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Author Correction: Federated learning enables big data for rare cancer boundary detection

Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Minimizing thin film thickness in TiN coatings using genetic algorithms

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AIP Conference Proceedings 2018年第1期2016卷

作者： M. I. Jarrah A. S. M. Jaya M. A. Azam M. R. Muhamad H. Akbar 1Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia 2Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka (UTeM) Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia 3Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Optimization of coating parameters is important in the machining process, as an optimized coating layer enhances efficient machining result by providing high wear resistance, saving time and effort, reducing cost, increasing productivity and leading to extend tool life for better surface finish. In terms of tool wear resistance, tools with suitable film coating are forty times better than uncoated tools. Cutting tools are usually coated by Titanium Nitride (TiN), since it has properties such as resistance to wear and hardness resulting in increased cutting performance. Genetic algorithms (GAs) belong to the powerful artificial intelligence optimization family of evolutionary algorithm-based metaheuristic techniques. Since their inception, they have been successfully applied in numerous areas of industrial sectors as material design, tools coating and cutting, alloy design, and so on. In this research work, predicting thin film coating thickness of titanium nitride (TiN) on tungsten carbide (WC) inserts is presented. First, (WC) inserts were coated by TiN in different conditions using Physical Vapor Deposition (PVD), and the surface profilometer was used to measure thickness of the WC coated specimens. Second, Response Surface Methodology (RSM) was used to generate the objective function which represents the process parameters and coating thickness. Third, using a fitness function model, GAs were established to optimize the thickness value of TiN layer with respect to changes in three process parameters, namely Nitrogen gas pressure (N2), Argon gas pressure (Ar), and Turntable Speed (TT). Finally, for performance measurement, three real experimental tests were carried out in terms of Prediction Interval (PI) and Residual Error (e) to validate the RSM model. The actual coating thickness of validation data fell within the 95% (PI) and the (e) values percentage were very low. In terms of optimization, GAs is capable optimizing thickness better than the average experimen

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HCVMultiscaleDyn: A simulator for the multiscale model of hepatitis C virus dynamics

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AIP Conference Proceedings 2018年第1期1978卷

作者： Alexander Churkin Vladimir Reinharz Harel Dahari Danny Barash 1Department of Software Engineering Shamoon College of Engineering Beer-Sheva Israel 2Department of Computer Science Ben-Gurion Universty Beer-Sheva Israel 3The Program for Experimental and Theoretical Modeling Division of Hepatology Department of Medicine Loyola University Medical Center Maywood Illinois USA

Multiscale mathematical models that incorporate both intracellular and extracellular hepatitis C virus (HCV) dynamics have been developed in recent years to study HCV-host dynamics and antivirals mode of action and efficacy. They were built upon the standard HCV dynamic model that keeps track of uninfected hepatocytes, HCV-infected hepatocytes, and free virus in blood (for each there is a differential equation), along with a fourth equation that considers the intracellular viral RNA (vRNA) dynamics in an infected cell. A multiscale model with vRNA dynamics consideration is substantially more difficult to solve and the governing differential equations are stiff. In one of the derived differential equations there is also an integral involving the vRNA that is more accurately computed by considering previous iterations, impeding the use of canned solvers. To solve this problem we have chosen to implement a stable and efficient numerical method that belongs to the family of adaptive stepsize methods and is implicit, the Rosenbrock method. We provide a user friendly simulator called HCVMultiscaleDyn with a Graphical User Interface that applies this method and is useful to simulate viral dynamics during antiviral treatment.

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Accurate pose measurement of label-attached small objects using a 3D vision technique

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Journal of Institute of Control, Robotics and Systems 2016年第10期22卷 839-846页

作者： Kim, Eung-Su Kim, Kye-Kyung Wijenayake, Udaya Park, Soon-Yong School of Computer Science and Engineering Kyungpook National University Korea Republic of Intelligent Cognitive Technology Research Department ETRI Korea Republic of

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and 0.2-0.6° in angle measurements. © ICROS 2016.

关键词： Template matching

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data

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Journal of Physics: Conference Series 2019年第1期1361卷

作者： Dahlan Abdullah H Hartono Erianto Ongko Cut Ita Erliana Defi Irwansyah Akbar Iskandar Ayu Esteka Sari Abdussakir Yuniningsih Nurintan Asyiah Siregar Muhammad Syahrul Kahar Raditya Faisal Waliulu Ainna Amalia Nuning Kurniasih Muhammad Arifin Syafiul Muzid Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang MalangIndonesia Department of Economic and Business Universitas Pembangunan Nasional 'Veteran' Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting.

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A constituent syntactic parse tree based discourse parser 20

A constituent syntactic parse tree based discourse parser

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20th SIGNLL Conference on Computational Natural Language Learning: Shared Task, CoNLL 2016

作者： Li, Zhongyi Zhao, Hai Pang, Chenxi Wang, Lili Wang, Huan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China Omron Institute of Sensing and Control Technology Shanghai China

ISBN: (纸本)1932432663

This paper describes our system in the CoNLL-2016 shared task. Our system takes a piece of newswire text as input and returns the discourse relations. In our system we use a pipeline to conduct each subtask. Our system is evaluated on the CoNLL-2016 Shared Task closed track and obtains 0.1515 in F1 measurement, especially the part of detecting connectives, which achieves 0.9838 on blind test set. © 2016 Association for Computational Linguistics.

关键词： Syntactics

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Convolutional neural networks with low-rank regularization 4

Convolutional neural networks with low-rank regularization

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4th International Conference on Learning Representations, ICLR 2016

作者： Tai, Cheng Xiao, Tong Zhang, Yi Wang, Xiaogang Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the low-rank constrained CNNs delivers significantly better performance than their non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves 91.31% accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs. © ICLR 2016: San Juan, Puerto Rico. All Rights Reserved.

关键词： Iterative methods

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Comparing exploration strategies for Q-learning in random stochastic mazes

Comparing exploration strategies for Q-learning in random st...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Arryon D. Tijsma Madalina M. Drugan Marco A. Wiering Institute of Artificial Intelligence and Cognitive Engineering University of Groningen Department of Mathematics and Computer Science Technical University Eindhoven

ISBN: (纸本)9781509042418

Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.

关键词： Learning (artificial intelligence) Navigation Markov processes Electronic mail Radiation detectors Boltzmann distribution

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Multisensory robotic therapy to promote natural emotional interaction for children with ASD 11

Multisensory robotic therapy to promote natural emotional in...

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11th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2016

作者： Bevill, Rachael Azzi, Paul Spadafora, Matthew Park, Chung Hyuk Kim, Hyung Jung Lee, Jong Won Raihan, Kazi Jeon, Myounghoon Howard, Ayanna M. Department of Biomedical Engineering School of Engineering and Applied Science George Washington University United States Centscere LLC United States Samsung Electronics Korea Republic of New York Institute of Technology United States Department of Cognitive and Learning Sciences College of Sciences and Arts Michigan Technological University United States School of Electrical and Computer Science College of Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781467383707

Children with autism have a hard time both with expressing their emotions, and with understanding the emotions of others. As the population of children with autism increases, it is crucial we create effective therapeutic programs that will improve their communication skills. We present an interactive robotic system that delivers emotional and social behaviors for multi-sensory therapy for children with autism spectrum disorders. Our framework includes emotion-based robotic gestures and facial expressions, as well as vision and audio-based monitoring system for quantitative measurement of the interaction. © 2016 IEEE.

关键词： Robotics

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Composite skyrmion bags in two-dimensional materials

arXiv

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arXiv 2018年

作者： Foster, David Kind, Charles Ackerman, Paul J. Tai, Jung-Shen B. Dennis, Mark R. Smalyukh, Ivan I. H. H. Wills Physics Laboratory University of Bristol BristolBS8 1TL United Kingdom School of Mathematics University of Bristol BristolBS8 1TW United Kingdom Department of Physics and Soft Materials Research Center University of Colorado BoulderCO80309 United States School of Physics and Astronomy University of Birmingham BirminghamB12 2TT United Kingdom Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado BoulderCO80309 United States

Skyrmions are particle-like topological excitations, studied in various condensed matter systems and models of high-energy physics (HEP). They occur as stable spin textures in certain planar magnetic materials [1-4] and as configurations in chiral nematic liquid crystals [5], having been originally proposed as model of atomic nuclei [10]. Since magnetic Skyrmions can be accelerated with a current [6-8], they have the potential to encode bits in low-power magnetic storage devices [9]. Drawing on techniques from HEP, we demonstrate that magnetic and liquid crystal Skyrmions interact like orientation dependent, localised particles, explaining previously observed Skyrmion behaviour. This interaction motivates the construction of Skyrmion bags: Textures of high topological degree which we realise experimentally in liquid crystals, and in magnetic materials by computer simulations. These Skyrmion bags configurations are nested multiple Skyrmions, which act like single Skyrmions in pairwise interaction, and under the inuence of a current in magnetic materials. These results emphasize equivalent Skyrmion behaviour in different physical systems, and suggest new, high-density magnetic memory storage devices based on Skyrmion bags. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic storage

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