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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

Project Hephaistos - II. Dyson sphere candidates from Gaia DR3, 2MASS, and WISE

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arXiv 2024年

作者： Suazo, Matías Zackrisson, Erik Mahto, Priyatam K. Lundell, Fabian Nettelblad, Carl Korn, Andreas J. Wright, Jason T. Majumdar, Suman Observational Astrophysics Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-751 20 Sweden Department of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore India Division of Scientific Computing Department of Information Technology Uppsala University UppsalaSE-751 05 Sweden Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Penn State Extraterrestrial Intelligence Center The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University University ParkPA16802 United States Department of Physics Blackett Laboratory Imperial College LondonSW7 2AZ United Kingdom Swedish Collegium for Advanced Study Linneanum Thunbergsvägen 2 UppsalaSE-752 38 Sweden

The search for extraterrestrial intelligence is currently being pursued using multiple techniques and in different wavelength bands. Dyson spheres, megastructures that could be constructed by advanced civilizations to harness the radiation energy of their host stars, represent a potential technosignature, that in principle may be hiding in public data already collected as part of large astronomical surveys. In this study, we present a comprehensive search for partial Dyson spheres by analyzing optical and infrared observations from Gaia, 2MASS, and WISE. We develop a pipeline that employs multiple filters to identify potential candidates and reject interlopers in a sample of five million objects, which incorporates a convolutional neural network to help identify confusion in WISE data. Finally, the pipeline identifies 7 candidates deserving of further analysis. All of these objects are M-dwarfs, for which astrophysical phenomena cannot easily account for the observed infrared excess emission. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

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A Multi-Task Mean Teacher for Semi-Supervised Shadow Detecti...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhihao Chen Lei Zhu Liang Wan Song Wang Wei Feng Pheng-Ann Heng College of Intelligence and Computing Tianjin University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Engineering University of South Carolina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.

关键词： Task analysis Image edge detection Feature extraction Training Radio frequency Fuses Solid modeling

Free Viewpoint Video Synthesis Based on DIBR

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Free Viewpoint Video Synthesis Based on DIBR

IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Xuesong Gao Keqiu Li Weiqiang Chen Zhongyao Yang Wenguo Wei Yangang Cai College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking (TANK) Tianjin China State Key Laboratory of Digital Multimedia Technology Hisense Co. Ltd. Qingdao China School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

The Free Viewpoint Video (FVV) system is an advanced visual media mode, which could satisfy the audience's demand of watching from any viewpoints. It has aroused great interests in the field of multimedia and vision. This paper proposes a set of solutions, including depth estimation, virtual view interpolation and post processing, to synthesize and optimize the virtual view images. Specifically, deep learning based method is proposed to compute depth images, which is more efficient than traditional stereo matching algorithm. The virtual view can be synthesized with the help of DIBR algorithm in an arbitrary position between two reference cameras. To remove the noise caused by rendering, some post processing techniques, such as median filtering and hole filling, are employed. The extensive experimental results demonstrate that the quality of the synthesized free viewpoint video has been significantly improved using our method.

关键词： Estimation Image color analysis Cameras Three-dimensional displays Conferences Rendering (computer graphics) Streaming media

Efficient and Privacy-Preserving Speaker Verification Scheme for Home Automation Devices

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Efficient and Privacy-Preserving Speaker Verification Scheme...

IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Xuesong Gao Keqiu Li Weiqiang Chen Weifeng Hu Ziling Zhang Qi Li College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking (TANK) Tianjin China State Key Laboratory of Digital Multimedia Technology Hisense Co. Ltd. Qingdao China School of Information Science and Engineering Shandong University Qingdao China School of Cyber Engineering Xidian University Xi'an China

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

Speaker recognition technology has been widely used in home automation recently, which can provide identification for the user efficiently. However, speaker recognition technology brings security risks as well as convenience, especially the privacy disclosure of the user's voiceprint templates. This paper proposes an efficient and privacy-preserving speaker verification scheme for home automation devices, named SEASON. We modify the high-accuracy deepspeaker framework in terms of privacy, based on residual network. With SEASON, users can access the home automation devices securely. Detailed security analysis shows that SEASON can actually resist various security threats. We implement SEASON with real datasets and 48 real records from four testers. Experiments reveal that SEASON can achieve efficient and accurate verification.

关键词： Spectrogram Authentication Home automation Privacy IEEE transactions Speaker recognition

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

A topology-preserving three-stage framework for fully-connected coronary artery extraction

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arXiv 2025年

作者： Qiu, Yuehui Shan, Dandan Wang, Yining Dong, Pei Wu, Dijia Yang, Xinnian Hong, Qingqi Shen, Dinggang Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Peking Union Medical College Hospital Beijing100006 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China City University of Hong Kong 999077 Hong Kong School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai Clinical Research and Trial Center Shanghai200231 China

Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53% and 85.07%, with Hausdorff Distances (HD) of 1.07mm and 1.63mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://***/YH-Qiu/CorSegRec. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

BAT: A Versatile Bipartite Attention-based Approach for Comprehensive Truth Inference in Mobile Crowdsourcing

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jiacheng Tang, Feilong Liu, Hao Chen, Long Yu, Yichuan Zhu, Yanmin Yu, Jiadi Hou, Xiaofeng Heng, Pheng-Ann The Chinese University of Hong Kong Department of Computer Science and Engineering 999077 Hong Kong Shanghai Jiao Tong University School of Computer Science Shanghai200240 China East China Normal University School of Data Science and Engineering China Simon Fraser University School of Computing Science Canada Huawei Technologies Co. Ltd China Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios. © 2002-2012 IEEE.

关键词： Graph neural networks