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Correction: Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

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BMC genomics 2023年第1期24卷 388页

作者： Elena V Demidova Ilya G Serebriiskii Ramilia Vlasenkova Simon Kelow Mark D Andrake Tiffiney R Hartman Tatiana Kent James Virtucio Gail L Rosen Richard T Pomerantz Roland L Dunbrack Erica A Golemis Michael J Hall David Y T Chen Mary B Daly Sanjeevani Arora Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Kazan Federal University Kazan 420008 Russia. Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia PA 19111 USA. Department of Biochemistry and Molecular Biophysics University of Pennsylvania Philadelphia PA 19104 USA. Arcadia University Glenside PA USA. Department of Biochemistry & Molecular Biology Sidney Kimmel Cancer Center Thomas Jeferson University Philadelphia PA 19107 USA. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University Philadelphia PA 19104 USA. Department of Cancer and Cellular Biology Lewis Katz School of Medicine Philadelphia PA 19140 USA. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Department of Surgical Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu.

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Application of LDPC Codes on PUF Error Correction Based on Code-offset Construction

Application of LDPC Codes on PUF Error Correction Based on C...

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2017 IEEE 12th International Conference on ASIC

作者： Kai Sun Yingjie Lao Weiqiang Liu Xiaohu You Chuan Zhang Lab of Efficient Architectures for Digital-communication and Signal-processing(LEADS) National Mobile Communications Research Laboratory Southeast University Department of Electrical and Computer Engineering Clemson University College of Electronic and Information Engineering NUAA

ISBN: (纸本)9781509066261;9781509066254

As a newly emerging technology,physical unclonable functions(PUFs) have drawn a lot of *** their cryptographic applications,error correction is an indispensable component to fight against environmental factors which influence PUFs' ***,BCH codes have been applied to the error correction part to enhance the stability of an cryptographic system using *** this paper,low-density parity-check(LDPC) codes are first used to correct errors caused by environmental factors and shown to perform better than BCH codes in some *** addition,normalized minsum algorithm has been considered to balance the performance and complexity of LDPC decoding.

关键词： Physical unclonable functions(PUFs) lowdensity parity-check(LDPC) codes key reproduction error correction

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DC MUX PUF:A Highly Reliable Feed-Back MUX PUF Based on Measuring Duty Cycle 12

DC MUX PUF:A Highly Reliable Feed-Back MUX PUF Based on Meas...

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2017 IEEE 12th International Conference on ASIC

作者： Yunhao Xu Yingjie Lao Weiqiang Liu Xiaohu You Chuan Zhang Lab of Efficient Architectures for Digital-communication and Signal-processing(LEADS) National Mobile Communications Research Laboratory Southeast University Department of Electrical and Computer Engineering Clemson University College of Electronic and Information Engineering Nanjing Uni.Aero.& Astro.

ISBN: (纸本)9781509066261;9781509066254

Ring oscillator physical unclonable function(RO PUF) and multiplexer physical unclonable function(MUX PUF)are the two mostly used structures that make use of gate delay,but their reliability is not satisfactory *** this paper,a new structure,duty cycle multiplexer physical unclonable function(DC MUX PUF) is proposed to improve the reliability.A feed-back structure is added to original MUX PUF and decision method is also changed by measuring duty *** a simplified model is built to calculate the reliability of DC MUX PUF,a primary Monte Carlo simulation with this model is *** reliability of DC MUX PUF and the original MUX PUF are compared,and it is suggested that DC MUX PUF performs better than original MUX PUF considering noise *** temperature variation is considered,and simulated by *** is suggested that reliability of them are both above 95% when temperature *** and randomness are all more than 90%.Then the complexity of two kinds of PUF are also taken into *** challenge length becomes long,complexity of this design is relatively lower than original MUX PUF.

关键词： Physical unclonable function feed-back MUX PUF Monte Carlo model reliability

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NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

arXiv

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arXiv 2020年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Ahn, Namhyuk Bai, Dongwoon Cai, Jie Cao, Yun Chen, Junyang Cheng, Kaihua Chun, SeYoung Deng, Wei El-Khamy, Mostafa Man Ho, Chiu Ji, Xiaozhong Kheradmand, Amin Kim, Gwantae Ko, Hanseok Lee, Kanghyu Lee, Jungwon Li, Hao Liu, Ziluan Liu, Zhi-Song Liu, Shuai Lu, Yunhua Meng, Zibo Navarrete Michelini, Pablo Micheloni, Christian Prajapati, Kalpesh Ren, Haoyu Hyeok Seo, Yong Siu, Wan-Chi Sohn, Kyung-Ah Tai, Ying Muhammad Umer, Rao Wang, Shuangquan Wang, Huibing Haoning Wu, Timothy Wu, Haoning Yang, Biao Yang, Fuzhi Yoo, Jaejun Zhao, Tongtong Zhou, Yuanbo Zhuo, Haijie Zong, Ziyao Zou, Xueyi Wang, Li-Wen Cani, Marie-Paule Siu, Wan-Chi Yang, Huan Fu, Jianlong Shi, Yukai Chen, Junyang Lee, Kanghyu Park, Jaihyun Lee, Junyeop Min, Jeongki Lee, Bokyeung Ko, Hanseok Yoo, Jaejun Sohn, Kyung-Ah Micheloni, Christian Hu, Fengshuo Wang, Yanhong Lu, Yunhua Peng, Jinjia Wang, Huibing Zhuo, Haijie Lee, Junyeop Min, Jeongki Lee, Bokyeung Park, Jaihyun Ko, Hanseok Tai, Lab Ying Jilin Li, Youtu Lab Liu, Shuai Yang, Biao Liu, Xing Chen, Shuaijun Zhao, Lei Wang, Zhan Lin, Yuxuan Jia, Xu Gao, Qinquan Deng, Wei Kheradmand, Amin El-Khamy, Mostafa Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Patel, Heena Chudasama, Vishal Upla, Kishor Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Meng, Zibo Ho, Chiu Man Computer Vision Lab ETH Zurich Switzerland LIX - Computer science laboratory at the cole polytechnique Palaiseau France Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong Shanghai Jiao Tong University China Microsoft Research Beijing China Guangdong University of Technology China Intelligent Signal Processing Laboratory Korea University Korea Republic of Ajou University Korea Republic of EPFL University of Udine Italy BOE Technology Group Co. Ltd Dalian Maritime University China Guangdong OPPO Mobile Telecommunications Corp. Ltd Department of Video Information Processing Korea University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Tencent Youtu Lab Yun Cao Tencent Youtu Tencent Youtu Lab Chengjie Wang Tencent Tencent Youtu Lab Feiyue Huang Tencent Youtu Lab Peking University China North China University of Technology China Huawei Technologies Co. Ltd China Fuzhou University Tong Tong Fuzhou University Imperial Vision Technology China Fuzhou University Imperial Vision Technology China Imperial Vision Technology SOC R&D Samsung Semiconductor Inc. United States Ulsan national institute of science and technology Korea Republic of Sardar Vallabhbhai National Institute Of Technology Surat India Norwegian University of Science and Technology Gjøvik Norway InnoPeak Technology

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r.t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Cyber-Attack Reconstruction via Sliding Mode Differentiation and Sparse Recovery Algorithm: electrical Power Networks Application

Cyber-Attack Reconstruction via Sliding Mode Differentiation...

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International Workshop on Variable Structure Systems, VSS

作者： Shamila Nateghi Y. Shtessel J-P Barbot Gang Zheng Lei Yu Department of Electrical and Computer Engineering University of Alabama in Huntsville Huntsville AL USA EA7393 Non-A INRIA Cergy-Pontoise France INRIA-Lille Nord Europe Villeneuve d'Ascq Cedex France Signal Processing Laboratory Wuhan University Wuhan China

In this work, the unknown cyber-attacks on cyber-physical systems are reconstructed using sliding mode differentiation techniques in concert with the sparse recovery algorithm, when only several unknown attacks out of a long list of possible attacks are considered non-zero. The approach is applied to a model of the electric power system, and finally, the efficacy of the proposed techniques is illustrated via simulations of a real electric power system.

关键词： Security Electronic mail Power systems Sensor systems Generators Actuators

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Compressively Sensed Image Recognition

arXiv

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arXiv 2018年

作者： Deǧerli, Ayşen Aslan, Sinem Yamaç, Mehmet Sankur, Bülent Gabbouj, Moncef Tampere University of Technology Laboratory of Signal Processing Tampere Finland Ca' Foscari University of Venice European Centre for Living Technology Venice Italy Ege University International Computer Institute Izmir Turkey Boǧaziçi University Electrical and Electronics Engineering Department Istanbul Turkey

—Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

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Secure quantum communication in the presence of phase- and polarization-dependent loss

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Physical Review A 2018年第4期98卷 042324-042324页

作者： Chenyang Li Marcos Curty Feihu Xu Olinka Bedroya Hoi-Kwong Lo Center for Quantum Information and Quantum Control Department of Electrical & Computer Engineering and Department of Physics University of Toronto Toronto Ontario M5S 3G4 Canada Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain Shanghai Branch Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Shanghai 201315 China

Silicon photonics holds the promise of the miniaturization of quantum communication devices. Recently, silicon chip optical transmitters for quantum key distribution (QKD) have been built and demonstrated experimentally. Nonetheless, these silicon chips suffer substantial phase- and polarization-dependent loss (PDL) which, if unchecked, could compromise the security of QKD systems due to overestimation of the secret key rate. Here, first, we restore the security by regarding single photons without phase and polarization dependence as untagged and secure qubits. Next, by using a postselection technique, we implement a secure QKD protocol that provides a high key generation rate even in the presence of severe phase- and polarization-dependent loss. Our solution is simple to realize in a practical experiment, as it does not require any hardware modification.

关键词： Quantum cryptography

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

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Nature methods 2024年第10期21卷 1959页

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

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A computationally efficient detector: Using both the test and training data for disturbance correlation estimation

A computationally efficient detector: Using both the test an...

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2017 International Conference on Radar Systems, Radar 2017

作者： Liu, Jun Zhao, Hong-Yan Liu, Weijian Zhu, Shengqi Liu, Hongwei Li, Hongbin National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Wuhan Electronic Information Institute Wuhan430019 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9781785615153

This paper examines a target detection problem in colored Gaussian disturbance with an unknown covariance matrix. In many classic adaptive detectors, the covariance estimator is formed by using only the training data. This necessitates calculating a new covariance estimator for each cell under test (CUT) during the cell-by-cell target search process. We consider herein an alternative approach that forms the covariance matrix estimate by using both test and training data for detection in homogeneous environments. This approach is computationally much more efficient since the covariance matrix estimator is computed only once and can be applied for target detection at each CUT. Using this estimator, we propose a new detector with two tunable parameters, which includes several existing detectors as special cases. Closed-form expressions for the probabilities of false alarm and detection are derived in the matched and mismatched cases. Simulation results reveal that the rejection capability of mismatched signals of the proposed detector can be flexibly controlled by adjusting its tunable parameters. © 2017 Institution of engineering and Technology. All rights reserved.

关键词： Covariance matrix

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Multilinear class-specific discriminant analysis

arXiv

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arXiv 2017年

作者： Tran, Dat Thanh Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University

There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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