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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence 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 Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on Mobile computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method

Computing-in-Memory Architecture Based on Field-Free SOT-MRA...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chao Wang Zhaohao Wang Yansong Xu Jianlei Yang Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beihang University School of Electronics and Information Engineering Beihang University School of Microelectronics Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

On the current computing platforms, the memory wall between processor and memory has become the toughest challenge for the traditional Von-Neumann computer architecture. computing-in-Memory (CIM) is taken as a promising approach to solving the above bottleneck in computing systems. In this paper, we propose a CIM platform with field-free spinorbit torque magnetic random access memory (SOT-MRAM). The self-reference (SelfRef) method is designed to enhance the read reliability and directly obtain logic results through memory-like read operations without adding logic cells. Memory read/write and logic operations, including NOT, AND/NAND and OR/NOR, can be implemented in the same SOT-MRAM chip. The speed and power penalties caused by SelfRef scheme are acceptable thanks to the ultrafast switching of the SOT. The read reliability and logic correctness of the proposed CIM are demonstrated by hybrid simulation on a 40 nm technology node.

关键词： Reliability Switches Resistance Magnetic tunneling Magnetization Torque Semiconductor device modeling

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Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks

Fine-grained Scheduling in FPGA-Based Convolutional Neural N...

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IEEE International Conference on Cloud computing and big data Analysis (ICCCBDA)

作者： Wei Zhang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13× of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4×+ performance improvement compared MKL with 20 threads running on 10 core Intel processors.

关键词： Field programmable gate arrays Computational modeling Training Convolution Acceleration Bandwidth Task analysis

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Time Domain Speech Enhancement Using SNR Prediction and Robust Speaker Classification

Time Domain Speech Enhancement Using SNR Prediction and Robu...

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Internet of Things and intelligent Applications (ITIA), International Conference on

作者： Ji-gang Ren Qi-rong Mao Hongjie Jia Jing-jing Chen School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center big data ubiquitous perception and intelligent agriculture applications Zhenjiang China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

Recently, deep neural networks (DNNs) have been successfully used for speech enhancement. Most of them are single-task models, and the extracted features are limited by the speech enhancement model. To extract more speech information, we propose a muti-task learning method for speech enhancement using signal to noise ratio(SNR) prediction. SNR prediction task extracts features of the relationship between noise and speech that cannot be obtained by the speech enhancement model. In addition, we added the task of robust speaker recognition to extract speaker features. By sending SNR features and speaker features to the speech enhancement task, the model can obtain more speech-related information to improve the effect of speech enhancement. Experimental results on a public dataset show that our method achieves the state-of-the-art performance and also gets good scores in terms of subjective quality in time-domain speech enhancement.

关键词： Speech enhancement Signal to noise ratio Noise measurement Training Task analysis Feature extraction Time-domain analysis

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Cloudlet Placement Algorithm Based on Ratio Cut and Its Kernel Solution

Cloudlet Placement Algorithm Based on Ratio Cut and Its Kern...

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Internet of Things and intelligent Applications (ITIA), International Conference on

作者： Hongjie Jia Liangjun Wang Heping Song Qirong Mao Chao Qi Jianming Zhang School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China big data ubiquitous perception and intelligent agriculture applications Jiangsu Engineering Research Center Zhenjiang China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

In wireless metropolitan area networks, mobile edge computing provides a way to reduce cloud service latency by establishing small cloud infrastructures (such as cloudlets at the edge of the network). The distribution of mobile users is very complex, and mobile devices have many computationally intensive tasks that need to be processed by cloudlet. How to choose suitable placement locations for cloudlets and minimize the average access latency of mobile users to cloudlets has become research hotspots in mobile edge computing. The existing cloudlet placement algorithms have poor generalization ability and are prone to produce uneven partitions. To solve this problem, we proposes a cloudlet placement algorithm based on ratio cut and its kernel solution, which transforms the cloudlet placement problem into the ratio cut problem on the graph, and uses the kernel clustering method to optimize the objective function of cloudlet placement to reduce mobile users' access delay and improve the service performance of mobile devices. Finally, simulation experiments prove the effectiveness of the proposed algorithm and it can meet the strict response time requirements of mobile users.

关键词： Cloud computing Task analysis Delays Kernel Metropolitan area networks Mathematical model Wireless communication

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Remote Sensing Image Scene Classification Based on Gated Pyramidal Convolution

Remote Sensing Image Scene Classification Based on Gated Pyr...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Hang Liu Shuai Bian Zhengang Wang Bing Liu Qiang Hu Dong-en Guo Chongqing MeiAnSen Technology Co. Ltd Chongqing China Chongqing Spatial Big Data Intelligent Engineering Research Center Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Deep learning technology has demonstrated superior performance in remote sensing image scene classification. However, the complex background and diverse target objects in remote sensing images have always been a major challenge in scene classification. To solve this problem, we propose a novel remote sensing scene classification method based on Gated Pyramidal Convolution (GPC). Specifically, pyramidal convolution (PyConv) is first introduced into the residual block to capture different levels of detailed features. Then, the Gated Unit (GU) is placed behind the last three residual units to filter useless background information. Finally, a multi-level feature fusion structure is introduced to fuse the features of the last three residual units to further enhance the feature discriminant ability. Extensive experiments on AID and NWPU-RESISC45 datasets demonstrate that the proposed GPC model outperforms the state-of-the-art baselines.

关键词： Image analysis Convolution Layout Logic gates Sensors Resource management Remote sensing

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Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence

arXiv

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arXiv 2024年

作者： Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Goh, Rick Siow Mong Liu, Yong Li, Zhen Shenzhen518172 China Shenzhen518172 China Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Research Institute of Big Data Shenzhen518172 China South China Hospital Health Science Center Shenzhen University Shenzhen518111 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Affiliated Hospital of North Sichuan Medical College Sichuan637000 China North Sichuan Medical College Sichuan637000 China Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China Thuwal23955-6900 Saudi Arabia Thuwal23955-6900 Saudi Arabia Beijing University of Posts and Telecommunications Beijing100876 China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4–8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. Copyri

关键词： Differential privacy

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Optimal Margin Distribution Machine with Sparsity Inducing Penalty

Optimal Margin Distribution Machine with Sparsity Inducing P...

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International Conference on big data and Smart computing (bigCOMP)

作者： Teng Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Recently a promising research direction of statistical learning has been advocated, i.e., the optimal margin distribution learning, with the central idea of optimizing the margin distribution. As the most representative approach of this new learning paradigm, the optimal margin distribution machine (ODM) considers maximizing the margin mean and minimizing the margin variance simultaneously. The standard ODM exploits the ℓ_2-norm penalty, which gives rise to a dense decision boundary. However, in some situations, the model with parsimonious representation is more preferred, due to the redundant noisy features or limited computing resources. In this paper, we propose the sparse optimal margin distribution machine (Sparse ODM), which aims to achieve better generalization performance with moderate model size. For optimization, we extends an efficient coordinate descent method to solve the final problem since the variables are decoupled. In each iteration, we propose a modified Newton method to solve the one-variable sub-problem. Experimental results on both synthetic and real data sets show the superiority of the proposed method.

关键词： Standards Newton method Noise measurement Optimization Support vector machines Computational modeling Training

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