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26th Optoelectronics and Communications Conference, OECC 2021

作者： Yang, Li Yang, Ziyi Xu, Huaxing Department of Electronic Engineering and Information Science University of Science and Technology of China Anhui Hefei230027 China Key Laboratory of Electromagnetic Space Information Chinese Academy of Sciences Anhui Hefei230027 China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information Technology Beijing100041 China

Even and odd vector modes in optical fibers are represented with linear combinations of orbital angular momentum modes, and considered as single-photon spin-orbit entangled states. It renders generating such states wi... 详细信息

ISBN: (纸本)9781557528209

关键词： Photons

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GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow

GraphFly: Efficient Asynchronous Streaming Graphs Processing...

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Supercomputing Conference

作者： Dan Chen Chuangyi Gui Yi Zhang Hai Jin Long Zheng Yu Huang Xiaofei Liao National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665454452

Existing streaming graph processing systems typically adopt two phases of refinement and recomputation to ensure the correctness of the incremental computation. However, severe redundant memory accesses exist due to the unnecessary synchronization among independent edge updates. In this paper, we present GraphFly, a high-performance asynchronous streaming graph processing system based on dependency-flows. GraphFly features three key designs: 1) Dependency trees (D-trees), which helps quickly identify independent graph updates with low cost; 2) Dependency-flow based processing model, which exploits the space-time dependent co-scheduling for cache efficiency; 3) Specialized graph data layout, which further reduces memory accesses. We evaluate GraphFly, and the results show that GraphFly significantly outperforms state-of-the-art systems KickStarter and GraphBolt by 5.81× and 1.78× on average, respectively. Also, GraphFly scales well with different sizes of update batch and compute resources.

关键词： Costs High performance computing Memory management Layout data models Synchronization

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A Hybrid Framework for Spatial Interpolation: Merging data-driven with Domain Knowledge

arXiv

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arXiv 2024年

作者： Zhang, Cong Du, Shuyi Song, Hongqing Wang, Yuhe National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology Beijing100190 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these datasets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation datasets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge. © 2024, CC BY.

关键词： Domain Knowledge

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Rice lodging disaster monitoring method based on multi-source remote sensing data

Rice lodging disaster monitoring method based on multi-sourc...

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2022 International Conference on Geographic Information and Remote Sensing technology, GIRST 2022

作者： Dai, Xuemei Chen, Shuisen Jiang, Hao Nainarpandian, Chandrasekar Guangzhou Institute of Geography Guangdong Academy of Sciences Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou China Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China Centre for Geotechnology Manonmaniam Sundaranar University Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9781510662186

Lodging is a major yield-limiting factor in rice. Accurate assessment of rice lodging is essential for yield damage estimation, agricultural insurance claims settlement and subsequent management decisions. This study aims to explore the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data. Based on this, an extraction method of lodging rice distribution using multi-source remote sensing data was proposed. The results showed that: (a) The lodging area of rice could be effectively extracted from dual-polarization Sentinel-1 image data with an accuracy of 87.87%;(b) The vegetation indices (NDVI, DVI and LSWI) extracted by Sentinel-2 are sensitive to lodging rice. Based on a certain threshold, lodging rice can be effectively extracted with an accuracy of 87.5%. Our findings demonstrate the potential of Sentinel data for near real-time detection of the rice lodging. © 2023 SPIE.

关键词： Polarization

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Robust Decoding of the Auditory Attention from EEG Recordings Through Graph Convolutional Networks

Robust Decoding of the Auditory Attention from EEG Recording...

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

作者： Siqi Cai Ran Zhang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China

Auditory attention decoding (AAD) with electroencephalography (EEG) holds great promise in brain-computer interface (BCI). Despite much progress, it remains a research topic on how to effectively evaluate the performance of EEG-based AAD algorithms under an appropriate setting that reflects the use scenarios. It is desired that systems are evaluated under cross-subject and cross-trial settings. However, systems are often reported under same-subject, same-trial settings, where test data are not truly separated from the training data, thus potentially leading to model overfitting due to data leakage. In this paper, we study the robustness of graph convolutional network (GCN), a novel approach to learning the intricate spatial patterns in multi-channel EEG signals, by comparing GCN across cross-subject, cross-trial, and same-subject, same-trial settings. On two publicly available AAD datasets, it is found that GCN exhibits remarkable robustness, outperforming previous conventional convolutional neural network (CNN) solutions. We confirm the superiority of our GCN-based AAD model in terms of generalization and robustness.

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

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IEEE Transactions on Multimedia 2025年

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui South China University of Technology School of Electronic and Information Engineering 381 Wushan Road Tianhe District Guangzhou510000 China Pazhou Lab Guangzhou510330 China South China University of Technology School of Future Technology 381 Wushan Road Tianhe District Guangzhou510000 China South China University of Technology School of Software Engineering Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. © 1999-2012 IEEE.

关键词： Benchmarking

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PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against data Poisoning Attacks on Non-IID data

arXiv

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arXiv 2025年

作者： Zhang, Hongliang Yu, Jiguo Xu, Fenghua Hu, Chunqiang Zhang, Yongzhao Wang, Xiaofen Yu, Zhongyuan Zhang, Xiaosong School of Computer Science and Technology Qilu University of Technology Shandong Jinan250353 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Cyber Security Institute University of Science and Technology of China Anhui Hefei230026 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China College of Computer Science and Technology China University of Petroleum Shandong Qingdao266580 China

Privacy-Preserving Federated Learning (PPFL) allows multiple clients to collaboratively train a deep learning model by submitting hidden model updates. Nonetheless, PPFL is vulnerable to data poisoning attacks due to the distributed training nature of clients. Existing solutions have struggled to improve the performance of cross-silo PPFL in poisoned Non-IID data. To address the issues, this paper proposes a privacy-preserving federated prototype learning framework, named PPFPL, which enhances the cross-silo FL performance in poisoned Non-IID data while effectively resisting data poisoning attacks. Specifically, we adopt prototypes as client-submitted model updates to eliminate the impact of tampered data distribution on federated learning. Moreover, we utilize two servers to achieve Byzantine-robust aggregation by secure aggregation protocol, which greatly reduces the impact of malicious clients. Theoretical analyses confirm the convergence of PPFPL, and experimental results on publicly available datasets show that PPFPL is effective for resisting data poisoning attacks with Non-IID conditions. © 2025, CC BY.

关键词： Deep learning

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Wideband polarization reconfigurable antenna based on double C-shaped radiators

Wideband polarization reconfigurable antenna based on double...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liang, Weilong Li, Long Wu, Zhao Huang, Yanhu Ma, Qingxiu Liang, Jiajun Nanjing Research Institute of Electronics Technology Nanjing210000 China Yulin Normal University School of Physics and Telecommunication Engineering Yulin537000 China Guangxi Colleges Universities Key Lab of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China

ISBN: (纸本)9781665452366

An enhancing bandwidth tri-polarization reconfigurable antenna based on double C-shaped radiators is proposed. The antenna consists of a modified ground plane, double C-shaped radiators, and a feedline with a simple parasitic stub. The modified ground plane and the parasitic stub can improve the impedance matching, meanwhile, adjust the axial ratio performance. Two PIN diodes are embedded between the C-shaped radiator and the feedline. Three polarization reconfigurable modes can be achieved caused by switching two PIN diodes state, linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP). As the simulated results show, the -10-dB impedance bandwidth reaches 126.6% (1.76-7.83 GHz) in LP mode and 78.4% (2.96-6.78 GHz) in CP modes. And the proposed antenna has a 3-dB axial ratio (AR) bandwidth of 84% (2.6-6.4 GHz) in two CP modes. Therefore, the proposed antenna can meet the requirement of the modern wireless and satellite communication system. © 2022 IEEE.

关键词： Radiators

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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第34届中国控制与决策会议

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology)

Road network restoration is an important issue in the post-disaster disposal and rescue,especially when extraordinarily serious natural disasters(e.g.,floods and earthquakes) *** to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand ***,most existing work focuses on the activities of the single repair crew in the static road network,which is unable to adapt to the dynamic changes of the road network caused by secondary disasters,such as rock slides and debris ***,this work is concentrated on multicrew dynamic scheduling and routing in road network ***,a model of multicrew dynamic scheduling and routing is first ***,the Markov decision process is adopted to construct a decision model for repair crews,based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing ***,experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex,dynamic emergency scenarios of post-disaster.

关键词： Road Network Restoration Multiple Repair Crews Dynamic Scheduling and Routing Multiagent Q-Learning

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

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