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CLEO: Science and Innovations, S and I 2022

作者： Shu, Haowen Shen, Bitao Chang, Lin Tao, Yuansheng Xie, Weiqiang Jin, Ming Netherton, Andrew Zhang, Xuguang Qin, Jun Chen, Ruixuan Yu, Shaohua Wang, Xingjun Bowers, John E. State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

We achieve 100 Gbps/Lane PAM4 transmission by employing an AlGaAsOI microcomb source and silicon modulators. With 20 parallel wavelength channels utilized at C-band, an aggregate data rate of 2 Tbit/s is achieved. ... 详细信息

ISBN: (纸本)9781557528209

关键词： Gallium compounds

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Solar Irradiance Forecasting using Gaussian Process Regression Model

Solar Irradiance Forecasting using Gaussian Process Regressi...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Nesrine Omri Jannet Jamii Majdi Mansouri Mohamed Faouzi Mimouni The Laboratory of Automatic Electrical System and Environment (LAS2E) National Engineering School of Monastir Tunisia Electrical and Computer Engineering Department Texas A&M University at Qatar Electrical Engineering Department National Engineering School at Monastir (ENIM) Tunisia

Photovoltaic power generation presents a significant problem for power system security due to their stochastic nature. Thus, photovoltaic energy forecasting plays a key role in addressing the challenges of grid stability. Then, accurate PV energy forecasting improve the output power and therefore reduce the need for reserve energy to balance energy. Also, it improves dispatch and planning power. In this paper, we study a short term forecasting of PV power using a Gaussian Process Regression (GPR) model. The meteorological conditions such as temperature is used as input, while solar irradiance, is used as output for GPR *** data set are illustrated from data NOAA'S Surface Radiation (SURFRAD) Network at Bondville station, located in the USA. A comparative study with other machine learning techniques is developed in terms of calculation time (CT), RMSE, MAE, and R 2 .

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A novel and highly stable 840 MHz repetition rate femtosecond fiber laser

A novel and highly stable 840 MHz repetition rate femtosecon...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Yang, Ruoao Zhao, Minghe Jin, Xingang Li, Qian Chen, Zhangyuan Wang, Aimin Zhang, Zhigang State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Jiaxing Xurui Electronics Tech Co Ltd Jiaxing314001 China

ISBN: (纸本)9798350350012

We demonstrate a novel and highly stable femtosecond fiber laser at a repetition rate of 840 MHz. Because the laser components are bonded on a silica glass, the repetition rate of the free running laser drifts within 1 kHz over 12 hrs. The timing jitter of the pulses measured by the balanced optical cross-correlation is 130 as for the integration range of 10 kHz to 1 MHz. © 2022 IEEE.

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A novel and highly stable 840 MHz repetition rate femtosecond fiber laser

A novel and highly stable 840 MHz repetition rate femtosecon...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

关键词： Silica

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

SSRN

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SSRN 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spa-tial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting

arXiv

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arXiv 2025年

作者： Wang, Weiyan Shi, Xingjian Shu, Ruiqi Gao, Yuan Chen, Rui Ray Wang, Kun Xu, Fan Xue, Jinbao Li, Shuaipeng Tao, Yangyu Wang, Di Wu, Hao Huang, Xiaomeng TEG Tencent China Boson AI United States Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore Department and Computer and Science University of Science and Technology of China China

In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose BeamVQ, a novel probabilistic framework to realize iterative self-training with new self-ensemble strategies, achieving better physical consistency and generalization on extreme events. Following any base forecasting model, we can encode its deterministic outputs into a latent space and retrieve multiple codebook entries to generate probabilistic outputs. Then BeamVQ extends the beam search from discrete spaces to the continuous state spaces in this field. We can further employ domain-specific metrics (e.g., Critical Success Index for extreme events) to filter out the top-k candidates and develop the new self-ensemble strategy by combining the high-quality candidates. The self-ensemble can not only improve the inference quality and robustness but also iteratively augment the training datasets during continuous self-training. Consequently, BeamVQ realizes the exploration of rare but critical phenomena beyond the original dataset. Comprehensive experiments on different benchmarks and backbones show that BeamVQ consistently reduces forecasting MSE (up to 39%), enhancing extreme events detection and proving its effectiveness in handling data scarcity. Our codes are available at https://***/easylearningscores/BeamVQ. © 2025, CC BY.

关键词： Spatio-temporal data

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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Mathematical modeling of mixed convective MHD Falkner-Skan squeezed Sutterby multiphase flow with non-Fourier heat flux theory and porosity

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Applied Mathematics and Mechanics(English Edition) 2023年第11期44卷 2005-2018页

作者： Shuguang LI M.I.KHAN F.ALI S.S.ABDULLAEV S.SAADAOUI HABIBULLAH School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005Shandong ProvinceChina Department of Mechanics and Engineering Science Peking UniversityBeijing 100871China Department of Mechanical Engineering Lebanese American UniversityBeirut 11020Lebanon Department of Mathematical Sciences Federal Urdu University of ArtsSciences&TechnologyGulshan-e-IqbalKarachi 75300Pakistan Faculty of Chemical Engineering New Uzbekistan UniversityTashkent 100070Uzbekistan Department of Science and Innovation Tashkent State Pedagogical University named after NizamiTashkent 100070Uzbekistan Department of Physics Faculty of Science and ArtsMohayel AserKing Khalid UniversityAbha 61421Saudi Arabia State Key Laboratory of Turbulence and Complex System Collaborative Innovative Center for Advanced Aero-EnginesPeking UniversityBeijing 100871China

In a wide variety of mechanical and industrial applications,e.g.,space cooling,nuclear reactor cooling,medicinal utilizations(magnetic drug targeting),energy generation,and heat conduction in tissues,the heat transfer phenomenon is ***’s law of heat conduction has been used as the foundation for predicting the heat transfer behavior in a variety of real-world *** model’s production of a parabolic energy expression,which means that an initial disturbance would immediately affect the system under investigation,is one of its main ***,numerous researchers worked on such problem to resolve this *** last,this problem was resolved by Cattaneo by adding relaxation time for heat flux in Fourier’s law,which was defined as the time required to establish steady heat conduction once a temperature gradient is *** offered a material invariant version of Cattaneo’s model by taking into account the upper-connected derivative of the Oldroyd ***,both models are combinedly known as the Cattaneo-Christov(CC)*** this attempt,the mixed convective MHD Falkner-Skan Sutterby nanofluid flow is addressed towards a wedge surface in the presence of the variable external magnetic *** CC model is incorporated instead of Fourier’s law for the examination of heat transfer features in the energy expression.A two-phase nanofluid model is utilized for the implementation of *** nonlinear system of equations is tackled through the bvp4c technique in the MATLAB software *** influence of pertinent flow parameters is discussed and displayed through different *** and important results are summarized in the conclusion ***,in both cases of wall-through flow(i.e.,suction and injection effects),the porosity parameters increase the flow speed,and decrease the heat transport and the influence of drag forces.

关键词： sporosity Cattaneo-Christov(CC)heat flux model Falkner-Skan Sutterby nanofluid mixed convection stagnation point

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SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning 39

SADBA: Self-Adaptive Distributed Backdoor Attack Against Fed...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Jun Lai, Yuzhe Sun, Hong Ren, Bocheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Economics Wuhan Textile University China School of Computer Science and Technology Hainan University China

ISBN: (纸本)157735897X

Backdoor attacks in federated learning (FL) face challenges such as lower attack success rates and compromised main task accuracy (MA) compared to local training. Existing methods like distributed backdoor attack (DBA) mitigate these issues by modifying malicious clients’ updates and partitioning global triggers to enhance backdoor persistence and stealth. The recent full combination backdoor attack (FCBA) further improves backdoor efficiency with a full combination strategy. However, these methods are mainly applicable in small-scale FL. In large-scale FL, small trigger patterns weaken impact, and scaling them requires controlling exponentially more clients, which poses significant challenges, while simply reverting to DBA may decrease backdoor performance. To overcome these challenges, we propose the self-adaptive distributed backdoor attack (SADBA), which achieves similar performance to FCBA with a lower percentage of malicious clients (PMC). It also adapts more flexibly through an optimized model poisoning strategy and a self-adaptive data poisoning strategy. Experiments demonstrate SADBA outperforms state-of-the-art methods, achieving higher or comparable backdoor performance and MA across various datasets with limited PMC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

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IEEE Transactions on Automatic Control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

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