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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

Full-space Cloud of Random Points with a Scrambling Metasurface

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Light(Science & Applications) 2018年第1期7卷 467-474页

作者： Zile Li Qi Dai Muhammad Q.Mehmood Guangwei Hu Boris Luk’yanchuk Jin Tao Chenglong Hao Inki Kim Heonyeong Jeong Guoxing Zheng Shaohua Yu Andrea Alù Junsuk Rho Cheng-Wei Qiu School of Electronic Information Wuhan UniversityWuhan 430072China NOEIC State Key Laboratory of Optical Communication Technologies and NetworksWuhan Research Institute of Posts and TelecommunicationsWuhan 430074China Department of Electrical Engineering Information Technology University of the PunjabFerozpur Road54000 LahorePakistan Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Advanced Science Research Center City University of New YorkNew York 10031USA Data Storage Institute A*STAR(Agency for ScienceTechnology and Research)2 Fusionopolis Way#08-01Innovis 138634Singapore Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea

With the rapid progress in computer science,including artificial intelligence,big data and cloud computing,full-space spot generation can be pivotal to many practical applications,such as facial recognition,motion detection,augmented reality,*** opportunities may be achieved by using diffractive optical elements(DOEs)or light detection and ranging(LIDAR).However,DOEs suffer from intrinsic limitations,such as demanding depth-controlled fabrication techniques,large thicknesses(more than the wavelength),Lambertian operation only in half space,*** nevertheless relies on complex and bulky scanning systems,which hinders the miniaturization of the spot ***,inspired by a Lambertian scatterer,we report a Hermitian-conjugate metasurface scrambling the incident light to a cloud of random points in full space with compressed information density,functioning in both transmission and reflection *** 4044 random spots are experimentally observed in the entire space,covering angles at nearly 90°.Our scrambling metasurface is made of amorphous silicon with a uniform subwavelength height,a nearly continuous phase coverage,a lightweight,flexible design,and low-heat ***,it may be mass produced by and integrated into existing semiconductor foundry *** work opens important directions for emerging 3D recognition sensors,such as motion sensing,facial recognition,and other applications.

关键词： conjugate directions amorphous

Can early joining participants contribute more? - Timeliness sensitive incentivization for crowdsensing

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arXiv 2017年

作者： Xu, Yuedong Zhou, Yifan Mao, Yifan Chen, Xu Li, Xiang Research Center of Smart Networks and Systems Fudan University Shanghai China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

This paper investigates the incentive mechanism design from a novel and practically important perspective in which mobile users as contributors do not join simultaneously and a requester desires large efforts from early contributors. A two-stage Tullock contest framework is constructed: at the second stage the potential contributors compete for splittable reward by exerting efforts, and at the first stage the requester can orchestrate the incentive mechanism to maximize his crowdsensing efficiency given the rewarding budget. A general reward discrimination mechanism is developed for timeliness sensitive crowdsensing where an earlier contributor usually has a larger maximum achievable reward and thus allocates more efforts. Owning to the lack of joining time information, two practical implementations, namely earliest-n and termination time, are announced to the contributors. For each of them, we formulate a Stackelberg Bayesian game in which the joining time of a contributor is his type and not available to his opponents. The uniqueness of Bayesian Nash equilibrium (BNE) is proved in each strategy. To maximize the requester's efficiency, we compute the optimal number of rewarded contributors in the earliest-n scheme and the optimal deadline in the termination time scheme. Our contest framework is applicable not only to the closed crowdsensing with fixed number of contributors, but also to the open crowdsensing that the arrival of contributors is governed by a stochastic process. Extensive simulations manifest that with appropriate reward discriminations, the requester is able to achieve a much higher efficiency with the optimal selection of the number of rewarded contributiors and the termination time. Copyright © 2017, The Authors. All rights reserved.

关键词： Joining

MedShapeNet - A Large-Scale dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

IDTxl: The information dynamics toolkit xl: A python package for the efficient analysis of multivariate information dynamics in networks

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arXiv 2018年

作者： Wollstadt, Patricia Lizier, Joseph T. Vicente, Raul Finn, Conor Martinez-Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael MEG Unit Brain Imaging Center Goethe-University Frankfurt Fankfurt am Main Germany Centre for Complex Systems Faculty of Engineering and IT University of Sydney SydneyNSW Australia Computational Neuroscience Lab Institute of Computer Science Tartu Estonia Data61 CSIRO EppingNSW Australia Communications and Signal Theory and Telematics Engineering University of Valladolid Valladolid Spain Computational Neurodynamics Group Department of Computing Imperial College London London United Kingdom Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Campus Institute for Dynamics of Biological Networks Georg-August Universität Göttingen Germany

We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://***/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006;MacKay, 2003;Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. Information theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable. TE has become popular in many scientific disciplines to infer dependencies and whole networks from data. Notable application domains include neuroscience (Wibral, Vicente, & Lindner, 2014) and dynamical systems analysis (Lizier, Prokopenko, & Zomaya, 2014) (see Bossomaier, Barnett, Harré, & Lizier (2016) for an introduction to TE and a comprehensive discussion of its application). In the majority of the applications, TE is used in a bivariate fashion, where information transfer is quantified between all sourcetarget pairs. In a multivariate setting, however, such a bivariate analysis may infer spurious or redundant interactions, where multiple sources provide the same information about the target. Conversely, bivariate analysis may also miss synergistic

关键词： High level languages

Risk-aversemodel predictive control

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arXiv 2017年

作者： Sopasakis, Pantelis Herceg, Domagoj Bemporad, Alberto Patrinos, Panagiotis University of Cyprus Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks 1 Panepistimiou Avenue Aglantzia Nicosia2109 IMT School for Advanced Studies Lucca Piazza San Francesco 19 Lucca55100 Italy STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Kasteelpark Arenberg 10 Leuven3001 Belgium

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for *** Codes 93E20 Copyright © 2017, The Authors. All rights reserved.

关键词： Model predictive control

Active self-paced learning for cost-effective and progressive face identification

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arXiv 2017年

作者： Lin, Liang Wang, Keze Meng, Deyu Zuo, Wangmeng Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China Engineering Research Center Advanced Computing Engineering Software of Ministry of Education China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University China School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://***/projects/aspl/ Copyright © 201

关键词： Cost effectiveness

A power-distance based handover triggering algorithm for LTE-R using WINNERII-D2a channel model

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A power-distance based handover triggering algorithm for LTE...

Asia-Pacific Conference on Communications

作者： Ehab Ahmed Ibrahim Ehab F. Badran M. R. M. Rizk Computer Networks and Data Center Arab Academy for science and Technology Alexandria Egypt Electronics and Engineering Department Arab Academy for Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9781509006779

Railway systems cannot be apart from the rapid evolution of wireless communication systems. The Long Term Evolution communication system for Railway (LTE-R) is believed to be the normal evolution for current Global System for Mobile Communications Railways (GSM-R). One of the essential targets of LTE is to provide seamless and fast handover from one cell to another to achieve a strict delay requirement while, at the same time, keeping network management as simple as possible. Hence, the decision to trigger a handover is very important in the design of handover process. For handover to be successful, it requires to choose handover parameters correctly and optimize their setting. The LTE-R system uses two parameters to decide handover triggering: Hysteresis and Time-to-Trigger (TTT). The handover triggering is highly depended on the train speed, which means that as the train speed changed, the handover triggering time is correspondingly varied. To determine a more accurate handover triggering point is a critical issue. Too late handover triggering caused by train speed change will lead to handover failure. In this paper, we propose a new handover triggering algorithm called Power-Distance Algorithm that assets to avoid this problem. It eliminates the dependency on the train speed, also, reduces the system processing power. This Algorithm depends on the distance between the two base stations and power received from them. At a certain (predefined) point, the handover triggering must occur. This triggering point is the same for all values of train speed i.e., it doesn't change as the train speed changes. The change occurs only when the Hysteresis value changes.

关键词： Handover Long Term Evolution Mobile communication Rail transportation Hysteresis 3GPP

An optimized LTE measurement handover procedure for high speed trains using WINNER II channel model

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An optimized LTE measurement handover procedure for high spe...

Asia-Pacific Conference on Communications

作者： Ehab Ahmed Ibrahim Ehab F. Badran M. R. M. Rizk Computer Networks and Data Center Arab Academy for science and Technology Alexandria Egypt Electronics and Communications Engineering Department Arab Academy for Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9781509006779

Wireless Communication network is essential for railway environments as it provides safety and reliability for railway operation as well as improved railway services. Currently, Global System for Mobile Communications Railways (GSM-R) is widely used in railway systems. It is believed that GSM-R system has availability and reliability thanks to its commercial success. At the same time, GSM-R has a lot of shortcomings in high speed transmission. As a result, the Long Term Evolution for Railway (LTE-R) is considered as the natural evolution for current (GSM-R) system. Trains travelling at very high speeds suffer from many handovers between cells. One of the essential targets of LTE is providing fast and seamless handover between cells. Hence, handover triggering decision is very important in the design of handover process. One of the major problems that cause radio link failure is the too late handover triggering. It occurs when the train is moving faster than the handover parameter settings. In this paper, we propose an optimized measurement handover procedure for High Speed Trains (HST) which can accelerate the measurement procedure when the train is moving toward a target cell with a higher speed than designed to compensate the train acceleration. As a result, too late handover probability will be reduced causing a subsequent reduction in radio link failure probability.

关键词： Handover Long Term Evolution Rail transportation Mobile communication Channel models Loss measurement