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arXiv 2019年

作者： Wei, Wei Feng, Xiangnan Wang, Jiannan Liu, Xue Zheng, Zhiming School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Research Institute of Frontier Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Center for Humans and Machines Max Planck Institute for Human Development Berlin14195 Germany

In this paper, solution space organization of minimum vertex-cover problem is deeply investigated using the König-Egérvary (KE) graph and theorem, in which a hierarchical decomposition mechanism named KE-layer structure of general graphs is proposed to reveal the complexity of vertex-cover. An algorithm to verify the KE graph is given by the solution space expression of vertex-cover, and the relation between multi-layer KE graphs and maximal matching is illustrated and proved. Furthermore, a framework to calculate the KE-layer number and approximate the minimal vertex-cover is provided, with different strategies of switching nodes and counting energy. The phase transition phenomenon between different KE-layers are studied with the transition points located, and searching of vertex-cover got by this strategy presents comparable advantage against several other methods. The graphical representation and hierarchical decomposition provide a new perspective to illustrate the intrinsic complexity for large-scale graphs/systems recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

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arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

arXiv

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arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

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Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis

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Neuroscience and Biobehavioral Reviews 2025年 174卷 106201页

作者： Zhang, Lin Jian, Liwen Long, Yiming Ren, Zhihong Calhoun, Vince D. Passos, Ives Cavalcante Tian, Xinyu Xiang, Yuhong School of Psychology Central China Normal University Wuhan 430079 China Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU) Ministry of Education Wuhan 430079 China Key Laboratory of Human Development and Mental Health of Hubei Province School of Psychology Central China Normal University Wuhan 430079 China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Klaus Advanced Computing Building 266 Ferst Drive Atlanta 30332-0765 Georgia Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul RS Porto Alegre Brazil Laboratory of Molecular Psychiatry Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC) Hospital de Clínicas de Porto Alegre (HCPA) RS Porto Alegre Brazil Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul R. Ramiro Barcelos 2400 Floresta RS Porto Alegre 90035002 Brazil

Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reducing the risk of misdiagnosis associated with conventional methods. The aim of this meta-analysis is to evaluate the overall classification accuracy of machine learning models in MDD and examine the effects of machine learning algorithms, biomarkers, diagnostic comparison groups, validation procedures, and participant age on classification performance. As of September 2024, a total of 176 studies were ultimately included in the meta-analysis, encompassing a total of 60,926 participants. A random-effects model was applied to analyze the extracted data, resulting in an overall classification accuracy of 0.825 (95 % CI [0.810;0.839]). Convolutional neural networks significantly outperformed support vector machines (SVM) when using electroencephalography and magnetoencephalography data. Additionally, SVM demonstrated significantly better performance with functional magnetic resonance imaging data compared to graph neural networks and gaussian process classification. The sample size was negatively correlated to classification accuracy. Furthermore, evidence of publication bias was also detected. Therefore, while this study indicates that machine learning models show high accuracy in distinguishing MDD from healthy controls and other psychiatric disorders, further research is required before these findings can be generalized to large-scale clinical practice. © 2025 Elsevier Ltd

关键词： Big data Biomarkers Machine Learning Major Depressive Disorder

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Threat Analysis of Natural Gas Control System Based on Physical Process Constraints

Threat Analysis of Natural Gas Control System Based on Physi...

引用

Chinese Automation Congress (CAC)

作者： Yangyang Geng Wenwen Liu Yang Liu Qiang Wei Ke Liu Department of Cyberspace Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Research and Development Center Beijing sifang automation co.ltd Beijing China

Natural gas is one of the essential energy sources for daily life. The increased reliance on energy makes control systems attractive targets for attackers. This paper explains how the control network works and describes the negative influence of attacking physical process. The paper proposes denial-of-service (DoS) attacks model and integrity attacks for the control system. In addition, our work focus on analyzing triethylene glycol (TEG) dehydration process flow for discovering the influencing factors, operational constraints and safety constraints in the dehydration process. Then, we evaluate physical and economic influences for DoS attacks and integrity attacks on TEG dehydration process in our physical testbed. The consequence shows that these kinds of attacks scenarios will drive the plant into an unsafe state. Finally, we propose an effective mechanism to defense DoS attacks and integrity attacks on TEG dehydration process.

关键词： Process control Sensors Natural gas Integrated circuits Actuators Production

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Anomaly Prediction in Passenger Flow with Knowledge Transfer Method

Anomaly Prediction in Passenger Flow with Knowledge Transfer...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhipu Xie Weifeng Lv Syed Muhammad Asim Ali Bowen Du Runhe Huang Key State Laboratory of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Hosei University Tokyo Japan

ISBN: (纸本)9781538675199

Predicting anomaly in travel demand is a crucial finding from smart card data analytics. The output of these predictions is a significant contribution to planning sustainable public transport system and generating possible knowledge for transportation learning models. This paper investigates the anomaly effects of the surge in bus passengers demand and compare it with an increase in taxi demand. Indeed, both short-term and long-term demands reveal different patterns of passengers in uncertain situations. In pursuit of our goal, we estimated the similarity in stations by both selected and latent features where pre-trained knowledge are combined as an ensemble with different weights. We present Surge Prediction and Knowledge Transfer (SPKT) model that uses Seq2Seq method combined with Multi-source Transfer Learning method on travel patterns extracted from smart card data to classify source stations and target station. To illustrate the demands blueprint, we considered multiple source stations as input to the predictor, to develop a mechanism that bridges the knowledge transfer learning with the targeted stations. To exemplify our method, we use a case study of an event with passenger surge. From experiments, we found that transferring knowledge can make the surge prediction better compared to only limited training data for the target stations. The results have proved the effectiveness of surge predictions and knowledge transfer for learning models.

关键词： Surges Knowledge transfer Predictive models Public transportation Feature extraction Real-time systems

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Effects of Sodium Hydroxide Treatment on LLDPE/DS Composites: Tensile Properties and Morphology 1

Effects of Sodium Hydroxide Treatment on LLDPE/DS Composites...

引用

1st International Conference on Green and Sustainable computing (ICoGeS)

作者： Alnaid, Abduati Noriman, N. Z. Dahham, Omar S. Hamzah, R. Sudin, S. Idrus, S. Z. Syed Shayfull, Z. Mohammed, M. Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) UniCITI Alam Campus Sungai Chucuh Padang Besar Perlis 02100 Malaysia School of Computer and Communication Engineering (SCCE) Green Advanced Computing and Technology (GREAT) Research Group-CEGeoGTech Universiti Malaysia Perlis (UniMAP) Malaysia School of Human Development and Techno-communication (IKOM) Green Advanced Computing and Technology (GREAT) Research Group-CEGeoGTech Universiti Malaysia Perlis (UniMAP) Malaysia School of Manufacturing Engineering Universiti Malaysia Perlis Arau 02600 Malaysia Green Design and Manufacture Research Group Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Universiti Malaysia Perlis Kangar 0100 Malaysia Center of Excellence Geopolymer and Green Technology (CEGeoGTech) School of Materials Engineering Universiti Malaysia Perlis (UniMAP) Kompleks Pengajian Jejawi 2 Arau 02600 Malaysia

In this research, the effect of different sodium hydroxide (NaOH) concentration (3, 6 and 9 wt.%) on the tensile properties of linear low density polyethylene / date seeds (LLDPE/DS) composites were investigated. The size of DS particles was measured at (125 - 250 m) and the ratio of LLDPE over DS was fixed at 90/10 wt.%. The samples were prepared using extrusion process (screw speed at 60 rpm and barrel and die temperature at 160 C and 165 C respectively) followed by injection moulding process (from feed to exit die zone temperature at 160, 165, 170 and 175 C respectively). Results suggest that the addition of NaOH particularly 3 wt% has improved the tensile strength. Additionally, the elongation at break has improved as well. However, the high concentration of NaOH, particularly 9 wt.% effects negatively on these properties. In contrast, the Modulus shows different trend, which increased as NaOH concentration increased in the composites. The SEM section shows the improvement on DS surface as low NaOH concentration used (3 wt.%) and deterioration on the DS surface as high NaOH concentration used (9 wt.%).

关键词： Sodium hydroxide

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A survey of industrial control system testbeds

引用

IOP Conference Series: Materials Science and Engineering 2019年第4期569卷

作者： Yangyang Geng Yi Wang Wenwen Liu Qiang Wei Ke Liu Haolan Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China. Department of Research and Development Center Beijing sifang automation co.ltd Beijing 100085 China

Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical.

关键词：

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Effects of Date Seeds Size and Loading on Properties of Linear Low-Density Polyethylene/Date Seeds Powder Composites 1

Effects of Date Seeds Size and Loading on Properties of Line...

引用

1st International Conference on Green and Sustainable computing (ICoGeS)

作者： Alnaid, Abduati Noriman, N. Z. Dahham, Omar S. Hamzah, R. Al-Samarrai, Marwa N. Idrus, S. Z. Syed Shayfull, Z. Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) UniCITI Alam Campus Sungai Chucuh Padang Besar Perlis 02100 Malaysia School of Human Development and Techno-communication (IKOM) Green Advanced Computing and Technology (GREAT) Research Group-CEGeoGTech Universiti Malaysia Perlis (UniMAP) Malaysia School of Manufacturing Engineering Universiti Malaysia Perlis Arau 02600 Malaysia Green Design and Manufacture Research Group Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Universiti Malaysia Perlis Kangar 0100 Malaysia

In this work, the influences of different date seeds (DS) size, fine size (125- 250 wn), medium size (250- 500 wn) and coarse size (500, 1000 wn) and different DS content (5, 10, 15, 20 wt%) on the tensile properties of linear low density polyethylene / date seeds (LLDPE/DS) composites were evaluated. The extrusion and injection molding process were used respectively to form LLDPE/DS composites. Results show that the addition of DS up to 10 wt%, particularly DS fine size has increased the tensile strength (TS) of LLDPE/DS composites. However, high DS content, particularly DS coarse size decreased the TS of the composites. The percentage of elongation at break (Eb) decreased while the modulus increased gradually as DS content increased in LLDPE/DS composites, which made the composite stiffer and less flexible. The SEM shows clearly the good distribution of DS fine size and the agglomeration of DS coarse size.

关键词： Tensile strength

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Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

arXiv

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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