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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guangjian Zheng Min Tan Jun Yu Qing Wu Jianping Fan Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781509060689

Bilinear convolutional neural networks (BCNN) model, the state-of-the-art in fine-grained image recognition, fails in distinguishing the categories with subtle visual differences. We design a novel BCNN model guided by user click data (C-BCNN) to improve the performance via capturing both the visual and semantical content in images. Specially, to deal with the heavy noise in large-scale click data, we propose a weakly supervised learning approach to learn the C-BCNN, namely W-C-BCNN. It can automatically weight the training images based on their reliability. Extensive experiments are conducted on the public Clickture-Dog dataset. It shows that: (1) integrating CNN with click feature largely improves the performance; (2) both the click data and visual consistency can help to model image reliability. Moreover, the method can be easily customized to medical image recognition. Our model performs much better than conventional BCNN models on both the Clickture-Dog and medical image dataset.

关键词： Reliability Visualization Image recognition Data models Training Feature extraction Computational modeling

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Experiments in Routing Vehicles for Municipal Services

Experiments in Routing Vehicles for Municipal Services

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International Conference on High Performance Computing and simulation

作者： Imran Mahmood Sahar Idwan Junaid Ahmed Zubairi Izzeddin Matar Center for Research in Modeling & Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology CS and Engineering Department School of Engineering American U of Ras Al Khaimah Ras al Khaimah UAE and Comp Sci & Apps Dept Hashemite University Computer and Information Sciences Department College of Liberal Arts and Sciences State University of New York at Fredonia Faculty of Information Technology Software Engineering Department University of Petra

ISBN: (纸本)9781538678800

In this paper, route planning of waste collection trucks using R&R (Ruin and Recreate) approach is explored. We assume the trucks are guided by the central depot in selecting the optimal route for waste collection. Heuristic algorithms are simulated to find the optimal routes for the waste management fleet. Through the use of smart dumpsters that can communicate the current level of waste using sensors and communication modules, we aim to reduce the number of trucks used, the total time taken and total distance traveled by the fleet in a day. Our work is based on variations of CVRPTW (Capacitated Vehicle Routing Problem with Time Window Constraint). The central management system selects the dumpsters, based on their waste levels, in descending order, and dispatches appropriate number of trucks, with path assignments using Ruin and Recreate (R&R) approach of the VRPTW strategy. The municipal authority saves transit time, fuel cost and service time by using our approach, through a simulation of smart waste collection.

关键词： Smart Dumpster Capacitated Vehicle Routing Problem with Time Window Constraint Ruin & Recreate Search heuristic Waste management simulation

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Towards blockchain empowered trusted and accountable data sharing and collaboration in mobile healthcare applications

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EAI Endorsed Transactions on Pervasive Health and Technology 2018年第15期4卷

作者： Liang, Xueping Shetty, Sachin Tosh, Deepak Bowden, Daniel Njilla, Laurent Kamhoua, Charles Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Department of Computer Science University of Texas at El Paso El PasoTX United States Sentara Healthcare NorfolkVA United States Cyber Assurance Branch Air Force Research Laboratory RomeNY United States Network Security Branch of the U.S Army Research Laboratory Adelphi MD United States

Enabled by mobile and wearable technology, personal health data delivers immense and increasing value for healthcare, benefiting both care providers and medical research. The secure and convenient sharing of personal health data is crucial to the improvement of the interaction and collaboration of the healthcare industry. Faced with the potential privacy issues and vulnerabilities existing in current personal health data storage and sharing systems, as well as the blockchain integration concerns summarized in this paper, an innovative user-centric health data sharing solution by utilizing a decentralized but permissioned blockchain is proposed to protect privacy and enhance access management, with the help of channel formation scheme supported by the blockchain. By developing a web application for Personal Health Data Management (PHDM) systems, the individuals are capable of synchronizing sensor data from wearable devices with online account and controlling data access from any third parties. A mobile application is deployed to collect health data from personal wearable devices, manual input, and medical devices, and synchronize data to the cloud for data sharing with healthcare providers and health insurance companies. To preserve the integrity of health data, a proof of integrity and validation, is made available to each record, which is permanently retrievable from cloud database and is anchored to the blockchain network. Moreover, for scalable and performance considerations, a tree-based data processing and batching method is adopted to deal with large data sets of personal health data collected and uploaded by the mobile platform. To enable a trusted data access record, the Intel Software Extensions technology is utilized to ensure the accountability for data access and token based access control scheme is enhanced with the trusted hardware. Analysis shows that the proposed approach provides user privacy and accountability with acceptable overhead and scalabili

关键词： Blockchain

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Deep Mixture of Experts with Diverse Task Spaces

Deep Mixture of Experts with Diverse Task Spaces

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jianping Fan Tianyi Zhao Zhenzhong Kuang Zhou Yu Jun Yu Department of Computer Science UNC-Charlotte Charlotte NC USA Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China

In this paper, a deep mixture algorithm is developed to support large-scale visual recognition (e.g., recognizing tens of thousands of object classes) by seamlessly combining a set of base deep CNNs (AlexNet) with diverse task spaces, e.g., such base deep CNNs (i.e., diverse experts) are trained to recognize different subsets of tens of thousands of object classes rather than the same set of object classes. Our experimental results have demonstrated that our deep mixture algorithm can achieve very competitive results on large-scale visual recognition.

关键词： Ontologies Proposals Prediction algorithms Visualization Semantics Atomic layer deposition

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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CRASH ME INSIDE THE CLOUD: A FAULT RESILIENT FRAMEWORK FOR PARALLEL AND DISCRETE EVENT simulation 17

CRASH ME INSIDE THE CLOUD: A FAULT RESILIENT FRAMEWORK FOR P...

引用

simulation Multiconference

作者： Asad W. Malik Imran Mahmood Center for Research in Modeling Simulation & Vision (Crimson) Department of Computing School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)

ISBN: (纸本)9781510838284

The adoption of cloud computing has increased over the years due to its pay-as-you-go model. The other important feature that cloud provides is scalability achieved through virtualization. The dynamic burst of users is served through virtual machines. The cloud data center comprises of pool of commodity hardware systems used to provide computing capacity on request. The use of commodity hardware increases the failure rate. Therefore, the execution of Parallel Discrete Event simulation (PDES) over fragile cloud environment can lead to erroneous simulation results. Moreover, simply replacing faulty VMs is not the possible solution for PDES. In this paper, we exploit the execution of PDES codes over cloud environment and propose a fault resilient framework that dynamically handles the VM failure without restarting the entire simulation. Our benchmarking results demonstrate the effectiveness of the proposed protocol, compared with the widely used and traditional PDES protocol i.e. Time Warp.

关键词： Virtual Machine Cloud computing PDES Time Warp Fault Tolerant diethylstilbestrol monophosphate Cloud Computing discrete event simulation Crash virtual machines Clouds Fault tolerance Event driven simulation Parallel Lines Resilience computational power

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Convolutional neural networks for intestinal hemorrhage detection in wireless capsule endoscopy images

Convolutional neural networks for intestinal hemorrhage dete...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Panpeng Li Ziyun Li Fei Gao Li Wan Jun Yu Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University School of Communication Engineering Hangzhou Dianzi University College of Computer Science Chongqing University

ISBN: (纸本)9781509060689

Wireless capsule endoscopy (WCE) can painlessly capture a large number of images inside the intestine. However, only a small portion of these WCE images contain hemorrhage. It is thus critical to develop automated hemorrhage detection method to facilitate the diagnosis of intestinal diseases. However, automated hemorrhage detection is complicated by 1) the extreme imbalance between the amount of hemorrhage images and that of normal images; and 2) the variety of the appearance, texture, and luminance inside the intestine. In this paper, we proposed to learn a robust intestinal hemorrhage detection model via Convolutional Neural Networks (CNNs), because of CNNs' extraordinary performance in solving various image understanding tasks. Specially, we explored different CNN architectures and data augmentation methods. Besides, we investigated the correlation between hemorrhage detection accuracy and image quality. Across about 1.3k hemorrhage images and 40k normal images, the learned CNN model achieves an F-measure of 98.87%.

关键词： Hemorrhaging Optical wavelength conversion Endoscopes Machine learning Biomedical imaging Training Wireless communication

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Vehicular ad hoc network (VANET): A survey, challenges, and applications

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2017年 548卷 39-51页

作者： Rasheed, Asim Gillani, Saira Ajmal, Sana Qayyum, Amir Capital University of Science & Technology Islamabad Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan Center of Modeling and Simulation National University of Sciences & Technology Islamabad Pakistan

An ad hoc network consisting of vehicles has emerged as an interesting but challenging domain where a lot of new application may find their place. Though research in this field is on since last two decades, large-scale practical implementation still require some time. In this paper, a survey of current challenges and potential applications, incorporating medium access control schemes, routing approaches, hardware and spectrum issues, and security and privacy issues for VANETs, is presented. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Surveys

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simulation of unsteady blood flows in a patient-specific compliant pulmonary artery with a highly parallel monolithically coupled fluid-structure interaction algorithm

arXiv

引用

arXiv 2018年

作者： Kong, Fande Kheyfets, Vitaly Finol, Ender Cai, Xiao-Chuan Modeling and Simulation Idaho National Laboratory P.O. Box 1625 Idaho FallsID83415-3840 United States School of Medicine University of Colorado Denver AuroraCO80045-7109 United States Department of Mechanical Engineering University of Texas at San Antonio San AntonioTX78249 United States Department of Computer Science University of Colorado Boulder BoulderCO80309-0430 United States

Computational fluid dynamics (CFD) is increasingly used to study blood flows in patient-specific arteries for understanding certain cardiovascular diseases. The techniques work quite well for relatively simple problems, but need improvements when the problems become harder in the case when (1) the geometry becomes complex (from a few branches to a full pulmonary artery), (2) the model becomes more complex (from fluid-only calculation to coupled fluid-structure interaction calculation), (3) both the fluid and wall models become highly nonlinear, and (4) the computer on which we run the simulation is a supercomputer with tens of thousands of processor cores. To push the limit of CFD in all four fronts, in this paper, we develop and study a highly parallel algorithm for solving a monolithically coupled fluid-structure system for the modeling of the interaction of the blood flow and the arterial wall. As a case study, we consider a patient-specific, full size pulmonary artery obtained from CT (Computed Tomography) images, with an artificially added layer of wall with a fixed thickness. The fluid is modeled with a system of incompressible Navier-Stokes equations and the wall is modeled by a geometrically nonlinear elasticity equation. As far as we know this is the first time the unsteady blood flow in a full pulmonary artery is simulated without assuming a rigid wall. The proposed numerical algorithm and software scale well beyond 10,000 processor cores on a supercomputer for solving the fluid-structure interaction problem discretized with a stabilized finite element method in space and an implicit scheme in time involving hundreds of millions of unknowns. Copyright © 2018, The Authors. All rights reserved.

关键词： Finite element method

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Data-driven adaptive threshold control for bike share systems 17

Data-driven adaptive threshold control for bike share system...

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simulation Winter Conference

作者： Felisa J. Vázquez-Abad Silvano Bernabel Michael C. Fu Department of Computer Science CUNY Institute for Computer Simulation Stochastic Modeling and Optimization New York NY USA Robert H. Smith School of Business University of Maryland College Park MD USA

ISBN: (纸本)9781538634271

When it comes to building a successful public bike share such as the NYC CitiBike system, there are many important questions to answer. There is no shortage of work being done on finding the most efficient way to redistribute bikes to stations. Equally important to how to distribute bikes is the question of when to redistribute bikes. Redistribute too infrequently and customers become frustrated, resulting in decreased revenue. Redistribute too frequently and the cost of redistribution becomes prohibitively high. In this piece of research, we attempt to find the optimal time to call for the redistribution of bikes to minimize cost and retain maximum membership.

关键词： Adaptation models Dynamic scheduling Vehicle dynamics Optimization Transportation Computational modeling Stochastic processes

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