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Palm-print recognition algorithm based on random projection and chaotic system 17

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Palm-print recognition algorithm based on random projection ...

5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017

作者： Dong, Jiwen Zhao, Ziru Li, Hengjian Feng, Guang Department of Computer Science and Technology Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan City Shandong Province China

ISBN: (纸本)9781450348270

To improve the efficiency and security of palm-print recognition, we proposed a palm-print algorithm which based on random projection and chaotic system. Using twice random projections based on chaotic system to process the palm-print image, it generated the random project matrix and palm-print which are equivalent to two-factor identity authentications. And the initial value of chaotic system also could be the key to protect the security of palm-print. Reducing the dimensionality of the transformation matrix which could be produced randomly and the transformation matrix was not depend on the original training sample completely. In addition, the process of producing the random project matrix is simple and has lower computational complexity. And users could change the key by regain the new random project matrix to protect their privacy. © 2017 ACM.

关键词： Palmprint recognition

Graduate employment prediction with bias

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arXiv 2019年

作者： Guo, Teng Xia, Feng Zhen, Shihao Bai, Xiaomei Zhang, Dongyu Liu, Zitao Tang, Jiliang Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian116620 China School of Science Engineering and Information Technology Federation University Australia BallaratVIC3353 Australia Computing Center Anshan Normal University Anshan114007 China TAL AI Lab TAL Education Group Beijing100080 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

Quantum computing for High-Energy Physics State of the Art and Challenges Summary of the QC4HEP Working Group

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arXiv 2023年

作者： Di Meglio, Alberto Jansen, Karl Tavernelli, Ivano Alexandrou, Constantia Arunachalam, Srinivasan Bauer, Christian W. Borras, Kerstin Carrazza, Stefano Crippa, Arianna Croft, Vincent de Putter, Roland Delgado, Andrea Dunjko, Vedran Egger, Daniel J. Fernández-Combarro, Elias Fuchs, Elina Funcke, Lena González-Cuadra, Daniel Grossi, Michele Halimeh, Jad C. Holmes, Zoë Kühn, Stefan Lacroix, Denis Lewis, Randy Lucchesi, Donatella Martinez, Miriam Lucio Meloni, Federico Mezzacapo, Antonio Montangero, Simone Nagano, Lento Radescu, Voica Ortega, Enrique Rico Roggero, Alessandro Schuhmacher, Julian Seixas, Joao Silvi, Pietro Spentzouris, Panagiotis Tacchino, Francesco Temme, Kristan Terashi, Koji Tura, Jordi Tüysüz, Cenk Vallecorsa, Sofia Wiese, Uwe-Jens Yoo, Shinjae Zhang, Jinglei GenevaCH-1211 Switzerland CQTA Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany Computation-based Science and Technology Research Center The Cyprus Institute 20 Constantinou Kavafi str. Nicosia2121 Cyprus IBM Quantum IBM Research – Zurich Rüschlikon8803 Switzerland Department of Physics University of Cyprus PO Box 20537 Nicosia1678 Cyprus IBM Quantum IBM Research 1101 Kitchawan Rd Yorktown HeightsNY United States Physics Division LBNL - M/S 50A5104 1 Cyclotron Rd BerkeleyCA United States Deutsches Elektronen-Synchrotron DESY Notkestrasse 85 Hamburg22607 Germany RWTH Aachen University Templergraben 55 Aachen52062 Germany TIF Lab Dipartimento di Fisica Università degli Studi di Milano INFN Sezione di Milano Milan Italy Institut für Physik Humboldt-Universität zu Berlin Newtonstr. 15 Berlin12489 Germany haQaLi Applied Quantum Algorithms Leiden Netherlands Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science Facultad de Ciencias University of Oviedo Asturias33007 Spain Institute of Theoretical Phyiscs Leibniz University Hannover Hannover30167 Germany Physikalisch-Technische Bundesanstalt Braunschweig38116 Germany University of Bonn Nußallee 14-16 Bonn53115 Germany Institute for Theoretical Physics University of Innsbruck Innsbruck6020 Austria Institute for Quantum Optics and Quantum Information the Austrian Academy of Sciences Innsbruck6020 Austria Department of Physics Arnold Sommerfeld Center for Theoretical Physics Ludwig-Maximilians-Universität München Germany Munich Center for Quantum Science and Technology Germany LausanneCH-1015 Switzerland Paris-Saclay University CNRS IN2P3 IJCLab Orsay91405 France Department of Physics and Astronomy York University TorontoONM3J 1P3 Canada Department of Physics and Astronomy Università di Padova V. Marzolo 8 PadovaI-35131 Italy INFN - Sezione di Padova Via Marzolo 8 Padova35131 Italy Ni

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing. © 2023, CC BY-SA.

关键词： High energy physics

An approach for security evaluation and certification of a complete quantum communication system

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arXiv 2019年

作者： Sajeed, Shihan Chaiwongkhot, Poompong Huang, Anqi Qin, Hao Egorov, Vladimir Kozubov, Anton Gaidash, Andrei Chistiakov, Vladimir Vasiliev, Artur Gleim, Artur Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Toronto M5S 3G4 Canada Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China CAS Quantum Network Co. Ltd. 99 Xiupu road Shanghai201315 China Faculty of Photonics and Optical Information ITMO University Kadetskaya line 3/2 St. Petersburg199034 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China

Although quantum communication systems are being deployed on a global scale, their realistic security certification is not yet available. Here we present a security evaluation and improvement protocol for complete quantum communication systems. The protocol subdivides a system by defining seven system implementation sub-layers based on a hierarchical order of information flow;then it categorises the known system implementation imperfections by hardness of protection and practical risk. Next, an initial analysis report lists all potential loopholes in its quantum-optical part. It is followed by interactions with the system manufacturer, testing and patching most loopholes, and re-assessing their status. Our protocol has been applied on multiple commercial quantum key distribution systems to improve their security. A detailed description of our methodology is presented with the example of a subcarrier-wave system. Our protocol is a step towards future security evaluation and security certification standards. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum communication

The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

Understanding adversarial attacks on deep learning based medical image analysis systems

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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

$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

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IEEE Transactions on information Forensics and Security 2018年第9期13卷 2151-2166页

作者： Xuebin Ren Chia-Mu Yu Weiren Yu Shusen Yang Xinyu Yang Julie A. McCann Philip S. Yu School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Taiwan Information Security Center Taipei Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Department of Computing Imperial College London London U.K. School of Engineering and Applied Science Aston University Birmingham U.K. Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Department of Computer Science University of Illinois at Chicago Chicago IL USA

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

关键词： Privacy Data privacy Servers Correlation Estimation Frequency estimation Sensors

Structural insights into conformational stability of both wild-type and mutant Insulin Receptor Gene

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Next Research

Next Research 2024年第2期1卷

作者： Chisom Soremekun Daudi Jjingo David Kateete Oyekanmi Nash Harald Grallert Annette Peters Tinashe Chikowore Chiara Batini Opeyemi Soremekun Segun Fatumo The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Uganda Research Unit Entebbe Uganda Department of Immunology and Molecular Biology School of Biomedical Sciences Makerere University College of Health Sciences Kampala Uganda H3Africa Bioinformatics Network (H3ABioNet) Node Centre for Genomics Research and Innovation NABDA/FMST Abuja Nigeria Helmholtz Zentrum München German Research Center for Environmental Health (GmbH) Institute of Epidemiology Ingolstädter Landstr. 1 85764 Neuherberg Germany African Center of Excellence in Bioinformatics (ACE-B) Makerere University Kampala 10101 Uganda Department of Computer Science College of Computing and Information Sciences Makerere University P.O Box 7062 Kampala Uganda German Center for Diabetes Research (DZD) München-Neuherberg Ingolstädter Landstr. 1 85764 Neuherberg Germany Chair of Epidemiology Institute for Medical Information Processing Biometry and Epidemiology Medical Faculty Ludwig-Maximilians-Universität München Munich Germany MRC/Wits Developmental Pathways for Health Research Unit Department of Paediatrics Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA Division of Genetics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA Genetic Epidemiology Group Department of Health Sciences University of Leicester Leicester UK Leicester National Institute for Health and Care Research Biomedical Research Centre Glenfield Hospital Leicester UK Institute of Translational Genomics Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany Molecular Bio-computation and Drug Design Laboratory School of Health Sciences University of KwaZulu-Natal Westville Campus Durban 4001 South Africa MRC/UVRI and LSHTM Uganda Research Unit Entebbe Uganda De

Type 2 diabetes (T2D) poses a health challenge. It can lead to complications such as heart disease, hypertension, heart failure, and stroke. Factors like obesity and lack of activity can contribute to insulin resistance. The insulin receptor gene ( INSR ) is responsible for producing insulin receptors. When this gene malfunctions, it can contribute to the development of T2D. In this study, we investigated the stability of the structure of variants of INSR using an extended molecular dynamics simulation and the perturbation effect of compound CheBI_88339 on the protein structure. During the analysis, we observed that all three systems—the wild-type INSR , the R1191Q variant, and the R1191Q variant bound to compound CheBI_88339 (R1191Q-D) reached equilibrium in 30ns without any instability. Throughout the simulation process, it was generally observed that the wild-type INSR exhibited higher stability than the R1191Q variant and R1191Q-D. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of INSR , R1191Q and the variant bound to compound CheBI_88339 (R1191Q-D) are 9.28Å, 10.35Å, 8.65Å, 2.59Å, 2.98Å, and 2.89Å respectively. These values indicate that the mutated INSR introduced levels of deviations and flexibility in the protein structure. However, considering the variant bound to compound CheBI_88339 suggests that this drug may contribute to stabilizing the dynamics of the mutant protein. Overall, our findings shed light on the effect of genetic variants and their impact on protein stability. This research provides further insight into the dynamics of INSR and the potential of CheBI_88339 in targeting INSR . However, this study is computational, and further experimental studies are required.

关键词： Insulin receptor Type-2 diabetes Molecular dynamic simulation