检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Qi, Mengshi Li, Weijian Yang, Zhengyuan Wang, Yunhong Luo, Jiebo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Department of Computer Science University of Rochester United States

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Online Robust Lagrangian Support Vector Machine against Adversarial Attack

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Procedia Computer Science 2018年 139卷 173-181页

作者： Yue Ma Yiwei He Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

In adversarial environment such as intrusion detection and spam filtering, the adversary-intruder or spam advertiser may attempt to produce contaminate training instance and manipulate the learning of classifier. In order to keep good classification performance, many robuster learning methods have been proposed to deal with the adversarial attack. Support Vector Machines(SVMs) is a kind of successful approach in the adversarial classification tasks and the investigation of robust SVMs is very popular. However, in many real application, the data including stain instance is coming dynamically. Batch learning which needs retraining when encountering new samples, will consume more computing resources. In this paper, we propose a robust Lagrangian support vector machine (RLSVM) with modified kernel matrix and explore the online learning algorithm on it. The experimental results show the robustness of RLSVM against label noise produced by adversaries under the online adversarial environment.

关键词： adversarial attack poison attack label noise online learning Lagrangian SVM

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Corrigendum to "Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario" [Radiother. Oncol. 161 (2021) 230-240]

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Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2021年 163卷 119-120页

作者： Ruijie Yang Xueying Yang Le Wang Dingjie Li Yuexin Guo Ying Li Yumin Guan Xiangyang Wu Shouping Xu Shuming Zhang Maria F Chan Lisheng Geng Jing Sui Department of Radiation Oncology Peking University Third Hospital Beijing China. School of Physics Beihang University Beijing China. Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China. Department of Radiation Therapy Henan Cancer Hospital Zhengzhou China. Department of Radiation Oncology The First Affiliated Hospital of Zhengzhou University Zhengzhou China. Department of Oncology The First Affiliated Hospital of Chongqing Medical University Chongqing China. Department of Radiation Therapy Yantai Yuhuangding Hospital Yantai China. Department of Radiotherapy Shanxi Provincial Cancer Hospital Xi'an China. Department of Radiation Oncology General Hospital of People's Liberation Army Beijing China. Department of Radiation Oncology Peking University Third Hospital Beijing China Department of Ultrasound Beijing Hospital Beijing China. Medical Physics Department Memorial Sloan Kettering Cancer Center New York NY United States. School of Physics Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing China. Electronic address: lisheng.geng@***. School of Artificial Intelligence University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Electronic address: jsui@***.

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Max-min fairness user scheduling and power allocation in full-duplex OFDMA systems

arXiv

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arXiv 2018年

作者： Zhang, Xiaozhou Chang, Tsung-Hui Liu, Ya-Feng Shen, Chao Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization

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Angular distribution measurement for ^12C fragmentation via ^12C ＋ ^12C scattering at 100 MeV/u

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Chinese Physics C 2018年第7期42卷 30-36页

作者： Wei-Wei Qu Gao-Long Zhang Guo-Yu Tian Zhi-Qiang Chen Shou-Ping Xu Royichi Wada State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and ProtectionSoochow UniversitySuzhou 215123China Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Suzhou 215123China School of Physics and Nuclear Energy Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing100083China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China Department of Radiation Oncology PLA General HospitalBeijing 100853China Cyclotron Institute Texas A&M UniversityCollege StationTexas 77843USA

The angular distributions and energy spectra of 11B, 10B, and 9Be fragments of 12C in the angular range from 1.0° to 7.5° at 100 MeV/u were obtained via 12C ＋ 12C scattering. Detailed comparisons are presented between the experimental data and the modified antisymmetrized molecular dynamics （AMD-FM）, binary intranuclear cascade model （BIC） and Liège intranuclear cascade model （INCL＋＋）. The experimental angular distributions and energy spectra are well reproduced by the AMD-FM calculations but fail to be reproduced by the physical models installed in the Geant4 program, including the BIC and INCL＋＋ models.

关键词： breakup angular distribution AMD Geant4 simulation

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Correction to: Performance study of trial M-THGEM

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Radiation Detection Technology and Methods 2020年第1期4卷 83-83页

作者： Shao-Qing Deng Wen-Qi Yan Dao-Jin Hong Bo-Xiang Yu Gao-Long Zhang Yu-Guang Xie School of Physics and Nuclear Energy Engineering Beihang University Beijing China Institute of High Energy Physics Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Beijing China University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China

Upon request from the authors, the sequence of grant numbers are corrected as: Nos. 11575221, 11975040, U1832130, 11475013.

关键词：

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Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer Science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

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Spatiotemporal data organization and application research

Spatiotemporal data organization and application research

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ISPRS Geospatial Week 2017

作者： Tan, Chengguo Yan, Suiyu Big Data Innovation Center State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Wuda Geoinformatics Co. Ltd. Wuhan China

Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications, this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle. © Authors 2017. CC BY 4.0 License.

关键词： Smart city

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How China Deals with big data

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Annals of data Science 2017年第4期4卷 433-440页

作者： Shi, Yong Shan, Zhiguang Li, Jianping Fang, Yufei School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Informatization Research Department State Information Center Beijing100045 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China School of Computer and Communication Engineering University of Science and Technology Beijing100083 China Informatization Research Department State Information Center Beijing100045 China

On September 5, 2015, the state Council of Chinese Government, China’s cabinet formally announced its Action Framework for Promoting big data (***, 2015). This is the milestone for China to catch up the global wave of big data. Since 2012 big data became a hot issue for scientific communities as well as the governments of many countries (Lazer et al. in Science 343:1203–1205, 2014;Einav et al. in Science 345:715, 2014;Cate in Science 346:818, 2014;Khoury and Ioannidis in Science 346:1054–1055, 2014). At the 2013 G8 Summit, the leaders of Canada, France, Germany, Italy, Japan, Russia, U.S.A. and United Kingdom agreed on an "open government plan" (***/government/publications/open-data-charter/g8-open-data-charter-and-technical-annex, 2013). China’s framework, however, mainly emphasizes the integration of all trans-departmental data and establishes a number of government-driven national big data platforms so as to provide big data services to research, public and enterprises. The framework not only demonstrates a strong commitment of the Chinese government on big data, but also covers a wide range of governmental branches, enterprises and institutions far more than that of other countries. In addition, the framework shows an interpretation of big data that differs from other countries. If its objective is achieved, China would become a strong "big data country". © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： big data

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MedShapeNet - A Large-Scale dataset of 3D Medical Shapes for Computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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