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arXiv 2024年

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen School of Computer Science and Technology UCAS United Kingdom Sino-Danish College UCAS United Kingdom Department of Computer Science NCSU United States School of Economics and Management UCAS United Kingdom Research Center on Fictitious Economy and Data Science CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS United Kingdom Institute of Computing Technology CAS China

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D4M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

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Energy-Efficient Over-the-Air Computation in UAV-Assisted IIoT Networks

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Yali Sun, Sheng Liu, Min Ai, Bo Wang, Yuwei Liu, Yunhao Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Zhongguancun Laboratory Beijing China University of Chinese Academy of Sciences Beijing100049 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Zhengzhou University Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou450001 China Peng Cheng Laboratory Research Center of Networks and Communications Shenzhen518055 China Tsinghua University Beijing100084 China

In remote industrial internet of things (IIoT) monitoring systems, the unmanned aerial vehicle (UAV) serves as supplementary infrastructure to aggregate data from a large number of distributed sensors, and achieve industrial operation intelligence. In the wireless data aggregation process, using conventional orthogonal multiple access techniques face challenges such as scarce bandwidth, high communication latency and energy consumption. To tackle these issues, the over-the-air computation (AirComp) technique has emerged. It allows concurrent data transmissions from sensors, as well as integrates communication and computation processes, ultimately enabling fast data aggregation. However, the energy consumption issue remains unresolved. In this paper, we exploit spatial correlations among sensor measurements, and design an energy-efficient AirComp in UAV-assisted IIoT networks, where only a subset of sensors transmit data instead of all sensors. Then, we derive a closed-form expression for the mean square error (MSE) of each combination under a specific number of sensor transmissions. By jointly optimizing the UAV deployment and pre-coding coefficients of sensors, we formulate the problem of minimizing MSE for each combination of transmitted sensors. Furthermore, the MSE optimization algorithm is developed to output the average MSE of all combinations. Finally, we evaluate the average MSE and network lifetime performance of proposed scheme. © 2002-2012 IEEE.

关键词： Mean square error

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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A novel noise reduction and interaction enrichment recommendation model via contrastive learning

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Neurocomputing 2025年 645卷

作者： Zhang, Zhiqiang Wu, Qi Deng, Jiangzhou Ye, Jianmei Yang, Fang Wang, Yong Khushvakhtzoda, Kobiljon Kh. Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing400065 China Research Center of Digital Intelligence Technology Innovation and Industry Development Chongqing University of Posts and Telecommunications Chongqing400065 China Mashang Consumer Finance Co. Ltd. Chongqing400065 China National Academy of Sciences of Tajikistan Dushanbe734025 Tajikistan

To address the challenge of noisy data in recommendation systems, we propose a novel model that combines adaptive denoising with structural similarity-based interaction filling to improve recommendation accuracy. Our method dynamically prunes noisy interactions using a threshold-based denoising strategy without requiring auxiliary information. To compensate for information loss and preserve data sparsity, we introduce a structural similarity-based approach that fills pruned interactions with high-reliability user–item pairs, computed via cosine similarity. We further enhance representation learning via a contrastive learning framework that separates reliable and unreliable interactions based on mutual information maximization. Experimental results on three public datasets demonstrate that our model outperforms state-of-the-art baselines, achieving up to 4.5% and 6.7% improvements in Recall@5 and NDCG@5, respectively, confirming its effectiveness and practical applicability. © 2025 Elsevier B.V.

关键词： Contrastive Learning

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How to use open-pFind in deep proteomics data analysis?—A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data

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Biophysics Reports 2021年第3期7卷 207-226页

作者： Guangcan Shao Yong Cao Zhenlin Chen Chao Liu Shangtong Li Hao Chi Meng-Qiu Dong School of Life Sciences Peking University National Institute of Biological Sciences Beijing Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CASInstitute of Computing TechnologyCAS University of Chinese Academy of Sciences Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang University

High-throughput proteomics based on mass spectrometry（MS） analysis has permeated biomedical science and propelled numerous research projects. p Find 3 is a database search engine for high-speed and in-depth proteomics data analysis. p Find 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using p Find 3 in conjunction with p Parse for data pre-processing and if needed, p Quant for quantitation. This streamlined p Parse-p Findp Quant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on15N-labeling, SILAC-labeling, or TMT/i TRAQ labeling.

关键词： Mass spectrometry Protein identification Search engine Open-pFind Quantitation

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Collaborative Scheduling of computing Tasks for Edge computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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2021 China Automation Congress, CAC 2021

作者： Liu, Yue Xiong, Gang Zhu, Fenghua Chen, Shichao Zhang, Liguo Liu, Xudong School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation The Cloud Computing Center Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verify the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network. © 2021 IEEE

关键词： Edge computing

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Discretizing Continuous Action Space With Unimodal Probability Distributions for On-Policy Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11285-11297页

作者： Yuanyang Zhu Zhi Wang Yuanheng Zhu Chunlin Chen Dongbin Zhao Department of Control Science and Intelligence Engineering School of Management and Engineering and the Laboratory of Data Intelligence and Interdisciplinary Innovation Nanjing University Nanjing China Department of Control Science and Intelligence Engineering School of Management and Engineering and the Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

For on-policy reinforcement learning (RL), discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient (PG) estimator. In this article, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy RL algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the PG estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process.

关键词： Task analysis Probability distribution Aerospace electronics Reinforcement learning Gaussian distribution Technological innovation Learning systems

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Searching for a new probability distribution for modeling non-scale-free heavy-tailed real-world networks

TechRxiv

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TechRxiv 2022年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Kumar, Uttam Jayavelu, Senthilnath Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore560100 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India Department of Data Science S P Jain School of Global Management Mumbai400070 India Institute for Infocomm Research Agency for Science Technology and Research 138632 Singapore

Perhaps the most controversial topic in network science research is whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] claimed that the degree distributions of real-world networks are rarely power-law under statistical tests. Such complex networks include social, biological, information, temporal, and brain networks are often heavy-tailed where the assumption on scale-free nature of real-world heavy-tailed networks becomes insignificant so as the complex system evolves over time. The failure of power-law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity in the entire degree distribution in a log-log scale of a heavy-tailed complex network. Here, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions, for modeling the entire degree distributions of complex networks and capturing the non-linearity of these heavy-tailed networks. The generalized Lomax Model is introduced to fit the degree distributions of non-scale-free real-world networks and applied to model a wide variety of large-scale real-world complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties are derived into this context. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world data sets in comparison with the state-of-the-art. © 2022, CC BY.

关键词： Statistical mechanics

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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

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17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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