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arXiv 2021年

作者： Zhang, Jiawei Ge, Songyang Chang, Tsung-Hui Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The Guangdong Provincial Key Laboratory of Big Data Computing The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-agent network. In the considered problem, each agent owns some local information and a local variable for jointly minimizing a cost function, but local variables are coupled by linear constraints. Most of the existing methods for such problems are only applicable for convex problems or problems with specific linear constraints. There still lacks a distributed algorithm for solving such problems with general linear constraints under the nonconvex setting. To tackle this problem, we propose a new algorithm, called proximal dual consensus (PDC) algorithm, which combines a proximal technique and a dual consensus method. We show that under certain conditions the proposed PDC algorithm can generate an ∊-Karush-Kuhn-Tucker solution in O(1/∊) iterations, achieving the lower bound for distributed non-convex problems up to a constant. Numerical results are presented to demonstrate the good performance of the proposed algorithms for solving two vertical learning problems in machine learning over a multi-agent network. © 2021, CC BY.

关键词： Cost functions

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Partial-order Algorithm of Model Checking in µ-Predicate Ambient Logic

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Journal of Computers (Taiwan) 2018年第5期29卷 142-159页

作者： Jiang, Hua Lin, Rong-De Zou, Fu-Min Li, Ling-Xiang Key Lab of Granular Computing Minnan Normal University ZhangZhou China School of Mathematical Science Huaqiao university QuanZhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou Fujian350008 China School of Electronics and Information Engineering Hunan University of Science and Engineering Yongzhou425199 China

This paper introduces a kind of partial-order algorithm of model-checking in finite-control mobile ambients against μ-predicate ambient logic(Ambient logic based on first-order μ-calculus). Based on Tarski's fixpoint theorem, nested predicate equations and Block Dependency Graph, a group of partial ordering relation between inter-mediate results in the process of model checking is given, and thus a kind of local model-checking algorithm is proposed. To our knowledge, this is the first algorithm which exponent of time complexity is d/2+2, the exponent of space complexity is d/2 (d is the alternate nesting depth of fixpoint operator in the formula) and this is the third model-checking algorithm for predicate ambient logic with recursion. This paper's contributions are: (1) getting a group of partial ordering relation between intermediate results in the computing process of model checking for µ-calculus first-order predicate ambient logic based on Tarski's fixpoint theorem;(2) using the partial ordering relation to design an algorithm for model checking in mobile ambients;(3) analyzing the complexity of the algorithm. © 2018 Computer Society of the Republic of China. All rights reserved.

关键词： Model checking

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Large Language Models as Evolutionary Optimizers 13

Large Language Models as Evolutionary Optimizers

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Shengcai Chen, Caishun Qu, Xinghua Tang, Ke Ong, Yew-Soon Southern University of Science and Technology Centre for Frontier Ai Research A∗star Department of Computer Science and Engineering China Centre for Frontier Ai Research A∗star Singapore Tianqiao & Chrissy Chen Institute China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering China Nanyang Technological University Centre for Frontier Ai Research A∗star College of Computing and Data Science Singapore

ISBN: (纸本)9798350308365

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges. © 2024 IEEE.

关键词： Traveling salesman problem

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Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

arXiv

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arXiv 2022年

作者： Lou, Wei Li, Haofeng Li, Guanbin Han, Xiaoguang Wan, Xiang Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Pazhou Lab Guangzhou510330 China

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Spatial-Temporal Context-Aware Location Prediction Based on Bidirectional Self-Attention Network

Spatial-Temporal Context-Aware Location Prediction Based on ...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Kuijie Lin Junxin Chen Xiaoqin Lian Weimin Mai Zhiheng Guo Xiang Chen Terng-Yin Hsu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China Department of Computer Science National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781665450867

The next-location prediction tasks get much attention because it is employed in many applications. The accuracy of location prediction has become the basis of these applications. The existing approaches related rely on transition matrices according to specific probabilistic rules or recurrent neural networks that cannot be applied to complex scenarios. Other works focus on extracting extra information in trajectory. In this paper, we propose a context-aware model with a bidirectional self-attention network for location prediction, which can capture implicit spatial-temporal patterns from the time stamps and geographical distances of locations. Besides, a training mechanism, Mask Locations, is employed to improve the prediction accuracy. We conduct experiments on two large-scale datasets: a check-in dataset and a Call Detail Record (CDR) dataset. The results show that our model significantly outperforms the competitive baseline methods.

关键词： Training Wireless communication Recurrent neural networks Predictive models Signal processing Probabilistic logic Trajectory

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale for Diagnosing Alzheimer’s Disease

Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale fo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaolu Lin Pinya Lu Jie Pan Hongqin Yang Xuemei Ding Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Provincial University Engineering Research Center for Big Data Analysis and Application Fujian Normal University Fuzhou China Xiamen Jingyi Zhikang Technology Co. Ltd Xiamen China Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computing Engineering and Intelligent Systems Ulster University Derry-Londonderry NI UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases.

关键词： Neuroimaging Accuracy Attention mechanisms Three-dimensional displays Resonance Convolutional neural networks Alzheimer's disease

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FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

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Research in Astronomy and Astrophysics 2024年第10期24卷 130-140页

作者： Long-Fei Chen Chao-Wei Tsai Jian-Yang Li Bin Yang Di Li Yan Duan Chih-Hao Hsia Zhichen Pan Lei Qian Donghui Quan Xue-Jian Jiang Xiaohu Li Ruining Zhao Pei Zuo School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550025China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082China Núcleo de Astronomía Facultad de Ingenieríay CienciasUniversidad Diego PortalesChile Department of Astronomy College of Physics and Electronic EngineeringQilu Normal UniversityJinan 250200China Department of Electronic and Optical Engineering Space Engineering UniversityBeijing 101416China Laboratory for Space Research Faculty of ScienceThe University of Hong KongHong Kong(SAR)China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China

We used the Five-hundred-meter Aperture Spherical radio Telescope(FAST)to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets,C/2020 F3(NEOWISE),C/2020 R4(ATLAS),C/2021 A1(Leonard),and 67P/Churyumov-Gerasimenko during or after their perihelion *** of molecular transition lines fall in this low-frequency range,many attributed to complex organic or prebiotic *** conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the *** several molecules have been detected at high frequencies of greater than100 GHz in comets,our results confirm that it is challenging to detect molecular transitions in the L-band frequency *** non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines,which could be caused by the masers or non-LTE *** the line strengths are predicted to be weak,for FAST,using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future.

关键词： astrochemistry ISM:molecules comets:general line:identification

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SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION WITH CO-DISTRIBUTION ALIGNMENT

arXiv

引用

arXiv 2023年

作者： Wang, Tao Huang, Zhongzheng Wu, Jiawei Cai, Yuanzheng Li, Zuoyong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University China College of Computer and Data Science Fuzhou University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China School of Electrical and Mechanical Engineering Fujian Agriculture and Forestry University China

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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