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arXiv 2021年

作者： Lin, Bing Lin, Chaowei Chen, Xing College of Physics and Energy Fujian Normal University Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China School of Computer Science Peking University 100871 China College of Mathematics and Computer Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China

Workflow decision making is critical to performing many practical workflow applications. Scheduling in edge-cloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources in edge-cloud environments and the complicated data dependencies among the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. The existing studies have been mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent the task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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MVMS-RCN: A Dual-Domain Unified CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction

arXiv

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arXiv 2024年

作者： Fan, Xiaohong Chen, Ke Yi, Huaming Yang, Yin Zhang, Jianping The School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China The Centre for Mathematical Imaging Techniques Department of Mathematical Sciences The University of Liverpool LiverpoolL6972L United Kingdom The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan411105 China

X-ray Computed Tomography (CT) is one of the most important diagnostic imaging techniques in clinical applications. Sparse-view CT imaging reduces the number of projection views to a lower radiation dose and alleviates the potential risk of radiation exposure. Most existing deep learning (DL) and deep unfolding sparse-view CT reconstruction methods: 1) do not fully use the projection data;2) do not always link their architecture designs to a mathematical theory;3) do not flexibly deal with multi-sparse-view reconstruction assignments. This paper aims to use mathematical ideas and design optimal DL imaging algorithms for sparse-view CT reconstructions. We propose a novel dual-domain unified framework that offers a great deal of flexibility for multi-sparse-view CT reconstruction through a single model. This framework combines the theoretical advantages of model-based methods with the superior reconstruction performance of DL-based methods, resulting in the expected generalizability of DL. We propose a refinement module that utilizes unfolding projection domain to refine full-sparse-view projection errors, as well as an image domain correction module that distills multi-scale geometric error corrections to reconstruct sparse-view CT. This provides us with a new way to explore the potential of projection information and a new perspective on designing network architectures. The multi-scale geometric correction module is end-to-end learnable, and our method could function as a plug-and-play reconstruction technique, adaptable to various applications. Extensive experiments demonstrate that our framework is superior to other existing state-of-the-art methods. Our source codes are available at https://***/fanxiaohong/MVMS-RCN. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

arXiv

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arXiv 2023年

作者： Fan, Xiaohong Yang, Yin Chen, Ke Feng, Yujie Zhang, Jianping The School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Xiangtan411105 China The Centre for Mathematical ImagingTechniques Department of Mathematical Sciences The University of Liverpool Department of Mathematics and Statistics University of Strathclyde Glasgow United Kingdom The School of Mathematics and Computational Science Xiangtan University The Key Laboratory of Intelligent Computing & Information Processing The Ministry of Education Xiangtan411105 China

Proximal gradient-based optimization is one of the most common strategies to solve inverse problem of images, and it is easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on the second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high-/low-frequency image features, but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction. Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration, and post-processing. In the image restoration step, a cascade geometric incremental learning module is designed to compensate for missing texture information from different geometric spectral decomposition domains. Inspired by the overlap-tile strategy, we also develop a post-processing module to remove the block effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable, an adaptive initialization technique of physical parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://***/fanxiaohong/Nest-DGIL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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DeepContact: High throughput quantification of membrane contact site based on electron microscopy imaging

DeepContact: High throughput quantification of membrane cont...

引用

2022生物物理大会

作者： Liqing Liu Shuxin Yang Yang Liu Xixia Li Junjie Hu Li Xiao Tao Xu National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences College of Life Science University of Chinese Academy of Sciences Ningbo HuaMei Hospital University of Chinese Academy of Sciences School of Biomedical Engineering Guangzhou Medical University

Membrane contact site (MCS)-mediated organelle interactions plays essential roles in the cell. Quantitative analysis of the MCS reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed "DeepContact", a deep learning protocol for optimizing organelle segmentation and contact analysis based on label-free electron microscopy(EM).

关键词： DeepContact High throughput quantification of membrane contact site based on electron microscopy imaging

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

引用

arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Color analysis of iron surface structure fabricated by circularly polarized femtosecond laser

引用

Guangxue Jingmi Gongcheng/Optics and Precision Engineering 2015年 23卷 52-56页

作者： Su, Ya-Hui Han, Xiao-Hu Fan, Zhen-Zhu Zhang, Hua-Yong Zhong, Hong-Mei School of Electrical Engineering and Automation Anhui University Hefei230601 China School of Electrical and Information Engineering Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University Hefei230039 China

A method to induce multi-colors on an iron surface by using Circularly Polarized Femtosecond Laser (CPFL) processing was proposed. The subwavelength ripples were fabricated by CPFL scanning over the iron surface where covered by randomly distributed nanoparticles with diameters from 50 nm to 190 nm. A while light was used to irradiate the treated iron surface, and the relation between surface colors and incident angles was explored. By changing the incident angles of a white light from 0° to 80°, diverse colors were displayed on the laser-treated iron surface. In order to understand the underlying mechanism of the experimental results, a model of spherical nanoparticles spread randomly on ripples of the iron surface was built by Finite-difference Time Domain (FDTD). The experiments show that the surface reflection spectrum is varied from blue to red by increasing the incident angle of white light, which is consistent with the experimental results. Furthermore, it is found that the reflection spectrum shifts towards blue and the peak increases by decreasing the sizes of nanoparticles. This work may find potential applications in the fields of information storage, identifying codes and anti-counterfeiting patterns. © 2015, Chinese Academy of Sciences. All right reserved.

关键词： Femtosecond lasers

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The improved Hagedorn wavepacket method for semiclassical Schrödinger equation

引用

International Journal of Modeling, Simulation, and Scientific computing 2014年第4期5卷

作者： Xueyang Li Aiguo Xiao Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan P. R. China

The Hagedorn wavepacket method is an important numerical method for solving the semiclassical time-dependent Schrödinger equation. In this paper, a new semi-discretization in space is obtained by wavepacket operator. In a sense, such semi-discretization is equivalent to the Hagedorn wavepacket method, but this discretization is more intuitive to show the advantages of wavepacket methods. Moreover, we apply the multi-time-step method and the Magnus-expansion to obtain the improved algorithms in time-stepping computation. The improved algorithms are of the Gauss–Hermite spectral accuracy to approximate the analytical solution of the semiclassical Schrödinger equation. And for the given accuracy, the larger time stepsize can be used for the higher oscillation in the semiclassical Schrödinger equation. The superiority is shown by the error estimation and numerical experiments.

关键词： High oscillation semiclassical Schrödinger equation Hagedorn wavepacket multi-time-step method Magnus-expansion

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MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

arXiv

引用

arXiv 2022年

作者： Jiang, Yangbangyan Li, Xiaodan Chen, Yuefeng He, Yuan Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Security Department of Alibaba Group Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this paper. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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A novel neural network classification model based on covering and Affinity Propagation clustering algorithm

引用

Journal of Computational information Systems 2013年第7期9卷 2565-2573页

作者： Li, Hui Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Jiangsu Normal University Xuzhou 221116 China

Affinity Propagation (AP) algorithm can automatically determine the cluster center and does not need a pre-determined number of clustering. This paper presents a novel neural network classification model, using AP clustering method as a pre-processor to cluster high dimensional massive data samples, and combining covering algorithm to automatically determine the center of the covering, and calculating its radius, and then converting these parameters into the weights and thresholds of the hidden layer of classification neural network. The number of hidden layer neurons is the number of covering. The output layer is the M-P neuron layer. By evaluating the input feature of hidden layer to complete the mapping from covering to the output, and gain the corresponding output category of the input feature. Simulation results show that our proposed neural network classification model can get better performance. Copyright © 2013 Binary information Press.

关键词： Clustering algorithms

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