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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering 1st

A Vehicular Positioning Enhancement with Connected Vehicle A...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Liu, Wenhao Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach called CV-IMM-EKF. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Global positioning system

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Modeling and Simulation on Cooperative Movement of Vehicle Group Based on the Behavior of Fish 1st

Modeling and Simulation on Cooperative Movement of Vehicle G...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Kang, Lu Zheng, Kunxian Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No.30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Fatigue driving might affect the traffic safety when the vehicles are on the cruising state in highway. Trying to solve this problem, this paper uses the movement pattern of the fish to the vehicles fleet, and develops a model of vehicle group with realistic restrictions based on the existed fish algorithms, the mobile behavioral model and cluster behavior model, and then demonstrates the feasibility of applying the fish behavior to the cooperative movement of vehicle groups through analyzing the trajectory, velocity and spacing of vehicles. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicles

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REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Network 1st

REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Netwo...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Dai, Ziyi Zheng, Kunxian Zhou, Jianshan Duan, Xuting Guo, Peng Rong, Hui Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Vehicular Ad-Hoc Network (VANET) has emerged as an increasingly dominant technology for future connected vehicle and vehicular networks, where the focus of the development of VANET lies in the standardization of message transmission and dissemination via multi-hop broadcasting. However, the current communication protocols concerning VANET face many challenges, including data flooding and collision, transmission delay and other problems. Most of the challenges are closely related to next-hop selection. Therefore, this paper proposes a new routing protocol named REliable and Fast Forwarding (REFF) to optimize the selection of nodes in VANET. In this protocol, node filtering and node evaluation are two main steps. Distance between previous node and candidate node, relative velocity between previous node and candidate nodes, included angle between direction of target node’s velocity and candidate node’s velocity and transmission power of candidate node are adopted as indexes to help select a specific node as the next hop using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). By using this technique, the number of candidates as next-hop is largely reduced, avoiding the data flooding and resulting transmission relay. In addition, simulations based on experiments are done to verify the feasibility. The results show that message achieves a faster and more reliable transmission using REFF. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicular ad hoc networks

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Introducing the HOBBIT platform into the Ontology Alignment Evaluation campaign 13

Introducing the HOBBIT platform into the Ontology Alignment ...

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13th International Workshop on Ontology Matching, OM 2018

作者： Jiménez-Ruiz, Ernesto Saveta, Tzanina Zamazal, Ondřej Hertling, Sven Röder, Michael Fundulaki, Irini Ngomo, Axel-Cyrille Ngonga Sherif, Mohamed Ahmed Annane, Amina Bellahsene, Zohra Ben Yahia, Sadok Diallo, Gayo Faria, Daniel Kachroudi, Marouen Khiat, Abderrahmane Lambrix, Patrick Li, Huanyu Mackeprang, Maximilian Mohammadi, Majid Rybinski, Maciej Balasubramani, Booma Sowkarthiga Trojahn, Cassia Alan Turing Institute London United Kingdom Department of Informatics University of Oslo Norway Institute of Computer Science - FORTH Greece University of Economics Prague Czech Republic Data and Web Science Group University of Mannheim Germany Paderborn University Data Science Group Pohlweg 51 PaderbornD-33098 Germany Ecole Nationale Superieure d'Informatique Alger Algeria LIRMM Universit de Montpellier CNRS Montpellier France Université de Tunis El Manar LIPAH-LR 1ES14 Tunis2092 Tunisia BPH Center INSERM U1219 Team ERIAS LaBRI UMR5800 University of Bordeaux France Instituto Gulbenkian de Ciência Portugal Human-Centered Computing Freie Universitat Berlin Berlin4195 Germany Linköping University Swedish e-Science Research Centre Linköping Sweden Delft University of Technology Netherlands Dpto. Lenguajes y Ciencias de la Computación University of Malaga Spain ADVIS Lab. Department of Computer Science University of Illinois Chicago United States Institut de Recherche en Informatique de Toulouse France

This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI. © 2018 CEUR-WS.

关键词： Ontology

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Comprehensive proteomics metadata and integrative web portals facilitate sharing and integration of LINCS multiomics data

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Molecular & cellular proteomics : MCP 2025年 100947页

作者： Dušica Vidović Behrouz Shamsaei Stephan C Schürer Phillip Kogan Szymon Chojnacki Michal Kouril Mario Medvedovic Wen Niu Evren U Azeloglu Marc R Birtwistle Yibang Chen Tong Chen Jens Hansen Bin Hu Ravi Iyengar Gomathi Jayaraman Hong Li Tong Liu Eric A Sobie Yuguang Xiong Matthew J Berberich Gary Bradshaw Mirra Chung Robert A Everley Ben Gaudio Marc Hafner Marian Kalocsay Caitlin E Mills Maulik K Nariya Peter K Sorger Kartik Subramanian Chiara Victor Maria Banuelos Victoria Dardov Ronald Holewinski Danica-Mae Manalo Berhan Mandefro Andrea D Matlock Loren Ornelas Dhruv Sareen Clive N Svendsen Vineet Vaibhav Jennifer E Van Eyk Vidya Venkatraman Steve Finkbiener Ernest Fraenkel Jeffrey Rothstein Leslie Thompson Jacob Asiedu Steven A Carr Karen E Christianson Desiree Davison Deborah O Dele-Oni Katherine C DeRuff Shawn B Egri Alvaro Sebastian Vaca Jacome Jacob D Jaffe Daniel Lam Lev Litichevskiy Xiaodong Lu James Mullahoo Adam Officer Malvina Papanastasiou Ryan Peckner Caidin Toder Joel Blanchard Michael Bula Tak Ko Li-Huei Tsai Jennie Z Young Vagisha Sharma Ajay Pillai Jarek Meller Michael J MacCoss BD2K-LINCS DCIC Department of Molecular and Cellular Pharmacology University of Miami Miami FL 33146 Sylvester Comprehensive Cancer Center Miller School of Medicine University of Miami FL 33146. BD2K-LINCS DCIC Department of Environmental and Public Health Sciences University of Cincinnati Cincinnati OH 45220. Sylvester Comprehensive Cancer Center Miller School of Medicine University of Miami FL 33146 BD2K-LINCS DCIC Institute for Data Science and Computing University of Miami FL 33146. DToxS Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York NY 10029. DToxS Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York NY 10029 DToxS Center for Advanced Proteomics Research Rutgers University New Jersey Medical School Newark NJ 07103. HMS LINCS Center Harvard Medical School Boston MA 02115. NeuroLINCS Cedars-Sinai Medical Center Los Angeles CA 90048. NeuroLINCS Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology University of California San Francisco San Francisco CA 94158. NeuroLINCS Department of Biological Engineering MIT Cambridge MA 02142. NeuroLINCS Department of Neuroscience Johns Hopkins University Baltimore MD 21205. NeuroLINCS Departments of Psychiatry and Human Behavior and Neurobiology and Behavior University of California Irvine Irvine CA 92697. PCCSE The Broad Institute of Harvard and MIT Cambridge MA 02142. PCCSE Picower Institute for Learning and Memory Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139. PCCSE Department of Genome Sciences University of Washington Seattle WA 98195. NIH Bethesda MD 20892. BD2K-LINCS DCIC Department of Environmental and Public Health Sciences University of Cincinnati Cincinnati OH 45220. Electronic address: mellerj@ucmail.uc.edu. PCCSE Department of Genome Sciences University of Washington Seattle WA 98195. Electronic address: maccoss@uw.edu.

The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function and molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive to cells. Because of the importance of proteins and their modifications to the response of specific cellular perturbations, four of the six LINCS centers have included significant proteomics efforts in the characterization of the resulting phenotype. This manuscript aims to describe this effort and the data harmonization and integration of the LINCS proteomics data discussed in recent LINCS papers.

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A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA. Simons Center Data Analysis Simons Foundation New York NY USA. Department of Mechanical Engineering MIT Cambridge MA USA. Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA. Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA. Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA. Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria. Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA. Department of Biological Physics Eotvos University Budapest Hungary. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. esb@media.mit.edu. Department of Biological Engineering MIT Cambridge MA USA. esb@media.mit.edu. MIT Center for Neurobiological Engineering MIT Cambridge MA USA. esb@media.mit.edu. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. esb@media.mit.edu. MIT McGovern Institute for Brain Research MIT Cambridge MA USA. esb@media.mit.edu.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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Influence maximization with ε-almost submodular threshold functions 17

Influence maximization with ε-almost submodular threshold f...

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Qiang Li Wei Chen Xiaoming Sun Jialin Zhang CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Microsoft Research

ISBN: (纸本)9781510860964

Influence maximization is the problem of selecting k nodes in a social network to maximize their influence spread. The problem has been extensively studied but most works focus on the submodular influence diffusion models. In this paper, motivated by empirical evidences, we explore influence maximization in the non-submodular regime. In particular, we study the general threshold model in which a fraction of nodes have non-submodular threshold functions, but their threshold functions are closely upper- and lower-bounded by some submodular functions (we call them ε-almost submodular). We first show a strong hardness result: there is no l/nγ/c approximation for influence maximization (unless P = NP) for all networks with up to nγ ε-almost submodular nodes, where γ is in (0,1) and c is a parameter depending on ε. This indicates that influence maximization is still hard to approximate even though threshold functions are close to submodular. We then provide (l - ε)ℓ(1 - 1/ε) approximation algorithms when the number of ε-almost submodular nodes is ℓ. Finally, we conduct experiments on a number of real-world datasets, and the results demonstrate that our approximation algorithms outperform other baseline algorithms.

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A High Performance Asynchronous Non-blocking data Communication Protocol 7

A High Performance Asynchronous Non-blocking Data Communicat...

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2016 IEEE 7th International Conference on Software Engineering and Service science

作者： Guimin Huang Zhi Zheng Ya Zhou Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education Guangxi Experiment Center of Information Science Research Center on Data Science & Social Computing Guilin University of Electronic Technology

ISBN: (纸本)9781467399050

In this paper,we design a high-performance data communication message-oriented protocol(ECP),which is based on asynchronous non-blocking I/O model,used reactor design mode,introduced pipeline,and is on top of TCP without modifying the operating system underlying *** is very effective in the environment that has a great demand of network communication,especially in the case of large-scale data communication and high concurrent requests.

关键词： asynchronous non-blocking pipeline protocol communication

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