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International Conference on Computer Vision (ICCV)

作者： Aiming Hao Yuecong Min Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665428132

In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network. However, due to the limitation of chain rules in back-propagation, the visual module is hard to adjust for seeking optimized visual features. As a result, it enforces that the contextual module focuses on contextual information optimization only rather than balancing efficient visual and contextual information. In this paper, we propose a Self-Mutual Knowledge Distillation (SMKD) method, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously. Specifically, the visual and contextual modules share the weights of their corresponding classifiers, and train with CTC loss simultaneously. Moreover, the spike phenomenon widely exists with CTC loss. Although it can help us choose a few of the key frames of a gloss, it does drop other frames in a gloss and makes the visual feature saturation in the early stage. A gloss segmentation is developed to relieve the spike phenomenon and decrease saturation in the visual module. We conduct experiments on two CSLR bench-marks: PHOENIX14 and PHOENIX14-T. Experimental results demonstrate the effectiveness of the SMKD.

关键词： Training Deep learning Visualization Computer vision Gesture recognition Assistive technologies Benchmark testing

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs

arXiv

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arXiv 2023年

作者： Yang, Sikun Zha, Hongyuan School of Computing and Information Technology Great Bay University Dongguan523000 China Great Bay Institute for Advanced Study Great Bay University China Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Chinese University of Hong Kong Shenzhen China

Continuously-observed event occurrences, often exhibit self- and mutually-exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynamics may also change over time, with certain periodic trends. We propose a novel variational auto-encoder to capture such a mixture of temporal dynamics. More specifically, the whole time interval of the input sequence is partitioned into a set of sub-intervals. The event dynamics are assumed to be stationary within each sub-interval, but could be changing across those sub-intervals. In particular, we use a sequential latent variable model to learn a dependency graph between the observed dimensions, for each sub-interval. The model predicts the future event times, by using the learned dependency graph to remove the non-contributing influences of past events. By doing so, the proposed model demonstrates its higher accuracy in predicting inter-event times and event types for several real-world event sequences, compared with existing state of the art neural point processes. © 2023, CC BY.

关键词： Dynamics

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

arXiv

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arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams

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An interpretability analysis of travel decision learning in cyber-physical-social-systems 1

An interpretability analysis of travel decision learning in ...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Guo, Miao Ye, Peijun Liu, Xudong Liu, Sheng Xiong, Gang Zhang, Liguo Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

In the few decades, the rise of Internet and social media has fundamentally changed the management, control, and operation modes of modern engineering and social systems like defense, energy, critical health care and ... 详细信息

ISBN: (纸本)9781665433372

关键词： Deep learning Social networking (online) Scalability Perturbation methods Neurons Transportation Transforms

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Fast calibration of agent-based model using mean-field approach 1

Fast calibration of agent-based model using mean-field appro...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Lu, Wanze Xiong, Gang Liu, Xudong Liu, Sheng Ye, Peijun Zhang, Liguo Beijing University of Technology Faculty of Information and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665433372

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software. © 2021 IEEE.

关键词： Calibration

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

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Fundamental Research 2023年第6期3卷 951-959页

作者： Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhouZhejiang 310058China International Campus Zhejiang UniversityHainingZhejiang 314400China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China School of Geosciences and Info-Physics Central South UniversitySouth Lushan RoadChangsha 410000China NSW Department of Primary Industries Wagga Wagga Agricultural InstitutePine Gully Road Wagga WaggaNSW 2650Australia Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA Department of Agricultural and Biological Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute of Applied Remote Sensing and Information Technology Zhejiang UniversityHangzhouZhejiang 310058China Key Laboratory of On Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhouZhejiang 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouZhejiang 310058China

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food *** the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains *** this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in *** found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in *** results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

关键词： Crop yield estimation Deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis

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Channel grouping vision transformer for lightweight fruit and vegetable recognition

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Expert Systems with Applications 2025年 292卷

作者： Chengxu Liu Weiqing Min Jingru Song Yancun Yang Guorui Sheng Tao Yao Lili Wang Shuqiang Jiang School of Information and Electrical Engineering Ludong University 264025 Yantai China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China University of Chinese Academy of Sciences 100049 Beijing China

Recognizing fruit and vegetable is crucial for improving processing efficiency, automating harvesting, and facilitating dietary nutrition management. The diverse applications of fruit and vegetable recognition require deployment on end devices with limited resources, such as memory and computing power. The key challenge lies in designing lightweight recognition algorithms. However, current lightweight methods still rely on simple CNN-based networks, which fail to deeply explore and specifically analyze the unique features of fruit and vegetable images, resulting in unsatisfactory recognition performance. To address this challenge, we propose a novel lightweight recognition network termed Channel Grouping Vision Transformer (CGViT). CGViT utilizes a channel grouping mechanism and half-convolution to enhance feature extraction capability while reducing complexity. This design enables the model to capture three discriminative types of features from images. Subsequently, the Transformer is employed for feature fusion and global information extraction, ultimately creating an efficient neural network model for fruit and vegetable recognition. The proposed CGViT approach achieved recognition accuracies of 71.26 %, 99.99 %, 98.92 %, and 61.33 % on four fruit and vegetable datasets, respectively, outperforming state-of-the-art methods (MobileViTV2, MixNet, MobileNetV2). The maximum memory usage during training is only 6.48GB, which is merely 13.8 % of that required by state-of-the-art methods(MobileViTv2). The fruit and vegetable recognition model proposed in this study offers a more profound and effective solution, providing valuable insights for future research and practical applications in this domain. The code is available at https://***/Axboexx/CGViT .

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction Real-time systems

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