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Core influence mechanism on vertex-cover problem through leaf-removal-core breaking

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arXiv 2018年

作者： Feng, Xiangnan Wei, Wei Li, Xing Zheng, Zhiming School of Mathematics and Systems Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

Leaf-Removal process has been widely researched and applied in many mathematical and physical fields to help understand the complex systems, and a lot of problems including the minimal vertex-cover are deeply related to this process and the Leaf-Removal cores. In this paper, based on the structural features of the Leaf-Removal cores, a method named Core Influence is proposed to break the graphs into No-Leaf-Removal-Core ones, which takes advantages of identifying some significant nodes by localized and greedy strategy. By decomposing the minimal vertex-cover problem into the Leaf-Removal cores breaking process and maximal matching of the remained graphs, it is proved that any minimal vertex-covers of the whole graph can be located into these two processes, of which the latter one is a P problem, and the best boundary is achieved at the transition point. Compared with other node importance indices, the Core Influence method could break down the Leaf-Removal cores much faster and get the no-core graphs by removing fewer nodes from the graphs. Also, the vertex-cover numbers resulted from this method are lower than existing node importance measurements, and compared with the exact minimal vertex-cover numbers, this method performs appropriate accuracy and stability at different scales. This research provides a new localized greedy strategy to break the hard Leaf-Removal Cores efficiently and heuristic methods could be constructed to help understand some NP *** Codes 05C82 Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

Efficient Algorithms for the Summed Area Tables Primitive on GPUs

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Efficient Algorithms for the Summed Area Tables Primitive on...

IEEE International Conference on Cluster computing

作者： Peng Chen Mohamed Wahib Shinichiro Takizawa Ryousei Takano Satoshi Matsuoka AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology National Institute of Advanced Industrial Science and Technology Tokyo Japan Dept. of Mathematical and Computing Science Tokyo Institute of Technology Tokyo Japan

Two-dimensional Summed Area Tables (SAT) is a fundamental primitive used in image processing and machine learning applications. We present a collection of optimization methods for computing SAT on CUDA-enabled GPUs. Conventional approaches rely on computing the prefix sum in one dimension in parallel, transposing the matrix, then computing the prefix sum for the other dimension in parallel. Additionally, conventional methods use the scratchpad memory as cache. We propose a collection of algorithms that are scalable with respect to problem size. We use the register cache technique instead of the scratchpad memory and also employ a naive serial scan on the thread level for computing the prefix sum for one of the dimensions. Using a novel transpose-in-registers method we increase the inter-thread parallelism and outperform conventional SAT implementations. In addition, we significantly reduce both the communication between threads and the number of arithmetic instructions. On an Nvidia Pascal P100 GPU and Volta V100, our evaluations demonstrate that our implementations outperform state of the art libraries and yield up to 2.3x and 3.2x speedup over OpenCV and Nvidia NPP libraries, respectively.

关键词： Registers Graphics processing units Instruction sets Two dimensional displays Computer architecture Libraries Acceleration

Susceptible individuals drive active social contagion

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Physical Review Research 2019年第3期1卷 033125-033125页

作者： N. N. Chung L. Y. Chew W. Chen R. M. D'Souza C. H. Lai Centre for University Core Singapore University of Social Sciences Singapore 599494 School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Complexity Institute Nanyang Technological University Singapore 637723 LMIB and School of Mathematical Sciences Beihang University Beijing 100191 China Peng Cheng Laboratory Shenzhen Guangdong 518055 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China University of California Davis Davis California 95616 USA Department of Physics National University of Singapore Singapore 117542

The catalyzing role that influential individuals play in driving large-scale social contagion has been the focus of many recent empirical and theoretical studies. Nevertheless, complementary studies suggest that the success of social contagion depends largely on having a group of highly susceptible individuals. Here we show that being influential is not due solely to having susceptible peers and that the relative importance of influential versus susceptible individuals depends on the underlying mechanism driving the contagion. Our mathematical models show that for passive social influence both influential and susceptible individuals catalyze the spread of the contagion progressively and equitably. In contrast, for active social influence, susceptible individuals facilitate rapid initial contagion, but then influential individuals are necessary for sustaining the growth of the contagion. We show that active influence characterizes certain online social networks, like retweeting behavior on Twitter, while passive influence characterizes other online social networks, like adoption of whom to follow on the Digg network.

关键词： Complex systems Spreading Social networks

A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types

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arXiv 2018年

作者： Wang, Huiwen Huang, Tingting Wang, Shanshan School of Economics and Management Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations Beijing China

Mixed spatial autoregressive (SAR) models with numerical covariates have been well studied. However, as non-numerical data, such as functional data and compositional data, receive substantial amounts of attention and are applied to economics, medicine and meteorology, it becomes necessary to develop exible SAR models with multiple data types. In this article, we integrate three types of covariates, functional, compositional and numerical, in an SAR model. The new model has the merits of classical functional linear models and compositional linear models with scalar responses. Moreover, we develop an estimation method for the proposed model, which is based on functional principal component analysis (FPCA), the isometric logratio (ilr) transformation and the maximum likelihood estimation method. Monte Carlo experiments demonstrate the effectiveness of the estimators. A real dataset is also used to illustrate the utility of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Principal component analysis

Creating bus timetables with maximal synchronization by a new algorithm based on the PSO 17

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Creating bus timetables with maximal synchronization by a ne...

17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Tian, Daxin Liu, Wenhao Guo, Peng Rong, Hui Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Quartermaster Equipment Institute of the General Logistics Dept. of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

This paper addresses the problem of receiving a timetable for a settled network with maximal synchronization. In this paper, a model has been developed to solve the problem which attaches importance to creating a timetable for maximizing their synchronization and enables the transfer of passengers from one route to another with least waiting times at transfer nodes. Next, this problem is formulated as a mixed integer linear programming problem, and a new algorithm based on the traditional particle swarm optimization (PSO) has been developed to solve the problem in a short time. Finally, the relationship of iterative time and the complexity of the given network is qualitatively established. © ASCE.

关键词： Synchronization

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

Graph convolutional neural networks via motif-based attention

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arXiv 2018年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang Ning, Yuanxing Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Department of Computer Science University of Illinois at Chicago

Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information. Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems. Analogous to image-based attentional convolution networks that operate on locally connected and weighted regions of the input, we also extend graph normalization from one-dimensional node sequence to two-dimensional node grid by leveraging motif-matching, and design self-attentional layers without requiring any kinds of cost depending on prior knowledge of the graph structure. Our results on both bioinformatics and social network datasets show that we can significantly improve graph classification benchmarks over traditional graph kernel and existing deep models. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

A New Control Strategy Integrated into the Desired Safety Margin Car-Following Model Considering the Disturbance Level

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Transportation Research Record 2018年

作者： Zhang, Junjie Wang, Yunpeng Lu, Guangquan Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies School of Transportation Science and Engineering Beihang University Haidian District Beijing China China

A vehicular communication system can provide motion information based on various sensors. Thus, adaptive cruise control (ACC) systems based on a reliable communication system can relieve traffic congestion in a platoon. However, acquiring vehicle information entails uncertainties because the disturbance of traffic environment and vehicular communication systems influences vehicle control performance. In this study, the desired safety margin (DSM) model is employed to investigate the influence of uncertainty on car-following performance, such as starting, emergency braking, and car-following processes. Based on the DSM model, the disturbance level on perceived safety margin is introduced to characterize the uncertainty of vehicle information. The stability criterion of the DSM model with the disturbance level is derived via linear stability theory. Analytical results indicate that a negative value of disturbance level will enlarge the stable region. By contrast, a positive value is conducive for maintaining the consensus state and achieving the high acceleration and deceleration of following vehicles in the starting process. Findings show that disturbance in the vehicular system significantly influences car-following performance. To enhance the smoothness and stability of traffic flow evolution, a new control strategy is proposed in this study. Numerical experiments illustrate the effectiveness of the proposed control strategy in stabilizing traffic flow. This study highlights the need to resolve the stability of sensors and vehicular communication systems, and develop a vehicular controller to help ACC system improve vehicle control performance in the car-following process. © 2018, National Academy of Sciences: Transportation Research Board 2018.

关键词： Stability criteria

Short Term Trend Forecast of On-Line Monitoring data of Dissolved Gas in Power Transformer

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Short Term Trend Forecast of On-Line Monitoring Data of Diss...

Electrical Insulation Conference and Electrical Manufacturing & Coil Winding Conference

作者： Peng Zhang Bo Qi Qipeng Chen Zhihai Rong Chengrong Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing P.R. China Global Energy Interconnection Research Institute Co. Ltd Advanced Computing and Big Data Laboratory of SGCC Beijing China

ISBN: (纸本)9781538641798

As the transformation equipment of electrical energy, transformer is the key equipment in power system. When it comes to breakdown, there will be a great loss. Dissolved gas analysis (DGA) online testing is an important indicator of transformer health assessment which is widely used in insolation testing as it is sensitive to discharge defect. Nowadays, most of researches on DGA online testing analysis are aimed at the faults diagnosis. However, in some conditions, the fault may develop very rapidly. The operation and maintenance personnel don't have enough time to figure the problem out before the breakdown occur. This problem can be solved by forecasting the DGA on-line testing data effectively. With the reveal of deterioration trend, the serious failure will be avoided and the reliability of transformer will be improved. This paper proposes a short term trend forecast method based on online data optimization for dissolved gas in oil, which is a time series forecast. This method is made up of five parts: data optimization, related gases selection, the orders selection, model parameters estimation and model checking, multi-step forecast. With the field interference and DGA online testing device status error, the DGA online data's quality can't be assured. To improve the accuracy of forecast model, the transformer online testing data needs to be optimized in first step, including Pauta criterion removing for singular value and linear interpolation for missing data. The second step, select related gases, as different gases have strong relationship. The third step is to build forecaster model based on Auto-Regressive and Moving Average Model (ARMA), using Akaike information criterion (AIC) to select the model orders. The forth step, estimate the unknown parameters by least square method. After that, the model should be verified by residual error testing to make sure the effective information of the time series is fully extracted. The final step, use the forecast model

关键词： Predictive models Gases Oil insulation Time series analysis Mathematical model Monitoring data models