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COMMUNICATIONS EARTH & ENVIRONMENT 2024年第1期5卷 701页

作者： Capotondi, Antonietta Rodrigues, Regina R. Sen Gupta, Alex Benthuysen, Jessica A. Deser, Clara Froelicher, Thomas L. Lovenduski, Nicole S. Amaya, Dillon J. Le Grix, Natacha Xu, Tongtong Hermes, Juliet Holbrook, Neil J. Martinez-Villalobos, Cristian Masina, Simona Roxy, Mathew Koll Schaeffer, Amandine Schlegel, Robert W. Smith, Kathryn E. Wang, Chunzai University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) Federal University of Santa Catarina Florianópolis Brazil (GRID:grid.411237.2) (ISNI:0000 0001 2188 7235) University of New South Wales Climate Change Research Centre and Centre for Marine Science and Innovation Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) University of New South Wales Australian Research Council Centre of Excellence for Climate Extremes Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) Australian Institute of Marine Science Crawley Australia (GRID:grid.1046.3) (ISNI:0000 0001 0328 1619) National Center for Atmospheric Research Climate and Global Dynamics Laboratory Boulder USA (GRID:grid.57828.30) (ISNI:0000 0004 0637 9680) University of Bern Climate and Environmental Physics Physics Institute Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Bern Oeschger Centre for Climate Change Research Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Colorado Boulder Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) South African Environmental Observation Network Cape Town South Africa (GRID:grid.507758.8) (ISNI:0000 0004 0499 441X) University of Tasmania Institute for Marine and Antarctic Studies Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) University of Tasmania ARC Centre of Excellence for Climate Extremes Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) Universidad Adolfo Ibáñez Faculty of Engineering and Science Santiago Chile (GRID:grid.440617.0) (ISNI:0000 0001 2162 5606) ANID Technology Center No. DO210001 Data Observatory Foundation Santiago Chile (GRID:grid.440617.0) CMCC Foundation—Euro-M

Marine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing *** understanding of marine heatwave predictability and impacts requires analysis of these extremes at full ocean depth, using models and observations capturing their key drivers at the relevant scales, according to a broad literature review.

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Comments on cut-set bounds on network function computation

Comments on cut-set bounds on network function computation

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作者： Huang, Cupjin Tan, Zihan Yang, Shenghao Guang, Xuan Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science University of Chicago ChicagoIL60637 United States School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Mathematical Sciences and LPMC Nankai University Tianjin China Institute of Network Coding Chinese University of Hong Kong Hong Kong Hong Kong

A function computation problem over a directed acyclic network has been considered in the literature, where a sink node is required to compute a target function correctly with the inputs arbitrarily generated at multiple source nodes. The network links are error free but capacity limited, and the intermediate nodes perform network coding. The computing rate of a network code is the average number of times that the target function is computed for one use of the network, i.e., each link in the network is used at most once. In the existing papers, two cut-set bounds were proposed on the computing rate. However, we in this paper show that these bounds are not valid for general network function computation problems. We analyze the reason of the invalidity and propose a general cut-set bound by using a new equivalence relation associated with the inputs of the target function. Moreover, some results in the existing papers were proved by applying the invalid upper bound. We also justify the validity of these results. © 1963-2012 IEEE.

关键词： network coding

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Cryptanalysis of novel extended multivariate public key cryptosystem with invertible cycle

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International Journal of network Security 2018年第3期20卷 509-514页

作者： Lu, Gang Xue, Linyuan Nie, Xuyun Qin, Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Section 2 North Jianshe Road Chengdu610054 China State Key Laboratory of Information Security Institute of Information Engineering China Network and Data Security Key Laboratory of Sichuan Province China

In 2016, Qiao et al. proposed a novel extended multivariate public key cryptosystem (EMC) to enhance the security of multivariate public key cryptosystem. They applied it on Matsumoto-Imai (MI) encryption scheme and claimed that the enhanced MI scheme can be secure against Linearization Equation (LE) attack. Through analysis, we found that the enhanced MI scheme satisfied Quadratization Equations (QE). After finding all the quadratization equations, we can recover the plaintext corresponding to a valid ciphertext of the enhanced MI scheme. © International Journal of network Security.

关键词： Public key cryptography

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Corrigendum to "Dissemination in time and space in presymptomatic granulin mutation carriers: A spatial chronnectome study" [Neurobiology of Aging Volume 108, December 2021, Pages 155-167]

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Neurobiology of aging 2022年 119卷 140-144页

作者： Enrico Premi Marcello Giunta Armin Iraji Srinivas Rachakonda VinceD Calhoun Stefano Gazzina Alberto Benussi Roberto Gasparotti Silvana Archetti Martina Bocchetta Dave Cash Emily Todd Georgia Peakman Rhian Convery John C van Swieten Lize Jiskoot Raquel Sanchez-Valle Fermin Moreno Robert Laforce Caroline Graff Matthis Synofzik Daniela Galimberti JamesB Rowe Mario Masellis Carmela Tartaglia Elizabeth Finger Rik Vandenberghe Alexandre de Mendonça Fabrizio Tagliavini Chris R Butler Isabel Santana Alexander Gerhard Isabelle Le Ber Florence Pasquier Simon Ducharme Johannes Levin Adrian Danek Sandro Sorbi Markus Otto Jonathan D Rohrer Barbara Borroni Stroke Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Centre for Neurodegenerative Disorders Department of Clinical and Experimental Sciences University of Brescia Brescia Italy. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Departments of Psychology and Computer Science Georgia State University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA. Neurophysiology Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Neuroradiology Unit University of Brescia Brescia Italy. Laboratory Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Department of Neurodegenerative Disease Dementia Research Centre UCL Institute of Neurology Queen Square London UK. Department of Neurology Erasmus Medical Centre Rotterdam the Netherlands. Alzheimer's disease and Other Cognitive Disorders Unit Neurology Service Hospital Clinic University of Barcelona Barcelona Spain. Cognitive Disorders Unit Department of Neurology Donostia University Hospital San Sebastian Gipuzkoa Spain. Clinique Interdisciplinaire de Memoire Department des Sciences Neurologiques CHU de Quebec and Faculte de Medecine Universite Laval Quebec Canada. Center for Alzheimer Research Division of Neurogeriatrics Department of Neurobiology Care Sciences and Society Bioclinicum Karolinska Institutet Solna Sweden. Department of Neurodegenerative Diseases Hertie-Institute for Clinical Brain Research and Center of Neurology University of Tubingen Tubingen Germany. Fondazione Ca' Granda IRCCS Ospedale Policlinico Milan Italy University of Milan

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Balanced distribution adaptation for transfer learning

arXiv

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arXiv 2018年

作者： Wang, Jindong Chen, Yiqiang Hao, Shuji Feng, Wenjie Shenk, Zhiqi Beijing Key Laboratory of Mobile Computing and Pervasive Device CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Institute of High Performance Computing A*STAR School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

arXiv

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arXiv 2018年

作者： Liang, Junjie Hu, Jinlong Dong, Shoubin Honavar, Vasant Artificial Intelligence Research Laboratory Center for Big Data Analytics and Discovery Informatics College of Information Sciences and Technology Pennsylvania State University University ParkPA16802 United States Guangdong Key Laboratory of Communication & Computer Network School of Computer Science and Engineering South China University of Technology Guangzhou510006 China

We propose Top-N-Rank, a novel family of list wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function;and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. Because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (ReLU). We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function. Then, a more efficient variant, ***, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear in the average number of items rated by users. The results of our experiments using two widely used benchmarks, namely, the MovieLens data set and the Amazon Video Games data set demonstrate that: (i) The "top-N truncation" of the objective function substantially improves the ranking quality of the top N recommendations;(ii) using the ReLU for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid;and (iii) *** substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Cubic medium field equation public key cryptosystem

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International Journal of network Security 2018年第3期20卷 472-477页

作者： Lu, Gang Xue, Linyuan Nie, Xuyun Qin, Zhiguang Liu, Bo School of Information and Software Engineering University of Electronic Science and Technology of China 4 Jianshe North Rd 2nd Section Chenghua Qu Chengdu610054 China State Key Laboratory of Information Security Institute of Information Engineering Beijing100093 China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China

Medium Field Equation (MFE) multivariate public key cryptosystems were broken by High Order Linearization Equation (HOLE) attack. In order to avoid HOLE attack, we proposed an improvement of MFE, Cubic MFE public key encryption scheme. In our construction, multiplications of three second order matrices were used to get a set of cubic polynomials in the central map. Through theoretical analysis and computer experiments, the Cubic MFE is shown to be secure against HOLE attack and other existing attacks. © International Journal of network Security.

关键词： Linearization

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Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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Journal of Healthcare engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Social cognitive optimization with tent map for combined heat and power economic dispatch

arXiv

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arXiv 2018年

作者： Sun, Jiaze Li, Yang School of Computer Science and Technology Xi 'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi 'an University of Posts and Telecommunications Xi 'an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Combined Heat and Power Economic Dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel Social Cognitive Optimization algorithm with Tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, Adaptive Constraints Relaxing (ACR) rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by carrying out two typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Functions

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Publisher Correction: A global overview of marine heatwaves in a changing climate

Communications Earth & Environment

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Communications Earth & Environment 2024年第1期5卷 1页

作者： Antonietta Capotondi Tongtong Xu Dillon J. Amaya Regina R. Rodrigues Alex Sen Gupta Jessica A. Benthuysen Clara Deser Thomas L. Frölicher Natacha Le Grix Nicole S. Lovenduski Juliet Hermes Neil J. Holbrook Cristian Martinez-Villalobos Simona Masina Mathew Koll Roxy Amandine Schaeffer Robert W. Schlegel Kathryn E. Smith Chunzai Wang Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA NOAA/Physical Sciences Laboratory Boulder CO USA Federal University of Santa Catarina Florianópolis Brazil Climate ChangeResearch Centre and CentreforMarine Science and Innovation University of New South Wales Sydney NSW Australia Australian Research Council Centre of Excellence for Climate Extremes University of New South Wales Sydney NSW Australia Australian Institute of Marine Science Crawley WA Australia Climate and Global Dynamics Laboratory National Center for Atmospheric Research Boulder CO USA Climate and Environmental Physics Physics Institute University of Bern Bern Switzerland Oeschger Centre for Climate Change Research University of Bern Bern Switzerland Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research University of Colorado Boulder Boulder CO USA South African Environmental Observation Network Cape Town South Africa Institute for Marine and Antarctic Studies University of Tasmania Hobart TAS Australia ARC Centre of Excellence for Climate Extremes University of Tasmania Hobart TAS Australia Faculty of Engineering and Science Universidad Adolfo Ibáñez Santiago Chile Data Observatory Foundation ANID Technology Center No. DO210001 Santiago Chile CMCC Foundation—Euro-Mediterranean Center on Climate Change Bologna Italy Centre for Climate Change Research Indian Institute of Tropical Meteorology Ministry of Earth Sciences Pune India School of Mathematics and Statistics UNSW Sydney NSW Australia Center for Marine Science and Innovation UNSW Sydney NSW Australia Laboratoire d’Océanographie de Villefranche Sorbonne University CNRS Villefranche-sur-mer France Marine Biological Association of the United Kingdom Plymouth UK State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China Global Ocean and Climate Research Center

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