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arXiv 2024年

作者： Demaine, Erik D. Langerman, Stefan Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Computer Science Department Université libre de Bruxelles Belgium

We prove that the following problem is co-RE-complete and thus undecidable: given three simple polygons, is there a tiling of the plane where every tile is an isometry of one of the three polygons (either allowing or forbidding reflections)? This result improves on the best previous construction which requires five polygons. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation 4

VRLVMix: Combating Noisy Labels with Sample Selection based ...

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4th International Conference on Big Data, artificial intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Wang, Hailun Tu, Zhengzheng Jiang, Bo Ding, Yuhe Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI Ai Joint Laboratory Hefei China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350343618

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss 'hard' samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. This method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of 'hard' samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact 1 of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample. © 2023 IEEE.

关键词： Semantics

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CONTINUAL IMPROVEMENT OF THRESHOLD-BASED NOVELTY DETECTION

arXiv

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arXiv 2023年

作者： Ejilemele, Abe Mendez-Mendez, Jorge Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

When evaluated in dynamic, open-world situations, neural networks struggle to detect unseen classes. This issue complicates the deployment of continual learners in realistic environments where agents are not explicitly informed when novel categories are encountered. A common family of techniques for detecting novelty relies on thresholds of similarity between observed data points and the data used for training. However, these methods often require manually specifying (ahead of time) the value of these thresholds, and are therefore incapable of adapting to the nature of the data. We propose a new method for automatically selecting these thresholds utilizing a linear search and leave-one-out cross-validation on the ID classes. We demonstrate that this novel method for selecting thresholds results in improved total accuracy on MNIST, Fashion MNIST, and CIFAR-10. © 2023, CC BY.

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Radar2ECG: Multi-Scale Bottleneck Fusion and Cross-modal Semantic Distillation for Conditional Electrocardiogram Generation from Radar Heart Sound

Radar2ECG: Multi-Scale Bottleneck Fusion and Cross-modal Sem...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Jinye Men, Aidong Liu, Yang Han, Pengda Chen, Qingchao School of Artificial Intelligence Beijing University of Post and Telecommunications China Wangxuan Institute of Computer Technology Peking University China National Institute of Health Data Science Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Beijing Emergency Medical Center China Institute of Medical Technology Peking University China

ISBN: (纸本)9798350368741

The field of conditional Electrocardiogram(ECG) generation focuses on generating specified ECGs under given conditions for medical purposes. Existing methods are typically based on conditions of simple inputs like text or lead types. However, they struggle to handle the complexity of radar heart sound signals due to the lack of effective feature extraction, which hinders capturing the intricate waveform correlations between radar heart sounds and ECGs. Considering that radar-detected heart sound signals are contactless, the application is of essential value in a real-world deployment like sleep scenarios. Moreover, no prior approaches have addressed this specific task. To tackle this challenge, we propose a novel multi-scale feature fusion network framework, Radar2ECG. This model leverages pre-trained autoencoders for heart sound and ECG signals, aligning and integrating multi-layer features through a bottleneck structure to enhance receptive fields and reduce redundant features, thereby capturing the correlations between heart sounds and ECGs. Finally, we employ knowledge distillation to transfer knowledge from the ECG decoder to the heart sound decoder. We present three anomaly type datasets and extensive experiments conducted on both normal and abnormal datasets demonstrate that our method outperforms existing models in both accuracy and robustness. The multi-scale feature fusion significantly improves performance, showcasing strong potential in ECG generation and heart sound anomaly detection tasks. © 2025 IEEE.

关键词： ECG synthesis knowledge distillation Radar-to-ECG

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Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems

arXiv

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arXiv 2023年

作者： Han, Weiqiao Jasour, Ashkan Williams, Brian Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

In this paper, we consider the closed-loop control problem of nonlinear robotic systems in the presence of probabilistic uncertainties and disturbances. More precisely, we design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. Existing approaches to address the control problem of probabilistic systems are limited to particular classes of uncertainties and systems such as Gaussian uncertainties and processes and linearized systems. We present an approach that deals with nonlinear dynamics models and arbitrary known probabilistic uncertainties. We formulate the controller design problem as an optimization problem in terms of statistics of the probability distributions including moments and characteristic functions. In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems. In order to reduce the tracking deviations, we minimize the uncertainty of the probabilistic states around the nominal trajectory by minimizing the trace and the determinant of the covariance matrix of the probabilistic states. To obtain the state feedback gains, we solve deterministic optimization problems in terms of moments, characteristic functions, and state feedback gains using off-the-shelf interior-point optimization solvers. To illustrate the performance of the proposed method, we compare our method with existing probabilistic control methods. © 2023, CC BY.

关键词： Optimization

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks

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Journal of Systems science & Complexity 2022年第6期35卷 2062-2076页

作者： FU Weiming QIN Jiahu LING Qing KANG Yu YE Baijia Department of Automation University of Science and Technology of ChinaHefei 230027China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-Sen UniversityGuangzhou 510006China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230027China

Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient *** stochastic variational inference can only be performed in a centralized manner,which limits its applications in a wide range of situations where data is possessed by multiple ***,this paper develops a novel trust-region based stochastic variational inference algorithm for a general class of conjugate-exponential models over distributed and asynchronous networks,where the global parameters are diffused over the network by using the Metropolis rule and the local parameters are updated by using the trust-region ***,a simple rule is introduced to balance the transmission frequencies between neighboring nodes such that the proposed distributed algorithm can be performed in an asynchronous *** utility of the proposed algorithm is tested by fitting the Bernoulli model and the Gaussian model to different datasets on a synthetic network,and experimental results demonstrate its effectiveness and advantages over existing works.

关键词： Asynchronous networks Bayesian inference distributed algorithm stochastic variational inference trust-region method

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FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and Data Heterogeneity 16

FedDGL: Federated Dynamic Graph Learning for Temporal Evolut...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning Knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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