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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jinyi Liu Xiaxu Leng Ying Liu School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Beijing China

Detection and classification of X-ray baggage security imagery are of great significance not only in daily life. However, up to now, it is still difficult for the traditional image processing methods to detect and distinguish objects which we want to detect in X-Ray baggage security imagery. Moreover, it is even more challenging to classify different types of objects. Although the current state-of-the-art detectors have achieved impressive performance on various public datasets with visible images, these detectors fail to deal with objects in X-ray images. This paper proposes an object detection algorithm for X-Ray baggage security screening images. Firstly, to outline the detected object from X-Ray baggage security imagery, we propose a fore-ground-background segmentation method which based on color information. Then, to classify and outline different type of object in X-Ray im-age, a deep convolutional neural networks (DCNNs) based object detection framework Faster R-CNN are proposed. In this stage we also use transfer learning method to speed up Faster R-CNN. The proposed method is proved to achieve 77% mAP by in a real-word dataset which contains 32,253 sub-way X-Ray baggage security screening images.

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Error Recovery of RDMA Packets in data Center networks

Error Recovery of RDMA Packets in Data Center Networks

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International Conference on Computer Communications and networks (ICCCN)

作者： Yi Wang Kexin Liu Chen Tian Bo Bai Gong Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Future Network Theory Lab Huawei Hong Kong China

Modern data center applications need high throughput (40Gbps) and ultra-low latency (

关键词： Packet loss Receivers Throughput data centers Switches Ethernet

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RDMA Load Balancing via data Partition

RDMA Load Balancing via Data Partition

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International Conference on Computer Communications and networks (ICCCN)

作者： Yi Wang Ya-nan Jiang Qiufang Ma Chen Tian Bo Bai Gong Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Future Network Theory Lab Huawei Hong Kong China

With the development of data center networks, traditional TCP/IP cannot support the demand in data centers. Remote Direct Memory Access (RDMA) technology could improve the performance of DCN significantly because of high throughput and low latency. However, load balancing is a key issue in RDMA which has not been solved distribute. This paper will propose an algorithm to solve the load balance problem in RDMA on application layer without hardware changing. The main idea is to divide data to chunks and data chunks on multiple reachable paths for transmission. However, it is no trivial to find the optimal chunk size and the path number, some empirical values are found by varieties of experiments and tests. Moreover, the chunk allocation scheme also needs to consider the traffic condition in DCNs to find more free paths to transmit. We evaluate the algorithm in application layer with ns3 simulator. The experiment results show that with our algorithm the completion time can decrease 81.03% at most.

关键词： Task analysis Load management data centers Servers Software Routing Receivers

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Reassessing Immediate Coal Phase-Out: Dual Imperatives of Capacity Control and Renewables Expansion in China’s Net-zero Strategy

Nexus

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Nexus 2025年

作者： Jiaxing Wang Xi Lu Hao Wang Yang Ou Jianxiao Wang Dan Tong Yan Li Ziwen Ruan Zihua Yin Wenlong Zhou Benjamin McLellan Emily Xing Anna M. MacLennan Jingli Fan Xian Zhang Michael B. McElroy Kebin He State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing 100084 China Institute for Carbon Neutrality Tsinghua University Beijing 100084 China Institute of Carbon Neutrality Peking University100091 Beijing China China Oil & Gas Pipeline Network Corporation Beijing 100013 China College of Environmental Sciences and Engineering Peking University Beijing 100091 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100091 China Department of Earth System Science Tsinghua University Beijing 100084 China College of environmental science and Engineering Tongji University Shanghai 200092 China Centre for Sustainable Development and Energy Policy Research School of Energy and Mining Engineering China University of Mining and Technology Beijing 100083 China Graduate School of Energy Science Kyoto University Kyoto 606-8501 Japan Harvard-China Project on Energy Economy and Environment Harvard University Cambridge MA 02138 USA The Administrative Centre for China’s Agenda 21 Ministry of Science and Technology Beijing 100038 China School of Engineering and Applied Science Harvard University Cambridge MA 02138 USA Department of Earth and Planetary Sciences Harvard University Cambridge MA 02138 USA

Immediate cessation of investments in new coal-fired power plants is widely regarded as a crucial measure for China to achieve net-zero emission. However, there exists a lack of systematic evaluations regarding the stringent removal of coal power from China's energy system, raising potential concerns around balancing emission reductions and sustainable transformation. In this study, a comprehensive analysis coupling an integrated assessment model with an hourly power system optimization model reveals that implementing stringent policies to limit coal power expansion yields immediate emission reduction benefit. However, premature and singular capacity control of coal power may hinder the electrification of Chinese society, adversely impacting the adequacy of the power system beyond 2050. These findings underscore the importance of implementing supporting measures when implementing coal power control policies, including the crucial deployment of renewable energy at a pace significantly exceeding the current rate to achieve complementarity with coal power control policies.

关键词： net-zero emission renewable energy expansion coal power control energy system transition

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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Sequential online prediction in the presence of outliers and change points: An instant temporal structure learning approach

arXiv

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arXiv 2019年

作者： Liu, Bin Qi, Yu Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing Jiangsu210023 China College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310058 China

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration to the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, on the basis of the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter value of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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Investigation on Automatic data Acquisition of Well Logging Curve based on Color Recognition 4

Investigation on Automatic Data Acquisition of Well Logging ...

引用

2019 4th International Conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Wang, Han Ye, Zhihui Chen, Dong Zhu, Dandan Li, Shouding College of Safety and Ocean Engineering China University of PetroleumBeijing Beijing102249 China State Key Laboratory of Petroleum Resources and Prospecting College of Petroleum Engineering China University of PetroleumBeijing Beijing102249 China Beijing Key Lab of Petroleum Data Mining Department of Computer Science and Technology Beijing102249 China Institute of Geology and Geophysics Chinese Academy of Sciences Key Laboratory of Shale Gas and Geoengineering Institute of Geology and Geophysics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100083 China Institutions of Earth Science Chinese Academy of Sciences Beijing100029 China

Well logging data are important to evaluate rock and fluid properties underground. Many well logging data, as functions of well depth, are conserved as figures in the literature. Digitizing technics should be applied when utilizing the well logging data. In order to address this problem, a method was proposed to automatically identify and acquire the well logging data from well logging curves. Based on the color recognition, this algorithm is able to extract curve features based on pixels. Comparing to other data digitizing methods, the background grid lines could be easily filtered and removed. The algorithm is verified by digitizing some typical well logging curves. This main contribution of this work is to provide a new and efficient way for data digitizing from well logging curves. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： data acquisition

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Time irreversibility and amplitude irreversibility measures for nonequilibrium processes in complex systems

arXiv

引用

arXiv 2020年

作者： Yao, Wenpo Wang, Jun Perc, Matjaž Yao, Wenli Dai, Jiafei Guo, Daqing Yao, Dezhong Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 Maribor2000 Slovenia Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria School of Mines China University of Mining and Technology Xuzhou221116 China Department of Neurology Jinling Hospital Medical School of Nanjing University NanjingJiangsu210002 China Sichuan Institute for Brain Science and Brain-Inspired Intelligence University of Electronic Science and Technology of China Chengdu611731 China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu2019RU035 China

Time irreversibility, which characterizes nonequilibrium processes, can be measured based on the probabilistic differences between symmetric vectors. To simplify the quantification of time irreversibility, symmetric permutations instead of symmetric vectors have been employed in some studies. However, although effective in practical applications, this approach is conceptually incorrect. Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility – a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations. Through theoretical and experimental analyses, we highlight the strong similarities and close associations between the time irreversibility and amplitude irreversibility measures. Our paper clarifies the connections of and the differences between the two types of permutation-based parameters for quantitative nonequilibrium, and by doing so, we bridge the concepts of amplitude irreversibility and time irreversibility and broaden the selection of quantitative tools for studying nonequilibrium processes in complex systems. Copyright © 2020, The Authors. All rights reserved.

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Overlapping Community Detection Based on Influence and Seeds Extension

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese Institute of Electronics. All right reserved.

关键词： Population dynamics

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