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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Dezhi Ran Yuanxing Zhang Wenhan Zhang Kaigui Bian School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Short-form video feed has become one of the most popular ways for billions of users to interact with content, where users watch short-form videos of a few seconds one-by-one in a session. The common solution to improve the quality of experience (QoE) for short-form video feed is to treat it as a common sequential item recommendation problem and maximize its click-through rate prediction. However, the QoE of short-form video streaming under dynamic network conditions is jointly determined by both recommendation accuracy and streaming efficiency, and thus merely considering recommendation will lead to the degradation of the QoE of the streaming system for the audience. In this paper, we propose SSR, namely the short-form video streaming and recommendation system, which consists of a Transformer-based recommendation module and a reinforcement learning (RL) based bitrate adaptation streaming module. Specifically, we use Transformer to encode the session into a representation vector and recommend proper short-form videos based on the user's recent interest and the timeliness characteristics of short-form video contents. Then, the RL module combines the representation of session and other observations within the playback, and yields the appropriate bitrate allocation for the next short-form video to optimize a given QoE objective. Trace-driven emulations verify the efficiency of SSR compared to several state-of-the-art recommender systems and streaming strategies with at least 10%-15% QoE improvement under various QoE objectives.

关键词： Bit rate Streaming media Robustness Sensors Quality of experience Feeds Optimization

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

arXiv

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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

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CFP: A Cross-layer Recommender System with Fine-grained Preloading for Short Video Streaming at Network Edge

CFP: A Cross-layer Recommender System with Fine-grained Prel...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Dezhi Ran Yuanxing Zhang Ye Yuan Kaigui Bian School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Nowadays, short video feed has attracted billions of mobile users all around the world to interact with content effortlessly, yielding an explosive growth of short video commerce. Typically, users watch full-screen short videos of a few seconds one-by-one in a watch-list generated by recommender systems, skipping those they are not interested in. However, the recommender system at the cloud makes a user-interest-specific decision mostly based on the users' behavior data collected within the application itself (e.g., users' view history), without examining the lower-layer network and communication statistics. When the playback choked due to the limited network bandwidth, the user will probably skip the video, leading to a waste of bandwidth and degradation of the user's quality of experience (QoE). Meanwhile, the excessive number of user requests to video contents raises a heavy computational load and communication cost for the recommender system at the cloud to determine which videos to be recommended and delivered to each user in a real-time manner. The advance of edge computing provides a promising avenue of deploying edge nodes with caches (e.g., household devices) beyond cloud and edge servers, such that the recommender system in the cloud can place popular video contents closer to client users, and meanwhile the contents are delivered to client users with good network condition. In this paper, we propose CFP, a cross-layer recommender system for short video streaming with fine-grained preloading technique at the network edge. CFP jointly optimizes the recommendation effect of the video application and the content preloading efficiency under various network conditions at the network edge. CFP takes a two-stage approach: the cloud server first seeks to perform edge-wise instead of user-interest-specific recommendation with neural collaborative filtering recommender, preloading a list of candidate videos to edge nodes, and each edge node, deploying the GRU with at

关键词： Cloud computing Emulation Bandwidth Streaming media Quality of experience Servers Recommender systems

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Event detection and evolution in multi-lingual social streams

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Frontiers of Computer science 2020年第5期14卷 213-227页

作者： Yaopeng Liu Hao Peng Jianxin Li Yangqiu Song Xiong Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Department of Computer Science and Engineering HKUSTHong Kong99907China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China

Real-life events are emerging and evolving in social and news *** methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual *** this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and ***,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec *** propose an 8-tuple to describe event for correlation analysis and evolution graph *** evaluate the MLEM model using a massive human-generated dataset containing real world *** results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

关键词： event detection event evolution stream processing multi-lingual anomaly detection

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A Survey on Graph Processing Accelerators:Challenges and Opportunities

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Journal of Computer science & Technology 2019年第2期34卷 339-371页

作者： Chuang-Yi Gui Long Zheng Bingsheng He Cheng Liu Xin-Yu Chen Xiao-Fei Liao Hai Jin National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Computing National University of Singapore Singapore 117418 Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Graph is a well known data structure to represent the associated relationships in a variety of applications,e.g.,data science and machine *** a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures,dedicated hardware solutions,also referred to as graph processing accelerators,are essential and emerging to provide the benefits significantly beyond what those pure software solutions can *** this paper,we conduct a systematical survey regarding the design and implementation of graph processing ***,we review the relevant techniques in three core components toward a graph processing accelerator:preprocessing,parallel graph computation,and runtime *** also examine the benchmarks and results in existing studies for evaluating a graph processing ***,we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware *** finally present and discuss several challenges in details,and further explore the opportunities for the future research.

关键词： graph processing accelerator domain-specific architecture performance energy efficiency

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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Global static stability and its relation to gravity waves in the middle atmosphere

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Earth and Planetary Physics 2020年第5期4卷 504-512页

作者： Xiao Liu JiYao Xu Jia Yue Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control School of Mathematics and Information SciencesHenan Normal UniversityXinxiang Henan 453007China State Key Laboratory of Space Weather Center for Space Science and Applied ResearchChinese Academy of SciencesBeijing 100190China School of Astronomyand Space Science University of the Chinese Academy of ScienceBeijing 100049China Catholic University of America WashingtonDC 20064USA National Aeronautics and Space Administration(NASA)Goddard Space Flight Center GreenbeltMD 20771USA

The global atmospheric static stability(N2)in the middle atmosphere and its relation to gravity waves(GWs)were investigated by using the temperature profiles measured by the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instrument from 2002 to *** low latitudes,a layer with enhanced N2 occurs at an altitude of^20 km and exhibits annual oscillations caused by tropopause inversion *** an altitude of^70 km,enhanced N2 exhibits semiannual oscillations at low latitudes caused by the mesosphere inversion layers and annual oscillations at high latitudes resulting from the downward shift of the summer *** correlation coefficients between N2 and GW amplitudes can be larger than 0.8 at latitudes poleward of^40°N/*** observation provides factual evidence that a large N2 supports large-amplitude GWs and indicates that N2 plays a dominant role in maintaining GWs at least at high latitudes of the middle *** evidence also partially explains the previous results regarding the phase changes of annual oscillations of GWs at high latitudes.

关键词： atmospheric static stability gravity waves annual oscillation semiannual oscillation mesopause

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Unsupervised domain adaptation in semantic segmentation based on pixel alignment and self-training

arXiv

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arXiv 2021年

作者： Dong, Hexin Yu, Fei Zhao, Jie Dong, Bin Zhang, Li Center for Data Science Peking University Beijing China Center for Data Science in Health and Medicine Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China

This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans. Experiment results show that PAST has outperformed the non-UDA baseline significantly, and it received rank-2 on CrossMoDA validation phase Leaderboard with a mean Dice score of 0.8395. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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Local global information aggregation graph convolution for skeleton-based action recognition

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Neurocomputing 2025年 640卷

作者： Xie, Shichong Li, Shengze Chen, Peng Wang, Bing Zhang, Jun School of Artificial Intelligence Anhui University Hefei230601 China School of Electrical Engineering and Automation Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Institute of Health Sciences and Technology & School of Internet Anhui University Hefei230601 China School of Management Science and Engineering Anhui University of Finance & Economics Bengbu233030 China Anhui Rocvision Intelligent Technology Co. Ltd Hefei230001 China

In the field of human action recognition based on skeletal data, how to capture the complex features of skeletal sequences in both spatial and temporal dimensions is a significant challenge. Existing Graph Convolutional Network (GCN) methods typically focus on extracting spatial features using various approaches, while neglecting the temporal characteristics inherent in human actions. In this paper, we propose the L(ocal) G(lobal)-GCN, a novel approach that emphasizes the under-explored temporal dimension by segmenting time to capture both local and global information. We also introduce a relative position embedding method tailored for skeletal data to enhance the representation capability of graph convolution. Additionally, we propose a partitioning method that reduces computational complexity while retaining all information, leveraging the sequential nature of skeletal data. Our LG-GCN achieves and surpasses the state-of-the-art accuracy across three widely employed datasets: NTU RGB+D with 97.3% (X-View), NTU RGB+D 120 with 91.1% (X-Set), and NW-UCLA with 96.6%. © 2025

关键词： Network theory (graphs)

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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