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arXiv 2025年

作者： Stevens, Tristan S.W. Overdevest, Jeroen Nolan, Oisín van Nierop, Wessel L. van Sloun, Ruud J.G. Eldar, Yonina C. Department of Electrical Engineering University of Technology Eindhoven Netherlands Signal Processing Algorithms NXP Semiconductors Netherlands Faculty of Mathematics and Computer Science Weizmann institute of Science Israel

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging. © The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://***/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. © 2025, CC BY-SA.

关键词： Inverse problems

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Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

arXiv

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arXiv 2025年

作者： Yang, Yiqiong Yuan, Yitian Ren, Baoxing Wu, Ye Feng, Yanqiu Zhang, Xinyuan School of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health The Ministry of Education Southern Medical University Guangzhou China Department of Radiology Shunde Hospital Southern Medical University The First People's Hospital of Shunde Foshan Foshan China

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Conservation laws for variable coefficient nonlinear wave equations with power nonlinearities

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Chinese Physics B 2011年第7期20卷 22-27页

作者：黄定江周水庚杨勤民 Department of Mathematics East China University of Science and Technology School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Fudan University

Conservation laws for a class of variable coefficient nonlinear wave equations with power nonlinearities are investigated. The usual equivalence group and the generalized extended one including transformations which are nonlocal with respect to arbitrary elements are introduced. Then, using the most direct method, we carry out a classification of local conservation laws with characteristics of zero order for the equation under consideration up to equivalence relations generated by the generalized extended equivalence group. The equivalence with respect to this group and the correct choice of gauge coefficients of the equations play the major roles for simple and clear formulation of the final results.

关键词： nonlinear wave equations conservation laws equivalence group symmetries

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ON FINDING THE STRONGLY CONNECTED COMPONENTS IN A DIRECTED GRAPH

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INFORMATION processing LETTERS 1994年第1期49卷 9-14页

作者： NUUTILA, E SOISALONSOININEN, E Laboratory of Information Processing Science Department of Computer Science Helsinki University of Technology Otakaari 1M SF-02150 Espoo Finland

We present two improved versions of Tarjan's algorithm for the detection of strongly connected components in a directed graph. Our new algorithms handle sparse graphs and graphs with many trivial components (containing only one node) more economically than Tarjan's original algorithm. As an application we present an efficient transitive closure algorithm.

关键词： DESIGN OF ALGORITHMS STRONGLY CONNECTED COMPONENTS TARJANS ALGORITHM TRANSITIVE CLOSURE

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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PSVM: a preference-enhanced SVM model using preference data for classification

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science China(Information sciences) 2017年第12期60卷 165-178页

作者： Lerong MA Dandan SONG Lejian LIAO Jingang WANG Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University Search Business Department Alibaba Group

Classification is an essential task in data mining, machine learning and pattern recognition *** classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine(PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization(SMO)algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation(KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI,Stat Lib and ***. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.

关键词： preference SVM classification sampling sequential minimal optimization(SMO)

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RDVC: Efficient Deep Video Compression with Regulable Rate and Complexity Optimization

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IEEE Transactions on Multimedia 2025年

作者： Wei, Xiaojie Lin, Jielian Xu, Jiawei Gao, Wei Zhao, Tiesong Putian University School of Mechanical Electrical & Information Engineering PutianFujian China Shenzhen Graduate School Peking University School of Electronic and Computer Engineering Shenzhen518055 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this paper, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%/58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2. Sourcecode is available at: https://***/WXJ0001/RDVC. © 2025 IEEE.

关键词： Signal distortion

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Improving vertex-frontier based GPU breadth-first search

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Journal of Central South University 2014年第10期21卷 3828-3836页

作者：杨博卢凯高颖慧徐凯王小平程志权 Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology Department of Electronic Science and Engineering National University of Defense Technology Avatar Science Company

Breadth-first search(BFS) is an important kernel for graph traversal and has been used by many graph processing applications. Extensive studies have been devoted in boosting the performance of BFS. As the most effective solution, GPU-acceleration achieves the state-of-the-art result of 3.3×109 traversed edges per second on a NVIDIA Tesla C2050 GPU. A novel vertex frontier based GPU BFS algorithm is proposed, and its main features are three-fold. Firstly, to obtain a better workload balance for irregular graphs, a virtual-queue task decomposition and mapping strategy is introduced for vertex frontier expanding. Secondly, a global deduplicate detection scheme is proposed to remove reduplicative vertices from vertex frontier effectively. Finally, a GPU-based bottom-up BFS approach is employed to process large frontier. The experimental results demonstrate that the algorithm can achieve 10% improvement over the state-of-the-art method on diverse graphs. Especially, it exhibits 2-3 times speedup on low-diameter and scale-free graphs over the state-of-the-art on a NVIDIA Tesla K20 c GPU, reaching a peak traversal rate of 11.2×109 edges/s.

关键词： breadth-first search GPU graph traversal vertex frontier

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery

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IEEE Transactions on Network science and Engineering 2025年

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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Cyber-physical System Security for Networked Industrial Processes

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International Journal of Automation and computing 2015年第6期12卷 567-578页

作者： Shuang Huang Chun-Jie Zhou Shuang-Hua Yang Yuan-Qing Qin School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Computer Science Loughborough University

Cyber-physical systems(CPSs) are integrations of networks, computation and physical processes, where embedded computing devices continually sense, monitor, and control the physical processes through networks. Networked industrial processes combining internet, real-time computer control systems and industrial processes together are typical CPSs. With the increasingly frequent cyber-attack, security issues have gradually become key problems for CPSs. In this paper, a cyber-physical system security protection approach for networked industrial processes, i.e., industrial CPSs, is proposed. In this approach, attacks are handled layer by layer from general information technology(IT) security protection, to active protection, then to intrusion tolerance and physical security protection. The intrusion tolerance implemented in real-time control systems is the most critical layer because the real time control system directly affects the physical layer. This novel intrusion tolerance scheme with a closed loop defense framework takes into account the special requirements of industrial CPSs. To illustrate the effectiveness of the CPS security protection approach, a networked water level control system is described as a case study in the architecture analysis and design language(AADL) environment. Simulation results show that 3 types of injected attacks can be quickly defended by using the proposed protection approach.

关键词： Critical infrastructures cyber-physical systems(CP

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