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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Wanli Wu, Jun Ren, Haoqi Zhang, Zhifeng Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Fudan University School of Computer Science Shanghai200438 China

ISBN: (纸本)9781665432061

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP. © 2021 IEEE.

关键词： Syntactics

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification

PM2: A New Prompting Multi-modal Model Paradigm for Few-shot...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenwei Wang Qiule Sun Bingbing Zhang Weijian Su Pengfei Wang Jianxin Zhang Qiang Zhang Key Laboratory of Social Computing and Cognitive Intelligence (Ministry of Education) Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Engineering Dalian Minzu University China School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Few-shot learning has become a key technical solution for addressing the challenges of limited data and difficult annotation acquisition in medical image classification. However, relying solely on a single image modality proves inadequate for capture conceptual categories. This paper proposes a novel medical image classification paradigm based on a multi-modal foundation model, called PM 2 . In addition to the image modality, PM 2 introduces supplementary text input (prompt) to further describe images or conceptual categories and facilitate cross-modal few-shot learning. We empirically studied five different prompting schemes under this new paradigm. Furthermore, linear probing in multi-modal models only takes class token as input, ignoring the rich statistical data contained in high-level visual tokens. Therefore, we alternately perform linear classification on the feature distributions of visual tokens and class token. To effectively extract statistical information, we use global covariance pool with efficient matrix power normalization to aggregate the visual tokens. We then combine two classification heads: one for handling image class token and prompt representations encoded by the text encoder, and the other for classifying the feature distributions of visual tokens. Experiments on two medical datasets demonstrate that regardless of the prompting scheme, our method PM 2 outperforms its counterparts, achieving state-of-the-art performance.

关键词： Visualization Head Foundation models Feature extraction Data models Data mining Few shot learning Covariance matrices Medical diagnostic imaging Image classification

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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

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IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

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EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning

arXiv

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arXiv 2024年

作者： Pei, Wenbin Dai, Ruohao Xue, Bing Zhang, Mengjie Zhang, Qiang Cheung, Yiu-Ming Xia, Shuyin The School of Computer Science and Technology Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Department of Computer Science Hong Kong Baptist University Hong Kong The Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this by oversampling the minority class to increase the number of its instances, followed by undersampling to remove low-quality instances. However, most existing sampling methods face difficulties in generating diverse high-quality instances and often fail to remove noise or low-quality instances on a larger scale effectively. This paper therefore proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling. During the oversampling process, genetic programming (GP) is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, we develop a granular ball-based undersampling method that removes noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 20 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Besides, ablation studies further indicate that allowing knowledge transfer accelerates the GP's evolutionary learning process. Copyright © 2024, The Authors. All rights reserved.

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Theoretical Study on the Macroscopic Thermodynamic Properties of Beh+ and Ch+

SSRN

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SSRN 2024年

作者： Hu, Minli Xu, Zhihao Fan, Qunchao Fan, Zhixiang Li, Huidong Fu, Jia Wu, Jizhou Ma, Jie Xie, Feng School of Science Key Laboratory of High Performance Scientific Computation Xihua University Chengdu610039 China State Key Laboratory of Quantum Optics and Quantum Optics Devices Laser Spectroscopy Laboratory College of Physics and Electronics Engineering Shanxi University Taiyuan030006 China Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education Collaborative Innovation Center of Advanced Nuclear Energy Technology Institute of Nuclear and New Energy Technology Tsinghua University Beijing100084 China

The variational improved Hulburt-Hirschfelder (VIHH) potential energy model was used to simulate the potential energy curves of BeH+ and CH+ in their ground electronic states. Subsequently, by numerically solving the radial Schrödinger equation using the VIHH potential energy function with the LEVEL program, the rovibrational energy levels of the ground electronic states of BeH+ and CH+ were obtained, determining the molar entropy, enthalpy, and reduced molar Gibbs free energy in the temperature range of 100-6000K within the framework of the overall theory of quantum statistics. © 2024, The Authors. All rights reserved.

关键词： Free energy

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Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network

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Advanced Photonics Nexus 2024年第2期3卷 8-17页

作者： Yi Xu Fu Li Jianqiang Gu Zhiwei Bi Bing Cao Quanlong Yang Jiaguang Han Qinghua Hu Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringMinistry of EducationKey Laboratory of Optoelectronic Information TechnologyTianjinChina Tianjin University College of Intelligence and ComputingTianjinChina Central South University School of Physics and ElectronicsHunan Key Laboratory of Nanophotonics and DevicesChangshaChina Guilin University of Electronic Technology School of Optoelectronic EngineeringGuangxi Key Laboratory of Optoelectronic Information ProcessingGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Recently,deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational ***,to obtain sufficiently accurate predictions,the conventional deep-learning-based method consumes excessive time to collect the data set,thus hindering its wide application in this interdisciplinary *** introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral *** demonstrate three transfer strategies and experimentally quantify their performance,among which the“frozen-none”robustly improves the prediction accuracy by∼26%compared to direct *** propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by∼30%compared to its real-valued *** design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization *** simulated results successfully validate the capability of our *** work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands,which will pave the way toward the automated and mass production of metasurfaces.

关键词： transfer learning complex-valued deep neural network metasurface inverse design conditioned adaptive particle swarm optimization terahertz

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Deep Kernel Embedded Clustering Network

SSRN

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SSRN 2024年

作者： Ren, Lina Huang, Ruizhang Chen, Yanping Lin, Chuan Qin, Yongbin State Key Laboratory of Public Big Data Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Information Engineering Guizhou Light Industry Technical College Guiyang550025 China

In this paper, we propose a deep kernel embedded clustering network, namely DKEC, which learns data partitions with kernelized semantic embeddings of data samples via a self-supervised deep neural network. A kernelized semantic embedding learner is designed to learn high quality data representations, which not only capture the data semantic characteristics but also show good structural separable characteristics. Other than directly apply traditional predefined kernel mapping methods, the kernelized semantic embedding learner, investigated by a deep variational Gaussian process, is able to learn the kernel mapping according to the nature of various datasets automatically, while maintain reasonable computational complexity. The DKEC, which is an end-to-end deep clustering network, is invested based on the kernelized semantic embedding learner. A triplet self-supervised optimization objective is designed in the DKEC model to guide the clustering partition, the separable factor of the kernelized semantic embedding, and the data reconstruction factor of the kernelized semantic embedding, simultaneously. We conducted extensive experiments on $ 7 $ real-world datasets. The results unequivocally showcase the exceptional clustering performance of the proposed DKEC. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Schrödingerisation based computationally stable algorithms for ill-posed problems in partial differential equations

arXiv

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arXiv 2024年

作者： Jin, Shi Liu, Nana Ma, Chuwen School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China

We introduce a simple and stable computational method for ill-posed partial differential equation (PDE) problems. The method is based on Schrödingerisation, introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603], which maps all linear PDEs into Schrödinger-type equations in one higher dimension, for quantum simulations of these PDEs. Although the original problem is ill-posed, the Schrödingerised equations are Hamiltonian systems and time-reversible, allowing stable computation both forward and backward in time. The original variable can be recovered by data from suitably chosen domain in the extended dimension. We will use the (constant and variable coefficient) backward heat equation and the linear convection equation with imaginary wave speed as examples. Error analysis of these algorithms are conducted and verified numerically. The methods are applicable to both classical and quantum computers, and we also lay out quantum algorithms for these methods. Moreover, we introduce a smooth initialisation for the Schrödingerised equation which will lead to essentially spectral accuracy for the approximation in the extended space, if a spectral method is used. Consequently, the extra qubits needed due to the extra dimension, if a qubit based quantum algorithm is used, for both well-posed and ill-posed problems, becomes almost log log 1/Ε where Ε is the desired precision. This optimizes the complexity of the Schrödingerisation based quantum algorithms for any non-unitary dynamical system introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603]. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG

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Personal and Ubiquitous computing 2022年第4期26卷 1215-1224页

作者： Jiang, Du Li, Gongfa Sun, Ying Kong, Jianyi Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Eigenvalues and eigenfunctions

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