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Corrigendum to "Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data" [Comput. Biol. Med. 17 (2024) 108216]

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computers in biology and medicine 2024年 173卷 108352页

作者： Nuno Miguel Rodrigues José Guilherme de Almeida Ana Sofia Castro Verde Ana Mascarenhas Gaivão Carlos Bilreiro Inês Santiago Joana Ip Sara Belião Raquel Moreno Celso Matos Leonardo Vanneschi Manolis Tsiknakis Kostas Marias Daniele Regge Sara Silva Nickolas Papanikolaou Computational Clinical Imaging Group Champalimaud Foundation Portugal LASIGE Faculty of Sciences University of Lisbon Portugal. Electronic address: nmrodrigues@fc.ul.pt. Computational Clinical Imaging Group Champalimaud Foundation Portugal. Radiology Department Champalimaud Clinical Center Champalimaud Foundation Lisbon Portugal. NOVA Information Management School (NOVA IMS) Universidade Nova de Lisboa Campus de Campolide 1070-312 Lisboa Portugal. Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) GR-700 13 Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University GR-710 04 Heraklion Greece. Department of Electrical and Computer Engineering Hellenic Mediterranean University GR-710 04 Heraklion Greece Computational BioMedicine Laboratory (CBML) Institute of Computer Science Foundation for Research and Technology âĂŞ Hellas (FORTH) Heraklion Greece. Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale 142 Km 3.95 Candiolo Turin 10060 Italy Department of Surgical Sciences University of Turin Turin 10124 Italy. LASIGE Faculty of Sciences University of Lisbon Portugal. Department of Radiology Royal Marsden Hospital Sutton UK.

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Classes and phyla of the kingdom Fungi

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Fungal Diversity 2024年第5期124卷 1-165页

作者： Nalin N.Wijayawardene Kevin D.Hyde Kirill V.Mikhailov Gabor Peter Andre Aptroot Carmen L.A.Pires‑Zottarelli Bruno T.Goto Yuri S.Tokarev Danny Haelewaters Samantha C.Karunarathna Paul M.Kirk Andre L.C.M.de A.Santiago Ramesh K.Saxena Nathan Schoutteten Madhara K.Wimalasena Vladimir V.Aleoshin Abdullah M.S.Al‑Hatmi Kahandawa G.S.U.Ariyawansa Amanda R.Assuncao Thushara C.Bamunuarachchige Hans‑Otto Baral D.Jayarama Bhat Janusz Błaszkowski Teun Boekhout Nattawut Boonyuen Michael Brysch‑Herzberg Bin Cao Jonathan Cazabonne Xue‑Mei Chen Claudia Coleine Dong‑Qin Dai Heide‑Marie Daniel Suzana B.G.da Silva Francisco Adriano de Souza Somayeh Dolatabadi Manish K.Dubey Arun K.Dutta Aseni Ediriweera Eleonora Egidi Mostafa S.Elshahed Xinlei Fan Juliana R.B.Felix Mahesh C.A.Galappaththi Marizeth Groenewald Li‑Su Han Bo Huang Vedprakash G.Hurdeal Anastasia N.Ignatieva Gustavo H.Jeronimo Ana L.de Jesus Serhii Kondratyuk Jaturong Kumla Martin Kukwa Qirui Li Juliana L.R.Lima Xiao‑Yong Liu Wenhua Lu H.Thorsten Lumbsch Hugo Madrid Franco Magurno Guy Marson Eric H.C.McKenzie Audrius Menkis Armin Mešić Elaine C.R.Nascimento Elena S.Nassonova Yong Nie Naasson V.L.Oliveira Emilia A.Ossowska Julia Pawłowska Ursula Peintner Igor R.Pozdnyakov Bhagya M.Premarathne A.K.Hasith Priyashantha C.Alisha Quandt Mariana B.Queiroz Kunhiraman C.Rajeshkumar Mubashar Raza Niranjan Roy Milan C.Samarakoon Alessandra A.Santos Lidiane A.Santos Felix Schumm Laura Selbmann Faruk Selcuk D.Rabern Simmons Anastasia V.Simakova Maudy Th.Smith Onden Paraparath Sruthi Nakarin Suwannarach Kazuaki Tanaka Saowaluck Tibpromma Elias O.Tomas Merve Ulukapı Nicolas Van Vooren Dhanushka N.Wanasinghe Evi Weber Qianzhen Wu Er Fu Yang Ryuichi Yoshioka Noha H.Youssef Annemarie Zandijk Gui‑Qing Zhang Jin‑Yong Zhang Heng Zhao RuiLin Zhao Oleg A.Zverkov Marco Thines Sergey A.Karpov Center for Yunnan Plateau Biological Resources Protection and Utilization College of Biological Resource and Food EngineeringQujing Normal UniversityQujing 655011YunnanPeople’s Republic of China Tropical Microbiology Research Foundation 96/N/10Meemanagoda RoadPannipitiya 10230Sri Lanka Department of Bioprocess Technology Faculty of TechnologyRajarata UniversityMihintaleSri Lanka Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiangRai 57100Thailand Innovative Institutefor Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225People’s Republic of China CAS Key Laboratory for Plant Biodiversity and Biogeography of East Asia(KLPB) Kunming Institute of BotanyChinese Academy of ScienceKunmingYunnanChina Belozersky Institute of Physicochemical Biology Lomonosov Moscow State UniversityLeninskye Gory1Build.40Moscow 119991Russian Federation Kharkevich Institute for Information Transmission Problems Russian Academy of SciencesMoscow 127051Russian Federation National Collection of Agricultural and Industrial Microorganisms Institute of Food Science and TechnologyHungarian University of Agriculture and Life SciencesSomlóiút 14‑161118 BudapestHungary Instituto de Biociências Universidade Federal de Mato Grosso do SulAvenida Costa e Silvas/n Bairro UniversitárioCampo GrandeMS CEP 79070‑900Brazil Instituto de Pesquisas Ambientais Secretaria do Meio AmbienteInfraestrutura e LogísticaAv.Miguel Stéfano 3687São PauloSP CEP 04301‑902Brazil Departamento de Botânica e Zoologia Universidade Federal do Rio Grande do NorteCampus UniversitárioNatalRN 59078-900Brazil All-Russian Institute of Plant Protection Sh.Podbelskogo 3PushkinSt.Petersburg 196608Russia Research Group Mycology Department of BiologyGhent University9000 GhentBelgium Faculty of Science University of South Bohemia37005ČeskéBudějoviceCzech Republic Department of Ecology and Evolutionary Biology University of Colora

Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 *** higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al.,and the definition and the higher ranks(e.g.,phyla)of the‘true fungi’have been revised in several subsequent *** accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the *** study provides an updated classification of the kingdom Fungi,drawing upon a comprehensive phylogenomic analysis of Holomycota,with which we outline well-supported nodes of the fungal tree and explore more contentious *** accept 19 phyla of Fungi,***,Ascomycota,Basidiobolomycota,Basidiomycota,Blastocladiomycota,Calcarisporiellomycota,Chytridiomycota,Entomophthoromycota,Entorrhizomycota,Glomeromycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota,Mucoromycota,Neocallimastigomycota,Olpidiomycota,Rozellomycota,Sanchytriomycota,and *** the phylogenies,Caulochytriomycota resides in Chytridiomycota;thus,the former is regarded as a synonym of the latter,while Caulochytriomycetes is viewed as a class in *** provide a description of each phylum followed by its classes.A new subphylum,Sanchytriomycotina Karpov is introduced as the only subphylum in *** subclass Pneumocystomycetidae Kirk et *** Pneumocystomycetes,Ascomycota is invalid and thus *** of fossil fungi in phyla and classes are also discussed,providing examples.

关键词： Caulochytriomycota Phylogenomic analyses Pneumocystomycetidae Sanchytriomycotina

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Program disturb research and error avoidance algorithm design of 3D-TLC NAND flash memory

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International Journal of Performability engineering 2018年第8期14卷 1685-1694页

作者： Yang, Xiaoshan Zhu, Ligu Zhang, Qicong Zhang, Meng Wu, Fei Zhang, Wei School of Computer Communication University of China Beijing100024 China National Computer Quality Supervising and Testing Center 15th Institute of China Electronics Technology Group Corporation Beijing100083 China School of Information Engineering Shandong Management University Jinan250000 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Information Center China Datang Corporation Science and Technology Research Institute Beijing100000 China

This paper focuses on finding, settling and reducing the program disturb error of 3D-TLC NAND flash memory. Experimental analysis of the FPGA test platform determines characteristics of the program disturb error. The program disturb error makes the state shift rate of storage cells lose balance. MSB, CSB and LSB pages have unbalanced bit error ratio and bit error rate distribution. The page program disturb bit error is unbalanced as the number of program/erase cycles changes. Based on the experimental results of the error rate imbalance, an error avoidance algorithm is designed, which can shift the data state that is subject to program disturb to one that is not vulnerable to program disturb. The test results show that the algorithm can reduce the error rate of program disturb by 20% to 90%. In this sense, the program disturb error phenomenon found and error avoidance algorithm designed in this paper are helpful for improving the reliability of 3D-TLC NAND flash memory. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Errors

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

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The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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Perspectives on adaptive dynamical systems

arXiv

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin Institute of technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

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Online Intelligent Resource Management for Power-Delay Tradeoff in Backhaul-Limited Mobile Edge Computing Systems

Online Intelligent Resource Management for Power-Delay Trade...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuan Sun Gang Yang Xing-She Zhou Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computer Science and Engineering Northwestern Polytechnical University Xi???an China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the quality of experience for mobile device users, as it can offload computation intensive tasks from mobile devices to edge clouds. Nevertheless, task offloading also incurs additional overhead in both power consumption and execution delay. In essence, energy consumption and task delay cannot be optimized simultaneously as these two objectives contradict each other. Although many studies have been conducted to make tradeoff between them, the backhaul limitation of MEC-enabled wireless networks has not been tackled. In this paper, we study the power-delay tradeoff for MEC in backhaul-limited wireless networks. An average weighted sum power consumption minimization problem with task buffer stability constraints is formulated to investigate the problem, and a Lyapunouv Optimization based online intelligent computation and communication resource management algorithm is also proposed to solve it. In particular, at each time slot, optimal CPU-cycle frequencies, transmit power and backhaul capacity allocation are efficiently determined. Performance analysis indicates that under the proposed algorithm an [O(1/V), O(V)] tradeoff exists between the power consumption and execution delay, where V is a control parameter. Finally, simulations are conducted to validate the analytic results and demonstrate the changes of the system performance with various algorithm parameters.

关键词： Task analysis Mobile handsets Power demand Resource management Delays Optimization Wireless communication

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NTIRE 2021 Challenge on Image Deblurring

arXiv

引用

arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

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