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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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Prediction of solar energetic events impacting space weather conditions

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Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a big data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling big data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Improving pix2code based Bi-directional LSTM

Improving pix2code based Bi-directional LSTM

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Yanbin Liu Qidi Hu Kunxian Shu Chongqing Key Laboratory on Big Data for Bio Intelligence CQUPT Chongqing China College of Computer Science and Technology CQUPT Chongqing China

ISBN: (纸本)9781538678626;9781538678619

Pix2code is a framework based on deep learning to transform a graphical user interface screenshot created by the designer into computer coder with 77% of accuracy. The architecture is based on CNN and *** has been broadly applied to natural language processing about language model, which is both general and effective at capturing long-term dependencies. However, the standard LSTM predicting in time sequence ignores the contextual information of the future, but sometimes it is not enough just to look at the previous word. computer code is a relative spatial relationship and not only needs to recognize token but also fully understands the structure of all sequences. In order to solve the problem, the pix2code model is optimized by Bidirectional LSTM, which allows the output layer to get complete past and future context information for each point in the input sequence. The model's transforming accuracy in the test set has been significantly improved reaching 85%.

关键词： Training Decoding Feature extraction Graphical user interfaces DSL Mathematical model Context modeling

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Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

引用

In silico pharmacology 2023年第1期11卷 8页

作者： Anika Jabin Mohammad Fahim Uddin Salauddin Al Azad Ashfaque Rahman Fawzia Tabassum Pritthy Sarker A K M Helal Morshed Samiur Rahman Fatima Fairuz Raisa Musfiqur Rahman Sakib Abeer Hasan Olive Tabassum Islam Ramisha Tahsin Shahlaa Zernaz Ahmed Partha Biswas Mst Umme Habiba Mahbuba Siddiquy Maryam Jafary Department of Biochemistry and Microbiology North South University Dhaka 1229 Bangladesh. College of Material Science and Engineering Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang People's Republic of China. Key Laboratory of Industrial Biotechnology Ministry of Education School of Biotechnology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology Sylhet 3114 Bangladesh. Pathology and Pathophysiology Major Academy of Medical Science Zhengzhou University Zhengzhou City 450001 Henan Province People's Republic of China. Department of Electrical and Electronic Engineering Brac University Dhaka 1212 Bangladesh. Department of Pharmacy Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh. Department of Pharmacy East West University Dhaka 1212 Bangladesh. Department of Computer Science and Engineering East West University Dhaka 1212 Bangladesh. Department of Pharmaceutical Sciences North South University Dhaka 1229 Bangladesh. Department of Genetic Engineering and Biotechnology Jashore University of Science and Technology Jashore 7408 Bangladesh. Data Science Research Unit RPG Interface Lab Jashore 7400 Bangladesh. State Key Laboratory of Food Science and Technology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Division of Food Safety and Hygiene Department of Environmental Health Engineering School of Public Health Tehran University of Medical Sciences Tehran *** Iran.

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å); Rg (nm); PSA (Å); SASA (Å), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where -amyrin scored the most significant values in all aspects.

关键词： CD81 receptor Co-expressed genes behind HCV invasion DMN-induced hepatic fibrosis HCV infection In-silico pharmacokinetics and pharmacodynamics Target-specificity of amyrins

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Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

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China Communications 2018年第4期15卷 72-85页

作者： Lin Jiang Ruimin Hu Xiaochen Wang Weiping Tu Maosheng Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan 430072 China Institute of Big Data and internet Innovation Hunan University of Commerce Changsha 410205 China Software College East China University of Technology Nanchang 330013 China Collaborative Innovation Center for Economics crime investigation and prevention technology Jiangxi Province Nanchang 330103China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

关键词： audio coding non-blind audiobandwidth extension context correlation deeprecurrent neural network

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Feature extraction for hyperspectral imagery: The evolution from shallow to deep

arXiv

引用

arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

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Logic could be learned from images

arXiv

引用

arXiv 2019年

作者： Guo, Qian Qian, Yuhua Liang, Xinyan She, Yanhong Li, Deyu Liang, Jiye Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China College of Science Xi’an Shiyou University Shanxi Xi'an710065 China

Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to design some reasoning patterns beforehand. This has led to an interesting question: can logic reasoning patterns be directly learned from given data? The problem is termed as a data concept logic. In this study, a learning logic task from images, called a LiLi task, first is proposed. This task is to learn and reason the logic relation from images, without presetting any reasoning patterns. As a preliminary exploration, we design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition, Subtraction and Multiplication), in which each image is embedded with a n-digit number. It is worth noting that a learning model beforehand does not know the meaning of the n-digit numbers embedded in images and the relation between the input images and the output image. In order to tackle the task, in this work we use many typical neural network models and produce fruitful results. However, these models have the poor performances on the difficult logic task. For furthermore addressing this task, a novel network framework called a divide and conquer model by adding some label information is designed, achieving a high testing accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： computer circuits

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Risk factors of SARS-CoV-2 infection in healthcare workers: a retrospective study of a nosocomial outbreak

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Sleep Medicine: X 2020年 2卷 100028页

作者： Wang, Xuan Jiang, Xiaobing Huang, Qimin Wang, Han Gurarie, David Ndeffo-Mbah, Martial Fan, Fei Fu, Peng Horn, Mary Ann Mondal, Anirban King, Charles Xu, Shuai Zhao, Hongyang Bai, Yansen Department of Neurosurgery Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 China Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland 44106 OH United States Department of Biochemistry Case Western Reserve University Cleveland 44106 OH United States Center for Global Health and Diseases School of Medicine Case Western Reserve University Cleveland 44106 OH United States Department of Veterinary and Integrative Biosciences College of Veterinary and Biomedical Sciences Texas A&M University College Station 77840 TX United States School of Public Health Texas A&M University College Station 77840 TX United States Computer and Data Science Department Case Western Reserve University Cleveland 44106 OH United States Department of Occupational and Environmental Health State Key Laboratory of Environmental Health (Incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan 430030 China

Background: Healthcare workers (HCWs) are at the forefront of fighting against the COVID-19 pandemic. However, they are at high risk of acquiring the pathogen from infected patients and transmitting to other HCWs. We aimed to investigate risk factors for nosocomial COVID-19 infection among HCWs in a non-COVID-19 hospital yard. Methods: Retrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (including 12 COVID-19 HCWs) at Union Hospital of Wuhan, China. Sleep quality and working pressure were evaluated by the Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. The follow-up duration was from Dec 25, 2019, to Feb 15, 2020. Results: A high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt working under pressure (66.7% vs. 32.1%) than uninfected HCWs. SARS-CoV-2 infected HCWs had significantly higher scores of PSQI and NSI than uninfected HCWs (P < 0.001). Specifically, scores of 5 factors (sleep quality, sleep time, sleep efficiency, sleep disorder, and daytime dysfunction) in PSQI were higher among infected HCWs. For NSI, its 5 subscales (nursing profession and work, workload and time allocation, working environment and resources, patient care, management and interpersonal relations) were all higher in infected than uninfected nurse. Furthermore, total scores of PSQI (HR = 2.97, 95%CI = 1.86−4.76;P <0.001) and NSI (HR = 4.67, 95%CI = 1.42−15.45;P = 0.011) were both positively associated with the risk of SARS-CoV-2 infection. Conclusion: Our analysis shows that poor sleep quality and higher working pressure may increase the risk of nosocomial SARS-CoV-2 infection among HCWs. © 2020 The Authors

关键词： Healthcare workers Pittsburgh sleep quality index Risk factors SARS-CoV-2 The nurse stress index

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s-LWSR: Super lightweight super-resolution network

arXiv

引用

arXiv 2019年

作者： Li, Biao Liu, Jiabin Wang, Bo Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States College of Information Science and Technology University of Nebraska OmahaNE68182 United States

Deep learning (DL) architectures for super-resolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Correction: Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

引用

BMC public health 2023年第1期23卷 269页

作者： Hansong Zhu Si Chen Wen Lu Kaizhi Chen Yulin Feng Zhonghang Xie Zhifang Zhang Lingfang Li Jianming Ou Guangmin Chen Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. hszhu33@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. hszhu33@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. hszhu33@***. Climate Assessment Office of Fujian Climate Center Fuzhou 350007 Fujian China. Shengli Clinical Medical College of Fujian Medical University Department of Health Management of Fujian Provincial Hospital Fuzhou 350001 Fujian China. College of Computer and Data Science Fuzhou University Fuzhou 350108 Fujian China. School of Public Health Fujian Medical University Fuzhou 350108 Fujian China. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. Science and Technology Information and Management Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. ojm@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. ojm@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. ojm@***. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. 231147698@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. 231147698@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. 231147698@qq.com.

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