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arXiv 2023年

作者： Zhou, Xiaolan Xu, Chuanju School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen University Xiamen361005 China

We propose in this paper efficient first/second-order time-stepping schemes for the evolutional Navier-Stokes-Nernst-Planck-Poisson equations. The proposed schemes are constructed using an auxiliary variable reformulation and sophisticated treatment of the terms coupling different equations. By introducing a dynamic equation for the auxiliary variable and reformulating the original equations into an equivalent system, we construct first- and second-order semi-implicit linearized schemes for the underlying problem. The main advantages of the proposed method are: (1) the schemes are unconditionally stable in the sense that a discrete energy keeps decay during the time stepping;(2) the concentration components of the discrete solution preserve positivity and mass conservation;(3) the delicate implementation shows that the proposed schemes can be very efficiently realized, with computational complexity close to a semi-implicit scheme. Some numerical examples are presented to demonstrate the accuracy and performance of the proposed method. As far as the best we know, this is the first second-order method which satisfies all the above properties for the Navier-Stokes-Nernst-Planck-Poisson equations. © 2023, CC BY.

关键词： Numerical methods

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BioNet+: A Comprehensive Interaction Model of Finding Therapeutics for Diseases with Graph Deep Learning

BioNet+: A Comprehensive Interaction Model of Finding Therap...

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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Jinglun Ma Yanlong Qiu Canqun Yang Chengkun Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer Science and Techology National University of Defense Technology Changsha China

The prediction of interactions between biomedical entities is crucial for the discovery of therapeutics for diseases. The fundamental step is to build an interaction model with a comprehensive coverage on literature and curated databases. The massive amount of heterogeneous data in interaction networks of biomedical entities poses significant challenges to relevant prediction tasks. Previous research has mainly focused on constructing networks with limited types of entity relations. In this paper, we construct a large-scale heterogeneous graph model, BioNet+, using a data-driven approach to extract latent information and predict various relations among biomedical entities. BioNet+ provides a crucial update to our previous BioNet model, with the addition of disease entities. The model employs a graph convolution encoder to learn embedding information from the intricate interactions among chemicals (C), genes (G), diseases (D), and biological pathways (P). Furthermore, a decoder that applies two tensor decomposition methods is deployed to predict multiple types of relations, including CG, CD, and GD interactions. The model constructs a graph network consisting of 81,987 entity nodes and 206,195,703 interaction edges. To handle this massive model, BioNet+ reduces the computational complexity via data categorization and proposes a parallel model training algorithm for cross-node GPUs. Evaluation experiments demonstrate that BioNet+ achieves superior predictive performance, as demonstrated by its highest area under the receiver operating characteristic curve (AUROC) of 0.964.

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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A Technical Design of Inland Intelligent 64 TEU Container Ship for Beijing-Hangzhou Canal 7

A Technical Design of Inland Intelligent 64 TEU Container Sh...

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7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023

作者： Xu-Ming, Wang Xue-Min, Song Wei-Qin, Liu Xueqing, Bi Hong-Liang, Dai Wuhan University of Technology National Engineering Research Center for Water Transportation Safety Wuhan China CO. Ltd Nanjing China Wuhan University of Technology Ministry of Education Key Laboratory of High Performance Ship Technology Wuhan China Zhejiang Scientific Research Institution of Transport Hangzhou310023 China

ISBN: (纸本)9798350308532

With the rise and breakthrough of big data, cloud computing, artificial intelligence and other technologies, the shipping industry is transitioning from the era of automation and information to the era of intelligence. Intelligent shipping is a new modern shipping business form formed by the deep integration of modern information, artificial intelligence and other high-tech and shipping elements. In this paper, firstly the stat of art for inland intelligent container ship is introduced, the overview of the inland intelligent 64 TEU container ship is presented. Secondly, the design of 64 TEU container ship is applied in the consideration of optimization. For the integrated design scheme of the ship, the power system, propulsion device, power system layout, power distribution system, power system monitoring, engine room layout optimization, energy conversion, ship-engine and oar matching and propulsion efficiency are integrated into the overall design. Finally, by systematically studying such problems as rapid construction and real-time service of holographic navigation scene map in inland water network area, design and development of shipboard and shore-based information infrastructure of ship-shore collaboration, intelligent cross-domain computing of ship-shore collaboration, etc., the enhanced navigation and navigation support technology of inland intelligent container ship are performed. In the conclusion, the guidance of the design for intelligent 64 TEU container ship is proposed. © 2023 IEEE.

关键词： Ships

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FIG-LP: Feature-Inverse-Graph based Link Prediction in Graph Stream

FIG-LP: Feature-Inverse-Graph based Link Prediction in Graph...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Xu Zhang XiaoQiang Xiao GuoWei Li WeiXun Ning JianTong Song School of Computer National University of Defense Technology ChangSha China State Key Laboratory of High Performance Computing ChangSha China

Link prediction is one of the essential issues in network science, which aims to find the unknown link or estimate the future link in networks. Existing methods are mainly based on the assumption that the network data is completely available and has stable distribution before analysis. In practice, however, complex networks evolve lifelong with massive data. The data in those networks is associated with previous ones, and the distribution will be non-stationary. Compared with conventional link prediction methods, online link prediction in the largescale dynamic network has three main challenges: i) How to analyze massive data with acceptable expenditure; ii) How to predict future links with less topological information; iii) How to make link prediction stably in a dynamic evolutionary network. In this paper, we propose a streaming link prediction model based on lifelong learning and graph neural networks (GNNs), which converts the link prediction problems to graph classification. Our main idea is to design a new topology, the Feature-Inverse-Graph, which turns node pairs into independent graphs and takes the features of the node pair as new vertices. Additionally, we apply a two-phase sampling sketch to deal with the massive data so that the complexity of the model within lifelong evolving networks could be acceptable. Then, the link prediction tasks in the regular graph is converted into a series of individually Feature-Inverse-Graph classifications. In this case, the computational cost of our model will not increase dramatically with the increase of network data, which is further verified by analyzing the computational complexity. The experimental results demonstrate the efficiency and effectiveness of our model by continuously predicting future links of classical datasets. In our experiments, several topological link prediction measures are chosen as features of node pair. For future work, the FIG-LP model can be used as the basic research of intelligent systems

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FRCNet: Frequency and Region Consistency for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： He, Along Li, Tao Wu, Yanlin Zou, Ke Fu, Huazhu Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin China Haihe Lab of ITAI China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan Chengdu China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is important for lesion regions located at low frequencies and with significant scale changes. In this paper, we introduce two consistency regularization strategies for semi-supervised medical image segmentation, including frequency domain consistency (FDC) to assist the feature learning in frequency domain and multi-granularity region similarity consistency (MRSC) to perform multi-scale region-level local context information feature learning. With the help of the proposed FDC and MRSC, we can leverage the powerful feature representation capability of them in an effective and efficient way. We perform comprehensive experiments on two datasets, and the results show that our method achieves large performance gains and exceeds other state-of-the-art methods. © 2024, CC BY.

关键词： Deep learning

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FPGA-Based Multi-precision Architecture for Accelerating Large-Scale Floating-Point Matrix computing 17th

FPGA-Based Multi-precision Architecture for Accelerating Lar...

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17th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2020

作者： Zhang, Longlong Peng, Yuanxi Hu, Xiao Huang, Ahui Tian, Tian State Key Laboratory of High Performance Computing School of Computer National University of Defense Technology Changsha China Institute of Microelectronics School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030794774

Matrix computing plays a vital role in many scientific and engineering applications, but previous work can only handle the data with specified precision based on FPGA. This study first presents algorithms, data flows, and mapping strategies to match the hardware structure for matrix computing of different precisions. Then, we propose a unified multi-precision matrix computing unit core that can handle three precisions and three matrix operation modes and can be used as a coprocessor for large-scale matrix computing which has advantages of low storage and high efficiency. Finally, we build a complete matrix computing acceleration system and deploy it on FPGA using 128 processing elements (PEs). The experimental results show that the accelerator achieves a maximum frequency of 180 MHz, and matrix computing of double-precision, single-precision, and half-precision floating-point data performs 46.1 GFLOPS, 92.1 GFLOPS, and 184.3 GFLOPS respectively, which is superior to other current designs in terms of application range and performance. © 2021, IFIP International Federation for Information Processing.

关键词： Field programmable gate arrays (FPGA)

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The analytic smoothing effect of solutions for the nonlinear spatially homogeneous Landau equation with hard potentials

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Science China Mathematics 2022年第10期65卷 2079-2098页

作者： Hao-Guang Li Chao-Jiang Xu School of Mathematics and Statistics South-Central Minzu UniversityWuhan 430074China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) Ministry of Industry and Information TechnologyNanjing 211106China Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Laboratoire de Mathematiques Universite de Rouen NormandieCNRS UMR 6085Saint-Etienne-du-Rouvray 76801France

In this work,we study the Cauchy problem of the nonlinear spatially homogeneous Landau equation with hard potentials in a close-to-equilibrium *** prove that the solution to the Cauchy problem with the initial datum in L^(2)enjoys an analytic regularizing effect,and the evolution of the analytic radius is the same as that of heat equations.

关键词： spatially homogeneous Landau equation analytic smoothing effect hard potentials

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Excellent high-pressure-sustainable thermoelectric performance driven by metal-insulator topological phase transition in semimetal CaCdGe

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Physical Review B 2024年第3期109卷 035166-035166页

作者： Liangyu Li Zhenyu Ding Rongman Gao Miao Li Shuo-Wang Yang Gang Wu Xiaoping Yang High Magnetic Field Laboratory of Anhui Province High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei 230031 China Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 China Materials Science and Chemistry Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore

Recent advances in thermoelectric research have shed light on the promising properties of topological semimetals, which exhibit superior carrier mobility and electrical conductivity compared to traditional thermoelectric materials. Herein, we employ a first-principles method and semiclassical Boltzmann transport theory to investigate the electronic structures, lattice dynamics, and thermoelectric performance of CaCdGe, a representative nodal-line semimetal, along with their response to pressure. Our findings reveal that CaCdGe possesses an ultralow lattice thermal conductivity at room temperature (0.765 Wm−1K−1) and exhibits excellent n-type thermoelectric behavior in a wide temperature range. Specifically, we identify in-plane (out-of-plane) maximum thermoelectric figures of merit, ZTmax, reaching 1.02 (0.65) at 700 K(1100 K). Remarkably, even under extreme conditions of high pressure (up to 50 GPa), CaCdGe maintains its structural stability without undergoing any structural phase transition. Moreover, we explore the impact of spin-orbit coupling and observe a pressure-driven Lifshitz transition of the Fermi surface around 40 GPa. This transition results in CaCdGe undergoing an electronic phase transition from a charge-compensated semimetal to a topological insulator. Intriguingly, the thermoelectric parameters exhibit unconventional pressure response, embodied by the insensitivity to high pressure after electronic phase transition. Notably, even at 50 GPa, the in-plane (out-of-plane) ZTmax reaches 1.2 (0.7) at 1500 K(500 K). Our comprehensive theoretical study demonstrates the immense potential of CaCdGe and related topological semimetals for thermoelectric applications, even in extreme conditions of high pressure and high temperature.

关键词： Thermoelectric effects Topological phase transition Node-line semimetals Boltzmann theory First-principles calculations

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