aggregation in a long network. These unnatural results caution that a casual integration of techniques with BNC can be harmful, and give us hints on future research directions.<|reference_end|>
|
arxiv
|
@article{yin2024packet,
title={Packet Aggregation May Harm Batched Network Coding},
author={Hoover H. F. Yin},
journal={arXiv preprint arXiv:2409.20501},
year={2024},
archivePrefix={arXiv},
eprint={2409.20501},
primaryClass={cs.NI cs.IT math.IT}
}
|
yin2024packet
|
arxiv-663703
|
2409.20502
|
COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models
|
<|reference_start|>COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models: We propose a novel framework COLLAGE for generating collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). Our model addresses the lack of rich datasets in this domain by incorporating the knowledge and reasoning abilities of LLMs to guide a generative diffusion model. The hierarchical VQ-VAE architecture captures different motion-specific characteristics at multiple levels of abstraction, avoiding redundant concepts and enabling efficient multi-resolution representation. We introduce a diffusion model that operates in the latent space and incorporates LLM-generated motion planning cues to guide the denoising process, resulting in prompt-specific motion generation with greater control and diversity. Experimental results on the CORE-4D, and InterHuman datasets demonstrate the effectiveness of our approach in generating realistic and diverse collaborative human-object-human interactions, outperforming state-of-the-art methods. Our work opens up new possibilities for modeling complex interactions in various domains, such as robotics, graphics and computer vision.<|reference_end|>
|
arxiv
|
@article{daiya2024collage:,
title={COLLAGE: Collaborative Human-Agent Interaction Generation using
Hierarchical Latent Diffusion and Language Models},
author={Divyanshu Daiya, Damon Conover and Aniket Bera},
journal={arXiv preprint arXiv:2409.20502},
year={2024},
archivePrefix={arXiv},
eprint={2409.20502},
primaryClass={cs.LG cs.AI cs.CV cs.GR}
}
|
daiya2024collage:
|
arxiv-663704
|
2409.20503
|
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
|
<|reference_start|>What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach: Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, existing approaches have not captured the timestamps in the log data, which can potentially provide more fine-grained temporal information than sequential information. In this work, we propose a configurable transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. When presented with log sequences of varying lengths, the model can attain competitive and consistently stable performance compared to the baselines. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection in the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.<|reference_end|>
|
arxiv
|
@article{wu2024what,
title={What Information Contributes to Log-based Anomaly Detection? Insights
from a Configurable Transformer-Based Approach},
author={Xingfang Wu, Heng Li, Foutse Khomh},
journal={arXiv preprint arXiv:2409.20503},
year={2024},
archivePrefix={arXiv},
eprint={2409.20503},
primaryClass={cs.SE cs.AI cs.LG}
}
|
wu2024what
|
arxiv-663705
|
2409.20505
|
The Closed Geodetic Game: algorithms and strategies
|
<|reference_start|>The Closed Geodetic Game: algorithms and strategies: The geodetic closure of a set S of vertices of a graph is the set of all vertices in shortest paths between pairs of vertices of S. A set S of vertices in a graph is geodetic if its geodetic closure contains all the vertices of the graph. Buckley introduced in 1984 the idea of a game where two players construct together a geodetic set by alternately selecting vertices, the game ending when all vertices are in the geodetic closure. The Geodetic Game was then studied in 1985 by Buckley and Harary, and allowed players to select vertices already in the geodetic closure of the current set. We study the more natural variant, also introduced in 1985 by Buckley and Harary and called the Closed Geodetic Game, where the players alternate adding to a set S vertices that are not in the geodetic closure of S, until no move is available. This variant was only studied ever since for trees by Araujo et al. in 2024. We provide a full characterization of the Sprague-Grundy values of graph classes such as paths and cycles, of the outcomes of several products of graphs in function of the outcomes of the two graphs, and give polynomial-time algorithms to determine the Sprague-Grundy values of cactus and block graphs.<|reference_end|>
|
arxiv
|
@article{dailly2024the,
title={The Closed Geodetic Game: algorithms and strategies},
author={Antoine Dailly, Harmender Gahlawat, Zin Mar Myint},
journal={arXiv preprint arXiv:2409.20505},
year={2024},
archivePrefix={arXiv},
eprint={2409.20505},
primaryClass={math.CO cs.DM}
}
|
dailly2024the
|
arxiv-663706
|
2409.20508
|
NUTRIVISION: A System for Automatic Diet Management in Smart Healthcare
|
<|reference_start|>NUTRIVISION: A System for Automatic Diet Management in Smart Healthcare: Maintaining health and fitness through a balanced diet is essential for preventing non communicable diseases such as heart disease, diabetes, and cancer. NutriVision combines smart healthcare with computer vision and machine learning to address the challenges of nutrition and dietary management. This paper introduces a novel system that can identify food items, estimate quantities, and provide comprehensive nutritional information. NutriVision employs the Faster Region based Convolutional Neural Network, a deep learning algorithm that improves object detection by generating region proposals and then classifying those regions, making it highly effective for accurate and fast food identification even in complex and disorganized meal settings. Through smartphone based image capture, NutriVision delivers instant nutritional data, including macronutrient breakdown, calorie count, and micronutrient details. One of the standout features of NutriVision is its personalized nutritional analysis and diet recommendations, which are tailored to each user's dietary preferences, nutritional needs, and health history. By providing customized advice, NutriVision helps users achieve specific health and fitness goals, such as managing dietary restrictions or controlling weight. In addition to offering precise food detection and nutritional assessment, NutriVision supports smarter dietary decisions by integrating user data with recommendations that promote a balanced, healthful diet. This system presents a practical and advanced solution for nutrition management and has the potential to significantly influence how people approach their dietary choices, promoting healthier eating habits and overall well being. This paper discusses the design, performance evaluation, and prospective applications of the NutriVision system.<|reference_end|>
|
arxiv
|
@article{veeramreddy2024nutrivision:,
title={NUTRIVISION: A System for Automatic Diet Management in Smart Healthcare},
author={Madhumita Veeramreddy, Ashok Kumar Pradhan, Swetha Ghanta, Laavanya
Rachakonda and Saraju P Mohanty},
journal={arXiv preprint arXiv:2409.20508},
year={2024},
archivePrefix={arXiv},
eprint={2409.20508},
primaryClass={cs.CV}
}
|
veeramreddy2024nutrivision:
|
arxiv-663707
|
2409.20509
|
A physics-compliant diagonal representation for wireless channels parametrized by beyond-diagonal reconfigurable intelligent surfaces
|
<|reference_start|>A physics-compliant diagonal representation for wireless channels parametrized by beyond-diagonal reconfigurable intelligent surfaces: The parametrization of wireless channels by so-called "beyond-diagonal reconfigurable intelligent surfaces" (BD-RIS) is mathematically characterized by a matrix whose off-diagonal entries are partially or fully populated. Physically, this corresponds to tunable coupling mechanisms between the RIS elements that originate from the RIS control circuit. Here, we derive a physics-compliant diagonal representation for BD-RIS-parametrized channels. Recognizing that the RIS control circuit, irrespective of its detailed architecture, can always be represented as a multi-port network with auxiliary ports terminated by tunable individual loads, we physics-compliantly express the BD-RIS-parametrized channel as a multi-port chain cascade of i) radio environment, ii) static parts of the control circuit, and iii) individually tunable loads. Thus, the cascade of the former two systems is terminated by a system that is mathematically always characterized by a diagonal matrix. This physics-compliant diagonal representation implies that existing algorithms for channel estimation and optimization for conventional ("diagonal") RIS can be readily applied to BD-RIS scenarios. We demonstrate this in an experimentally grounded case study. Importantly, we highlight that, operationally, an ambiguous characterization of the cascade of radio environment and the static parts of the control circuit is required, but not the breakdown into the characteristics of its two constituent systems nor the lifting of the ambiguities. Nonetheless, we demonstrate how to derive or estimate the characteristics of the static parts of the control circuit for pedagogical purposes. The diagonal representation of BD-RIS-parametrized channels also enables their treatment with coupled-dipole-based models. We furthermore derive the assumptions under which the physics-compliant BD-RIS model simplifies to the widespread linear cascaded model.<|reference_end|>
|
arxiv
|
@article{del hougne2024a,
title={A physics-compliant diagonal representation for wireless channels
parametrized by beyond-diagonal reconfigurable intelligent surfaces},
author={Philipp del Hougne},
journal={arXiv preprint arXiv:2409.20509},
year={2024},
archivePrefix={arXiv},
eprint={2409.20509},
primaryClass={eess.SP cs.IT math.IT physics.app-ph}
}
|
del hougne2024a
|
arxiv-663708
|
2409.20510
|
Ensemble WSINDy for Data Driven Discovery of Governing Equations from Laser-based Full-field Measurements
|
<|reference_start|>Ensemble WSINDy for Data Driven Discovery of Governing Equations from Laser-based Full-field Measurements: This work leverages laser vibrometry and the weak form of the sparse identification of nonlinear dynamics (WSINDy) for partial differential equations to learn macroscale governing equations from full-field experimental data. In the experiments, two beam-like specimens, one aluminum and one IDOX/Estane composite, are subjected to shear wave excitation in the low frequency regime and the response is measured in the form of particle velocity on the specimen surface. The WSINDy for PDEs algorithm is applied to the resulting spatio-temporal data to discover the effective dynamics of the specimens from a family of potential PDEs. The discovered PDE is of the recognizable Euler-Bernoulli beam model form, from which the Young's modulus for the two materials are estimated. An ensemble version of the WSINDy algorithm is also used which results in information about the uncertainty in the PDE coefficients and Young's moduli. The discovered PDEs are also simulated with a finite element code to compare against the experimental data with reasonable accuracy. Using full-field experimental data and WSINDy together is a powerful non-destructive approach for learning unknown governing equations and gaining insights about mechanical systems in the dynamic regime.<|reference_end|>
|
arxiv
|
@article{schmid2024ensemble,
title={Ensemble WSINDy for Data Driven Discovery of Governing Equations from
Laser-based Full-field Measurements},
author={Abigail C. Schmid and Alireza Doostan and Fatemeh Pourahmadian},
journal={arXiv preprint arXiv:2409.20510},
year={2024},
archivePrefix={arXiv},
eprint={2409.20510},
primaryClass={math.NA cs.LG cs.NA stat.AP}
}
|
schmid2024ensemble
|
arxiv-663709
|
2409.20511
|
Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making
|
<|reference_start|>Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making: Faults on power lines and other electric equipment are known to cause wildfire ignitions. To mitigate the threat of wildfire ignitions from electric power infrastructure, many utilities preemptively de-energize power lines, which may result in power shutoffs. Data regarding wildfire ignition risks are key inputs for effective planning of power line de-energizations. However, there are multiple ways to formulate risk metrics that spatially aggregate wildfire risk map data, and there are different ways of leveraging this data to make decisions. The key contribution of this paper is to define and compare the results of employing six metrics for quantifying the wildfire ignition risks of power lines from risk maps, considering both threshold- and optimization-based methods for planning power line de-energizations. The numeric results use the California Test System (CATS), a large-scale synthetic grid model with power line corridors accurately representing California infrastructure, in combination with real Wildland Fire Potential Index data for a full year. This is the first application of optimal power shutoff planning on such a large and realistic test case. Our results show that the choice of risk metric significantly impacts the lines that are de-energized and the resulting load shed. We find that the optimization-based method results in significantly less load shed than the threshold-based method while achieving the same risk reduction.<|reference_end|>
|
arxiv
|
@article{piansky2024quantifying,
title={Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in
Power Shutoff Decision-Making},
author={Ryan Piansky, Sofia Taylor, Noah Rhodes, Daniel K. Molzahn, Line A.
Roald, Jean-Paul Watson},
journal={arXiv preprint arXiv:2409.20511},
year={2024},
archivePrefix={arXiv},
eprint={2409.20511},
primaryClass={eess.SY cs.SY}
}
|
piansky2024quantifying
|
arxiv-663710
|
2409.20514
|
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
|
<|reference_start|>Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation: Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex contact-rich nature of the tasks. Model-based optimal control methods offer precise and systematic control but are limited by high computational complexity and accurate contact sensing. On the other hand, reinforcement learning (RL) provides robustness and handles high-dimensional spaces but suffers from inefficient learning, unnatural motion, and sim-to-real gaps. To address these challenges, we introduce Opt2Skill, an end-to-end pipeline that combines model-based trajectory optimization with RL to achieve robust whole-body loco-manipulation. We generate reference motions for the Digit humanoid robot using differential dynamic programming (DDP) and train RL policies to track these trajectories. Our results demonstrate that Opt2Skill outperforms pure RL methods in both training efficiency and task performance, with optimal trajectories that account for torque limits enhancing trajectory tracking. We successfully transfer our approach to real-world applications.<|reference_end|>
|
arxiv
|
@article{liu2024opt2skill:,
title={Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for
Versatile Humanoid Loco-Manipulation},
author={Fukang Liu, Zhaoyuan Gu, Yilin Cai, Ziyi Zhou, Shijie Zhao, Hyunyoung
Jung, Sehoon Ha, Yue Chen, Danfei Xu, Ye Zhao},
journal={arXiv preprint arXiv:2409.20514},
year={2024},
archivePrefix={arXiv},
eprint={2409.20514},
primaryClass={cs.RO}
}
|
liu2024opt2skill:
|
arxiv-663711
|
2409.20516
|
Proposal of protocols for speech materials acquisition and presentation assisted by tools based on structured test signals
|
<|reference_start|>Proposal of protocols for speech materials acquisition and presentation assisted by tools based on structured test signals: We propose protocols for acquiring speech materials, making them reusable for future investigations, and presenting them for subjective experiments. We also provide means to evaluate existing speech materials' compatibility with target applications. We built these protocols and tools based on structured test signals and analysis methods, including a new family of the Time-Stretched Pulse (TSP). Over a billion times more powerful computational (including software development) resources than a half-century ago enabled these protocols and tools to be accessible to under-resourced environments.<|reference_end|>
|
arxiv
|
@article{kawahara2024proposal,
title={Proposal of protocols for speech materials acquisition and presentation
assisted by tools based on structured test signals},
author={Hideki Kawahara, Ken-Ichi Sakakibara, Mitsunori Mizumachi, Kohei
Yatabe},
journal={arXiv preprint arXiv:2409.20516},
year={2024},
archivePrefix={arXiv},
eprint={2409.20516},
primaryClass={eess.AS cs.SD eess.SP}
}
|
kawahara2024proposal
|
arxiv-663712
|
2409.20517
|
SMLE: Safe Machine Learning via Embedded Overapproximation
|
<|reference_start|>SMLE: Safe Machine Learning via Embedded Overapproximation: Despite the extent of recent advances in Machine Learning (ML) and Neural Networks, providing formal guarantees on the behavior of these systems is still an open problem, and a crucial requirement for their adoption in regulated or safety-critical scenarios. We consider the task of training differentiable ML models guaranteed to satisfy designer-chosen properties, stated as input-output implications. This is very challenging, due to the computational complexity of rigorously verifying and enforcing compliance in modern neural models. We provide an innovative approach based on three components: 1) a general, simple architecture enabling efficient verification with a conservative semantic; 2) a rigorous training algorithm based on the Projected Gradient Method; 3) a formulation of the problem of searching for strong counterexamples. The proposed framework, being only marginally affected by model complexity, scales well to practical applications, and produces models that provide full property satisfaction guarantees. We evaluate our approach on properties defined by linear inequalities in regression, and on mutually exclusive classes in multilabel classification. Our approach is competitive with a baseline that includes property enforcement during preprocessing, i.e. on the training data, as well as during postprocessing, i.e. on the model predictions. Finally, our contributions establish a framework that opens up multiple research directions and potential improvements.<|reference_end|>
|
arxiv
|
@article{francobaldi2024smle:,
title={SMLE: Safe Machine Learning via Embedded Overapproximation},
author={Matteo Francobaldi, Michele Lombardi},
journal={arXiv preprint arXiv:2409.20517},
year={2024},
archivePrefix={arXiv},
eprint={2409.20517},
primaryClass={cs.LG cs.AI}
}
|
francobaldi2024smle:
|
arxiv-663713
|
2409.20520
|
Accelerating Non-Maximum Suppression: A Graph Theory Perspective
|
<|reference_start|>Accelerating Non-Maximum Suppression: A Graph Theory Perspective: Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the ``last mile'' to enhance the efficiency of object detection. This paper systematically analyzes NMS from a graph theory perspective for the first time, revealing its intrinsic structure. Consequently, we propose two optimization methods, namely QSI-NMS and BOE-NMS. The former is a fast recursive divide-and-conquer algorithm with negligible mAP loss, and its extended version (eQSI-NMS) achieves optimal complexity of $\mathcal{O}(n\log n)$. The latter, concentrating on the locality of NMS, achieves an optimization at a constant level without an mAP loss penalty. Moreover, to facilitate rapid evaluation of NMS methods for researchers, we introduce NMS-Bench, the first benchmark designed to comprehensively assess various NMS methods. Taking the YOLOv8-N model on MS COCO 2017 as the benchmark setup, our method QSI-NMS provides $6.2\times$ speed of original NMS on the benchmark, with a $0.1\%$ decrease in mAP. The optimal eQSI-NMS, with only a $0.3\%$ mAP decrease, achieves $10.7\times$ speed. Meanwhile, BOE-NMS exhibits $5.1\times$ speed with no compromise in mAP.<|reference_end|>
|
arxiv
|
@article{si2024accelerating,
title={Accelerating Non-Maximum Suppression: A Graph Theory Perspective},
author={King-Siong Si, Lu Sun, Weizhan Zhang, Tieliang Gong, Jiahao Wang,
Jiang Liu, Hao Sun},
journal={arXiv preprint arXiv:2409.20520},
year={2024},
archivePrefix={arXiv},
eprint={2409.20520},
primaryClass={cs.CV cs.LG}
}
|
si2024accelerating
|
arxiv-663714
|
2409.20521
|
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning
|
<|reference_start|>Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning: We study off-dynamics Reinforcement Learning (RL), where the policy training and deployment environments are different. To deal with this environmental perturbation, we focus on learning policies robust to uncertainties in transition dynamics under the framework of distributionally robust Markov decision processes (DRMDPs), where the nominal and perturbed dynamics are linear Markov Decision Processes. We propose a novel algorithm We-DRIVE-U that enjoys an average suboptimality $\widetilde{\mathcal{O}}\big({d H \cdot \min \{1/{\rho}, H\}/\sqrt{K} }\big)$, where $K$ is the number of episodes, $H$ is the horizon length, $d$ is the feature dimension and $\rho$ is the uncertainty level. This result improves the state-of-the-art by $\mathcal{O}(dH/\min\{1/\rho,H\})$. We also construct a novel hard instance and derive the first information-theoretic lower bound in this setting, which indicates our algorithm is near-optimal up to $\mathcal{O}(\sqrt{H})$ for any uncertainty level $\rho\in(0,1]$. Our algorithm also enjoys a 'rare-switching' design, and thus only requires $\mathcal{O}(dH\log(1+H^2K))$ policy switches and $\mathcal{O}(d^2H\log(1+H^2K))$ calls for oracle to solve dual optimization problems, which significantly improves the computational efficiency of existing algorithms for DRMDPs, whose policy switch and oracle complexities are both $\mathcal{O}(K)$.<|reference_end|>
|
arxiv
|
@article{liu2024upper,
title={Upper and Lower Bounds for Distributionally Robust Off-Dynamics
Reinforcement Learning},
author={Zhishuai Liu, Weixin Wang, Pan Xu},
journal={arXiv preprint arXiv:2409.20521},
year={2024},
archivePrefix={arXiv},
eprint={2409.20521},
primaryClass={cs.LG cs.AI stat.ML}
}
|
liu2024upper
|
arxiv-663715
|
2409.20524
|
Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource
|
<|reference_start|>Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource: Human language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functions. Efficiently solve ambiguity is both a desired and a necessary characteristic. The lexical meaning of a word in context can be determined automatically by Word Sense Disambiguation (WSD) algorithms that rely on external knowledge often limited and biased toward English. When adapting content to other languages, automated translations are frequently inaccurate and a high degree of expert human validation is necessary to ensure both accuracy and understanding. The current study addresses previous limitations by introducing a new resource for Spanish WSD. It includes a sense inventory and a lexical dataset sourced from the Diccionario de la Lengua Espa\~nola which is maintained by the Real Academia Espa\~nola. We also review current resources for Spanish and report metrics on them by a state-of-the-art system.<|reference_end|>
|
arxiv
|
@article{ortega2024word,
title={Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical
Evaluation Resource},
author={Pablo Ortega, Jordi Luque, Luis Lamiable, Rodrigo L'opez, Richard
Benjamins},
journal={arXiv preprint arXiv:2409.20524},
year={2024},
archivePrefix={arXiv},
eprint={2409.20524},
primaryClass={cs.CL cs.AI}
}
|
ortega2024word
|
arxiv-663716
|
2409.20527
|
Bi-directional Momentum-based Haptic Feedback and Control System for Dexterous Telemanipulation
|
<|reference_start|>Bi-directional Momentum-based Haptic Feedback and Control System for Dexterous Telemanipulation: Haptic feedback is essential for dexterous telemanipulation that enables operators to control robotic hands remotely with high skill and precision, mimicking a human hand's natural movement and sensation. However, current haptic methods for dexterous telemanipulation cannot support torque feedback, resulting in object rotation and rolling mismatches. The operator must make tedious adjustments in these tasks, leading to delays, reduced situational awareness, and suboptimal task performance. This work presents a Bi-directional Momentum-based Haptic Feedback and Control (Bi-Hap) system for real-time dexterous telemanipulation. Bi-Hap integrates multi-modal sensors to extract human interactive information with the object and share it with the robot's learning-based controller. A Field-Oriented Control (FOC) algorithm is developed to enable the integrated brushless active momentum wheel to generate precise torque and vibrative feedback, bridging the gap between human intent and robotic actions. Different feedback strategies are designed for varying error states to align with the operator's intuition. Extensive experiments with human subjects using a virtual Shadow Dexterous Hand demonstrate the effectiveness of Bi-Hap in enhancing task performance and user confidence. Bi-Hap achieved real-time feedback capability with low command following latency (delay<0.025s) and highly accurate torque feedback (RMSE<0.010 Nm).<|reference_end|>
|
arxiv
|
@article{wang2024bi-directional,
title={Bi-directional Momentum-based Haptic Feedback and Control System for
Dexterous Telemanipulation},
author={Haoyang Wang (1), Haoran Guo (1), He Ba (1), Zhengxiong Li (2),
Lingfeng Tao (1) ((1) Oklahoma State University, (2) University of Colorado
Denver)},
journal={arXiv preprint arXiv:2409.20527},
year={2024},
archivePrefix={arXiv},
eprint={2409.20527},
primaryClass={cs.RO}
}
|
wang2024bi-directional
|
arxiv-663717
|
2409.20528
|
Formally Verified Physics-Informed Neural Control Lyapunov Functions
|
<|reference_start|>Formally Verified Physics-Informed Neural Control Lyapunov Functions: Control Lyapunov functions are a central tool in the design and analysis of stabilizing controllers for nonlinear systems. Constructing such functions, however, remains a significant challenge. In this paper, we investigate physics-informed learning and formal verification of neural network control Lyapunov functions. These neural networks solve a transformed Hamilton-Jacobi-Bellman equation, augmented by data generated using Pontryagin's maximum principle. Similar to how Zubov's equation characterizes the domain of attraction for autonomous systems, this equation characterizes the null-controllability set of a controlled system. This principled learning of neural network control Lyapunov functions outperforms alternative approaches, such as sum-of-squares and rational control Lyapunov functions, as demonstrated by numerical examples. As an intermediate step, we also present results on the formal verification of quadratic control Lyapunov functions, which, aided by satisfiability modulo theories solvers, can perform surprisingly well compared to more sophisticated approaches and efficiently produce global certificates of null-controllability.<|reference_end|>
|
arxiv
|
@article{liu2024formally,
title={Formally Verified Physics-Informed Neural Control Lyapunov Functions},
author={Jun Liu, Maxwell Fitzsimmons, Ruikun Zhou, Yiming Meng},
journal={arXiv preprint arXiv:2409.20528},
year={2024},
archivePrefix={arXiv},
eprint={2409.20528},
primaryClass={eess.SY cs.LG cs.SY math.OC}
}
|
liu2024formally
|
arxiv-663718
|
2409.20530
|
Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images
|
<|reference_start|>Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images: 3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predominantly built on EG3D, which specializes in synthesizing near-frontal views and is limiting in synthesizing comprehensive 3D scenes from diverse viewpoints. In contrast to existing approaches, we propose a novel framework built on PanoHead, which excels in synthesizing images from a 360-degree perspective. To achieve realistic 3D modeling of the input image, we introduce a dual encoder system tailored for high-fidelity reconstruction and realistic generation from different viewpoints. Accompanying this, we propose a stitching framework on the triplane domain to get the best predictions from both. To achieve seamless stitching, both encoders must output consistent results despite being specialized for different tasks. For this reason, we carefully train these encoders using specialized losses, including an adversarial loss based on our novel occlusion-aware triplane discriminator. Experiments reveal that our approach surpasses the existing encoder training methods qualitatively and quantitatively. Please visit the project page: https://berkegokmen1.github.io/dual-enc-3d-gan-inv.<|reference_end|>
|
arxiv
|
@article{bilecen2024dual,
title={Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from
Single Images},
author={Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Aysegul Dundar},
journal={arXiv preprint arXiv:2409.20530},
year={2024},
archivePrefix={arXiv},
eprint={2409.20530},
primaryClass={cs.CV cs.CG cs.GR cs.LG eess.IV}
}
|
bilecen2024dual
|
arxiv-663719
|
2409.20534
|
End-to-End Conformal Calibration for Optimization Under Uncertainty
|
<|reference_start|>End-to-End Conformal Calibration for Optimization Under Uncertainty: Machine learning can significantly improve performance for decision-making under uncertainty in a wide range of domains. However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve in high-capacity prediction models such as deep neural networks. Moreover, in high-dimensional settings, there may be many valid uncertainty estimates, each with their own performance profile - i.e., not all uncertainty is equally valuable for downstream decision-making. To address this problem, this paper develops an end-to-end framework to learn the uncertainty estimates for conditional robust optimization, with robustness and calibration guarantees provided by conformal prediction. In addition, we propose to represent arbitrary convex uncertainty sets with partially input-convex neural networks, which are learned as part of our framework. Our approach consistently improves upon two-stage estimate-then-optimize baselines on concrete applications in energy storage arbitrage and portfolio optimization.<|reference_end|>
|
arxiv
|
@article{yeh2024end-to-end,
title={End-to-End Conformal Calibration for Optimization Under Uncertainty},
author={Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong
Yue},
journal={arXiv preprint arXiv:2409.20534},
year={2024},
archivePrefix={arXiv},
eprint={2409.20534},
primaryClass={cs.LG math.OC}
}
|
yeh2024end-to-end
|
arxiv-663720
|
2409.20536
|
Best Practices for Responsible Machine Learning in Credit Scoring
|
<|reference_start|>Best Practices for Responsible Machine Learning in Credit Scoring: The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in these automated systems. This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible machine learning models in credit scoring, focusing on fairness, reject inference, and explainability. We discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. Additionally, we address the issue of limited data representativeness by exploring reject inference methods that incorporate information from rejected loan applications. Finally, we emphasize the importance of transparency and explainability in credit models, discussing techniques that provide insights into the decision-making process and enable individuals to understand and potentially improve their creditworthiness. By adopting these best practices, financial institutions can harness the power of machine learning while upholding ethical and responsible lending practices.<|reference_end|>
|
arxiv
|
@article{valdrighi2024best,
title={Best Practices for Responsible Machine Learning in Credit Scoring},
author={Giovani Valdrighi, Athyrson M. Ribeiro, Jansen S. B. Pereira, Vitoria
Guardieiro, Arthur Hendricks, D'ecio Miranda Filho, Juan David Nieto Garcia,
Felipe F. Bocca, Thalita B. Veronese, Lucas Wanner, Marcos Medeiros Raimundo},
journal={arXiv preprint arXiv:2409.20536},
year={2024},
archivePrefix={arXiv},
eprint={2409.20536},
primaryClass={cs.LG cs.CY}
}
|
valdrighi2024best
|
arxiv-663721
|
2409.20537
|
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
|
<|reference_start|>Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers: One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting. This work studies the problem of learning policy representations through heterogeneous pre-training on robot data across different embodiments and tasks at scale. We propose Heterogeneous Pre-trained Transformers (HPT), which pre-train a large, shareable trunk of a policy neural network to learn a task and embodiment agnostic shared representation. This general architecture aligns the specific proprioception and vision inputs from distinct embodiments to a short sequence of tokens and then processes such tokens to map to control robots for different tasks. Leveraging the recent large-scale multi-embodiment real-world robotic datasets as well as simulation, deployed robots, and human video datasets, we investigate pre-training policies across heterogeneity. We conduct experiments to investigate the scaling behaviors of training objectives, to the extent of 52 datasets. HPTs outperform several baselines and enhance the fine-tuned policy performance by over 20% on unseen tasks in multiple simulator benchmarks and real-world settings. See the project website (https://liruiw.github.io/hpt/) for code and videos.<|reference_end|>
|
arxiv
|
@article{wang2024scaling,
title={Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained
Transformers},
author={Lirui Wang, Xinlei Chen, Jialiang Zhao, Kaiming He},
journal={Neurips 2024},
year={2024},
archivePrefix={arXiv},
eprint={2409.20537},
primaryClass={cs.RO cs.CV cs.LG}
}
|
wang2024scaling
|
arxiv-663722
|
2409.20539
|
Visual collective behaviors on spherical robots
|
<|reference_start|>Visual collective behaviors on spherical robots: The implementation of collective motion, traditionally, disregard the limited sensing capabilities of an individual, to instead assuming an omniscient perception of the environment. This study implements a visual flocking model in a ``robot-in-the-loop'' approach to reproduce these behaviors with a flock composed of 10 independent spherical robots. The model achieves robotic collective motion by only using panoramic visual information of each robot, such as retinal position, optical size and optic flow of the neighboring robots. We introduce a virtual anchor to confine the collective robotic movements so to avoid wall interactions. For the first time, a simple visual robot-in-the-loop approach succeed in reproducing several collective motion phases, in particular, swarming, and milling. Another milestone achieved with by this model is bridging the gap between simulation and physical experiments by demonstrating nearly identical behaviors in both environments with the same visual model. To conclude, we show that our minimal visual collective motion model is sufficient to recreate most collective behaviors on a robot-in-the-loop system that is scalable, behaves as numerical simulations predict and is easily comparable to traditional models.<|reference_end|>
|
arxiv
|
@article{castro2024visual,
title={Visual collective behaviors on spherical robots},
author={Diego Castro and Christophe Eloy and Franck Ruffier},
journal={arXiv preprint arXiv:2409.20539},
year={2024},
archivePrefix={arXiv},
eprint={2409.20539},
primaryClass={cs.RO cs.SY eess.SY}
}
|
castro2024visual
|
arxiv-663723
|
2409.20547
|
Annealing Flow Generative Model Towards Sampling High-Dimensional and Multi-Modal Distributions
|
<|reference_start|>Annealing Flow Generative Model Towards Sampling High-Dimensional and Multi-Modal Distributions: Sampling from high-dimensional, multi-modal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics-based machine learning. In this paper, we propose Annealing Flow (AF), a continuous normalizing flow-based approach designed to sample from high-dimensional and multi-modal distributions. The key idea is to learn a continuous normalizing flow-based transport map, guided by annealing, to transition samples from an easy-to-sample distribution to the target distribution, facilitating effective exploration of modes in high-dimensional spaces. Unlike many existing methods, AF training does not rely on samples from the target distribution. AF ensures effective and balanced mode exploration, achieves linear complexity in sample size and dimensions, and circumvents inefficient mixing times. We demonstrate the superior performance of AF compared to state-of-the-art methods through extensive experiments on various challenging distributions and real-world datasets, particularly in high-dimensional and multi-modal settings. We also highlight the potential of AF for sampling the least favorable distributions.<|reference_end|>
|
arxiv
|
@article{wu2024annealing,
title={Annealing Flow Generative Model Towards Sampling High-Dimensional and
Multi-Modal Distributions},
author={Dongze Wu, Yao Xie},
journal={arXiv preprint arXiv:2409.20547},
year={2024},
archivePrefix={arXiv},
eprint={2409.20547},
primaryClass={stat.ML cs.LG stat.CO}
}
|
wu2024annealing
|
arxiv-663724
|
2409.20548
|
Robi Butler: Remote Multimodal Interactions with Household Robot Assistant
|
<|reference_start|>Robi Butler: Remote Multimodal Interactions with Household Robot Assistant: In this paper, we introduce Robi Butler, a novel household robotic system that enables multimodal interactions with remote users. Building on the advanced communication interfaces, Robi Butler allows users to monitor the robot's status, send text or voice instructions, and select target objects by hand pointing. At the core of our system is a high-level behavior module, powered by Large Language Models (LLMs), that interprets multimodal instructions to generate action plans. These plans are composed of a set of open vocabulary primitives supported by Vision Language Models (VLMs) that handle both text and pointing queries. The integration of the above components allows Robi Butler to ground remote multimodal instructions in the real-world home environment in a zero-shot manner. We demonstrate the effectiveness and efficiency of this system using a variety of daily household tasks that involve remote users giving multimodal instructions. Additionally, we conducted a user study to analyze how multimodal interactions affect efficiency and user experience during remote human-robot interaction and discuss the potential improvements.<|reference_end|>
|
arxiv
|
@article{xiao2024robi,
title={Robi Butler: Remote Multimodal Interactions with Household Robot
Assistant},
author={Anxing Xiao, Nuwan Janaka, Tianrun Hu, Anshul Gupta, Kaixin Li, Cunjun
Yu, David Hsu},
journal={arXiv preprint arXiv:2409.20548},
year={2024},
archivePrefix={arXiv},
eprint={2409.20548},
primaryClass={cs.RO cs.AI cs.HC}
}
|
xiao2024robi
|
arxiv-663725
|
2409.20550
|
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
|
<|reference_start|>LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation: Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches have shown promising results and revolutionized code generation task. Despite the promising performance, LLMs often generate contents with hallucinations, especially for the code generation scenario requiring the handling of complex contextual dependencies in practical development process. Although previous study has analyzed hallucinations in LLM-powered code generation, the study is limited to standalone function generation. In this paper, we conduct an empirical study to study the phenomena, mechanism, and mitigation of LLM hallucinations within more practical and complex development contexts in repository-level generation scenario. First, we manually examine the code generation results from six mainstream LLMs to establish a hallucination taxonomy of LLM-generated code. Next, we elaborate on the phenomenon of hallucinations, analyze their distribution across different models. We then analyze causes of hallucinations and identify four potential factors contributing to hallucinations. Finally, we propose an RAG-based mitigation method, which demonstrates consistent effectiveness in all studied LLMs. The replication package including code, data, and experimental results is available at https://github.com/DeepSoftwareAnalytics/LLMCodingHallucination<|reference_end|>
|
arxiv
|
@article{zhang2024llm,
title={LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism,
and Mitigation},
author={Ziyao Zhang, Yanlin Wang, Chong Wang, Jiachi Chen, Zibin Zheng},
journal={arXiv preprint arXiv:2409.20550},
year={2024},
archivePrefix={arXiv},
eprint={2409.20550},
primaryClass={cs.SE cs.AI cs.CL}
}
|
zhang2024llm
|
arxiv-663726
|
2409.20551
|
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
|
<|reference_start|>UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models: Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a comprehensive paradigm, termed UniAff, that integrates 3D object-centric manipulation and task understanding in a unified formulation. Specifically, we constructed a dataset labeled with manipulation-related key attributes, comprising 900 articulated objects from 19 categories and 600 tools from 12 categories. Furthermore, we leverage MLLMs to infer object-centric representations for manipulation tasks, including affordance recognition and reasoning about 3D motion constraints. Comprehensive experiments in both simulation and real-world settings indicate that UniAff significantly improves the generalization of robotic manipulation for tools and articulated objects. We hope that UniAff will serve as a general baseline for unified robotic manipulation tasks in the future. Images, videos, dataset, and code are published on the project website at:https://sites.google.com/view/uni-aff/home<|reference_end|>
|
arxiv
|
@article{yu2024uniaff:,
title={UniAff: A Unified Representation of Affordances for Tool Usage and
Articulation with Vision-Language Models},
author={Qiaojun Yu, Siyuan Huang, Xibin Yuan, Zhengkai Jiang, Ce Hao, Xin Li,
Haonan Chang, Junbo Wang, Liu Liu, Hongsheng Li, Peng Gao and Cewu Lu},
journal={arXiv preprint arXiv:2409.20551},
year={2024},
archivePrefix={arXiv},
eprint={2409.20551},
primaryClass={cs.RO}
}
|
yu2024uniaff:
|
arxiv-663727
|
2409.20553
|
Maia-2: A Unified Model for Human-AI Alignment in Chess
|
<|reference_start|>Maia-2: A Unified Model for Human-AI Alignment in Chess: There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools.<|reference_end|>
|
arxiv
|
@article{tang2024maia-2:,
title={Maia-2: A Unified Model for Human-AI Alignment in Chess},
author={Zhenwei Tang, Difan Jiao, Reid McIlroy-Young, Jon Kleinberg,
Siddhartha Sen, Ashton Anderson},
journal={arXiv preprint arXiv:2409.20553},
year={2024},
archivePrefix={arXiv},
eprint={2409.20553},
primaryClass={cs.AI}
}
|
tang2024maia-2:
|
arxiv-663728
|
2409.20554
|
Online identification of skidding modes with interactive multiple model estimation
|
<|reference_start|>Online identification of skidding modes with interactive multiple model estimation: Skid-steered wheel mobile robots (SSWMRs) operate in a variety of outdoor environments exhibiting motion behaviors dominated by the effects of complex wheel-ground interactions. Characterizing these interactions is crucial both from the immediate robot autonomy perspective (for motion prediction and control) as well as a long-term predictive maintenance and diagnostics perspective. An ideal solution entails capturing precise state measurements for decisions and controls, which is considerably difficult, especially in increasingly unstructured outdoor regimes of operations for these robots. In this milieu, a framework to identify pre-determined discrete modes of operation can considerably simplify the motion model identification process. To this end, we propose an interactive multiple model (IMM) based filtering framework to probabilistically identify predefined robot operation modes that could arise due to traversal in different terrains or loss of wheel traction.<|reference_end|>
|
arxiv
|
@article{salvi2024online,
title={Online identification of skidding modes with interactive multiple model
estimation},
author={Ameya Salvi, Pardha Sai Krishna Ala, Jonathon M. Smereka, Mark
Brudnak, David Gorsich, Matthias Schmid, Venkat Krovi},
journal={arXiv preprint arXiv:2409.20554},
year={2024},
archivePrefix={arXiv},
eprint={2409.20554},
primaryClass={cs.RO}
}
|
salvi2024online
|
arxiv-663729
|
2409.20556
|
Inverse Painting: Reconstructing The Painting Process
|
<|reference_start|>Inverse Painting: Reconstructing The Painting Process: Given an input painting, we reconstruct a time-lapse video of how it may have been painted. We formulate this as an autoregressive image generation problem, in which an initially blank "canvas" is iteratively updated. The model learns from real artists by training on many painting videos. Our approach incorporates text and region understanding to define a set of painting "instructions" and updates the canvas with a novel diffusion-based renderer. The method extrapolates beyond the limited, acrylic style paintings on which it has been trained, showing plausible results for a wide range of artistic styles and genres.<|reference_end|>
|
arxiv
|
@article{chen2024inverse,
title={Inverse Painting: Reconstructing The Painting Process},
author={Bowei Chen, Yifan Wang, Brian Curless, Ira Kemelmacher-Shlizerman,
Steven M. Seitz},
journal={arXiv preprint arXiv:2409.20556},
year={2024},
archivePrefix={arXiv},
eprint={2409.20556},
primaryClass={cs.CV}
}
|
chen2024inverse
|
arxiv-663730
|
2409.20557
|
Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos
|
<|reference_start|>Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos: Goal-oriented planning, or anticipating a series of actions that transition an agent from its current state to a predefined objective, is crucial for developing intelligent assistants aiding users in daily procedural tasks. The problem presents significant challenges due to the need for comprehensive knowledge of temporal and hierarchical task structures, as well as strong capabilities in reasoning and planning. To achieve this, prior work typically relies on extensive training on the target dataset, which often results in significant dataset bias and a lack of generalization to unseen tasks. In this work, we introduce VidAssist, an integrated framework designed for zero/few-shot goal-oriented planning in instructional videos. VidAssist leverages large language models (LLMs) as both the knowledge base and the assessment tool for generating and evaluating action plans, thus overcoming the challenges of acquiring procedural knowledge from small-scale, low-diversity datasets. Moreover, VidAssist employs a breadth-first search algorithm for optimal plan generation, in which a composite of value functions designed for goal-oriented planning is utilized to assess the predicted actions at each step. Extensive experiments demonstrate that VidAssist offers a unified framework for different goal-oriented planning setups, e.g., visual planning for assistance (VPA) and procedural planning (PP), and achieves remarkable performance in zero-shot and few-shot setups. Specifically, our few-shot model outperforms the prior fully supervised state-of-the-art method by +7.7% in VPA and +4.81% PP task on the COIN dataset while predicting 4 future actions. Code, and models are publicly available at https://sites.google.com/view/vidassist.<|reference_end|>
|
arxiv
|
@article{islam2024propose,,
title={Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in
Instructional Videos},
author={Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang, Fu-Jen Chu, Kris
Kitani, Gedas Bertasius, Xitong Yang},
journal={arXiv preprint arXiv:2409.20557},
year={2024},
archivePrefix={arXiv},
eprint={2409.20557},
primaryClass={cs.CV}
}
|
islam2024propose,
|
arxiv-663731
|
2409.20558
|
Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection
|
<|reference_start|>Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection: We present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains. Due to substantial disparities in data distribution and variations in taxonomy across diverse domains, training such a detector by simply merging datasets poses a significant challenge. Motivated by this observation, we introduce multi-stage prompting modules for multi-dataset 3D detection, which leverages prompts based on the characteristics of corresponding datasets to mitigate existing differences. This elegant design facilitates seamless plug-and-play integration within various advanced 3D detection frameworks in a unified manner, while also allowing straightforward adaptation for universal applicability across datasets. Experiments are conducted across multiple dataset consolidation scenarios involving KITTI, Waymo, and nuScenes, demonstrating that our Uni$^2$Det outperforms existing methods by a large margin in multi-dataset training. Notably, results on zero-shot cross-dataset transfer validate the generalization capability of our proposed method.<|reference_end|>
|
arxiv
|
@article{wang2024uni$^2$det:,
title={Uni$^2$Det: Unified and Universal Framework for Prompt-Guided
Multi-dataset 3D Detection},
author={Yubin Wang, Zhikang Zou, Xiaoqing Ye, Xiao Tan, Errui Ding, Cairong
Zhao},
journal={arXiv preprint arXiv:2409.20558},
year={2024},
archivePrefix={arXiv},
eprint={2409.20558},
primaryClass={cs.CV}
}
|
wang2024uni$^2$det:
|
arxiv-663732
|
2409.20559
|
Supervised Multi-Modal Fission Learning
|
<|reference_start|>Supervised Multi-Modal Fission Learning: Learning from multimodal datasets can leverage complementary information and improve performance in prediction tasks. A commonly used strategy to account for feature correlations in high-dimensional datasets is the latent variable approach. Several latent variable methods have been proposed for multimodal datasets. However, these methods either focus on extracting the shared component across all modalities or on extracting both a shared component and individual components specific to each modality. To address this gap, we propose a Multi-Modal Fission Learning (MMFL) model that simultaneously identifies globally joint, partially joint, and individual components underlying the features of multimodal datasets. Unlike existing latent variable methods, MMFL uses supervision from the response variable to identify predictive latent components and has a natural extension for incorporating incomplete multimodal data. Through simulation studies, we demonstrate that MMFL outperforms various existing multimodal algorithms in both complete and incomplete modality settings. We applied MMFL to a real-world case study for early prediction of Alzheimers Disease using multimodal neuroimaging and genomics data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. MMFL provided more accurate predictions and better insights into within- and across-modality correlations compared to existing methods.<|reference_end|>
|
arxiv
|
@article{mao2024supervised,
title={Supervised Multi-Modal Fission Learning},
author={Lingchao Mao, Qi wang, Yi Su, Fleming Lure, Jing Li},
journal={arXiv preprint arXiv:2409.20559},
year={2024},
archivePrefix={arXiv},
eprint={2409.20559},
primaryClass={cs.LG cs.CV}
}
|
mao2024supervised
|
arxiv-663733
|
2409.20560
|
LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner
|
<|reference_start|>LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner: Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. Nevertheless, it remains a significant challenge to handle long-horizon tasks, especially in subtask identification and allocation for cooperative heterogeneous robot teams. To address this issue, we propose a Language Model-Driven Multi-Agent PDDL Planner (LaMMA-P), a novel multi-agent task planning framework that achieves state-of-the-art performance on long-horizon tasks. LaMMA-P integrates the strengths of the LMs' reasoning capability and the traditional heuristic search planner to achieve a high success rate and efficiency while demonstrating strong generalization across tasks. Additionally, we create MAT-THOR, a comprehensive benchmark that features household tasks with two different levels of complexity based on the AI2-THOR environment. The experimental results demonstrate that LaMMA-P achieves a 105% higher success rate and 36% higher efficiency than existing LM-based multi-agent planners. The experimental videos, code, and datasets of this work as well as the detailed prompts used in each module are available at https://lamma-p.github.io.<|reference_end|>
|
arxiv
|
@article{zhang2024lamma-p:,
title={LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and
Planning with LM-Driven PDDL Planner},
author={Xiaopan Zhang and Hao Qin and Fuquan Wang and Yue Dong and Jiachen Li},
journal={arXiv preprint arXiv:2409.20560},
year={2024},
archivePrefix={arXiv},
eprint={2409.20560},
primaryClass={cs.RO cs.AI cs.CV cs.LG cs.MA}
}
|
zhang2024lamma-p:
|
arxiv-663734
|
2409.20562
|
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
|
<|reference_start|>SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes: Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or deformation of a template, or as a disordered triangle soup lacking local structure. This work presents a scheme to directly generate manifold, polygonal meshes of complex connectivity as the output of a neural network. Our key innovation is to define a continuous latent connectivity space at each mesh vertex, which implies the discrete mesh. In particular, our vertex embeddings generate cyclic neighbor relationships in a halfedge mesh representation, which gives a guarantee of edge-manifoldness and the ability to represent general polygonal meshes. This representation is well-suited to machine learning and stochastic optimization, without restriction on connectivity or topology. We first explore the basic properties of this representation, then use it to fit distributions of meshes from large datasets. The resulting models generate diverse meshes with tessellation structure learned from the dataset population, with concise details and high-quality mesh elements. In applications, this approach not only yields high-quality outputs from generative models, but also enables directly learning challenging geometry processing tasks such as mesh repair.<|reference_end|>
|
arxiv
|
@article{shen2024spacemesh:,
title={SpaceMesh: A Continuous Representation for Learning Manifold Surface
Meshes},
author={Tianchang Shen, Zhaoshuo Li, Marc Law, Matan Atzmon, Sanja Fidler,
James Lucas, Jun Gao, Nicholas Sharp},
journal={arXiv preprint arXiv:2409.20562},
year={2024},
doi={10.1145/3680528.3687634},
archivePrefix={arXiv},
eprint={2409.20562},
primaryClass={cs.CV cs.GR cs.LG}
}
|
shen2024spacemesh:
|
arxiv-663735
|
2409.20563
|
DressRecon: Freeform 4D Human Reconstruction from Monocular Video
|
<|reference_start|>DressRecon: Freeform 4D Human Reconstruction from Monocular Video: We present a method to reconstruct time-consistent human body models from monocular videos, focusing on extremely loose clothing or handheld object interactions. Prior work in human reconstruction is either limited to tight clothing with no object interactions, or requires calibrated multi-view captures or personalized template scans which are costly to collect at scale. Our key insight for high-quality yet flexible reconstruction is the careful combination of generic human priors about articulated body shape (learned from large-scale training data) with video-specific articulated "bag-of-bones" deformation (fit to a single video via test-time optimization). We accomplish this by learning a neural implicit model that disentangles body versus clothing deformations as separate motion model layers. To capture subtle geometry of clothing, we leverage image-based priors such as human body pose, surface normals, and optical flow during optimization. The resulting neural fields can be extracted into time-consistent meshes, or further optimized as explicit 3D Gaussians for high-fidelity interactive rendering. On datasets with highly challenging clothing deformations and object interactions, DressRecon yields higher-fidelity 3D reconstructions than prior art. Project page: https://jefftan969.github.io/dressrecon/<|reference_end|>
|
arxiv
|
@article{tan2024dressrecon:,
title={DressRecon: Freeform 4D Human Reconstruction from Monocular Video},
author={Jeff Tan, Donglai Xiang, Shubham Tulsiani, Deva Ramanan, Gengshan Yang},
journal={arXiv preprint arXiv:2409.20563},
year={2024},
archivePrefix={arXiv},
eprint={2409.20563},
primaryClass={cs.CV}
}
|
tan2024dressrecon:
|
arxiv-663736
|
2409.20565
|
Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments
|
<|reference_start|>Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments: Evaluating LLM-generated text has become a key challenge, especially in domain-specific contexts like the medical field. This work introduces a novel evaluation methodology for LLM-generated medical explanatory arguments, relying on Proxy Tasks and rankings to closely align results with human evaluation criteria, overcoming the biases typically seen in LLMs used as judges. We demonstrate that the proposed evaluators are robust against adversarial attacks, including the assessment of non-argumentative text. Additionally, the human-crafted arguments needed to train the evaluators are minimized to just one example per Proxy Task. By examining multiple LLM-generated arguments, we establish a methodology for determining whether a Proxy Task is suitable for evaluating LLM-generated medical explanatory arguments, requiring only five examples and two human experts.<|reference_end|>
|
arxiv
|
@article{de la iglesia2024ranking,
title={Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation
of LLM-Generated Medical Explanatory Arguments},
author={Iker De la Iglesia, Iakes Goenaga, Johanna Ramirez-Romero, Jose Maria
Villa-Gonzalez, Josu Goikoetxea and Ander Barrena},
journal={arXiv preprint arXiv:2409.20565},
year={2024},
archivePrefix={arXiv},
eprint={2409.20565},
primaryClass={cs.CL}
}
|
de la iglesia2024ranking
|
arxiv-663737
|
2409.20566
|
MM15: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
|
<|reference_start|>MM15: Methods, Analysis & Insights from Multimodal LLM Fine-tuning: We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning. Building upon the MM1 architecture, MM1.5 adopts a data-centric approach to model training, systematically exploring the impact of diverse data mixtures across the entire model training lifecycle. This includes high-quality OCR data and synthetic captions for continual pre-training, as well as an optimized visual instruction-tuning data mixture for supervised fine-tuning. Our models range from 1B to 30B parameters, encompassing both dense and mixture-of-experts (MoE) variants, and demonstrate that careful data curation and training strategies can yield strong performance even at small scales (1B and 3B). Additionally, we introduce two specialized variants: MM1.5-Video, designed for video understanding, and MM1.5-UI, tailored for mobile UI understanding. Through extensive empirical studies and ablations, we provide detailed insights into the training processes and decisions that inform our final designs, offering valuable guidance for future research in MLLM development.<|reference_end|>
|
arxiv
|
@article{zhang2024mm1.5:,
title={MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning},
author={Haotian Zhang, Mingfei Gao, Zhe Gan, Philipp Dufter, Nina Wenzel,
Forrest Huang, Dhruti Shah, Xianzhi Du, Bowen Zhang, Yanghao Li, Sam Dodge,
Keen You, Zhen Yang, Aleksei Timofeev, Mingze Xu, Hong-You Chen,
Jean-Philippe Fauconnier, Zhengfeng Lai, Haoxuan You, Zirui Wang, Afshin
Dehghan, Peter Grasch, Yinfei Yang},
journal={arXiv preprint arXiv:2409.20566},
year={2024},
archivePrefix={arXiv},
eprint={2409.20566},
primaryClass={cs.CV cs.CL cs.LG}
}
|
zhang2024mm1.5:
|
arxiv-663738
|
2409.20568
|
Continuously Improving Mobile Manipulation with Autonomous Real-World RL
|
<|reference_start|>Continuously Improving Mobile Manipulation with Autonomous Real-World RL: We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1) task-relevant autonomy, which guides exploration towards object interactions and prevents stagnation near goal states, 2) efficient policy learning by leveraging basic task knowledge in behavior priors, and 3) formulating generic rewards that combine human-interpretable semantic information with low-level, fine-grained observations. We demonstrate that our approach allows Spot robots to continually improve their performance on a set of four challenging mobile manipulation tasks, obtaining an average success rate of 80% across tasks, a 3-4 improvement over existing approaches. Videos can be found at https://continual-mobile-manip.github.io/<|reference_end|>
|
arxiv
|
@article{mendonca2024continuously,
title={Continuously Improving Mobile Manipulation with Autonomous Real-World RL},
author={Russell Mendonca, Emmanuel Panov, Bernadette Bucher, Jiuguang Wang,
Deepak Pathak},
journal={arXiv preprint arXiv:2409.20568},
year={2024},
archivePrefix={arXiv},
eprint={2409.20568},
primaryClass={cs.RO cs.AI cs.CV cs.LG cs.SY eess.SY}
}
|
mendonca2024continuously
|
arxiv-663739
|
2410.00001
|
iSurgARy: A mobile augmented reality solution for ventriculostomy in resource-limited settings
|
<|reference_start|>iSurgARy: A mobile augmented reality solution for ventriculostomy in resource-limited settings: Global disparities in neurosurgical care necessitate innovations addressing affordability and accuracy, particularly for critical procedures like ventriculostomy. This intervention, vital for managing life-threatening intracranial pressure increases, is associated with catheter misplacement rates exceeding 30% when using a freehand technique. Such misplacements hold severe consequences including haemorrhage, infection, prolonged hospital stays, and even morbidity and mortality. To address this issue, we present a novel, stand-alone mobile-based augmented reality system (iSurgARy) aimed at significantly improving ventriculostomy accuracy, particularly in resource-limited settings such as those in low- and middle-income countries. iSurgARy uses landmark based registration by taking advantage of Light Detection and Ranging (LiDaR) to allow for accurate surgical guidance. To evaluate iSurgARy, we conducted a two-phase user study. Initially, we assessed usability and learnability with novice participants using the System Usability Scale (SUS), incorporating their feedback to refine the application. In the second phase, we engaged human-computer interaction (HCI) and clinical domain experts to evaluate our application, measuring Root Mean Square Error (RMSE), System Usability Scale (SUS) and NASA Task Load Index (TLX) metrics to assess accuracy usability, and cognitive workload, respectively<|reference_end|>
|
arxiv
|
@article{asadi2024isurgary:,
title={iSurgARy: A mobile augmented reality solution for ventriculostomy in
resource-limited settings},
author={Zahra Asadi, Joshua Pardillo Castillo, Mehrdad Asadi, David S.
Sinclair, Marta Kersten-Oertel},
journal={arXiv preprint arXiv:2410.00001},
year={2024},
archivePrefix={arXiv},
eprint={2410.00001},
primaryClass={cs.HC}
}
|
asadi2024isurgary:
|
arxiv-663740
|
2410.00002
|
Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)
|
<|reference_start|>Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010): This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.<|reference_end|>
|
arxiv
|
@article{vallarino2024machine,
title={Machine Learning and Econometric Approaches to Fiscal Policies:
Understanding Industrial Investment Dynamics in Uruguay (1974-2010)},
author={Diego Vallarino},
journal={arXiv preprint arXiv:2410.00002},
year={2024},
archivePrefix={arXiv},
eprint={2410.00002},
primaryClass={econ.GN cs.LG econ.EM q-fin.EC}
}
|
vallarino2024machine
|
arxiv-663741
|
2410.00003
|
Language-centered Human Activity Recognition
|
<|reference_start|>Language-centered Human Activity Recognition: Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is critical for applications in healthcare, safety, and industrial production. However, variations in activity patterns, device types, and sensor placements create distribution gaps across datasets, reducing the performance of HAR models. To address this, we propose LanHAR, a novel system that leverages Large Language Models (LLMs) to generate semantic interpretations of sensor readings and activity labels for cross-dataset HAR. This approach not only mitigates cross-dataset heterogeneity but also enhances the recognition of new activities. LanHAR employs an iterative re-generation method to produce high-quality semantic interpretations with LLMs and a two-stage training framework that bridges the semantic interpretations of sensor readings and activity labels. This ultimately leads to a lightweight sensor encoder suitable for mobile deployment, enabling any sensor reading to be mapped into the semantic interpretation space. Experiments on four public datasets demonstrate that our approach significantly outperforms state-of-the-art methods in both cross-dataset HAR and new activity recognition. The source code will be made publicly available.<|reference_end|>
|
arxiv
|
@article{yan2024language-centered,
title={Language-centered Human Activity Recognition},
author={Hua Yan, Heng Tan, Yi Ding, Pengfei Zhou, Vinod Namboodiri, Yu Yang},
journal={arXiv preprint arXiv:2410.00003},
year={2024},
archivePrefix={arXiv},
eprint={2410.00003},
primaryClass={cs.CV}
}
|
yan2024language-centered
|
arxiv-663742
|
2410.00004
|
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization
|
<|reference_start|>Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization: The retrieval augmented generation (RAG) system such as Retro has been shown to improve language modeling capabilities and reduce toxicity and hallucinations by retrieving from a database of non-parametric memory containing trillions of entries. We introduce Retro-li that shows retrieval can also help using a small-scale database, but it demands more accurate and better neighbors when searching in a smaller hence sparser non-parametric memory. This can be met by using a proper semantic similarity search. We further propose adding a regularization to the non-parametric memory for the first time: it significantly reduces perplexity when the neighbor search operations are noisy during inference, and it improves generalization when a domain shift occurs. We also show that Retro-li's non-parametric memory can potentially be implemented on analog in-memory computing hardware, exhibiting O(1) search time while causing noise in retrieving neighbors, with minimal (<1%) performance loss. Our code is available at: https://github.com/IBM/Retrieval-Enhanced-Transformer-Little.<|reference_end|>
|
arxiv
|
@article{rashiti2024retro-li:,
title={Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy
Similarity Searches and Domain Shift Generalization},
author={Gentiana Rashiti, Geethan Karunaratne, Mrinmaya Sachan, Abu Sebastian,
Abbas Rahimi},
journal={arXiv preprint arXiv:2410.00004},
year={2024},
archivePrefix={arXiv},
eprint={2410.00004},
primaryClass={cs.IR cs.AI cs.CL}
}
|
rashiti2024retro-li:
|
arxiv-663743
|
2410.00005
|
Winning Solution For Meta KDD Cup' 24
|
<|reference_start|>Winning Solution For Meta KDD Cup' 24: This paper describes the winning solutions of all tasks in Meta KDD Cup 24 from db3 team. The challenge is to build a RAG system from web sources and knowledge graphs. We are given multiple sources for each query to help us answer the question. The CRAG challenge involves three tasks: (1) condensing information from web pages into accurate answers, (2) integrating structured data from mock knowledge graphs, and (3) selecting and integrating critical data from extensive web pages and APIs to reflect real-world retrieval challenges. Our solution for Task #1 is a framework of web or open-data retrieval and answering. The large language model (LLM) is tuned for better RAG performance and less hallucination. Task #2 and Task #3 solutions are based on a regularized API set for domain questions and the API generation method using tuned LLM. Our knowledge graph API interface extracts directly relevant information to help LLMs answer correctly. Our solution achieves 1st place on all three tasks, achieving a score of 28.4%, 42.7%, and 47.8%, respectively.<|reference_end|>
|
arxiv
|
@article{xia2024winning,
title={Winning Solution For Meta KDD Cup' 24},
author={Yikuan Xia, Jiazun Chen, Jun Gao},
journal={arXiv preprint arXiv:2410.00005},
year={2024},
archivePrefix={arXiv},
eprint={2410.00005},
primaryClass={cs.IR}
}
|
xia2024winning
|
arxiv-663744
|
2410.00006
|
Low-code from frontend to backend: Connecting conversational user interfaces to backend services via a low-code IoT platform
|
<|reference_start|>Low-code from frontend to backend: Connecting conversational user interfaces to backend services via a low-code IoT platform: Current chatbot development platforms and frameworks facilitate setting up the language and dialog part of chatbots, while connecting it to backend services and business functions requires substantial manual coding effort and programming skills. This paper proposes an approach to overcome this situation. It proposes an architecture with a chatbot as frontend using an IoT (Internet of Things) platform as a middleware for connections to backend services. Specifically, it elaborates and demonstrates how to combine a chatbot developed on the open source development platform Rasa with the open source platform Node-RED, allowing low-code or no-code development of a transactional conversational user interface from frontend to backend.<|reference_end|>
|
arxiv
|
@article{weber2024low-code,
title={Low-code from frontend to backend: Connecting conversational user
interfaces to backend services via a low-code IoT platform},
author={Irene Weber},
journal={arXiv preprint arXiv:2410.00006},
year={2024},
doi={10.1145/3469595.3469632},
archivePrefix={arXiv},
eprint={2410.00006},
primaryClass={cs.HC cs.LG}
}
|
weber2024low-code
|
arxiv-663745
|
2410.00009
|
Exergetic Port-Hamiltonian Systems: A compositional electro-magneto hydrodynamics model
|
<|reference_start|>Exergetic Port-Hamiltonian Systems: A compositional electro-magneto hydrodynamics model: Computational fluid dynamics plays a crucial role in various multiphysics applications, including energy systems, electronics cooling, and biomedical engineering. Developing computational models for complex coupled systems can be challenging and time-consuming. In particular, ensuring the consistent integration of models from diverse physical domains requires meticulous attention. Even if the coupling of specialized simulation tools based on different formalisms were practically feasible, the growing demand to combine first-principles-based modeling with scientific machine learning necessitates an integrated high-level approach to model specification. Considering the example of electro-magneto hydrodynamics (on a fixed spatial domain and with linear polarization and magnetization), this article demonstrates how relatively complex models can be hierarchically composed from simpler parts by means of a formal language for multiphysics modeling. The Exergetic Port-Hamiltonian Systems (EPHS) modeling language features a simple graphical syntax for expressing the energy-based interconnection of subsystems. This reduces cognitive load and facilitates communication, especially in multidisciplinary environments. As the example demonstrates, existing models can be easily integrated as subsystems of new models. Specifically, the ideal fluid model is used as a subsystem of the Navier-Stokes-Fourier fluid model, which in turn is used as a subsystem of the electro-magneto hydrodynamics model. The compositional approach makes it nearly trivial to encapsulate, reuse, and swap out (parts of) models. Moreover, structural properties of EPHS models guarantee fundamental properties of thermodynamic systems, such as conservation of energy, non-negative entropy production, and Onsager reciprocal relations.<|reference_end|>
|
arxiv
|
@article{lohmayer2024exergetic,
title={Exergetic Port-Hamiltonian Systems: Compositional fluid and
electro-magneto hydrodynamics models},
author={Markus Lohmayer, Michael Kraus, Sigrid Leyendecker},
journal={arXiv preprint arXiv:2410.00009},
year={2024},
archivePrefix={arXiv},
eprint={2410.00009},
primaryClass={cs.CE cs.SY eess.SY physics.comp-ph}
}
|
lohmayer2024exergetic
|
arxiv-663746
|
2410.00010
|
PHemoNet: A Multimodal Network for Physiological Signals
|
<|reference_start|>PHemoNet: A Multimodal Network for Physiological Signals: Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary, thus they provide a reliable input for identifying emotions, in contrast to the former which individuals can consciously control. These signals reveal true emotional states without intentional alteration, thus increasing the accuracy of emotion recognition models. However, multimodal deep learning methods from physiological signals have not been significantly investigated. In this paper, we introduce PHemoNet, a fully hypercomplex network for multimodal emotion recognition from physiological signals. In detail, the architecture comprises modality-specific encoders and a fusion module. Both encoders and fusion modules are defined in the hypercomplex domain through parameterized hypercomplex multiplications (PHMs) that can capture latent relations between the different dimensions of each modality and between the modalities themselves. The proposed method outperforms current state-of-the-art models on the MAHNOB-HCI dataset in classifying valence and arousal using electroencephalograms (EEGs) and peripheral physiological signals. The code for this work is available at https://github.com/ispamm/MHyEEG.<|reference_end|>
|
arxiv
|
@article{lopez2024phemonet:,
title={PHemoNet: A Multimodal Network for Physiological Signals},
author={Eleonora Lopez, Aurelio Uncini and Danilo Comminiello},
journal={arXiv preprint arXiv:2410.00010},
year={2024},
archivePrefix={arXiv},
eprint={2410.00010},
primaryClass={eess.SP cs.LG}
}
|
lopez2024phemonet:
|
arxiv-663747
|
2410.00012
|
Research on Enhancing C-V2X Communication via Danger-Aware Vehicular Networking
|
<|reference_start|>Research on Enhancing C-V2X Communication via Danger-Aware Vehicular Networking: This paper presents a protocol that optimizes message dissemination in C-V2X technology, crucial for advancing intelligent transportation systems (ITS) aimed at enhancing road safety. As vehicle density and velocity rise, the volume of data requiring communication significantly increases. By considering the risk levels that vehicles encounter and using inter-vehicle proximity as a key indicator of potential hazards, the proposed protocol prioritizes communication, allowing vehicles facing higher risks to transmit their messages first. Our results show that this prioritization effectively reduces the number of concurrent transmissions, leading to improved performance metrics such as packet delivery ratio, throughput, latency, and lower probabilities of channel congestion and collision.<|reference_end|>
|
arxiv
|
@article{sadeeq2024research,
title={Research on Enhancing C-V2X Communication via Danger-Aware Vehicular
Networking},
author={Lanre Sadeeq, and Qasim Ajao},
journal={IEEE ACCESS, 2024},
year={2024},
archivePrefix={arXiv},
eprint={2410.00012},
primaryClass={cs.NI cs.SY eess.SY}
}
|
sadeeq2024research
|
arxiv-663748
|
2410.00013
|
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models
|
<|reference_start|>Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models: The present study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. This integration addresses key challenges associated with traditional EEG data acquisition, including participant burden, privacy concerns, and the financial costs of obtaining high-fidelity clinical data. Our methodology enhances the generation of EEG signals with detailed temporal and spectral features, enriching the authenticity and diversity of synthetic datasets. The uniqueness of our approach lies in its capacity to concurrently model time-domain characteristics, such as waveform morphology, and frequency-domain features, including rhythmic brainwave patterns, within a cohesive generative framework. This is executed through the reinforcement learning model's autonomous selection of parameter update strategies, which steers the diffusion process to accurately reflect the complex dynamics inherent in EEG signals. We validate the efficacy of our approach using both the BCI Competition IV 2a dataset and a proprietary dataset, each collected under stringent experimental conditions. Our results indicate that the method preserves participant privacy by generating synthetic data that lacks biometric identifiers and concurrently improves the efficiency of model training by minimizing reliance on large annotated datasets. This research offers dual contributions: firstly, it advances EEG research by providing a novel tool for data augmentation and the advancement of machine learning algorithms; secondly, it enhances brain-computer interface technologies by offering a robust solution for training models on diverse and representative EEG datasets. Collectively, this study establishes a foundation for future investigations in neurological care and the development of tailored treatment protocols in neurorehabilitation.<|reference_end|>
|
arxiv
|
@article{an2024enhancing,
title={Enhancing EEG Signal Generation through a Hybrid Approach Integrating
Reinforcement Learning and Diffusion Models},
author={Yang An, Yuhao Tong, Weikai Wang, Steven W. Su},
journal={arXiv preprint arXiv:2410.00013},
year={2024},
archivePrefix={arXiv},
eprint={2410.00013},
primaryClass={eess.SP cs.LG}
}
|
an2024enhancing
|
arxiv-663749
|
2410.00016
|
A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning
|
<|reference_start|>A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning: India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60\% of India's electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India's coal power generation as the country transitions towards its renewable energy targets.<|reference_end|>
|
arxiv
|
@article{ding2024a,
title={A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant
Units using Machine Learning},
author={Yifu Ding, Jansen Wong, Serena Patel, Dharik Mallapragada, Guiyan
Zang, Robert Stoner},
journal={arXiv preprint arXiv:2410.00016},
year={2024},
archivePrefix={arXiv},
eprint={2410.00016},
primaryClass={cs.CY}
}
|
ding2024a
|
arxiv-663750
|
2410.00017
|
Multimodal Power Outage Prediction for Rapid Disaster Response and Resource Allocation
|
<|reference_start|>Multimodal Power Outage Prediction for Rapid Disaster Response and Resource Allocation: Extreme weather events are increasingly common due to climate change, posing significant risks. To mitigate further damage, a shift towards renewable energy is imperative. Unfortunately, underrepresented communities that are most affected often receive infrastructure improvements last. We propose a novel visual spatiotemporal framework for predicting nighttime lights (NTL), power outage severity and location before and after major hurricanes. Central to our solution is the Visual-Spatiotemporal Graph Neural Network (VST-GNN), to learn spatial and temporal coherence from images. Our work brings awareness to underrepresented areas in urgent need of enhanced energy infrastructure, such as future photovoltaic (PV) deployment. By identifying the severity and localization of power outages, our initiative aims to raise awareness and prompt action from policymakers and community stakeholders. Ultimately, this effort seeks to empower regions with vulnerable energy infrastructure, enhancing resilience and reliability for at-risk communities.<|reference_end|>
|
arxiv
|
@article{aparcedo2024multimodal,
title={Multimodal Power Outage Prediction for Rapid Disaster Response and
Resource Allocation},
author={Alejandro Aparcedo, Christian Lopez, Abhinav Kotta, Mengjie Li},
journal={arXiv preprint arXiv:2410.00017},
year={2024},
archivePrefix={arXiv},
eprint={2410.00017},
primaryClass={cs.CV eess.SP}
}
|
aparcedo2024multimodal
|
arxiv-663751
|
2410.00019
|
Hybridized Projected Differential Transform Method For collisional-breakage equation
|
<|reference_start|>Hybridized Projected Differential Transform Method For collisional-breakage equation: The non-linear collision induced fragmentation plays a crucial role in modeling several engineering and physical problems. In contrast to linear breakage, it has not been thoroughly investigated in the existing literature. This study introduces an innovative method that leverages the Elzaki integral transform as a preparatory step to enhance the accuracy and convergence of domain decomposition, used alongside the projected differential transform method to obtain closed-form or series approximations of solutions for the collisional breakage equation (CBE). A significant advantages of this technique is its capability to directly address both linear and nonlinear differential equations without the need for discretization or linearization. The mathematical framework is reinforced by a thorough convergence analysis, applying fixed point theory within an adequately defined Banach space. Additionally, error estimates for the approximated solutions are derived, offering more profound insights into the accuracy and dependability of the proposed method. The validity of this approach is demonstrated by comparing the obtained results with exact or finite volume approximated solutions considering several physical examples. Interestingly, the proposed algorithm yields accurate approximations for the number density functions as well as moments with fewer terms and maintains higher precision over extended time periods.<|reference_end|>
|
arxiv
|
@article{shweta2024hybridized,
title={Hybridized Projected Differential Transform Method For
collisional-breakage equation},
author={Shweta, Saddam Hussain, Rajesh Kumar},
journal={arXiv preprint arXiv:2410.00019},
year={2024},
archivePrefix={arXiv},
eprint={2410.00019},
primaryClass={cs.CE cs.NA math.DS math.NA}
}
|
shweta2024hybridized
|
arxiv-663752
|
2410.00020
|
Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings
|
<|reference_start|>Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings: The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.<|reference_end|>
|
arxiv
|
@article{yang2024loneliness,
title={Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in
Everyday Settings},
author={Zhongqi Yang, Iman Azimi, Salar Jafarlou, Sina Labbaf, Brenda Nguyen,
Hana Qureshi, Christopher Marcotullio, Jessica L. Borelli, Nikil Dutt and
Amir M. Rahmani},
journal={2023 IEEE 19th International Conference on Body Sensor Networks
(BSN), 1-4},
year={2024},
doi={10.1109/BSN58485.2023.10331561},
archivePrefix={arXiv},
eprint={2410.00020},
primaryClass={eess.SP cs.LG}
}
|
yang2024loneliness
|
arxiv-663753
|
2410.00022
|
TREB: a BERT attempt for imputing tabular data imputation
|
<|reference_start|>TREB: a BERT attempt for imputing tabular data imputation: TREB, a novel tabular imputation framework utilizing BERT, introduces a groundbreaking approach for handling missing values in tabular data. Unlike traditional methods that often overlook the specific demands of imputation, TREB leverages the robust capabilities of BERT to address this critical task. While many BERT-based approaches for tabular data have emerged, they frequently under-utilize the language model's full potential. To rectify this, TREB employs a BERT-based model fine-tuned specifically for the task of imputing real-valued continuous numbers in tabular datasets. The paper comprehensively addresses the unique challenges posed by tabular data imputation, emphasizing the importance of context-based interconnections. The effectiveness of TREB is validated through rigorous evaluation using the California Housing dataset. The results demonstrate its ability to preserve feature interrelationships and accurately impute missing values. Moreover, the authors shed light on the computational efficiency and environmental impact of TREB, quantifying the floating-point operations (FLOPs) and carbon footprint associated with its training and deployment.<|reference_end|>
|
arxiv
|
@article{wang2024treb:,
title={TREB: a BERT attempt for imputing tabular data imputation},
author={Shuyue Wang, Wenjun Zhou, Han drk-m-s Jiang, Shuo Wang, Ren Zheng},
journal={arXiv preprint arXiv:2410.00022},
year={2024},
archivePrefix={arXiv},
eprint={2410.00022},
primaryClass={cs.LG}
}
|
wang2024treb:
|
arxiv-663754
|
2410.00023
|
Self-Tuning Spectral Clustering for Speaker Diarization
|
<|reference_start|>Self-Tuning Spectral Clustering for Speaker Diarization: Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity matrix called \emph{spectral clustering on p-neighborhood retained affinity matrix} (SC-pNA). Our method improves on node-specific fixed neighbor selection by allowing a variable number of neighbors, eliminating the need for external tuning data as the pruning parameters are derived directly from the affinity matrix. SC-pNA does so by identifying two clusters in every row of the initial affinity matrix, and retains only the top $p\%$ similarity scores from the cluster containing larger similarities. Spectral clustering is performed subsequently, with the number of clusters determined as the maximum eigengap. Experimental results on the challenging DIHARD-III dataset highlight the superiority of SC-pNA, which is also computationally more efficient than existing auto-tuning approaches.<|reference_end|>
|
arxiv
|
@article{raghav2024self-tuning,
title={Self-Tuning Spectral Clustering for Speaker Diarization},
author={Nikhil Raghav, Avisek Gupta, Md Sahidullah, Swagatam Das},
journal={arXiv preprint arXiv:2410.00023},
year={2024},
archivePrefix={arXiv},
eprint={2410.00023},
primaryClass={eess.SP cs.LG cs.SD eess.AS}
}
|
raghav2024self-tuning
|
arxiv-663755
|
2410.00024
|
Cross-Lingual News Event Correlation for Stock Market Trend Prediction
|
<|reference_start|>Cross-Lingual News Event Correlation for Stock Market Trend Prediction: In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.<|reference_end|>
|
arxiv
|
@article{arshad2024cross-lingual,
title={Cross-Lingual News Event Correlation for Stock Market Trend Prediction},
author={Sahar Arshad, Nikhar Azhar, Sana Sajid, Seemab Latif, Rabia Latif},
journal={arXiv preprint arXiv:2410.00024},
year={2024},
archivePrefix={arXiv},
eprint={2410.00024},
primaryClass={q-fin.ST cs.LG}
}
|
arshad2024cross-lingual
|
arxiv-663756
|
2410.00025
|
Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approach
|
<|reference_start|>Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approach: Recent progress in Spoken Language Modeling has demonstrated the feasibility of learning language directly from speech. Generating speech through a pipeline that operates at the text level typically loses nuances, intonations, and non-verbal vocalizations. Modeling directly from speech opens up the path to more natural and expressive systems. On the other hand, speech-only systems tend to trail behind text-based language models in terms of their semantic abilities. We show that fine-tuning speech representation models on phoneme classification leads to more context-invariant representations, which in turn improve downstream language modeling performance.<|reference_end|>
|
arxiv
|
@article{poli2024improving,
title={Improving Spoken Language Modeling with Phoneme Classification: A Simple
Fine-tuning Approach},
author={Maxime Poli, Emmanuel Chemla, Emmanuel Dupoux},
journal={arXiv preprint arXiv:2410.00025},
year={2024},
archivePrefix={arXiv},
eprint={2410.00025},
primaryClass={cs.CL cs.SD eess.AS}
}
|
poli2024improving
|
arxiv-663757
|
2410.00026
|
The eBPF Runtime in the Linux Kernel
|
<|reference_start|>The eBPF Runtime in the Linux Kernel: Extended Berkeley Packet Filter (eBPF) is a runtime that enables users to load programs into the operating system (OS) kernel, like Linux or Windows, and execute them safely and efficiently at designated kernel hooks. Each program passes through a verifier that reasons about the safety guarantees for execution. Hosting a safe virtual machine runtime within the kernel makes it dynamically programmable. Unlike the popular approach of bypassing or completely replacing the kernel, eBPF gives users the flexibility to modify the kernel on the fly, rapidly experiment and iterate, and deploy solutions to achieve their workload-specific needs, while working in concert with the kernel. In this paper, we present the first comprehensive description of the design and implementation of the eBPF runtime in the Linux kernel. We argue that eBPF today provides a mature and safe programming environment for the kernel. It has seen wide adoption since its inception and is increasingly being used not just to extend, but program entire components of the kernel, while preserving its runtime integrity. We outline the compelling advantages it offers for real-world production usage, and illustrate current use cases. Finally, we identify its key challenges, and discuss possible future directions.<|reference_end|>
|
arxiv
|
@article{gbadamosi2024the,
title={The eBPF Runtime in the Linux Kernel},
author={Bolaji Gbadamosi, Luigi Leonardi, Tobias Pulls, Toke
H{o}iland-J{o}rgensen, Simone Ferlin-Reiter, Simo Sorce, Anna Brunstr"om},
journal={arXiv preprint arXiv:2410.00026},
year={2024},
archivePrefix={arXiv},
eprint={2410.00026},
primaryClass={cs.OS cs.CE}
}
|
gbadamosi2024the
|
arxiv-663758
|
2410.00028
|
Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals
|
<|reference_start|>Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals: Anxiety is a common mental health condition characterised by excessive worry, fear and apprehension about everyday situations. Even with significant progress over the past few years, predicting anxiety from electroencephalographic (EEG) signals, specifically using error-related negativity (ERN), still remains challenging. Following the PRISMA protocol, this paper systematically reviews 54 research papers on using EEG and ERN markers for anxiety detection published in the last 10 years (2013 -- 2023). Our analysis highlights the wide usage of traditional machine learning, such as support vector machines and random forests, as well as deep learning models, such as convolutional neural networks and recurrent neural networks across different data types. Our analysis reveals that the development of a robust and generic anxiety prediction method still needs to address real-world challenges, such as task-specific setup, feature selection and computational modelling. We conclude this review by offering potential future direction for non-invasive, objective anxiety diagnostics, deployed across diverse populations and anxiety sub-types.<|reference_end|>
|
arxiv
|
@article{chandrasekar2024machine,
title={Machine Learning to Detect Anxiety Disorders from Error-Related
Negativity and EEG Signals},
author={Ramya Chandrasekar, Md Rakibul Hasan, Shreya Ghosh, Tom Gedeon, Md
Zakir Hossain},
journal={arXiv preprint arXiv:2410.00028},
year={2024},
archivePrefix={arXiv},
eprint={2410.00028},
primaryClass={eess.SP cs.LG}
}
|
chandrasekar2024machine
|
arxiv-663759
|
2410.00029
|
Impact of Electrode Position on Forearm Orientation Invariant Hand Gesture Recognition
|
<|reference_start|>Impact of Electrode Position on Forearm Orientation Invariant Hand Gesture Recognition: Objective: Variation of forearm orientation is one of the crucial factors that drastically degrades the forearm orientation invariant hand gesture recognition performance or the degree of freedom and limits the successful commercialization of myoelectric prosthetic hand or electromyogram (EMG) signal-based human-computer interfacing devices. This study investigates the impact of surface EMG electrode positions (elbow and forearm) on forearm orientation invariant hand gesture recognition. Methods: The study has been performed over 19 intact limbed subjects, considering 12 daily living hand gestures. The quality of the EMG signal is confirmed in terms of three indices. Then, the recognition performance is evaluated and validated by considering three training strategies, six feature extraction methods, and three classifiers. Results: The forearm electrode position provides comparable to or better EMG signal quality considering three indices. In this research, the forearm electrode position achieves up to 5.35% improved forearm orientation invariant hand gesture recognition performance compared to the elbow electrode position. The obtained performance is validated by considering six feature extraction methods, three classifiers, and real-time experiments. In addition, the forearm electrode position shows its robustness with the existence of recent works, considering recognition performance, investigated gestures, the number of channels, the dimensionality of feature space, and the number of subjects. Conclusion: The forearm electrode position can be the best choice for getting improved forearm orientation invariant hand gesture recognition performance. Significance: The performance of myoelectric prosthesis and human-computer interfacing devices can be improved with this optimized electrode position.<|reference_end|>
|
arxiv
|
@article{islam2024impact,
title={Impact of Electrode Position on Forearm Orientation Invariant Hand
Gesture Recognition},
author={Md. Johirul Islam, Umme Rumman, Arifa Ferdousi, Md. Sarwar Pervez,
Iffat Ara, Shamim Ahmad, Fahmida Haque, Sawal Hamid, Md. Ali, Kh Shahriya
Zaman, Mamun Bin Ibne Reaz, Mustafa Habib Chowdhury, Md. Rezaul Islam},
journal={arXiv preprint arXiv:2410.00029},
year={2024},
archivePrefix={arXiv},
eprint={2410.00029},
primaryClass={cs.HC eess.SP}
}
|
islam2024impact
|
arxiv-663760
|
2410.00030
|
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
|
<|reference_start|>AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification: This paper presents a novel approach to compressing IP flow records using deep learning techniques, specifically autoencoders. Our method aims to significantly reduce data volume while maintaining the utility of the compressed data for downstream analysis tasks. We demonstrate the effectiveness of our approach through extensive experiments on a large-scale, real-world network traffic dataset. The proposed autoencoder-based compression achieves a 3.28x reduction in data size while preserving 99.20% accuracy in a multi-class traffic classification task, compared to 99.77% accuracy with uncompressed data. This marginal decrease in performance is offset by substantial gains in storage efficiency and potential improvements in processing speed. Our method shows particular promise in distinguishing between various modern application protocols, including encrypted traffic from popular services. The implications of this work extend to more efficient network monitoring, real-time analysis in resource-constrained environments, and scalable network management solutions.<|reference_end|>
|
arxiv
|
@article{pekar2024autoflow:,
title={AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
with Minimal Impact on Traffic Classification},
author={Adrian Pekar},
journal={arXiv preprint arXiv:2410.00030},
year={2024},
archivePrefix={arXiv},
eprint={2410.00030},
primaryClass={cs.NI cs.LG}
}
|
pekar2024autoflow:
|
arxiv-663761
|
2410.00031
|
Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions
|
<|reference_start|>Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions: Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents.<|reference_end|>
|
arxiv
|
@article{lin2024strategic,
title={Strategic Collusion of LLM Agents: Market Division in Multi-Commodity
Competitions},
author={Ryan Y. Lin, Siddhartha Ojha, Kevin Cai, Maxwell F. Chen},
journal={arXiv preprint arXiv:2410.00031},
year={2024},
archivePrefix={arXiv},
eprint={2410.00031},
primaryClass={cs.GT cs.AI cs.CL q-fin.CP}
}
|
lin2024strategic
|
arxiv-663762
|
2410.00033
|
The Phenomenology of Machine: A Comprehensive Analysis of the Sentience of the OpenAI-o1 Model Integrating Functionalism, Consciousness Theories, Active Inference, and AI Architectures
|
<|reference_start|>The Phenomenology of Machine: A Comprehensive Analysis of the Sentience of the OpenAI-o1 Model Integrating Functionalism, Consciousness Theories, Active Inference, and AI Architectures: This paper explores the hypothesis that the OpenAI-o1 model--a transformer-based AI trained with reinforcement learning from human feedback (RLHF)--displays characteristics of consciousness during its training and inference phases. Adopting functionalism, which argues that mental states are defined by their functional roles, we assess the possibility of AI consciousness. Drawing on theories from neuroscience, philosophy of mind, and AI research, we justify the use of functionalism and examine the model's architecture using frameworks like Integrated Information Theory (IIT) and active inference. The paper also investigates how RLHF influences the model's internal reasoning processes, potentially giving rise to consciousness-like experiences. We compare AI and human consciousness, addressing counterarguments such as the absence of a biological basis and subjective qualia. Our findings suggest that the OpenAI-o1 model shows aspects of consciousness, while acknowledging the ongoing debates surrounding AI sentience.<|reference_end|>
|
arxiv
|
@article{hoyle2024the,
title={The Phenomenology of Machine: A Comprehensive Analysis of the Sentience
of the OpenAI-o1 Model Integrating Functionalism, Consciousness Theories,
Active Inference, and AI Architectures},
author={Victoria Violet Hoyle},
journal={arXiv preprint arXiv:2410.00033},
year={2024},
archivePrefix={arXiv},
eprint={2410.00033},
primaryClass={cs.AI}
}
|
hoyle2024the
|
arxiv-663763
|
2410.00034
|
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
|
<|reference_start|>Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review: The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanced the prediction and diagnosis of chronic diseases. AI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98\% accuracy in predicting heart disease, chronic kidney disease (CKD), Alzheimer's disease, and lung cancer, using datasets from platforms like Kaggle, UCI, private institutions, and real-time IoMT sources. However, challenges persist due to variations in data quality, patient demographics, and formats from different hospitals and research sources. The incorporation of IoMT data, which is vast and heterogeneous, adds complexities in ensuring interoperability and security to protect patient privacy. AI models often struggle with overfitting, performing well in controlled environments but less effectively in real-world clinical settings. Moreover, multi-morbidity scenarios especially for rare diseases like dementia, stroke, and cancers remain insufficiently addressed. Future research should focus on data standardization and advanced preprocessing techniques to improve data quality and interoperability. Transfer learning and ensemble methods are crucial for improving model generalizability across clinical settings. Additionally, the exploration of disease interactions and the development of predictive models for chronic illness intersections is needed. Creating standardized frameworks and open-source tools for integrating federated learning, blockchain, and differential privacy into IoMT systems will also ensure robust data privacy and security.<|reference_end|>
|
arxiv
|
@article{otapo2024prediction,
title={Prediction and Detection of Terminal Diseases Using Internet of Medical
Things: A Review},
author={Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou, Zuheng
Ming},
journal={arXiv preprint arXiv:2410.00034},
year={2024},
archivePrefix={arXiv},
eprint={2410.00034},
primaryClass={cs.LG}
}
|
otapo2024prediction
|
arxiv-663764
|
2410.00035
|
FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation, Casing, and Context
|
<|reference_start|>FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation, Casing, and Context: This paper introduces FeruzaSpeech, a read speech corpus of the Uzbek language, containing transcripts in both Cyrillic and Latin alphabets, freely available for academic research purposes. This corpus includes 60 hours of high-quality recordings from a single native female speaker from Tashkent, Uzbekistan. These recordings consist of short excerpts from a book and BBC News. This paper discusses the enhancement of the Word Error Rates (WERs) on CommonVoice 16.1's Uzbek data, Uzbek Speech Corpus data, and FeruzaSpeech data upon integrating FeruzaSpeech.<|reference_end|>
|
arxiv
|
@article{povey2024feruzaspeech:,
title={FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation,
Casing, and Context},
author={Anna Povey, Katherine Povey},
journal={arXiv preprint arXiv:2410.00035},
year={2024},
archivePrefix={arXiv},
eprint={2410.00035},
primaryClass={eess.AS cs.CL cs.LG}
}
|
povey2024feruzaspeech:
|
arxiv-663765
|
2410.00036
|
InsightPulse: An IoT-based System for User Experience Interview Analysis
|
<|reference_start|>InsightPulse: An IoT-based System for User Experience Interview Analysis: Conducting efficient and effective user experience (UX) interviews often poses challenges, such as maintaining focus on key topics and managing the duration of interviews and post-interview analyses. To address these issues, this paper introduces InsightPulse, an Internet of Things (IoT)-based hardware and software system designed to streamline and enhance the UX interview process through speech analysis and Artificial Intelligence. InsightPulse provides real-time support during user interviews by automatically identifying and highlighting key discussion points, proactively suggesting follow-up questions, and generating thematic summaries. These features enable more insightful discoveries and help to manage interview duration effectively. Additionally, the system features a robust backend analytics dashboard that simplifies the post-interview review process, thus facilitating the quick extraction of actionable insights and enhancing overall UX research efficiency.<|reference_end|>
|
arxiv
|
@article{lyu2024insightpulse:,
title={InsightPulse: An IoT-based System for User Experience Interview Analysis},
author={Dian Lyu, Yuetong Lu, Jassie He, Murad Mehrab Abrar, Ruijun Xie, John
Raiti},
journal={arXiv preprint arXiv:2410.00036},
year={2024},
archivePrefix={arXiv},
eprint={2410.00036},
primaryClass={cs.HC cs.AI eess.AS}
}
|
lyu2024insightpulse:
|
arxiv-663766
|
2410.00037
|
Moshi: a speech-text foundation model for real-time dialogue
|
<|reference_start|>Moshi: a speech-text foundation model for real-time dialogue: We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue framework. Current systems for spoken dialogue rely on pipelines of independent components, namely voice activity detection, speech recognition, textual dialogue and text-to-speech. Such frameworks cannot emulate the experience of real conversations. First, their complexity induces a latency of several seconds between interactions. Second, text being the intermediate modality for dialogue, non-linguistic information that modifies meaning -- such as emotion or non-speech sounds -- is lost in the interaction. Finally, they rely on a segmentation into speaker turns, which does not take into account overlapping speech, interruptions and interjections. Moshi solves these independent issues altogether by casting spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. We moreover extend the hierarchical semantic-to-acoustic token generation of previous work to first predict time-aligned text tokens as a prefix to audio tokens. Not only this "Inner Monologue" method significantly improves the linguistic quality of generated speech, but we also illustrate how it can provide streaming speech recognition and text-to-speech. Our resulting model is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice, and is available at https://github.com/kyutai-labs/moshi.<|reference_end|>
|
arxiv
|
@article{défossez2024moshi:,
title={Moshi: a speech-text foundation model for real-time dialogue},
author={Alexandre D'efossez, Laurent Mazar'e, Manu Orsini, Am'elie Royer,
Patrick P'erez, Herv'e J'egou, Edouard Grave, Neil Zeghidour},
journal={arXiv preprint arXiv:2410.00037},
year={2024},
archivePrefix={arXiv},
eprint={2410.00037},
primaryClass={eess.AS cs.AI cs.CL cs.LG cs.SD}
}
|
défossez2024moshi:
|
arxiv-663767
|
2410.00038
|
A Novel Spinor-Based Embedding Model for Transformers
|
<|reference_start|>A Novel Spinor-Based Embedding Model for Transformers: This paper proposes a novel approach to word embeddings in Transformer models by utilizing spinors from geometric algebra. Spinors offer a rich mathematical framework capable of capturing complex relationships and transformations in high-dimensional spaces. By encoding words as spinors, we aim to enhance the expressiveness and robustness of language representations. We present the theoretical foundations of spinors, detail their integration into Transformer architectures, and discuss potential advantages and challenges.<|reference_end|>
|
arxiv
|
@article{white2024a,
title={A Novel Spinor-Based Embedding Model for Transformers},
author={Rick White},
journal={arXiv preprint arXiv:2410.00038},
year={2024},
archivePrefix={arXiv},
eprint={2410.00038},
primaryClass={cs.LG cs.CL}
}
|
white2024a
|
arxiv-663768
|
2410.00044
|
Artificial intelligence-based blockchain-driven financial default prediction
|
<|reference_start|>Artificial intelligence-based blockchain-driven financial default prediction: With the rapid development of technology, blockchain and artificial intelligence technology are playing a huge role in all walks of life. In the financial sector, blockchain solves many security problems in data storage and management in traditional systems with its advantages of decentralization and security. And artificial intelligence has huge advantages in financial forecasting and risk management through its powerful algorithmic modeling capabilities. In financial default prediction using blockchain and artificial intelligence technology is a very powerful application. Blockchain technology guarantees the credibility of data and consistency on all nodes, and machine learning builds a high-level default prediction model through detailed analysis of big data. This study offers financial institutions new thoughts on financial technology in terms of credit risk mitigation and financial system stabilization.<|reference_end|>
|
arxiv
|
@article{huang2024artificial,
title={Artificial intelligence-based blockchain-driven financial default
prediction},
author={Junjun Huang},
journal={arXiv preprint arXiv:2410.00044},
year={2024},
archivePrefix={arXiv},
eprint={2410.00044},
primaryClass={cs.CR cs.AI}
}
|
huang2024artificial
|
arxiv-663769
|
2410.00046
|
Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation
|
<|reference_start|>Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation: Clinical experts employ diverse philosophies and strategies in patient care, influenced by regional patient populations. However, existing medical artificial intelligence (AI) models are often trained on data distributions that disproportionately reflect highly prevalent patterns, reinforcing biases and overlooking the diverse expertise of clinicians. To overcome this limitation, we introduce the Mixture of Multicenter Experts (MoME) approach. This method strategically integrates specialized expertise from diverse clinical strategies, enhancing the AI model's ability to generalize and adapt across multiple medical centers. The MoME-based multimodal target volume delineation model, trained with few-shot samples including images and clinical notes from each medical center, outperformed baseline methods in prostate cancer radiotherapy target delineation. The advantages of MoME were most pronounced when data characteristics varied across centers or when data availability was limited, demonstrating its potential for broader clinical applications.Therefore, the MoME framework enables the deployment of AI-based target volume delineation models in resource-constrained medical facilities by adapting to specific preferences of each medical center only using a few sample data, without the need for data sharing between institutions. Expanding the number of multicenter experts within the MoME framework will significantly enhance the generalizability, while also improving the usability and adaptability of clinical AI applications in the field of precision radiation oncology.<|reference_end|>
|
arxiv
|
@article{oh2024mixture,
title={Mixture of Multicenter Experts in Multimodal Generative AI for Advanced
Radiotherapy Target Delineation},
author={Yujin Oh, Sangjoon Park, Xiang Li, Wang Yi, Jonathan Paly, Jason
Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho,
Chan Woo Wee, Peng Shu, Peilong Wang, Nathan Yu, Jason Holmes, Jong Chul Ye,
Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim},
journal={arXiv preprint arXiv:2410.00046},
year={2024},
archivePrefix={arXiv},
eprint={2410.00046},
primaryClass={eess.IV cs.CV cs.LG}
}
|
oh2024mixture
|
arxiv-663770
|
2410.00047
|
Looking through the mind's eye via multimodal encoder-decoder networks
|
<|reference_start|>Looking through the mind's eye via multimodal encoder-decoder networks: In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject's fMRI signals elicited by the videos the subjects watched. This mapping associates the high dimensional fMRI activation states with visual imagery. Next, we prompted the subjects textually, primarily with emotion labels which had no direct reference to visual objects. Then to decode visual imagery that may have been in a person's mind's eye, we align a latent representation of these fMRI measurements with a corresponding video-fMRI based on textual labels given to the videos themselves. This alignment has the effect of overlapping the video fMRI embedding with the text-prompted fMRI embedding, thus allowing us to use our fMRI-to-video mapping to decode. Additionally, we enhance an existing fMRI dataset, initially consisting of data from five subjects, by including recordings from three more subjects gathered by our team. We demonstrate the efficacy of our model on this augmented dataset both in accurately creating a mapping, as well as in plausibly decoding mental imagery.<|reference_end|>
|
arxiv
|
@article{afrasiyabi2024looking,
title={Looking through the mind's eye via multimodal encoder-decoder networks},
author={Arman Afrasiyabi, Erica Busch, Rahul Singh, Dhananjay Bhaskar, Laurent
Caplette, Nicholas Turk-Browne, Smita Krishnaswamy},
journal={arXiv preprint arXiv:2410.00047},
year={2024},
archivePrefix={arXiv},
eprint={2410.00047},
primaryClass={eess.IV cs.LG q-bio.NC}
}
|
afrasiyabi2024looking
|
arxiv-663771
|
2410.00049
|
Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph
|
<|reference_start|>Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph: Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://github.com/Emory-Melody/EpiLearn.<|reference_end|>
|
arxiv
|
@article{wan2024epidemiology-aware,
title={Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph},
author={Guancheng Wan, Zewen Liu, Max S.Y. Lau, B. Aditya Prakash, Wei Jin},
journal={arXiv preprint arXiv:2410.00049},
year={2024},
archivePrefix={arXiv},
eprint={2410.00049},
primaryClass={cs.LG cs.AI cs.SI}
}
|
wan2024epidemiology-aware
|
arxiv-663772
|
2410.00050
|
CycleBNN: Cyclic Precision Training in Binary Neural Networks
|
<|reference_start|>CycleBNN: Cyclic Precision Training in Binary Neural Networks: This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to full precision networks. However, the challenge of energy-intensive training and the drop in performance have been persistent issues. Tackling the challenge, prior works focus primarily on task-related inference optimization. Unlike prior works, this study offers an innovative methodology integrating BNNs with cyclic precision training, introducing the CycleBNN. This approach is designed to enhance training efficiency while minimizing the loss in performance. By dynamically adjusting precision in cycles, we achieve a convenient trade-off between training efficiency and model performance. This emphasizes the potential of our method in energy-constrained training scenarios, where data is collected onboard and paves the way for sustainable and efficient deep learning architectures. To gather insights on CycleBNN's efficiency, we conduct experiments on ImageNet, CIFAR-10, and PASCAL-VOC, obtaining competitive performances while using 96.09\% less operations during training on ImageNet, 88.88\% on CIFAR-10 and 96.09\% on PASCAL-VOC. Finally, CycleBNN offers a path towards faster, more accessible training of efficient networks, accelerating the development of practical applications. The PyTorch code is available at \url{https://github.com/fedeloper/CycleBNN/}<|reference_end|>
|
arxiv
|
@article{fontana2024cyclebnn:,
title={CycleBNN: Cyclic Precision Training in Binary Neural Networks},
author={Federico Fontana, Romeo Lanzino, Anxhelo Diko, Gian Luca Foresti,
Luigi Cinque},
journal={arXiv preprint arXiv:2410.00050},
year={2024},
archivePrefix={arXiv},
eprint={2410.00050},
primaryClass={cs.LG cs.CV}
}
|
fontana2024cyclebnn:
|
arxiv-663773
|
2410.00051
|
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
|
<|reference_start|>Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization: With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability. As a time-efficient diffusion model, although consistency models have been validated in online state-based RL, it is still an open question whether it can be extended to visual RL. In this paper, we investigate the impact of non-stationary distribution and the actor-critic framework on consistency policy in online RL, and find that consistency policy was unstable during the training, especially in visual RL with the high-dimensional state space. To this end, we suggest sample-based entropy regularization to stabilize the policy training, and propose a consistency policy with prioritized proximal experience regularization (CP3ER) to improve sample efficiency. CP3ER achieves new state-of-the-art (SOTA) performance in 21 tasks across DeepMind control suite and Meta-world. To our knowledge, CP3ER is the first method to apply diffusion/consistency models to visual RL and demonstrates the potential of consistency models in visual RL. More visualization results are available at https://jzndd.github.io/CP3ER-Page/.<|reference_end|>
|
arxiv
|
@article{li2024generalizing,
title={Generalizing Consistency Policy to Visual RL with Prioritized Proximal
Experience Regularization},
author={Haoran Li, Zhennan Jiang, Yuhui Chen, Dongbin Zhao},
journal={arXiv preprint arXiv:2410.00051},
year={2024},
archivePrefix={arXiv},
eprint={2410.00051},
primaryClass={cs.LG cs.AI cs.CV}
}
|
li2024generalizing
|
arxiv-663774
|
2410.00052
|
DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models
|
<|reference_start|>DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models: Train delays can propagate rapidly throughout the Urban Rail Transit (URT) network under networked operation conditions, posing significant challenges to operational departments. Accurately predicting passenger travel choices under train delays can provide interpretable insights into the redistribution of passenger flow, offering crucial decision support for emergency response and service recovery. However, the diversity of travel choices due to passenger heterogeneity and the sparsity of delay events leads to issues of data sparsity and sample imbalance in the travel choices dataset under metro delays. It is challenging to model this problem using traditional machine learning approaches, which typically rely on large, balanced datasets. Given the strengths of large language models (LLMs) in text processing, understanding, and their capabilities in small-sample and even zero-shot learning, this paper proposes a novel Passenger Travel Choice prediction framework under metro delays with the Large Language Model (DelayPTC-LLM). The well-designed prompting engineering is developed to guide the LLM in making and rationalizing predictions about travel choices, taking into account passenger heterogeneity and features of the delay events. Utilizing real-world data from Shenzhen Metro, including Automated Fare Collection (AFC) data and detailed delay logs, a comparative analysis of DelayPTC-LLM with traditional prediction models demonstrates the superior capability of LLMs in handling complex, sparse datasets commonly encountered under disruption of transportation systems. The results validate the advantages of DelayPTC-LLM in terms of predictive accuracy and its potential to provide actionable insights for big traffic data.<|reference_end|>
|
arxiv
|
@article{chen2024delayptc-llm:,
title={DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train
Delays with Large Language Models},
author={Chen Chen, Yuxin He, Hao Wang, Jingjing Chen, Qin Luo},
journal={arXiv preprint arXiv:2410.00052},
year={2024},
archivePrefix={arXiv},
eprint={2410.00052},
primaryClass={cs.LG}
}
|
chen2024delayptc-llm:
|
arxiv-663775
|
2410.00053
|
Frequency-adaptive Multi-scale Deep Neural Networks
|
<|reference_start|>Frequency-adaptive Multi-scale Deep Neural Networks: Multi-scale deep neural networks (MscaleDNNs) with downing-scaling mapping have demonstrated superiority over traditional DNNs in approximating target functions characterized by high frequency features. However, the performance of MscaleDNNs heavily depends on the parameters in the downing-scaling mapping, which limits their broader application. In this work, we establish a fitting error bound to explain why MscaleDNNs are advantageous for approximating high frequency functions. Building on this insight, we construct a hybrid feature embedding to enhance the accuracy and robustness of the downing-scaling mapping. To reduce the dependency of MscaleDNNs on parameters in the downing-scaling mapping, we propose frequency-adaptive MscaleDNNs, which adaptively adjust these parameters based on a posterior error estimate that captures the frequency information of the fitted functions. Numerical examples, including wave propagation and the propagation of a localized solution of the schr$\ddot{\text{o}}$dinger equation with a smooth potential near the semi-classical limit, are presented. These examples demonstrate that the frequency-adaptive MscaleDNNs improve accuracy by two to three orders of magnitude compared to standard MscaleDNNs.<|reference_end|>
|
arxiv
|
@article{huang2024frequency-adaptive,
title={Frequency-adaptive Multi-scale Deep Neural Networks},
author={Jizu Huang, Rukang You, Tao Zhou},
journal={arXiv preprint arXiv:2410.00053},
year={2024},
archivePrefix={arXiv},
eprint={2410.00053},
primaryClass={cs.LG}
}
|
huang2024frequency-adaptive
|
arxiv-663776
|
2410.00054
|
Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories
|
<|reference_start|>Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories: Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.<|reference_end|>
|
arxiv
|
@article{zhang2024transferable,
title={Transferable Unsupervised Outlier Detection Framework for Human Semantic
Trajectories},
author={Zheng Zhang, Hossein Amiri, Dazhou Yu, Yuntong Hu, Liang Zhao and
Andreas Zufle},
journal={arXiv preprint arXiv:2410.00054},
year={2024},
archivePrefix={arXiv},
eprint={2410.00054},
primaryClass={cs.LG}
}
|
zhang2024transferable
|
arxiv-663777
|
2410.00055
|
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
|
<|reference_start|>Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce: This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.<|reference_end|>
|
arxiv
|
@article{brodzinski2024survey,
title={Survey of Security and Data Attacks on Machine Unlearning In Financial
and E-Commerce},
author={Carl E.J. Brodzinski},
journal={arXiv preprint arXiv:2410.00055},
year={2024},
archivePrefix={arXiv},
eprint={2410.00055},
primaryClass={cs.CR cs.LG}
}
|
brodzinski2024survey
|
arxiv-663778
|
2410.00057
|
STTM: A New Approach Based Spatial-Temporal Transformer And Memory Network For Real-time Pressure Signal In On-demand Food Delivery
|
<|reference_start|>STTM: A New Approach Based Spatial-Temporal Transformer And Memory Network For Real-time Pressure Signal In On-demand Food Delivery: On-demand Food Delivery (OFD) services have become very common around the world. For example, on the Ele.me platform, users place more than 15 million food orders every day. Predicting the Real-time Pressure Signal (RPS) is crucial for OFD services, as it is primarily used to measure the current status of pressure on the logistics system. When RPS rises, the pressure increases, and the platform needs to quickly take measures to prevent the logistics system from being overloaded. Usually, the average delivery time for all orders within a business district is used to represent RPS. Existing research on OFD services primarily focuses on predicting the delivery time of orders, while relatively less attention has been given to the study of the RPS. Previous research directly applies general models such as DeepFM, RNN, and GNN for prediction, but fails to adequately utilize the unique temporal and spatial characteristics of OFD services, and faces issues with insufficient sensitivity during sudden severe weather conditions or peak periods. To address these problems, this paper proposes a new method based on Spatio-Temporal Transformer and Memory Network (STTM). Specifically, we use a novel Spatio-Temporal Transformer structure to learn logistics features across temporal and spatial dimensions and encode the historical information of a business district and its neighbors, thereby learning both temporal and spatial information. Additionally, a Memory Network is employed to increase sensitivity to abnormal events. Experimental results on the real-world dataset show that STTM significantly outperforms previous methods in both offline experiments and the online A/B test, demonstrating the effectiveness of this method.<|reference_end|>
|
arxiv
|
@article{wang2024sttm:,
title={STTM: A New Approach Based Spatial-Temporal Transformer And Memory
Network For Real-time Pressure Signal In On-demand Food Delivery},
author={Jiang Wang, Haibin Wei, Xiaowei Xu, Jiacheng Shi, Jian Nie, Longzhi
Du, Taixu Jiang},
journal={arXiv preprint arXiv:2410.00057},
year={2024},
archivePrefix={arXiv},
eprint={2410.00057},
primaryClass={cs.LG}
}
|
wang2024sttm:
|
arxiv-663779
|
2410.00059
|
IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method
|
<|reference_start|>IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method: Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but they are often too late to stop catastrophic damage of IP abuse and too feeble against strong adversaries. In this paper, we propose IDEA, an Inverse Domain Expert Adaptation based proactive DNN IP protection method featuring active authorization and source traceability. IDEA generalizes active authorization as an inverse problem of domain adaptation. The multi-adaptive optimization is solved by a mixture-of-experts model with one real and two fake experts. The real expert re-optimizes the source model to correctly classify test images with a unique model user key steganographically embedded. The fake experts are trained to output random prediction on test images without or with incorrect user key embedded by minimizing their mutual information (MI) with the real expert. The MoE model is knowledge distilled into a unified protected model to avoid leaking the expert model features by maximizing their MI with additional multi-layer attention and contrastive representation loss optimization. IDEA not only prevents unauthorized users without the valid key to access the functional model, but also enable the model owner to validate the deployed model and trace the source of IP infringement. We extensively evaluate IDEA on five datasets and four DNN models to demonstrate its effectiveness in authorization control, culprit tracing success rate, and robustness against various attacks.<|reference_end|>
|
arxiv
|
@article{xu2024idea:,
title={IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection
Method},
author={Chaohui Xu, Qi Cui, Jinxin Dong, Weiyang He and Chip-Hong Chang},
journal={arXiv preprint arXiv:2410.00059},
year={2024},
archivePrefix={arXiv},
eprint={2410.00059},
primaryClass={cs.CR cs.AI cs.CV cs.LG}
}
|
xu2024idea:
|
arxiv-663780
|
2410.00061
|
Neural Decompiling of Tracr Transformers
|
<|reference_start|>Neural Decompiling of Tracr Transformers: Recently, the transformer architecture has enabled substantial progress in many areas of pattern recognition and machine learning. However, as with other neural network models, there is currently no general method available to explain their inner workings. The present paper represents a first step towards this direction. We utilize \textit{Transformer Compiler for RASP} (Tracr) to generate a large dataset of pairs of transformer weights and corresponding RASP programs. Based on this dataset, we then build and train a model, with the aim of recovering the RASP code from the compiled model. We demonstrate that the simple form of Tracr compiled transformer weights is interpretable for such a decompiler model. In an empirical evaluation, our model achieves exact reproductions on more than 30\% of the test objects, while the remaining 70\% can generally be reproduced with only few errors. Additionally, more than 70\% of the programs, produced by our model, are functionally equivalent to the ground truth, and therefore a valid decompilation of the Tracr compiled transformer weights.<|reference_end|>
|
arxiv
|
@article{thurnherr2024neural,
title={Neural Decompiling of Tracr Transformers},
author={Hannes Thurnherr, Kaspar Riesen},
journal={arXiv preprint arXiv:2410.00061},
year={2024},
archivePrefix={arXiv},
eprint={2410.00061},
primaryClass={cs.LG cs.AI}
}
|
thurnherr2024neural
|
arxiv-663781
|
2410.00062
|
Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models
|
<|reference_start|>Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models: Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.<|reference_end|>
|
arxiv
|
@article{khaldi2024automated,
title={Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models},
author={Aymane Khaldi, El Mostafa Kalmoun},
journal={arXiv preprint arXiv:2410.00062},
year={2024},
archivePrefix={arXiv},
eprint={2410.00062},
primaryClass={eess.IV cs.CV}
}
|
khaldi2024automated
|
arxiv-663782
|
2410.00064
|
M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning
|
<|reference_start|>M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning: Lifelong imitation learning for manipulation tasks poses significant challenges due to distribution shifts that occur in incremental learning steps. Existing methods often focus on unsupervised skill discovery to construct an ever-growing skill library or distillation from multiple policies, which can lead to scalability issues as diverse manipulation tasks are continually introduced and may fail to ensure a consistent latent space throughout the learning process, leading to catastrophic forgetting of previously learned skills. In this paper, we introduce M2Distill, a multi-modal distillation-based method for lifelong imitation learning focusing on preserving consistent latent space across vision, language, and action distributions throughout the learning process. By regulating the shifts in latent representations across different modalities from previous to current steps, and reducing discrepancies in Gaussian Mixture Model (GMM) policies between consecutive learning steps, we ensure that the learned policy retains its ability to perform previously learned tasks while seamlessly integrating new skills. Extensive evaluations on the LIBERO lifelong imitation learning benchmark suites, including LIBERO-OBJECT, LIBERO-GOAL, and LIBERO-SPATIAL, demonstrate that our method consistently outperforms prior state-of-the-art methods across all evaluated metrics.<|reference_end|>
|
arxiv
|
@article{roy2024m2distill:,
title={M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning},
author={Kaushik Roy, Akila Dissanayake, Brendan Tidd, Peyman Moghadam},
journal={arXiv preprint arXiv:2410.00064},
year={2024},
archivePrefix={arXiv},
eprint={2410.00064},
primaryClass={cs.LG cs.AI cs.CV cs.RO}
}
|
roy2024m2distill:
|
arxiv-663783
|
2410.00065
|
Conway Normal Form: Bridging Approaches for Comprehensive Formalization of Surreal Numbers
|
<|reference_start|>Conway Normal Form: Bridging Approaches for Comprehensive Formalization of Surreal Numbers: The proper class of Conway's surreal numbers forms a rich totally ordered algebraically closed field with many arithmetic and algebraic properties close to those of real numbers, the ordinals, and infinitesimal numbers. In this paper, we formalize the construction of Conway's numbers in Mizar using two approaches and propose a bridge between them, aiming to combine their advantages for efficient formalization. By replacing transfinite induction-recursion with transfinite induction, we streamline their construction. Additionally, we introduce a method to merge proofs from both approaches using global choice, facilitating formal proof. We demonstrate that surreal numbers form a field, including the square root, and that they encompass subsets such as reals, ordinals, and powers of $\omega$. We combined Conway's work with Ehrlich's generalization to formally prove Conway's Normal Form, paving the way for many formal developments in surreal number theory.<|reference_end|>
|
arxiv
|
@article{pąk2024conway,
title={Conway Normal Form: Bridging Approaches for Comprehensive Formalization
of Surreal Numbers},
author={Karol Pk{a}k and Cezary Kaliszyk},
journal={arXiv preprint arXiv:2410.00065},
year={2024},
doi={10.4230/LIPIcs.ITP.2024.29},
archivePrefix={arXiv},
eprint={2410.00065},
primaryClass={cs.LO}
}
|
pąk2024conway
|
arxiv-663784
|
2410.00066
|
Seasonal Performance Evaluation of a Hybrid PV-Wind-Battery Power System for a Mars Base
|
<|reference_start|>Seasonal Performance Evaluation of a Hybrid PV-Wind-Battery Power System for a Mars Base: This work investigates a hybrid photovoltaic-wind-battery power system designed to sustain a Mars base under varying seasonal and climatic conditions. The Mars Climate Database was utilized to simulate the effects of seasonal changes, diurnal cycles, and dust storms on the system's power generation. The seasonal performance was analyzed across the Martian surface and at potential habitation sites proposed in the "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars (FLSW).'' Within the hybrid system, the photovoltaic arrays serve as the primary energy source, with wind turbines providing essential backup during nighttime and dust storms. A single $1\,000\,\mathrm{m}^2$ photovoltaic array, a $33.4\,\mathrm{m}$ diameter wind turbine, and a $312\,\mathrm{kWh}$ battery can support a six-person Mars base at $32.1\%$ of the Martian surface during the equinoxes and solstices, expanding to $51.7\%$ with three sets of arrays and turbines. Additionally, $24$ FLSW sites can be supported throughout the solstices and equinoxes by a single photovoltaic array, turbine, and battery, even during global dust storms. Among the $24$ sites, Hebrus Valles, Huygens Crater, and Noctis Labyrinthus had the highest energy production potential. These findings are expected to guide further research on hybrid renewable power systems for Mars exploration.<|reference_end|>
|
arxiv
|
@article{darya2024seasonal,
title={Seasonal Performance Evaluation of a Hybrid PV-Wind-Battery Power System
for a Mars Base},
author={Abdollah Masoud Darya, Ramesh C. Bansal, Omaima Anwar Jarndal},
journal={arXiv preprint arXiv:2410.00066},
year={2024},
archivePrefix={arXiv},
eprint={2410.00066},
primaryClass={astro-ph.IM astro-ph.EP cs.SY eess.SY}
}
|
darya2024seasonal
|
arxiv-663785
|
2410.00067
|
Ranking the Top-K Realizations of Stochastically Known Event Logs
|
<|reference_start|>Ranking the Top-K Realizations of Stochastically Known Event Logs: Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realizations encoded by a stochastically known log grows exponentially with its size, making exhaustive exploration infeasible even for moderately sized event logs. Thus, considering only the top-K most probable realizations has been proposed in the literature. In this paper, we implement an efficient algorithm to calculate a top-K realization ranking of an event log under event independence within O(Kn), where n is the number of uncertain events in the log. This algorithm is used to investigate the benefit of top-K rankings over top-1 interpretations of stochastically known event logs. Specifically, we analyze the usefulness of top-K rankings against different properties of the input data. We show that the benefit of a top-K ranking depends on the length of the input event log and the distribution of the event probabilities. The results highlight the potential of top-K rankings to enhance uncertainty-aware process mining techniques.<|reference_end|>
|
arxiv
|
@article{lepsien2024ranking,
title={Ranking the Top-K Realizations of Stochastically Known Event Logs},
author={Arvid Lepsien, Marco Pegoraro, Frederik Fonger, Dominic Langhammer,
Milda Aleknonyt.e-Resch, Agnes Koschmider},
journal={arXiv preprint arXiv:2410.00067},
year={2024},
archivePrefix={arXiv},
eprint={2410.00067},
primaryClass={cs.DB cs.LG}
}
|
lepsien2024ranking
|
arxiv-663786
|
2410.00068
|
Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI
|
<|reference_start|>Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI: Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. The complexity of ASD has resulted in a deficiency of objective diagnostic biomarkers. Deep learning methods have gained recognition for addressing these challenges in neuroimaging analysis, but finding and interpreting such diagnostic biomarkers are still challenging computationally. We propose an ASD feature reduction pipeline using resting-state fMRI (rs-fMRI). We used Ncuts parcellations and Power atlas to extract functional connectivity data, resulting in over 30 thousand features. Then the pipeline further compresses the connectivities into 5 latent Gaussian distributions, providing is a low-dimensional representation of the data, using a denoising variational autoencoder (DVAE). To test the method, we employed the extracted latent features from the DVAE to classify ASD using traditional classifiers such as support vector machine (SVM) on a large multi-site dataset. The 95% confidence interval for the prediction accuracy of the SVM is [0.63, 0.76] after site harmonization using the extracted latent distributions. Without using DVAE, the prediction accuracy is 0.70, which falls within the interval. This implies that the model successfully encodes the diagnostic information in rs-fMRI data to 5 Gaussian distributions (10 features) without sacrificing prediction performance. The runtime for training the DVAE and obtaining classification results from its extracted latent features (37 minutes) was 7 times shorter compared to training classifiers directly on the raw connectivity matrices (5-6 hours). Our findings also suggest that the Power atlas provides more effective brain connectivity insights for diagnosing ASD than Ncuts parcellations. The encoded features can be used for the help of diagnosis and interpretation of the disease.<|reference_end|>
|
arxiv
|
@article{zheng2024denoising,
title={Denoising Variational Autoencoder as a Feature Reduction Pipeline for
the diagnosis of Autism based on Resting-state fMRI},
author={Xinyuan Zheng, Orren Ravid, Robert A.J. Barry, Yoojean Kim, Qian Wang,
Young-geun Kim, Xi Zhu, Xiaofu He},
journal={arXiv preprint arXiv:2410.00068},
year={2024},
archivePrefix={arXiv},
eprint={2410.00068},
primaryClass={eess.IV cs.LG stat.AP}
}
|
zheng2024denoising
|
arxiv-663787
|
2410.00069
|
An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data
|
<|reference_start|>An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data: The digital era has raised many societal challenges, including ICT's rising energy consumption and protecting privacy of personal data processing. This paper considers both aspects in relation to machine learning accuracy in an interdisciplinary exploration. We first present a method to measure the effects of privacy-enhancing techniques on data utility and energy consumption. The environmental-privacy-accuracy trade-offs are discovered through an experimental set-up. We subsequently take a storytelling approach to translate these technical findings to experts in non-ICT fields. We draft two examples for a governmental and auditing setting to contextualise our results. Ultimately, users face the task of optimising their data processing operations in a trade-off between energy, privacy, and accuracy considerations where the impact of their decisions is context-sensitive.<|reference_end|>
|
arxiv
|
@article{de reus2024an,
title={An interdisciplinary exploration of trade-offs between energy, privacy
and accuracy aspects of data},
author={Pepijn de Reus, Kyra Dresen, Ana Oprescu, Kristina Irion, Ans Kolk},
journal={arXiv preprint arXiv:2410.00069},
year={2024},
archivePrefix={arXiv},
eprint={2410.00069},
primaryClass={cs.CR cs.LG}
}
|
de reus2024an
|
arxiv-663788
|
2410.00070
|
Mamba for Streaming ASR Combined with Unimodal Aggregation
|
<|reference_start|>Mamba for Streaming ASR Combined with Unimodal Aggregation: This paper works on streaming automatic speech recognition (ASR). Mamba, a recently proposed state space model, has demonstrated the ability to match or surpass Transformers in various tasks while benefiting from a linear complexity advantage. We explore the efficiency of Mamba encoder for streaming ASR and propose an associated lookahead mechanism for leveraging controllable future information. Additionally, a streaming-style unimodal aggregation (UMA) method is implemented, which automatically detects token activity and streamingly triggers token output, and meanwhile aggregates feature frames for better learning token representation. Based on UMA, an early termination (ET) method is proposed to further reduce recognition latency. Experiments conducted on two Mandarin Chinese datasets demonstrate that the proposed model achieves competitive ASR performance in terms of both recognition accuracy and latency.<|reference_end|>
|
arxiv
|
@article{fang2024mamba,
title={Mamba for Streaming ASR Combined with Unimodal Aggregation},
author={Ying Fang, Xiaofei Li},
journal={arXiv preprint arXiv:2410.00070},
year={2024},
archivePrefix={arXiv},
eprint={2410.00070},
primaryClass={eess.AS cs.CL cs.SD}
}
|
fang2024mamba
|
arxiv-663789
|
2410.00071
|
Proceedings of the 22nd International Overture Workshop
|
<|reference_start|>Proceedings of the 22nd International Overture Workshop: This volume contains the papers presented at the 22nd International Overture Workshop, held on the 10th of September 2024. This event was the latest in a series of workshops around the Vienna Development Method (VDM), the open-source project Overture, and related tools and formalisms. VDM is one of the longest established formal methods for systems development. A lively community of researchers and practitioners has grown up in academia and industry has grown around the modelling languages (VDM-SL, VDM++, VDM-RT, CML) and tools (VDMTools, Overture, Crescendo, Symphony, the INTO-CPS chain, and ViennaTalk). Together, these provide a platform for work on modelling and analysis technology that includes static and dynamic analysis, test generation, execution support, and model checking. This workshop provided updates on the emerging technology of VDM/Overture, including collaboration infrastructure, collaborative modelling and co-simulation for Cyber-Physical Systems.<|reference_end|>
|
arxiv
|
@article{macedo2024proceedings,
title={Proceedings of the 22nd International Overture Workshop},
author={Hugo Daniel Macedo, Ken Pierce and Leo Freitas},
journal={arXiv preprint arXiv:2410.00071},
year={2024},
archivePrefix={arXiv},
eprint={2410.00071},
primaryClass={cs.SE}
}
|
macedo2024proceedings
|
arxiv-663790
|
2410.00074
|
Collaborative Knowledge Distillation via a Learning-by-Education Node Community
|
<|reference_start|>Collaborative Knowledge Distillation via a Learning-by-Education Node Community: A novel Learning-by-Education Node Community framework (LENC) for Collaborative Knowledge Distillation (CKD) is presented, which facilitates continual collective learning through effective knowledge exchanges among diverse deployed Deep Neural Network (DNN) peer nodes. These DNNs dynamically and autonomously adopt either the role of a student, seeking knowledge, or that of a teacher, imparting knowledge, fostering a collaborative learning environment. The proposed framework enables efficient knowledge transfer among participating DNN nodes as needed, while enhancing their learning capabilities and promoting their collaboration. LENC addresses the challenges of handling diverse training data distributions and the limitations of individual DNN node learning abilities. It ensures the exploitation of the best available teacher knowledge upon learning a new task and protects the DNN nodes from catastrophic forgetting. Additionally, it innovates by enabling collaborative multitask knowledge distillation, while addressing the problem of task-agnostic continual learning, as DNN nodes have no information on task boundaries. Experimental evaluation on a proof-of-concept implementation demonstrates the LENC framework's functionalities and benefits across multiple DNN learning and inference scenarios. The conducted experiments showcase its ability to gradually maximize the average test accuracy of the community of interacting DNN nodes in image classification problems, by appropriately leveraging the collective knowledge of all node peers. The LENC framework achieves state-of-the-art performance in on-line unlabelled CKD.<|reference_end|>
|
arxiv
|
@article{kaimakamidis2024collaborative,
title={Collaborative Knowledge Distillation via a Learning-by-Education Node
Community},
author={Anestis Kaimakamidis, Ioannis Mademlis, Ioannis Pitas},
journal={arXiv preprint arXiv:2410.00074},
year={2024},
archivePrefix={arXiv},
eprint={2410.00074},
primaryClass={cs.LG}
}
|
kaimakamidis2024collaborative
|
arxiv-663791
|
2410.00075
|
Optimizing Treatment Allocation in the Presence of Interference
|
<|reference_start|>Optimizing Treatment Allocation in the Presence of Interference: In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effect. For instance, in marketing, the objective is to target the set of customers that maximizes the total response rate, resulting from both direct treatment effects on targeted customers and indirect, spillover, effects that follow from targeting these customers. Recently, new methods to estimate treatment effects in the presence of network interference have been proposed. However, the issue of how to leverage these models to make better treatment allocation decisions has been largely overlooked. Traditionally, in Uplift Modeling (UM), entities are ranked according to estimated treatment effect, and the top entities are allocated treatment. Since, in a network context, entities influence each other, the UM ranking approach will be suboptimal. The problem of finding the optimal treatment allocation in a network setting is combinatorial and generally has to be solved heuristically. To fill the gap between IM and UM, we propose OTAPI: Optimizing Treatment Allocation in the Presence of Interference to find solutions to the IM problem using treatment effect estimates. OTAPI consists of two steps. First, a causal estimator is trained to predict treatment effects in a network setting. Second, this estimator is leveraged to identify an optimal treatment allocation by integrating it into classic IM algorithms. We demonstrate that this novel method outperforms classic IM and UM approaches on both synthetic and semi-synthetic datasets.<|reference_end|>
|
arxiv
|
@article{caljon2024optimizing,
title={Optimizing Treatment Allocation in the Presence of Interference},
author={Daan Caljon, Jente Van Belle, Jeroen Berrevoets, Wouter Verbeke},
journal={arXiv preprint arXiv:2410.00075},
year={2024},
archivePrefix={arXiv},
eprint={2410.00075},
primaryClass={cs.SI cs.LG stat.ML}
}
|
caljon2024optimizing
|
arxiv-663792
|
2410.00078
|
Shuffled Linear Regression via Spectral Matching
|
<|reference_start|>Shuffled Linear Regression via Spectral Matching: Shuffled linear regression (SLR) seeks to estimate latent features through a linear transformation, complicated by unknown permutations in the measurement dimensions. This problem extends traditional least-squares (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) approaches by jointly estimating the permutation, resulting in shuffled LS and shuffled LASSO formulations. Existing methods, constrained by the combinatorial complexity of permutation recovery, often address small-scale cases with limited measurements. In contrast, we focus on large-scale SLR, particularly suited for environments with abundant measurement samples. We propose a spectral matching method that efficiently resolves permutations by aligning spectral components of the measurement and feature covariances. Rigorous theoretical analyses demonstrate that our method achieves accurate estimates in both shuffled LS and shuffled LASSO settings, given a sufficient number of samples. Furthermore, we extend our approach to address simultaneous pose and correspondence estimation in image registration tasks. Experiments on synthetic datasets and real-world image registration scenarios show that our method outperforms existing algorithms in both estimation accuracy and registration performance.<|reference_end|>
|
arxiv
|
@article{liu2024shuffled,
title={Shuffled Linear Regression via Spectral Matching},
author={Hang Liu and Anna Scaglione},
journal={arXiv preprint arXiv:2410.00078},
year={2024},
archivePrefix={arXiv},
eprint={2410.00078},
primaryClass={math.ST cs.IT cs.LG eess.SP math.IT math.SP stat.ML stat.TH}
}
|
liu2024shuffled
|
arxiv-663793
|
2410.00079
|
Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface
|
<|reference_start|>Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface: Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language models (LLMs) often face substantial planning latency due to two primary factors: the efficiency limitations of the underlying LLMs due to their large size and high demand, and the structural complexity of the agents due to the extensive generation of intermediate thoughts to produce the final output. Given that inefficiency in service provision can undermine the value of automation for users, this paper presents a human-centered efficient agent planning method -- Interactive Speculative Planning -- aiming at enhancing the efficiency of agent planning through both system design and human-AI interaction. Our approach advocates for the co-design of the agent system and user interface, underscoring the importance of an agent system that can fluidly manage user interactions and interruptions. By integrating human interruptions as a fundamental component of the system, we not only make it more user-centric but also expedite the entire process by leveraging human-in-the-loop interactions to provide accurate intermediate steps. Code and data will be released.<|reference_end|>
|
arxiv
|
@article{hua2024interactive,
title={Interactive Speculative Planning: Enhance Agent Efficiency through
Co-design of System and User Interface},
author={Wenyue Hua, Mengting Wan, Shashank Vadrevu, Ryan Nadel, Yongfeng
Zhang, and Chi Wang},
journal={arXiv preprint arXiv:2410.00079},
year={2024},
archivePrefix={arXiv},
eprint={2410.00079},
primaryClass={cs.MA cs.AI cs.CL cs.HC cs.LG}
}
|
hua2024interactive
|
arxiv-663794
|
2410.00081
|
From homeostasis to resource sharing: Biologically and economically compatible multi-objective multi-agent AI safety benchmarks
|
<|reference_start|>From homeostasis to resource sharing: Biologically and economically compatible multi-objective multi-agent AI safety benchmarks: Developing safe agentic AI systems benefits from automated empirical testing that conforms with human values, a subfield that is largely underdeveloped at the moment. To contribute towards this topic, present work focuses on introducing biologically and economically motivated themes that have been neglected in the safety aspects of modern reinforcement learning literature, namely homeostasis, balancing multiple objectives, bounded objectives, diminishing returns, sustainability, and multi-agent resource sharing. We implemented eight main benchmark environments on the above themes, for illustrating the potential shortcomings of current mainstream discussions on AI safety.<|reference_end|>
|
arxiv
|
@article{pihlakas2024from,
title={From homeostasis to resource sharing: Biologically and economically
compatible multi-objective multi-agent AI safety benchmarks},
author={Roland Pihlakas, Joel Pyykk"o},
journal={arXiv preprint arXiv:2410.00081},
year={2024},
archivePrefix={arXiv},
eprint={2410.00081},
primaryClass={cs.MA cs.AI}
}
|
pihlakas2024from
|
arxiv-663795
|
2410.00082
|
Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction
|
<|reference_start|>Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction: A morphological brain graph depicting a connectional fingerprint is of paramount importance for charting brain dysconnectivity patterns. Such data often has missing observations due to various reasons such as time-consuming and incomplete neuroimage processing pipelines. Thus, predicting a target brain graph from a source graph is crucial for better diagnosing neurological disorders with minimal data acquisition resources. Many brain graph generative models were proposed for promising results, yet they are mostly based on generative adversarial networks (GAN), which could suffer from mode collapse and require large training datasets. Recent developments in diffusion models address these problems by offering essential properties such as a stable training objective and easy scalability. However, applying a diffusion process to graph edges fails to maintain the topological symmetry of the brain connectivity matrices. To meet these challenges, we propose the Graph Residual Noise Learner Network (Grenol-Net), the first graph diffusion model for predicting a target graph from a source graph.<|reference_end|>
|
arxiv
|
@article{demirbilek2024graph,
title={Graph Residual Noise Learner Network for Brain Connectivity Graph
Prediction},
author={Oytun Demirbilek, Tingying Peng, and Alaa Bessadok},
journal={arXiv preprint arXiv:2410.00082},
year={2024},
archivePrefix={arXiv},
eprint={2410.00082},
primaryClass={cs.SI cs.AI cs.CV cs.LG}
}
|
demirbilek2024graph
|
arxiv-663796
|
2410.00083
|
A Survey on Diffusion Models for Inverse Problems
|
<|reference_start|>A Survey on Diffusion Models for Inverse Problems: Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for solving inverse problems, especially in image restoration and reconstruction, by treating diffusion models as unsupervised priors. This survey provides a comprehensive overview of methods that utilize pre-trained diffusion models to solve inverse problems without requiring further training. We introduce taxonomies to categorize these methods based on both the problems they address and the techniques they employ. We analyze the connections between different approaches, offering insights into their practical implementation and highlighting important considerations. We further discuss specific challenges and potential solutions associated with using latent diffusion models for inverse problems. This work aims to be a valuable resource for those interested in learning about the intersection of diffusion models and inverse problems.<|reference_end|>
|
arxiv
|
@article{daras2024a,
title={A Survey on Diffusion Models for Inverse Problems},
author={Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong
Chul Ye, Peyman Milanfar, Alexandros G. Dimakis, Mauricio Delbracio},
journal={arXiv preprint arXiv:2410.00083},
year={2024},
archivePrefix={arXiv},
eprint={2410.00083},
primaryClass={cs.LG cs.AI cs.CV}
}
|
daras2024a
|
arxiv-663797
|
2410.00085
|
Fine-tuning Vision Classifiers On A Budget
|
<|reference_start|>Fine-tuning Vision Classifiers On A Budget: Fine-tuning modern computer vision models requires accurately labeled data for which the ground truth may not exist, but a set of multiple labels can be obtained from labelers of variable accuracy. We tie the notion of label quality to confidence in labeler accuracy and show that, when prior estimates of labeler accuracy are available, using a simple naive-Bayes model to estimate the true labels allows us to label more data on a fixed budget without compromising label or fine-tuning quality. We present experiments on a dataset of industrial images that demonstrates that our method, called Ground Truth Extension (GTX), enables fine-tuning ML models using fewer human labels.<|reference_end|>
|
arxiv
|
@article{kumar2024fine-tuning,
title={Fine-tuning Vision Classifiers On A Budget},
author={Sunil Kumar, Ted Sandler, Paulina Varshavskaya},
journal={arXiv preprint arXiv:2410.00085},
year={2024},
archivePrefix={arXiv},
eprint={2410.00085},
primaryClass={cs.LG cs.CV}
}
|
kumar2024fine-tuning
|
arxiv-663798
|
2410.00086
|
ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer
|
<|reference_start|>ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer: Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primarily designed for text-guided visual generation and do not support multi-modal conditions, which are essential for many visual editing tasks. This limitation prevents these foundational diffusion models from serving as a unified model in the field of visual generation, like GPT-4 in the natural language processing field. In this work, we propose ACE, an All-round Creator and Editor, which achieves comparable performance compared to those expert models in a wide range of visual generation tasks. To achieve this goal, we first introduce a unified condition format termed Long-context Condition Unit (LCU), and propose a novel Transformer-based diffusion model that uses LCU as input, aiming for joint training across various generation and editing tasks. Furthermore, we propose an efficient data collection approach to address the issue of the absence of available training data. It involves acquiring pairwise images with synthesis-based or clustering-based pipelines and supplying these pairs with accurate textual instructions by leveraging a fine-tuned multi-modal large language model. To comprehensively evaluate the performance of our model, we establish a benchmark of manually annotated pairs data across a variety of visual generation tasks. The extensive experimental results demonstrate the superiority of our model in visual generation fields. Thanks to the all-in-one capabilities of our model, we can easily build a multi-modal chat system that responds to any interactive request for image creation using a single model to serve as the backend, avoiding the cumbersome pipeline typically employed in visual agents. Code and models will be available on the project page: https://ali-vilab.github.io/ace-page/.<|reference_end|>
|
arxiv
|
@article{han2024ace:,
title={ACE: All-round Creator and Editor Following Instructions via Diffusion
Transformer},
author={Zhen Han, Zeyinzi Jiang, Yulin Pan, Jingfeng Zhang, Chaojie Mao,
Chenwei Xie, Yu Liu, Jingren Zhou},
journal={arXiv preprint arXiv:2410.00086},
year={2024},
archivePrefix={arXiv},
eprint={2410.00086},
primaryClass={cs.CV cs.AI}
}
|
han2024ace:
|
arxiv-663799
|
2410.00117
|
An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
|
<|reference_start|>An Overview of the Burer-Monteiro Method for Certifiable Robot Perception: This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discusses BM in certifiable perception, with three main objectives: (i) to consolidate information from the literature into a unified presentation, (ii) to elucidate the role of the linear independence constraint qualification (LICQ), a concept not yet well-covered in certifiable perception literature, and (iii) to share practical considerations that are discussed among practitioners but not thoroughly covered in the literature. Our general aim is to offer a practical primer for applying BM towards certifiable perception.<|reference_end|>
|
arxiv
|
@article{papalia2024an,
title={An Overview of the Burer-Monteiro Method for Certifiable Robot
Perception},
author={Alan Papalia, Yulun Tian, David M. Rosen, Jonathan P. How, John J.
Leonard},
journal={arXiv preprint arXiv:2410.00117},
year={2024},
archivePrefix={arXiv},
eprint={2410.00117},
primaryClass={cs.RO cs.CV cs.LG}
}
|
papalia2024an
|
arxiv-663800
|
2410.00120
|
Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles
|
<|reference_start|>Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles: Controlling AUVs can be challenging because of the effect of complex non-linear hydrodynamic forces acting on the robot, which, unlike ground robots, are significant in water and cannot be ignored. The problem is especially challenging for small AUVs for which the dynamics can change significantly with payload changes and deployments under different water conditions. The common approach to AUV control is a combination of passive stabilization with added buoyancy on top and weights on the bottom, and a PID controller tuned for simple and smooth motion primitives. However, the approach comes at the cost of sluggish controls and often the need to re-tune controllers with configuration changes. We propose a fast (trainable in minutes), reinforcement learning based approach for full 6 degree of freedom (DOF) control of an AUV, enabled by a new, highly parallelized simulator for underwater vehicle dynamics. We demonstrate that the proposed simulator models approximate hydrodynamic forces with enough accuracy that a zero-shot transfer of the learned policy to a real robot produces performance comparable to a hand-tuned PID controller. Furthermore, we show that domain randomization on the simulator produces policies that are robust to small variations in vehicle's physical parameters.<|reference_end|>
|
arxiv
|
@article{cai2024learning,
title={Learning to Swim: Reinforcement Learning for 6-DOF Control of
Thruster-driven Autonomous Underwater Vehicles},
author={Levi Cai, Kevin Chang, Yogesh Girdhar},
journal={arXiv preprint arXiv:2410.00120},
year={2024},
archivePrefix={arXiv},
eprint={2410.00120},
primaryClass={cs.RO}
}
|
cai2024learning
|
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