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Quantum key distribution with distinguishable decoy states

Physical Review A 2018年第1期98卷 012330-012330页

作者： Anqi Huang Shi-Hai Sun Zhihong Liu Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada College of Science National University of Defense Technology Changsha 410073 People's Republic of China College of Mechatronic Engineering and Automation National University of Defense Technology Changsha 410073 People's Republic of China Russian Quantum Center and MISIS University Moscow Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada

The decoy-state protocol has been considered to be one of the most important methods to protect the security of quantum key distribution (QKD) with a weak coherent source. Here we test two experimental approaches to generating the decoy states with different intensities: modulation of the pump current of a semiconductor laser diode, and external modulation by an optical intensity modulator. The former approach shows a side channel in the time domain that allows an attacker to distinguish s signal state from a decoy state, breaking a basic assumption in the protocol. We model a photon-number-splitting attack based on our experimental data, and show that it compromises the system's security. Then, based on the work of Tamaki et al. [New J. Phys. 18, 065008 (2016)], we obtain two analytical formulas to estimate the yield and error rate of single-photon pulses when the signal and decoy states are distinguishable. The distinguishability reduces the secure key rate below that of a perfect decoy-state protocol. To mitigate this reduction, we propose to calibrate the transmittance of the receiver (Bob's) unit. We apply our method to three QKD systems and estimate their secure key rates.

关键词： Quantum communication Quantum cryptography

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GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

arXiv

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arXiv 2018年

作者： Chen, Hung-I Harry Chiu, Yu-Chiao Zhang, Tinghe Zhang, Songyao Huang, Yufei Chen, Yidong Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States Greehey Children’s Cancer Research Institute University of Texas Health Science Center at San Antonio San AntonioTX78229 United States Department of Epidemiology & Biostatistics University of Texas Health Science Center at San Antonio San AntonioTX78229 United States Laboratory of Information Fusion Technology of Ministry of Education School of Automation Northwestern Polytechnical University Xi'an Shaanxi710072 China

Background Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists’ capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. Copyright © 2018, The Authors. All rights reserved.

关键词： Gene expression

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Predicting drug response of tumors from integrated genomic profiles by deep neural networks

arXiv

引用

arXiv 2018年

作者： Chiu, Yu-Chiao Chen, Hung-I Harry Zhang, Tinghe Zhang, Songyao Gorthi, Aparna Wang, Li-Ju Huang, Yufei Chen, Yidong Greehey Children's Cancer Research Institute University of Texas Health Science Center at San Antonio San AntonioTX78229 United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States Laboratory of Information Fusion Technology of Ministry of Education School of Automation Northwestern Polytechnical University Xi'an Shaanxi710072 China Department of Epidemiology and Biostatistics University of Texas Health Science Center at San Antonio San AntonioTX78229 United States

Background The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep neural networks (DNNs) have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. Results We proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains three subnetworks, i) a mutation encoder pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNNs of our model, including DNNs built without TCGA pretraining, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in

关键词： Genome

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Corrections to “Multi-Sensor Kalman Filtering With Intermittent Measurements” [Mar 18 797-804]

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IEEE Transactions on Automatic Control 2018年第5期63卷 1545-1545页

作者： Chao Yang Jianying Zheng Xiaoqiang Ren Wen Yang Hongbo Shi Ling Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes Department of Automation East China University of Science and Technology Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

In the above technical note [ibid., vol. 63, no. 3, pp. 797–804, Mar. 2018], one of the author's names in the byline was misspelled and should have read as follows. Chao Yang, Jianying Zheng, Xiaoqiang Ren, Wen Y... 详细信息

关键词： Kalman filters Stochastic processes Sensor fusion Estimation error

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A Correct Method based on Retinex for the Solar Image with Uneven Illumination

A Correct Method based on Retinex for the Solar Image with U...

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2017 IEEE 8th International Conference on Software engineering and Service science

作者： Hongjuan Yang Kaifan Ji Yunfei Yang Wenliang Duan Hui Deng Bo Liang Yunnan Key Laboratory of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Observatory Chinese Academy of Sciences Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences

The traditional mask method and background fitting method can not correct effectively the solar image with uneven illumination. We adopt a correct method based on Retinex for the solar image with uneven illumination. The quality of image is improved by compressing the range of brightness and increasing the contrast by separately processing the effect of illumination and reflectivity to the intensity value. It could reveal the detailed character of the shadow area of the image. By contrasting four evaluate indexes, including the mean intensity, the image entropy, the image contrast and the peak signal to noise ratio(PSNR), this method is proved to be effective in the solar image with uneven illumination.

关键词： uneven illumination correct Retinex

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Enhancing Constraint based Test Generation by Local Search

Enhancing Constraint based Test Generation by Local Search

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2017年第六届软件与计算机应用国际会议

作者： Mengxiang Lin Xiaomei Hou Rui Liu Linyan Ge School of Mechanical Engineering and Automation State Key Laboratory of Software Development EnvironmentBeihang University School of Computer Science and Technology State Key Laboratory of Software Development EnvironmentBeihang University School of Information Management Wuhan University

The core operation of symbolic execution based test data generation is to generate a path constraint in terms of input variables for a selected path. Solutions to the path constraint are test data which will be used to execute the target path. So far, the limitations of constraint solving still prevent its widespread use in practice. Most of the constraint solvers aim at a particular kind of constraint and might have no way to deal with complex path constraints derived in a real-world software application. To tackle the problem, we propose a hybrid solving strategy for path constraints containing multiple kinds of constraints. In particular, the path constraint is converted and divided into two parts that can be solved by different constraint solving techniques. A local search method is combined with a linear constraint solver to solve complex mathematical constraints. We have extended the symbolic execution engine KLEE with our approach and evaluated its effectiveness on a set of mathematical programs. The preliminary evaluation shows that our approach can solve a majority of complex path constraints in the experiments in reasonable time.

关键词： Automated test generation symbolic execution constraint solving local search

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Transfer deep learning for low-resource chinese word segmentation with a novel neural network

arXiv

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arXiv 2017年

作者： Xu, Jingjing Ma, Shuming Zhang, Yi Wei, Bingzhen Cai, Xiaoyan Sun, Xu MOE Key Laboratory of Computational Linguistics Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China School of Automation Northwestern Polytechnical University Xi'an China

Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. We propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets 4. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural networks

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Gone with the Wind: Nonlinear Guidance for Small Fixed-Wing Aircraft in Arbitrarily Strong Windfields

Gone with the Wind: Nonlinear Guidance for Small Fixed-Wing ...

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American Control Conference

作者： Luca Furieri Thomas Stastny Lorenzo Marconi Roland Siegwart Igor Gilitschenski Laboratory of Automation and Robotics Department of Electronics Computer Science and Systems University of Bologna Italy Autonomous Systems Lab Department of Mechanical & Processing Engineering Swiss Federal Institute of Technology (ETH Zurich) Switzerland

ISBN: (纸本)9781509045839

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this paper we present a principled nonlinear guidance strategy, addressing this problem. More broadly, we propose a methodology for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

关键词： Arbitrarily Strong Windfields Fixed wing aircraft Guidance Wind Wings Unmanned Aerial Vehicle low speed level control vehicle visual tracking wall coping nonlinearity Platform

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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2017 IEEE International Conference on Robotics and automation, ICRA 2017

作者： Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781509046331

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： Mobile robots

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Fusing R Features and Local Features with Context-Aware Kernels for Action Recognition

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International Journal of computer Vision 2016年第2期118卷 151-171页

作者： Yuan, Chunfeng Wu, Baoxin Li, Xi Hu, Weiming Maybank, Stephen Wang, Fangshi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Systems Birkbeck College London United Kingdom School of Software Engineering Beijing Jiaotong University Beijing China

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition. © 2015, Springer science+Business Media New York.

关键词： Principal component analysis

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