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arXiv 2022年

作者： Zhou, Yue Liu, Zhigang Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The Chongqing School University of Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model. Finally, experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency. © 2022, CC BY-NC-ND.

关键词： Multivariant analysis

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Precision Medicine:What Challenges Are We Facing?

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Genomics, Proteomics & Bioinformatics 2016年第5期14卷 253-261页

作者： Yu Xue Eric-Wubbo Lameijer Kai Ye Kunlin Zhang Suhua Chang Xiaoyue Wang Jianmin Wu Ge Gao Fangqing Zhao Jian Li Chunsheng Han Shuhua Xu Jingfa Xiao Xuerui Yang Xiaomin Ying Xuegong Zhang Wei-Hua Chen Yun Liu Zhang Zhang Kun Huang Jun Yu MOE Key Lahoratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Brain ScienceHuazhong University of Science and TechnologyWuhan 430074China School of Electronic and Information Engineering.Xi'an Jiaotong University.Xi'an 710049 China CAS Key Laboratory of Mental Health Institute of PsychologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of'Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100005China MOE Beijing Key Laboratory of Carcinogenesis and Translatiooal ResearchCenter for Cancer BioinformaticsPeking University Cancer Hospital&InstituteBeijing 100142China Center for Bioinformatics State Key Laboratory of Protein and Plant Gene ResearchSchool of Life SciencesPeking UniversityBeijing 100871China Beijing Institutes of Life Science.Chinese Academy of Sciences Beijing 100101China MOE Key Lahoratory of Developnlental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing 210096China State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of SciencesBeijing 100101China Max Planck Independent Research Group on Population Genomics CAS-MPG Partner Institute for Computational Biology(PICB)Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China CAS Ker Laboratory of Genome Sciences and Information Chinese Academy of Sciences.Beijing 100101China MOE Key Laboratory of Bioinformatics School of Life Sciences Center for Synthetic and Systems BiologyTsinghua UniversityBeijing 100084China Computational Omics Lahoratory Center of Computational BiologyBeijing Institute of Basic Medical SciencesBeijing 100850China Bioinformatics Division TNLIST and MOE Key Laboratory for Bioinformatics Department of AutomationTsinghua UniversityBeijing 100084China Department of Bioinformatics and Systems Biology College of LifeScience and Technol

Following the publication of the US National Research Council （N RC） report ＂ Toward PrecMon Medicine.＂ Building a Knowledge network for Biomedical Research and a New Taxonomy of Diseases＂ in 2011 [1], several n... 详细信息

关键词： PMI Precision Medicine data We

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Rec-Symnet: Symbolic network-Based Rectifiable Learning Framework for Symbolic Regression

SSRN

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SSRN 2023年

作者： Liu, Jingyi Li, Weijun Yu, Lina Wu, Min Sun, Linjun Li, Wenqiang Li, Yanjie Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China

Symbolic regression (SR) can be utilized to unveil the underlying mathematical expressions that describe a given set of observed data. At present, SR can be categorized into two methods: learning-from-scratch and learning-with-experience. Compared to learning-from-scratch, learning-with-experience yields results that are comparable to those of several benchmarks and incurs significantly lower time costs for obtaining expressions. However, the learning-with-experience model performs poorly in terms of unseen data distributions and lacks a rectification tool, apart from constant optimization, which exhibits limited performance. In this study, we propose a Symbolic network-based Rectifiable Learning Framework (Rec-SymNet) that possesses the ability to correct errors. Rec-SymNet adopts Symbolic network (SymNet) to represent an expression, and the encoding of SymNet is designed to provide supervised information, with numerous self-generated expressions, to train a policy net (PolicyNet). The training of PolicyNet can offer prior knowledge to guide effective searches. Subsequently, the incorrectly predicted expressions are revised via a rectification mechanism. This rectification mechanism endows Rec-SymNet with broader applicability. Experimental results demonstrate that our proposed method achieves the highest averaged coefficient of determination on self-generated datasets when compared with other state-of-the-art methods and yields more accurate results in public datasets. © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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PruneSymNet: A Symbolic Neural network and Pruning Algorithm for Symbolic Regression

arXiv

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arXiv 2024年

作者： Wu, Min Li, Weijun Yu, Lina Li, Wenqiang Liu, Jingyi Li, Yanjie Hao, Meilan AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China

Symbolic regression aims to derive interpretable symbolic expressions from data in order to better understand and interpret data. In this study, a symbolic network called PruneSymNet is proposed for symbolic regression. This is a novel neural network whose activation function consists of common elementary functions and operators. The whole network is differentiable and can be trained by gradient descent method. Each subnetwork in the network corresponds to an expression, and our goal is to extract such subnetworks to get the desired symbolic expression. Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting. The proposed greedy pruning algorithm preserves the edge with the least loss in each pruning, but greedy algorithm often can not get the optimal solution. In order to alleviate this problem, we combine beam search during pruning to obtain multiple candidate expressions each time, and finally select the expression with the smallest loss as the final result. It was tested on the public data set and compared with the current popular algorithms. The results showed that the proposed algorithm had better accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Gradient methods

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A neural-guided dynamic symbolic network for exploring mathematical expressions from data 24

A neural-guided dynamic symbolic network for exploring mathe...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Wenqiang Li Weijun Li Lina Yu Min Wu Linjun Sun Jingyi Liu Yanjie Li Shu Wei Yusong Deng Meilan Hao AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits University of Chinese Academy of Sciences Beijing China and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China and Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don't scale well to unseen problems. In this work, we propose DYSYMNET, a novel neural-guided Dynamic Symbolic network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DYSYMNET shows clear superiority over several representative baseline models. Open source code is available at https://***/AILWQ/DySymNet.

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AsyncSC: An Asynchronous Sidechain for Multi-Domain data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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MemNetAR: Memory network with Adversative Relation for Target-Level Sentiment Classification

MemNetAR: Memory Network with Adversative Relation for Targe...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yiwei Gao Jianwei Niu Xuefeng Liu Kaili Mao Shui Yu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Target-level sentiment classification aims to identify the sentiment of multiple targets in a sentence. Although existing approaches based on neural network have achieved good performance in this task, we find that many approaches tend to give the same predictions for instances that have multiple targets, and this tendency can make a low accuracy for those instances that have different classes for different targets. Based on this observation, we propose MemNetAR, a memory network which can explicitly leverage the adversative relation among multiple targets in a sentence. Specifically, we add an adversative loss to the cross-entropy loss when there are adversative words between targets. The experimental results on public laptop and restaurant datasets prove that our model can improve 0.84% and 0.8% on total test dataset, and improve 2.97% and 2.68% on the dataset consisting of those instances with multiple targets but different classes by leveraging this new adversative information.

关键词： Task analysis Feature extraction Sentiment analysis Portable computers Machine learning Training Predictive models

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Time-Bin-Encoded Boson Sampling with a Single-Photon Device

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Physical Review Letters 2017年第19期118卷 190501-190501页

作者： Yu He X. Ding Z.-E. Su H.-L. Huang J. Qin C. Wang S. Unsleber C. Chen H. Wang Y.-M. He X.-L. Wang W.-J. Zhang S.-J. Chen C. Schneider M. Kamp L.-X. You Z. Wang S. Höfling Chao-Yang Lu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Technische Physik Physikalisches Instität and Wilhelm Conrad Röntgen-Center for Complex Material Systems Universitat Würzburg Am Hubland D-97074 Wüzburg Germany State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China SUPA School of Physics and Astronomy University of St Andrews St Andrews KY16 9SS United Kingdom

Boson sampling is a problem strongly believed to be intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator. Here, we implement the first time-bin-encoded boson sampling using a highly indistinguishable (∼94%) single-photon source based on a single quantum-dot-micropillar device. The protocol requires only one single-photon source, two detectors, and a loop-based interferometer for an arbitrary number of photons. The single-photon pulse train is time-bin encoded and deterministically injected into an electrically programmable multimode network. The observed three- and four-photon boson sampling rates are 18.8 and 0.2 Hz, respectively, which are more than 100 times faster than previous experiments based on parametric down-conversion.

关键词： Optical tests of quantum theory Quantum algorithms Quantum optics Computational complexity

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Research on the Distribution Characteristics of Standardized Big data Resources Based on data Visualization——Taking Standardization Practice in Shandong Province as an Example

Research on the Distribution Characteristics of Standardized...

引用

IEEE International Conference on data science in Cyberspace (DSC)

作者： Jinyang Sun Dongdong Peng Zhe Lu Mengran Zhai Ning Qi Yuanhua Qi SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics; (2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution"; (3) the distribution of think-tank experts presents obvious "agglomeration effect".

关键词： Industries Technological innovation Heavily-tailed distribution Animals Urban areas data visualization Forestry Big data Fisheries Standards

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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