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作者： Zhu, Suguo Fang, Zhenying Gao, Fei Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China

Recently, deep learning is widely developed in computer vision applications. In this paper, a novel simple tracker with deep learning is proposed to complete the tracking task. A simple fully convolutional Siamese network is applied to capture the similarity between different frames. Nevertheless, the detailed information from lower layers, which is also important for locating the target object, is not considered into the tracking task. In this paper, the detailed information from two lower layers is considered into the response map to improve the performance and not to increase much time spent. This leads more significant improvement for feature representation and localization of the target object. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the baseline and the state-of-the-art trackers. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Correction to: Batch mode active learning via adaptive criteria weights

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Applied Intelligence 2020年第6期51卷 3490-3490页

作者： Li, Hao Wang, Yongli Li, Yanchao Xiao, Gang Hu, Peng Zhao, Ruxin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Science and Technology on Complex Systems Simulation Laboratory Beijing China

A correction to this paper has been published: https://***/10.1007/s10489-020-02146-9

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Multimodal unified attention networks for vision-and-language interactions

arXiv

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arXiv 2019年

作者： Yu, Zhou Cui, Yuhao Yu, Jun Tao, Dacheng Tian, Qi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney Australia Noah's Ark Lab Huawei China

Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents. Existing state-of-the-art approaches use co-attention models to associate each visual object (e.g., image region) with each textual object (e.g., query word). Despite the success of these co-attention models, they only model inter-modal interactions while neglecting intra-modal interactions. Here we propose a general 'unified attention' model that simultaneously captures the intra- and inter-modal interactions of multimodal features and outputs their corresponding attended representations. By stacking such unified attention blocks in depth, we obtain the deep Multimodal Unified Attention Network (MUAN), which can seamlessly be applied to the visual question answering (VQA) and visual grounding tasks. We evaluate our MUAN models on two VQA datasets and three visual grounding datasets, and the results show that MUAN achieves top level performance on both tasks without bells and whistles. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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A Fine-grained Privacy-preserving k-means Clustering Algorithm Upon Negative Databases

A Fine-grained Privacy-preserving k-means Clustering Algorit...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Dongdong Zhao Xiaoyi Hu Shengwu Xiong Jing Tian Jianwen Xiang Jing Zhou Huanhuan Li Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan Hubei China Dalian Naval Academy Operation Software and Simulation Institute School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Nowadays, privacy protection has become an important issue in data mining. k-means algorithm is one of the most classical data mining algorithms, and it has been widely studied in the past decade. Negative database (NDB) is a new type of data representation which can protect privacy while supporting distance estimation, so it is promising to apply NDBs to privacy-preserving k-means clustering. Existing privacy-preserving k-means clustering algorithms based on NDBs could effectively protect data privacy, but their clustering performance has a non-negligible degradation. In this paper, we propose a new NDB generation algorithm (named QK-hidden algorithm), and based on this algorithm, we propose a privacy-preserving k-means algorithm. The proposed algorithm can control the accuracy of distance estimation in a fine-grained manner, and thus it can control the clustering results granularly. Experimental results demonstrate the proposed algorithm has better clustering performance than existing privacy-preserving k-means algorithms based on NDBs.

关键词： Clustering algorithms Estimation Databases Data privacy Euclidean distance computer science Degradation

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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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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Sanitation approach toward resource recovery in rural and semi-urban centers: Insight from South South Nigeria

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Environmental Quality Management 2018年第1期28卷 13-19页

作者： Odey, Emmanuel Alepu Li, Zifu Giwa, Abdulmoseen Segun Etokidem, Esther Uduak School of Energy and Environmental Engineering Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing China State key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing China Department of Electronics and Computer Technology University of Calabar Calabar Nigeria

Recent sanitation situations in Nigeria present public health problems, especially in rural and semi-urban centers. Local and national government bodies in the country have defined policies to provide adequate sanitation for the populations under their control as stated in relevant decrees and policies. However, those who work as health providers in Nigeria regard conditions in many parts of the country as unhygienic and unhealthy due to a lack of adequate sanitation and because the sanitation facilities that are in place are often ineffective. Interviews with householders in rural and semi-urban centers showed that more than 72% of the people in rural communities lack access to adequate sanitation and toilet systems, especially in public institutions. While in semi-urban centers about 91% of the residents use septic tanks without treatment facility, the remaining 8% use toilet systems such as community toilets, wet pit latrines, and open defecation. Both rural and semi-urban centers do not use urine-diverting dry toilets or septic tanks with treatment facilities. Given the present sanitation situation, this study proposes a modern toilet system in rural communities that will enhance the recovery of biogas and nutrients as fertilizer for soil amendment through appropriate implementation, use, and maintenance. The results could be useful as a support for decisions on policies and strategies for adequate and improved sanitation facilities in rural and semi-urban centers in South South Nigeria. © 2018 Wiley Periodicals, Inc.

关键词： biogas fecal sludge rural centers sanitation semi-urban centers soil amendment South South Nigeria

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Combining Spectrum-Based Fault Localization and Statistical Debugging: An Empirical Study

Combining Spectrum-Based Fault Localization and Statistical ...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Jiajun Jiang Ran Wang Yingfei Xiong Xiangping Chen Lu Zhang Key Laboratory of High Confidence Software Technologies Ministry of Education (PKU) Department of Computer Science and Technology Peking University Beijing China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and Design Sun Yat-sen University Guangzhou China

Program debugging is a time-consuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrum-based fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored. In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores. Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, fine-grained data collection significantly outperforms coarse-grained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches. According to our empirical study, we propose a new fault localization approach, PREDFL (Predicate-based Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a state-of-the-art fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of state-of-the-art fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top-1, which reveals that PREDFL complements existing techniques.

关键词： Debugging Data collection Instruments Runtime Systematics Computational modeling Data models

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Li, An, and Morozov Reply:

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Physical Review Letters 2019年第11期123卷 119602-119602页

作者： Guodong Li Qi An Sergey I. Morozov Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan 430070 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China Department of Chemical and Materials Engineering University of Nevada Reno Reno Nevada 89557 USA Department of Computer Simulation and Nanotechnology South Ural State University Chelyabinsk 454080 Russia

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CNN features for emotional impact of movies task

CNN features for emotional impact of movies task

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2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018

作者： Yi, Yun Wang, Hanli Li, Qinyu Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques Gannan Normal University Ganzhou341000 China Department of Computer Science and Technology Tongji University Shanghai201804 China Department of Computer Science Lanzhou City University Lanzhou730070 China

A framework is proposed to predict the emotional impact of movies by using the audio, action, object and scene features. First, four state-of-the-art features are extracted from four pre-trained convolutional neural networks to depict video contents, and an early fusion strategy is used to combine vectors of these features. Then, the linear support vector regression or linear support vector machine is employed to separately learn affective models or fear models, and the strategy of cross-validation is utilized to select training parameters. Finally, the Gaussian blur function is used to smooth scores of video segments. The experiments show that the combination of these features obtains promising results. Copyright held by the owner/author(s). © 2018 CEUR-WS. All rights reserved.

关键词： Neural networks

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