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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Radu Timofte Shuhang Gu Jiqing Wu Luc Van Gool Lei Zhang Ming-Hsuan Yang Muhammad Haris Greg Shakhnarovich Norimichi Ukita Shijia Hu Yijie Bei Zheng Hui Xiao Jiang Yanan Gu Jie Liu Yifan Wang Federico Perazzi Brian McWilliams Alexander Sorkine-Hornung Olga Sorkine-Hornung Christopher Schroers Jiahui Yu Yuchen Fan Jianchao Yang Ning Xu Zhaowen Wang Xinchao Wang Thomas S. Huang Xintao Wang Ke Yu Tak-Wai Hui Chao Dong Liang Lin Chen Change Loy Dongwon Park Kwanyoung Kim Se Young Chun Kai Zhang Pengjv Liu Wangmeng Zuo Shi Guo Jiye Liu Jinchang Xu Yijiao Liu Fengye Xiong Yuan Dong Hongliang Bai Alexandru Damian Nikhil Ravi Sachit Menon Cynthia Rudin Junghoon Seo Taegyun Jeon Jamyoung Koo Seunghyun Jeon Soo Ye Kim Jae-Seok Choi Sehwan Ki Soomin Seo Hyeonjun Sim Saehun Kim Munchurl Kim Rong Chen Kun Zeng Jinkang Guo Yanyun Qu Cuihua Li Namhyuk Ahn Byungkon Kang Kyung-Ah Sohn Yuan Yuan Jiawei Zhang Jiahao Pang Xiangyu Xu Yan Zhao Wei Deng Sibt Ul Hussain Muneeb Aadil Rafia Rahim Xiaowang Cai Fang Huang Yueshu Xu Pablo Navarrete Michelini Dan Zhu Hanwen Liu Jun-Hyuk Kim Jong-Seok Lee Yiwen Huang Ming Qiu Liting Jing Jiehang Zeng Ying Wang Manoj Sharma Rudrabha Mukhopadhyay Avinash Upadhyay Sriharsha Koundinya Ankit Shukla Santanu Chaudhury Zhe Zhang Yu Hen Hu Lingzhi Fu ETH Zurich Zurich Switzerland Merantix Germany NTIRE Eidgenossische Technische Hochschule Zurich Zurich ZH CH ESAT KU Leuven Belgium University of California at Merced CA USA Toyota Technological Institute TTI-Chicago Chicago US Duke University US School of Electronic Engineering Xidian University China Disney Research Facebook Oculus University of Illinois at Urbana-Champaign Illinois US Snap Inc. Snap Inc. Adobe Research Stevens Institute of Technology US University of Illinois at Urbana-Champaign US The Chinese University of Hong Kong Hong Kong China SenseTime Research School of Electrical and Computer Engineering National Institute of Science and Technolog Korea School of Electrical and Computer Engineering National Institute of Science and Technology Korea School of Computer Science and Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications China Beijing Faceall Technology Co. Ltd China Satrec Initiative R&D Center Korea Advanced Institute of Science and Technology Korea Xiamen University China Xiamen Universi China Ajou University Korea Electrical and Computer Engineering University of Alberta Canada Fuzhou University China National University of Computer & Emerging Sciences Islamabad Pakistan BOE Technology Group Co. Ltd. Yonsei University Korea Wenhua College China Software School Xiamen University China CSIR-CEERI India University of Wisconsin-Madison Madison Wisconsin US Institute of Integrated Automation School of Electronic and Information Engineering Xian Jiaotong University China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661017

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

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Effective image retrieval via multilinear multi-index fusion

arXiv

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arXiv 2017年

作者： Zhang, Zhizhong Xie, Yuan Zhang, Wensheng Tian, Qi Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Multi-index fusion has demonstrated impressive performances in retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via neighbor structure, ignoring the high order information among different visual representations. In this paper, we propose a new multi-index fusion scheme for image retrieval. By formulating this procedure as a multilinear based optimization problem, the complementary information hidden in different indexes can be explored more thoroughly. Specially, we first build our multiple indexes from various visual representations. Then a so-called indexspecific functional matrix, which aims to propagate similarities, is introduced for updating the original index. The functional matrices are then optimized in a unified tensor space to achieve a refinement, such that the relevant images can be pushed more closer. The optimization problem can be efficiently solved by the augmented Lagrangian method with theoretical convergence guarantee. Unlike the traditional multi-index fusion scheme, our approach embeds the multi-index subspace structure into the new indexes with sparse constraint, thus it has little additional memory consumption in online query stage. Experimental evaluation on three benchmark datasets reveals that the proposed approach achieves the state-of-The-Art performance, i.e., N-score 3.94 on UKBench, mAP 94.1% on Holiday and 62.39% on Market-1501. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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On ${\mathcal H}_{\infty }$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks

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IEEE Transactions on Automatic control 2017年第9期62卷 4884-4890页

作者： Lei Zou Zidong Wang Jun Hu Huijun Gao College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University London Uxbridge U.K. Department of Applied Mathematics Harbin University of Science and Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

This paper is concerned with the H ∞ filtering problem for a class of time-varying nonlinear delayed system under high-rate communication network and stochastic protocol (SP). The communication between the sensors and the state estimator is implemented via a shared high-rate communication network in which multiple transmissions are generated between two adjacent sampling instants of sensors. At each transmission instant, only one sensor is allowed to get access to the communication network in order to avoid data collisions and the SP is employed to determine which sensor obtains access to the network at a certain instant. The mapping technology is applied to characterize the randomly switching behavior of the data transmission resulting from the utilization of the SP. The aim of the problem addressed is to design an estimator such that the H ∞ disturbance attenuation level is guaranteed for the estimation error dynamics over a given finite horizon. Sufficient conditions are derived for the existence of the finite-horizon filter satisfying the prescribed H ∞ performance requirement, and the explicit expression of the time-varying filter gains is characterized by resorting toa set of recursive matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filter design scheme.

关键词： Protocols Communication networks Symmetric matrices Time-varying systems Linear matrix inequalities Sensor phenomena and characterization

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LQ control of Lotka-Volterra Systems Based on their Locally Linearized Dynamics

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IFAC-PapersOnLine 2016年第10期49卷 241-245页

作者： Lipták, Görgy Magyar, Attila Hangos, Katalin M. University of Pannonia Faculty of Information Technology Department of Electrical Engineering and Information Systems H-8200 Egyetem street 10 Veszprém Hungary HAS Computers and Automation Research Institute Process Control Research Group H-1111 Kende street 13-17 Budapest Hungary

This work applies the LQ control framework to the class of quasi-polynomial and Lotka-Volterra systems through the linearized version of their nonlinear system model. The primary aim is to globally stabilize the original system with a suboptimal LQ state feedback by means of a well-known entropy-like Lyapunov function that is related to the diagonal stability of linear systems. This aim can only be reached in the case when the quasi-monomial composition matrix is invertible. In the rank-deficient case only the local stabilization of the system is possible with an LQ controller that is designed using the locally linearized model of the closed-loop system model. © 2016

关键词： System stability Biological systems Closed loop systems Linear matrix inequalities Linear systems Linearization Lyapunov functions Nonlinear systems State feedback Closed loop system modeling Composition matrixes Linearized dynamics Local stabilizations Lotka Volterra systems LQ control Lyapunov stability Nonlinear system modeling

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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Using system entity structures to model the elements of a scenario in a research flight simulator

Using system entity structures to model the elements of a sc...

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AIAA Modeling and Simulation Technologies Conference, 2017

作者： Durak, Umut Pruter, Insa Gerlach, Torsten Jafer, Shafagh Pawletta, Thorsten Hartmann, Sven Institute of Flight Systems Lilienthalplatz 7 Braunschweig38108 Germany Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University - Daytona Beach Campus Lehman 343 600 S Clyde Morris Blvd Daytona BeachFL32114-3900 United States Computational Engineering & Automation Research Group University of Wismar PF 1210 WismarD-23952 Germany Department of Informatics Clausthal University of Technnology Julius-Albert-Strasse 4 Clausthal-Zellerfeld38678 Germany

ISBN: (数字)9781624104510

ISBN: (纸本)9781624104510

While any simulation study starts with a scenario, scenario development is usually conducted in an unstructured and ad hoc manner. In order to streamline scenario development, a formal approach is envisioned in the research flight simulator facility of German Aerospace Center (DLR), namely Air Vehicle Simulator (AVES). System Entity Structure (SES) which is a high level ontology that was introduced to specify a set of system structures and parameter settings is proposed as the foundations. The paper outlines a model-based methodology for scenario development. SES is exploited for metamodeling in order to capture all possible elements of a scenario that can be simulated in AVES. Then a scenario modeling methodology is built upon this metamodel. © 2017, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Flight simulators

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Joint hierarchical category structure learning and large-scale image classification

arXiv

引用

arXiv 2017年

作者： Qu, Yanyun Lin, Li Shen, Fumin Lu, Chang Wu, Yang Xie, Yuan Tao, Dacheng Department of Computer Science Xiamen University Xiamen361005 China School of Computer Science and Engineering University of Electric Science and technology of China Chengdu611731 China Institute for Research Initiatives Nara Institute of Science and Technology Japan Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia

—We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification. Copyright © 2017, The Authors. All rights reserved.

关键词： Image classification

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Cascade IMC-PID control of Superheated Steam Temperature based on Closed-loop Identification in the Frequency Domain

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IFAC-PapersOnLine 2016年第18期49卷 91-97页

作者： Li, Xiao-Feng Ding, Dong-Jie Wang, Ya-Gang Huang, Zekun The Instrument and Power Plant Control Department Electric Power Research Institute of Guangdong Power Group Co. Guangzhou510080 China The School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Jungong Road Shanghai200093 China

This paper proposes an IMC-PID Cascade control of Superheated Steam Temperature based on closed-loop identification in the frequency domain. The process models of superheated steam temperature are firstly identified with set-point change tests in the inner and outer control loops. The process frequency responses are estimated, and then transform functions are obtained by least square method in both amplitude and phase. The IMC-PID controllers of the Steam Temperature Cascade control loop (SST) is designed by using the internal model control (IMC) and improved Skogestad-IMC (SIMC) method based on the obtained model. It is shown that this control strategy achieves better control performance and minimizes the effects of external and internal disturbances. © 2016

关键词： Closed loop control systems Cascade control systems control equipment Frequency response Least squares approximations Model predictive control Oceanography Steam Three term control systems Closed loop identification control performance H infinity controls IMC PID Internal model control Least square methods Steam temperature control Superheated steam temperature

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