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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Pre-training of an artificial neural network for software fault prediction 7

Pre-training of an artificial neural network for software fa...

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7th International Conference on Computer and Knowledge Engineering, ICCKE 2017

作者： Owhadi-Kareshk, Moein Sedaghat, Yasser Akbarzadeh, Mohammad-R.-T. Center of Excellence On Soft Computing and Intelligent Information Processing Ireland Dependable Distributed Embedded Systems Laboratory Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9781538608043

Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve this classification problem. We propose to use a pre-training technique for a shallow, i.e. with fewer hidden layers, Artificial Neural Network (ANN). While this method is usually employed to prevent over-fitting in deep ANNs, our results indicate that even in a shallow network, it improves the accuracy by escaping from local minima. We compare the proposed method with four SVM-based classifiers and a regular ANN without pre-training on seven datasets from NASA codes in the PROMISE repository. Results confirm that the pre-training improves accuracy by achieving the best overall ranking of 1.43. Among seven datasets, our method has higher accuracy in four of them, while ANN and support vector machine are the best for two and one datasets, respectively. © 2017 IEEE.

关键词： Software testing

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Understanding adversarial attacks on deep learning based medical image analysis systems

arXiv

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Quantifying quantumness of channels without entanglement

arXiv

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arXiv 2021年

作者： Ku, Huan-Yu Kadlec, Josef Černoch, Antonín Quintino, Marco Túlio Zhou, Wenbin Lemr, Karel Lambert, Neill Miranowicz, Adam Chen, Shin-Liang Nori, Franco Chen, Yueh-Nan National Cheng Kung University Tainan701 Taiwan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Saitama Wako-shi351-0198 Japan Palacký University in Olomouc Faculty of Science Joint Laboratory of Optics Palacký University Institute of Physics AS CR 17. listopadu 12 Olomouc771 46 Czech Republic Institute of Physics The Czech Academy of Sciences Joint Laboratory of Optics of PU and IP AS CR 17. listopadu 50A Olomouc772 07 Czech Republic Faculty of Physics University of Vienna Boltzmanngasse 5 Vienna1090 Austria Austrian Academy of Sciences Boltzmanngasse 3 ViennaA-1090 Austria Graduate School of Informatics Nagoya University Chikusa-ku Nagoya464-8601 Japan Institute of Spintronics and Quantum Information Faculty of Physics Adam Mickiewicz University Poznan61-614 Poland Department of Physics National Chung Hsing University Taichung40227 Taiwan Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany RIKEN Center for Quantum Computing Saitama Wako351-0198 Japan Department of Physics The University of Michigan Ann ArborMI48109-1040 United States

Quantum channels breaking entanglement, incompatibility, or nonlocality are defined as such because they are not useful for entanglement-based, one-sided device-independent, or device-independent quantum information processing, respectively. Here, we show that such breaking channels are related to complementary tests of macrorealism i.e., temporal separability, channel unsteerability, temporal unsteerability, and the temporal Bell inequality. To demonstrate this we first define a steerability-breaking channel, which is conceptually similar to entanglement and nonlocality-breaking channels and prove that it is identical to an incompatibility-breaking channel. A hierarchy of quantum non-breaking channels is derived, akin to the existing hierarchy relations for temporal and spatial quantum correlations. We then introduce the concept of channels that break temporal correlations, explain how they are related to the standard breaking channels, and prove the following results: (1) A robustness-based measure for non-entanglement-breaking channels can be probed by temporal nonseparability. (2) A non-steerability-breaking channel can be quantified by channel steering. (3) Temporal steerability and non-macrorealism can be used for, respectively, distinguishing unital steerability-breaking channels and nonlocality-breaking channels for a maximally entangled state. Finally, a two-dimensional depolarizing channel is experimentally implemented as a proof-of-principle example to demonstrate the hierarchy relation of non-breaking channels using temporal quantum correlations. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum entanglement

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Author Correction: Cell fate decisions are specified by the dynamic ERK interactome

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Nature cell biology 2022年第3期24卷 400页

作者： Alex von Kriegsheim Daniela Baiocchi Marc Birtwistle David Sumpton Willy Bienvenut Nicholas Morrice Kayo Yamada Angus Lamond Gabriella Kalna Richard Orton David Gilbert Walter Kolch Signalling and Proteomics Laboratory The Beatson Institute for Cancer Research Glasgow G61 1BD UK. Experimental Pathology University of Bologna 40126 Bologna Italy. Systems Biology Ireland University College Dublin Dublin 4 Ireland. MRC Protein Phosphorylation Unit University of Dundee Dundee DD1 5EH Scotland UK. Wellcome Trust Centre for Gene Regulation and Expression University of Dundee Dundee DD1 5EH Scotland UK. Sir Henry Wellcome Functional Genomics Facility University of Glasgow Glasgow G12 8QQ UK. Bioinformatics Research Centre Department of Computing Science University of Glasgow Glasgow G12 8QQ UK. School of Information Systems Computing & Mathematics Brunel University Uxbridge Middlesex UB8 3PH UK. Signalling and Proteomics Laboratory The Beatson Institute for Cancer Research Glasgow G61 1BD UK. walter.kolch@ucd.ie. Sir Henry Wellcome Functional Genomics Facility University of Glasgow Glasgow G12 8QQ UK. walter.kolch@ucd.ie. Systems Biology Ireland University College Dublin Dublin 4 Ireland. walter.kolch@ucd.ie.

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Exploring representativeness and informativeness for active learning

arXiv

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arXiv 2019年

作者： Du, Bo Wang, Zengmao Zhang, Lefei Zhang, Liangpei Liu, Wei Shen, Jialie Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Collaborative Innovation Center of Geospatial Technology State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States School of Information Systems Singapore Management University Singapore QCIS University of Technology SydneyNSW2007 Australia

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified Best-versus-Second-Best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Secure Beamforming Design for MISO SWIPT systems: An Indirectly Optimized Approach

Secure Beamforming Design for MISO SWIPT Systems: An Indirec...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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Building quantum neural networks based on swap test

arXiv

引用

arXiv 2019年

作者： Zhao, Jian Zhang, Yuan-Hang Shao, Chang-Peng Wu, Yu-Chun Guo, Guang-Can Guo, Guo-Ping Key Laboratory of Quantum Information Chinese Academy of Sciences School of Physics University of Science and Technology of China Hefei Anhui230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China School of the Gifted Young University of Science and Technology of China Hefei Anhui230026 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Origin Quantum Computing Hefei Anhui230026 China

Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurons

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Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

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Naive gabor networks for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Liu, Chenying Li, Jun He, Lin Plaza, Antonio Li, Shutao Li, Bo Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Electrical and Information Engineering Hunan University Changsha410082 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura CáceresE-10071 Spain Beijing Key Laboratory of Digital Media School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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