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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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Attentive relational networks for mapping images to scene graphs

arXiv

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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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Finding Patterns of Stock Returns Based on Sequence Alignment

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Procedia Computer Science 2018年 139卷 256-262页

作者： Yong Shi Ye-ran Tang Wen Long School of Economics & Management University of Chinese Academy of Sciences Beijing 100190 P.R. China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 P.R. China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

In this paper, we propose the method based on sequence alignment to find patterns of stock returns. We use 5 minutes high frequency data of CSI 300 index to test this method, and find we can predict the sharply rise or drop for the stock returns according to patterns of the sample symbol sequence. The empirical analysis suggests it is possible to find and predict patterns in stock returns based on the method of sequence alignment.

关键词： finding patterns sequence alignment stock returns

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Deep Multi-Metric Learning for Person Re-Identification

Deep Multi-Metric Learning for Person Re-Identification

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongxin Ge Xinqian Gu Min Chen Hongxing Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education (Chongqing University) Chongqing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

关键词： Measurement Image color analysis Feature extraction Training Machine learning Kernel Learning systems

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

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22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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

arXiv

引用

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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Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer Science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

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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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Soft-gated warping-GAN for pose-guided person image synthesis 18

Soft-gated warping-GAN for pose-guided person image synthesi...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Haoye Dong Xiaodan Liang Ke Gong Hanjiang Lai Jia Zhu Jian Yin School of Data and Computer Science Sun Yat-sen University and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Intelligent Systems Engineering Sun Yat-sen University School of Data and Computer Science Sun Yat-sen University School of Computer Science South China Normal University

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets.

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Dynamic Harmonic Modeling and Characteristics Analysis of Photovoltaic Converter

Dynamic Harmonic Modeling and Characteristics Analysis of Ph...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ding Wang Yangwu Shen Xun Mao Ao Pi Bing Zeng Chengye Zhang Power grid technology center State Grid Hunan Electric Power Company Limited Research Institute Changsha China Power grid technology center State Grid Anhui Electric Power Co. Ltd. Research Institute Hefei China Big data center state key laboratory of offshore wind power technology and testing XEMC Wind power Co. Ltd. Changsha China School of Electrical & Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

Photovoltaic converters whose dynamic characteristics determine the behavior of grid-connected device are important equipment for new energy generation. Therefore, to analyze the harmonic characteristics generated by grid-connected photovoltaic devices, it is necessary to firstly study the mechanism of harmonic generation, then build the dynamic harmonic model of photovoltaic power generation unit, and discuss the influencing factors. In this paper, based on the deep comprehension of harmonic characteristics, a Norton equivalent circuit that described the harmonic characteristics of grid-connected converter of photovoltaic power generation unit is established. Besides, the effects of the power output of photovoltaic power generation unit and the filter structure on converter's harmonic output are investigated. The accuracy of the proposed model is verified by comparing the results between the theoretical calculation and simulation.

关键词： Power harmonic filters Harmonic analysis Modulation Photovoltaic systems

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