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Modeling hydrological consequences of 21st-Century climate and land use/land cover changes in a mid-high latitude watershed

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Geoscience Frontiers 2024年第5期15卷 118-135页

作者： Chuanqi Liu Chi Xu Zhijie Zhang Shengqing Xiong Wanchang Zhang Bo Zhang Hao Chen Yongxin Xu Shuhang Wang Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China University of Chinese Academy of Sciences Beijing 100049China National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Chinese Research Academy of Environmental SciencesBeijing 100012China Natural Resources Aero-geophysical and Remote Sensing Centre China Geological Survey BureauBeijingChina School of Geography Development and EnvironmentThe University of ArizonaTucsonAZ 85719USA School of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China Department of Earth Sciences University of the Western CapeCape Town 7535South Africa

The Naoli River Basin(NRB),a pivotal agricultural production area in China,is poised to undergo substantial impacts on water resources due to projected climate and land use/cover(LULC)*** its significance in the context of China’s expanding farmland construction in the NRB,there exists limited research on the potential repercussions of future shifts in runoff,soil water content(SWC),and evapotranspiration(ET)on crop productivity and water availability(both in terms of quantity and timing).This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models(GCMs)across three scenarios to drive the well-calibrated distributed hydrological model,*** objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term(2026–2050),middle-term(2051–2075),and far-term(2076–2100)future in comparison to the baseline period of 1990–*** consistently indicate an increase trend in annual average ET,runoff,and SWC in the NRB across all three future periods under the three SSP *** changes emerge as the primary driver influencing regional hydrological processes in the near ***,under high-emission scenarios,monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the *** shift is attributed to the anticipated warming of winter and spring,leading to a transition in peak snowmelt from April to ***,the expansion of cropland intensifies crop evapotranspiration demand,potentially exacerbating water stress during the early stages of crop growth in *** findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling *** planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing wat

关键词： Climate and LULC changes ESSI-3 Hydrological modeling Snowmelt Naoli River Basin

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless Networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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IEEE Conference on Wireless Communications and Networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory data mining

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Joint Participation Incentive and Network Pricing Design for Federated Learning

Joint Participation Incentive and Network Pricing Design for...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Ningning Ding Lin Gao Jianwei Huang Department of Electrical and Computer Engineering Northwestern University Evanston IL USA Shenzhen Research Institute of Big Data Shenzhen China School of Electronics and Information Engineering and the Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China

Federated learning protects users’ data privacy though sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server’s cost by up to 24.87% comparing with the state-of-the-art benchmarks.

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Neural Enhanced Variational Bayesian Inference on Graphs for Localized Statistical Channel Modeling

Neural Enhanced Variational Bayesian Inference on Graphs for...

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IEEE International Conference on Communications (ICC)

作者： Yiheng Wang Shutao Zhang Ye Xue Tianshu Yu Qingjiang Shi Tsung-Hui Chang Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen Guangdong China School of Data Science The Chinese University of Hong Kong-Shenzhen Guangdong China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper proposes an innovative graph neural network (GNN)-based approach to address the challenge of recovering ill-conditioned sparse signals within the task of multi-grid localized statistical channel modeling (LSCM). Our proposed GNN architecture captures the structural sparsity inherent in the channel angular power spectrum (APS) by leveraging reference signal receiving power (RSRP) measured from multiple grids. It can effectively mitigate the severe coherence in the measurement matrix. Furthermore, we present a novel online unsupervised training scheme that enables real-time adaptability for multi-grid LSCM applications. Through extensive simulations, we demonstrate the superior performance of our GNN-based method in the context of multi-grid LSCM, showcasing its advantages over existing sparse recovery techniques.

关键词： Training Adaptation models Power measurement Communication channels Graph neural networks Real-time systems Bayes methods

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： Image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing data mining Few shot learning Residual neural networks

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Accelerating Parallel Sampling of Diffusion Models

arXiv

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arXiv 2024年

作者： Tang, Zhiwei Tang, Jiasheng Luo, Hao Wang, Fan Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China DAMO Academy Alibaba Group China Hupan Lab Zhejiang Province China Shenzhen Research Institute of Big Data Shenzhen China

Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent autoregressive nature of their sampling process. In this work, we propose a novel approach that accelerates the sampling of diffusion models by parallelizing the autoregressive process. Specifically, we reformulate the sampling process as solving a system of triangular nonlinear equations through fixed-point iteration. With this innovative formulation, we explore several systematic techniques to further reduce the iteration steps required by the solving process. Applying these techniques, we introduce ParaTAA, a universal and training-free parallel sampling algorithm that can leverage extra computational and memory resources to increase the sampling speed. Our experiments demonstrate that ParaTAA can decrease the inference steps required by common sequential sampling algorithms such as DDIM and DDPM by a factor of 4∼14 times. Notably, when applying ParaTAA with 100 steps DDIM for Stable Diffusion, a widely-used text-to-image diffusion model, it can produce the same images as the sequential sampling in only 7 inference steps. The code is available at https://***/TZW1998/ParaTAA-Diffusion. © 2024, CC BY.

关键词： Nonlinear equations

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BackdoorBench: a comprehensive benchmark of backdoor learning 22

BackdoorBench: a comprehensive benchmark of backdoor learnin...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Baoyuan Wu Hongrui Chen Mingda Zhang Zihao Zhu Shaokui Wei Danni Yuan Chao Shen School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen School of Cyber Science and Engineering Xi'an Jiaotong University

ISBN: (纸本)9781713871088

Backdoor learning is an emerging and vital topic for studying deep neural networks' vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being proposed, successively or concurrently, in the status of a rapid arms race. However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and the difficulties of implementation and reproducibility. Without thorough evaluations and comparisons, it is not easy to track the current progress and design the future development roadmap of the literature. To alleviate this dilemma, we build a comprehensive benchmark of backdoor learning called BackdoorBench. It consists of an extensible modular-based codebase (currently including implementations of 8 state-of-the-art (SOTA) attacks and 9 SOTA defense algorithms) and a standardized protocol of complete backdoor learning. We also provide comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5 poisoning ratios, based on 5 models and 4 datasets, thus 8,000 pairs of evaluations in total. We present abundant analysis from different perspectives about these 8,000 evaluations, studying the effects of different factors in backdoor learning.

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Bidirectional Cross-scale Feature Fusion for Long Video Micro-Expression 3D Spotting Network 21

Bidirectional Cross-scale Feature Fusion for Long Video Micr...

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21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： He, Xiaosong Wu, Xiao Peng, Jun Li, Qingxia Ma, Xinkai He, Yuanmin Informatization Office Chongqing University of Science and Technology Chongqing401331 China College of Mathematics Physics and Data Science Chongqing Sino-German Future Factory Research Institute Chongqing401331 China College of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing401331 China

ISBN: (纸本)9781665490849

Psycho-cognitive computing is an important part of intelligent human-computer interaction technology, which has received extensive attention in recent years. The research of micro-expressions can reflect the depth and breadth of mental cognitive computing. Micro-expression(ME) is a spontaneous, short-lived, and inadvertent facial expression. The research on ME is of great significance in sentiment analysis, criminal investigation, and psychology research. ME spotting refers to locating sequences of micro-expressions in a long video. ME detection is an extremely important step in the field of ME analysis. Based on the I3D backbone network of long video optical flow extraction and original video feature extraction, this paper extracts effective feature layers, performs downsampling, and uses the BiFPN module with a spatial attention mechanism to selectively fuse the extracted multi-scale feature layers. The final classification-regression network discriminates the detected expressions and locates the temporal boundaries where the expressions occur. The experimental results show that the proposed method effectively improves the F1 score index. Compared with other deep learning methods, this method performs better on both CAS(ME)2 and SAMM. The proposed method provides a reliable foundation for the downstream tasks of ME research, such as ME recognition, and also provides an effective method for collecting natural macro-or micro-expression datasets from a large amount of video data in the future. © 2022 IEEE.

关键词： Extraction

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High-Resolution Natural Image Matting by Refining Low-resolution Alpha Mattes

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IEEE Transactions on Image Processing 2025年 34卷 3323-3335页

作者： Ye, Xianmin Liang, Yihui Tan, Mian Feng, Fujian Wang, Lin Huang, Han Guizhou Minzu University College of Data Science and Information Engineering Guiyang 550025 China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang 550025 China University of Electronic Science and Technology of China School of Computer Science Zhongshan Institute Zhongshan 528400 China South China University of Technology School of Software Engineering Guangzhou 510006 China Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China Guangzhou 510006 China Guangdong Engineering Center for Large Model and GenAI Technology Guangzhou 510006 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China

High-resolution natural image matting plays an important role in image editing, film-making and remote sensing due to its ability of accurately extract the foreground from a natural background. However, due to the complexity brought about by the proliferation of resolution, the existing image matting methods cannot obtain high-quality alpha mattes on high-resolution images in reasonable time. To overcome this challenge, we introduce a high-resolution image matting framework based on alpha matte refinement from low-resolution to high-resolution (HRIMF-AMR). The proposed framework transforms the complex high-resolution image matting problem into low-resolution image matting problem and high-resolution alpha matte refinement problem. While the first problem is solved by adopting an existing image matting method, the latter is addressed by applying the Detail Difference Feature Extractor (DDFE) designed as a part of our work. The DDFE extracts detail difference features from high-resolution images by measuring the image feature difference between high-resolution images and low-resolution images. The low-resolution alpha matte is refined according to the extracted detail difference feature, providing the high-resolution alpha matte. In addition, the Matte Detail Resolution Difference (MDRD) loss is introduced to train the DDFE, which imposes an additional constraint on the extraction of detail difference features with mattes. Experimental results show that integrating HRIMF-AMR significantly enhances the performance of existing matting methods on high-resolution images of Transparent-460 and Alphamatting. © 1992-2012 IEEE.

关键词： alpha matte detail detail difference feature High-resolution image matting natural image matting

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Dual Backbone Multi-Attention Hierarchical Fusion and Feature Enhancement Network for Crowd Counting

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IEEE Transactions on Consumer Electronics 2025年

作者： Zheng, Chunling Gao, Xingyu Chen, Zhenyu Lyu, Lei Shandong Normal University School of Information Science and Engineering Jinan250358 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China State Grid Corporation of China Big Data Center Beijing100031 China China Electric Power Research Institute Beijing100192 China

In recent years, significant progress has been made in crowd counting with the development of convolutional neural networks (CNNs). However, while CNNs excel at extracting local features, their limited receptive fields restrict their ability to model global context. In contrast, Transformers can effectively model long-distance dependencies, but are inferior to CNN in capturing local detail features. Local details and global context information are crucial to handle large-scale changes in crowds. To address this problem, we propose a novel dual backbone network (DBNet) that integrates CNN and Transformer architectures, aiming to capture and aggregate both global semantic information and local detail features at multiple levels. Specifically, the dual backbone structure is designed to extract fine-grained local features while modeling long-range contextual relationships. Additionally, we introduce a multi-attention hierarchical fusion module (MAHF) that integrates global and local features from the two backbones while suppressing background noise. To further enhance accuracy in the presence of multi-scale variations, we also employ a Feature Enhancement Module (FEM), which enables the network to more effectively identify edge features and facilitates more effective multi-scale feature modeling. Extensive experiments on ShanghaiTech, UCF-QNRF, and JHU-Crowd++ datasets demonstrate that DBNet achieves competitive performance, validating the effectiveness of our approach. © 1975-2011 IEEE.

关键词： Convolutional neural networks

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