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arXiv 2019年

作者： Shao, Shuai Wang, Yan-Jiang Liu, Bao-Di Xu, Rui Li, Ye College of Information and Control Engineering China University of Petroleum Qingdao266580 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Jinan250000 China

Dictionary learning methods can be split into two categories: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use the discriminative information. With the first category, samples of different classes are mapped to different subspaces which leads to some redundancy in the base vectors. For the second category, the samples in each specific class can not be described well. Moreover, most class shared dictionary learning methods use the 0-norm regularization term as the sparse constraint. In this paper, we first propose a novel class shared dictionary learning method named label embedded dictionary learning (LEDL) by introducing the 1-norm sparse constraint to replace the conventional 0-norm regularization term in LC-KSVD method. Then we propose a novel network named hybrid dictionary learning network (HDLN) to combine the class specific dictionary learning with class shared dictionary learning together to fully describe the feature to boost the performance of classification. Extensive experimental results on six benchmark datasets illustrate that our methods are capable of achieving superior performance compared to several conventional classification algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Binary Channel Fuzzy Self-Adjusted Neural Network for Solving Time-Changing QP Problems

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IEEE transactions on neural networks and learning systems 2025年 PP卷 PP页

作者： Yamei Luo Qingyi Ren Zihao Zheng Siyuan Chen Xin Ma Yu Liu Xiaoli Li Junzhi Yu Zhijun Zhang The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern University of Science and Technology Institute for Robotics Shenzhen518055 China Peking University College of Engineering Beijing100871 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Laboratory Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong 723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established; then, using Lagrange's law, the time-changing QP problem is transformed into a time-changing matrix equation; and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN).

关键词： Recurrent neural networks

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Research and Prospect of Digital Economy Development in Shandong Province

Research and Prospect of Digital Economy Development in Shan...

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2019 5th International Conference on Humanities and Social Science Research (ICHSSR 2019)

作者： Zhengqian Feng Hao Wang Liping Wei Shandong Information Resources Application Association Qilu University of Technology(Shandong Academy of Sciences) Shandong Computer Science Center(National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong University of Science and Technology

Digital economy refers to the economic model that takes digital technology as the core to drive the whole economic activity process and create *** the future, all economic links may be driven by digital technology, digital technology will be the main driving force for world economic development, and digital economy will be the new engine for world economic *** great transformation of digital economy is a new opportunity for economic *** development level of digital economy in Shandong province has entered the first echelon in China, but there are still some factors restricting its *** digital economy has become the core force leading the scientific and technological revolution and industrial transformation, and human society is entering a new stage marked by digital *** province must correctly grasp the general trend of the development of digital economy, do a good job in planning and creating momentum and taking advantage of it, accelerate the construction of a strong digital province, make the digital economy become an important starting point to promote the conversion of old and new drives and an important supporting force of "lead the way".

关键词： Digital Economy Development Status Evaluation Index

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Surface Reconstruction from Normals: A Robust DGP-based Discontinuity Preservation Approach

Surface Reconstruction from Normals: A Robust DGP-based Disc...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Wuyuan Xie Miaohui Wang Mingqiang Wei Jianmin Jiang Jing Qin College of Computer and Software Engineering Shenzhen University (SZU) Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Nursing Hong Kong Polytechnic University

ISBN: (纸本)9781728132945

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

关键词： Surface reconstruction discontinuities angular misalignment Solution space CONSERVATION optimum performance Arts complexity classes

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Pressure-induced enhancement of optoelectronic properties in PtS2

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Chinese Physics B 2018年第6期27卷 345-349页

作者： Yi-Fang Yuan Zhi-Tao Zhang Wei-Ke Wang Yong-Hui Zhou Xu-Liang Chen Chao An Ran-Ran Zhang Ying Zhou Chuan-Chuan Gu Liang Li Xin-Jian Li Zhao-Rong Yang Department of Physics and Laboratory of Material Physics Zhengzhou UniversityZhengzhou 450052China Anhui Provincial Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field LaboratoryChinese Academy of SciencesHefei 230031China Synergetic Innovation Center for Quantum Effects and Application Key Laboratory of Low-dimensional Quantum Structures and Quantum Control of Ministry of EducationCollege of Physics and Information ScienceHunan Normal UniversityChangsha 410081China State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology(HUST)Wuhan 430074China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China

PtS2, which is one of the group-10 transition metal dichalcogenides, attracts increasing attention due to its extraordinary properties under external modulations as predicted by theory, such as tunable bandgap and indirect-to-direct gap transition under strain; however, these properties have not been verified experimentally. Here we report the first experimental exploration of its optoelectronic properties under external pressure. We find that the photocurrent is weakly pressuredependent below 3 GPa but increases significantly in the pressure range of 3 GPa–4 GPa, with a maximum ～ 6 times higher than that at ambient pressure. X-ray diffraction data shows that no structural phase transition can be observed up to26.8 GPa, which indicates a stable lattice structure of PtS2 under high pressure. This is further supported by our Raman measurements with an observation of linear blue-shifts of the two Raman-active modes to 6.4 GPa. The pressure-enhanced photocurrent is related to the indirect-to-direct/quasi-direct bandgap transition under pressure, resembling the gap behavior under compression strain as predicted theoretically.

关键词： high pressure optoelectronic properties transition metal disulfide

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One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Interpretable Saliency Map for Deep Reinforcement Learning

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Hong Zheng Yinglong Dai Fumin Yu Yuezhen Hu School of Physics and Electronics Hunan Normal University Changsha 410081 China College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.

关键词：

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Efficient parallel linear scaling method to get the response density matrix in all-electron real-space density-functional perturbation theory

arXiv

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arXiv 2020年

作者： Shang, Honghui Liang, Wanzhen Zhang, Yunquan Yang, Jinlong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Physical Chemistry of Solid Surfaces Collaborative Innovation Center of Chemistry for Energy Materials Fujian Provincial KeyLaboratory of Theoretical and Computational Chemistry Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University XiamenFujian361005 China Hefei National Laboratory for Physical Sciences at Microscale Department of Chemical Physics Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China

The real-space density-functional perturbation theory (DFPT) for the computations of the response properties with respect to the atomic displacement and homogeneous electric field perturbation has been recently developed and implemented into the all-electron, numeric atom-centered orbitals electronic structure package FHI-aims. It is found that the bottleneck for large scale applications is the computation of the response density matrix, which scales as O(N3). Here for the response properties with respect to the homogeneous electric field, we present an efficient parallel linear scaling algorithm for the response density matrix calculation. Our scheme is based on the second-order trace-correcting purification and the parallel sparse matrix-matrix multiplication algorithms. The new scheme reduces the formal scaling from O(N3) to O(N), and shows good parallel scalability over tens of thousands of cores. As demonstrated by extensive validation, we achieve a rapid computation of accurate polarizabilities using DFPT. Finally, the computational efficiency of this scheme has been illustrated by making the scaling tests and scalability tests on massively parallel computer systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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A Handwritten Chinese Text Recognizer Applying Multi-level Multimodal Fusion Network

A Handwritten Chinese Text Recognizer Applying Multi-level M...

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International Conference on Document Analysis and Recognition

作者： Yuhuan Xiu Qingqing Wang Hongjian Zhan Man Lan Yue Lu School of Computer Science and Software Engineering East China Normal University Shanghai P.R.China University of Technology Sydney Shanghai Key Laboratory of Multidimensional Information Processing

Handwritten Chinese text recognition (HCTR) has received extensive attention from the community of pattern recognition in the past decades. Most existing deep learning methods consist of two stages, i.e., training a text recognition network on the base of visual information, followed by incorporating language constrains with various language models. Therefore, the inherent linguistic semantic information is often neglected when designing the recognition network. To tackle this problem, in this work, we propose a novel multi-level multimodal fusion network and properly embed it into an attention-based LSTM so that both the visual information and the linguistic semantic information can be fully leveraged when predicting sequential outputs from the feature vectors. Experimental results on the ICDAR-2013 competition dataset demonstrate a comparable result with the state-of-the-art approaches.

关键词： Visualization Semantics Linguistics Convolution Text recognition Feature extraction Hidden Markov models

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