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Physical origins of ruled surfaces on the reduced density matrices geometry

Science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 1-8页

作者： Ji-Yao Chen Zhengfeng Ji Zheng-Xin Liu Xiaofei Qi Nengkun Yu Bei Zeng Duanlu Zhou State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Department of Physics Renmin University of China Beijing 100872 China Department of Mathematics Shanxi University Taiyuan 030000 China Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2W1 Canada Institute of Physics Chinese Academy of Sciences Beijing 100190 China

The reduced density matrices （RDMs） of many-body quantum states form a convex set. The boundary of low dimensional projections of this convex set may exhibit nontrivial geometry such as ruled surfaces. In this paper, we study the physical origins of these ruled surfaces for bosonic systems. The emergence of ruled surfaces was recently proposed as signatures of symmetry- breaking phase. We show that, apart from being signatures of symmetry-brealdng, ruled surfaces can also be the consequence of gapless quantum systems by demonstrating an explicit example in terms of a two-mode Ising model. Our analysis was largely simplified by the quantum de Finetti＇s theorem--in the limit of large system size, these RDMs are the convex set of all the symmetric separable states. To distinguish ruled surfaces originated from gapless systems from those caused by symmetry- breaking, we propose to use the finite size scaling method for the corresponding geometry. This method is then applied to the two-mode XY model, successfully identifying a ruled surface as the consequence of gapless systems.

关键词： ruled surfaces reduced density matrices bosonic system geometry

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Improving Learning Algorithm Performance for Spiking Neural Networks

Improving Learning Algorithm Performance for Spiking Neural ...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Qiang Fu Yuling Luo Junxiu Liu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposes three methods to improve the learning algorithm for spiking neural networks（SNNs）. The aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. The performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. The exclusive-or（XOR） and Wisconsin breast cancer（WBC） classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period.

关键词： spiking neural network optimization method learning performance

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Chemical Substance Classification Using Long Short-Term Memory Recurrent Neural Network

Chemical Substance Classification Using Long Short-Term Memo...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jinlei Zhang Junxiu Liu Yuling Luo Qiang Fu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks(LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

关键词： recurrent neural networks chemical substances long short-term memory feed forward neural networks

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Digital health technologies in the accelerating medicines Partnership® Schizophrenia Program

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Schizophrenia (Heidelberg, Germany) 2025年第1期11卷 83页

作者： Johanna T W Wigman Ann Ee Ching Yoonho Chung Habiballah Rahimi Eichi Erlend Lane Carsten Langholm Aditya Vaidyam Andrew Jin Soo Byun Anastasia Haidar Jessica Hartmann Angela Nunez Dominic Dwyer Adibah Amani Nasarudin Owen Borders Isabelle Scott Zailyn Tamayo Priya Matneja Kang-Ik Cho Jean Addington Luis K Alameda Celso Arango Nicholas J K Breitborde Matthew R Broome Kristin S Cadenhead Monica E Calkins Eric Yu Hai Chen Jimmy Choi Philippe Conus Cheryl M Corcoran Barbara A Cornblatt Covadonga M Diaz-Caneja Lauren M Ellman Paolo Fusar-Poli Pablo A Gaspar Carla Gerber Louise Birkedal Glenthøj Leslie E Horton Christy Lai Ming Hui Joseph Kambeitz Lana Kambeitz-Ilankovic Matcheri S Keshavan Sung-Wan Kim Nikolaos Koutsouleris Kerstin Langbein Daniel Mamah Daniel H Mathalon Vijay A Mittal Merete Nordentoft Godfrey D Pearlson Jesus Perez Diana O Perkins Albert R Powers Jack Rogers Fred W Sabb Jason Schiffman Jai L Shah Steven M Silverstein Stefan Smesny Walid Yassin William S Stone Gregory P Strauss Judy L Thompson Rachel Upthegrove Swapna Verma Jijun Wang Daniel H Wolf Phillip Wolff Laura M Rowland Simon D'Alfonso Ofer Pasternak Sylvain Bouix Patrick D McGorry Rene S Kahn John M Kane Carrie E Bearden Scott W Woods Martha E Shenton Barnaby Nelson Justin T Baker John Torous University of Groningen University Medical Center Groningen Department of Psychiatry Interdisciplinary Centre of Psychopathology and Emotion regulation Groningen The Netherlands. Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia. Orygen Parkville VIC Australia. McLean Hospital Belmont MA USA. Harvard Medical School Boston MA USA. Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Brigham and Women's Hospital Harvard Medical School Boston MA USA. Yale University New Haven CT USA. Northwell Health Glen Oaks NY USA. Department of Psychiatry Hotchkiss Brain Institute University of Calgary Calgary AB Canada. Service de Psychiatrie Générale Dép. de Psychiatrie CHUV Lausanne Lausanne Switzerland. Department of Child and Adolescent Psychiatry Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM) CIBERSAM ISCIII School of Medicine Universidad Complutense Madrid Spain. Department of Psychiatry and Behavioral Health Ohio State University Columbus OH USA. Department of Psychology Ohio State University Columbus OH USA. Institute for Mental Health School of Psychology University of Birmingham Birmingham UK. Birmingham Women's and Children's NHS Foundation Trust Birmingham UK. University of California San Diego CA USA. Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Psychiatry School of Clinical Medicine Li Ka Shing Faculty of Medicine University of Hong Kong Hong Kong Hong Kong. Olin Neuropsychiatry Research Center Hartford HealthCare Behavioral Health Network Hartford CT USA. Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA. Department of Psychiatry Donald and Barbara Zucker School of Medicine Hempstead NY USA. Feinstein Institute for Medical Research Manhasset NY USA. Department of Psych

Although meta-analytic studies have shown that 25-33% of those at Clinical High Risk (CHR) for psychosis transition to a first episode of psychosis within three years, less is known about estimating the risk of transition at an individual level. Digital phenotyping offers a novel approach to explore the nature of CHR and may help to improve personalized risk prediction. Specifically, digital data enable detailed mapping of experiences, moods and behaviors during longer periods of time (e.g., weeks, months) and offer more insight into patterns over time at the individual level across their routine daily life. However, while novel digital health technologies open up many new avenues of research, they also come with specific challenges, including replicability of results and the adherence of participants. This paper outlines the design of the digital component of the Accelerating Medicines Partnership® Schizophrenia Program (AMP SCZ) project, a large international collaborative project that follows individuals at CHR for psychosis over a period of two years. The digital component comprises one-year smartphone-based digital phenotyping and actigraphy. Smartphone-based digital phenotyping includes 30-item short daily self-report surveys and voice diaries as well as passive data capture (geolocation, on/off screen state, and accelerometer). Actigraphy data are collected via an Axivity wristwatch. The aim of this paper is to describe the design and the three goals of the digital measures used in AMP SCZ to: (i) better understand the symptoms, real-life experiences, and behaviors of those at CHR for psychosis, (ii) improve the prediction of transition to psychosis and other health outcomes in this population based on digital phenotyping and, (iii) serve as an example for replicable and ethical research across geographically diverse regions and cultures. Accordingly, we describe the rationale, protocol and implementation of these digital components of the AMP SCZ project. **

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Sparse Tensor Discriminative Locality Alignment for Gait Recognition

Sparse Tensor Discriminative Locality Alignment for Gait Rec...

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International Joint Conference on Neural Networks

作者： Nengwen Zhao Lefei Zhang Bo Du Liangpei Zhang Dacheng Tao Jane You State Key Laboratory of Software Engineering School of Computer Wuhan University Department of Computing The Hong Kong Polytechnic University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781509006212

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and sub-sequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.

关键词： Gait recognition dimensionality reduction images computer vision Tensor Locomotion Gait principal components analysis vector representation Alignment

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Mobile application testing in industrial contexts: An exploratory multiple case-study 14th

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14th International Conference on New Trends in Intelligent software Methodology, Tools, and Techniques, SoMeT 2015

作者： Zein, Samer Salleh, Norsaremah Grundy, John Department of Computer Science International Islamic University Kuala Lumpur Malaysia Centre for Computing and Engineering Software Systems Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783319226880

Recent empirical studies in the area of mobile application testing indicate the need for specific testing techniques and methods for mobile applications. This is due to mobile applications being significantly different than traditional web and desktop applications, particularly in terms of the physical constraints of mobile devices and the very different features of their operating systems. In this paper, we presented a multiple case-study involving four software development companies in the area of mobile and smartphones application. We aimed to identify testing techniques currently being applied by developers and challenges that they are facing. Our principle results are that many industrial teams seem to lack sufficient knowledge on how to test mobile applications, particularly in the areas of mobile application life-cycle conformance, context-awareness, and integration testing. We also found that there is no formal testing approach or methodology that can facilitate a development team to systematically test a critical mobile application. © Springer International Publishing Switzerland 2015.

关键词： Integration testing

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Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint

Regularizing Deep Convolutional Neural Networks with a Struc...

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IEEE International Conference on Data Mining (ICDM)

作者： Wei Xiong Bo Du Lefei Zhang Ruimin Hu Dacheng Tao Computer School of Wuhan University National Engineering Research Center for Multimedia Software Wuhan China Collaborative Innovation Center of Geospatial Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW Australia

ISBN: (纸本)9781509054749

Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).

关键词： Neurons Correlation Decorrelation Training Data models Electronic mail Biological neural networks

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Denoising Auto-Encoders toward Robust Unsupervised Feature Representation

Denoising Auto-Encoders toward Robust Unsupervised Feature R...

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International Joint Conference on Neural Networks

作者： Wei Xiong Bo Du Lefei Zhang Liangpei Zhang Dacheng Tao State Key Laboratory of Software Engineering School of Computer Science Wuhan University China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW 2007 Australia

ISBN: (纸本)9781509006212

Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked convolutional denoising auto-encoders (SCDAE), which can map raw images to hierarchical representations in an unsupervised manner. Our network is elaborately designed to fit for the visual recognition tasks. It is established by stacking the denoising auto-encoders. Unlike the prior works, in the training phase, the auto-encoders are trained patch-wisely so that the latent features can be applied to powerful regularizers for better representation;in the inference phase, the denoising auto-encoders are stacked convolutionally, hence the generated feature maps in the higher layers can preserve the coherent structures within the features in the lower layers. To achieve better performance, we apply whitening to each layer to sphere the input features. Our network is evaluated on the challenging image datasets MNIST, CIFAR-10 and STL-10 and demonstrates superior performance to the state-of-the-art unsupervised networks.

关键词： Noise reduction Convolutional codes Training Feature extraction Kernel Optimized production technology Robustness

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Supporting Scientists in Re-engineering Sequential Programs to Parallel Using Model-Driven engineering

Supporting Scientists in Re-engineering Sequential Programs ...

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IEEE/ACM International Workshop on software engineering for High Performance computing in Science (SE4HPCS)

作者： Mohamed Almorsy John Grundy Centre for Computing and Engineering Software and Systems Swinburne University of Technology Hawthorn Australia

Developing complex computational-intensive and data-intensive scientific applications requires effective utilization of the computational power of the available computing platforms including grids, clouds, clusters, multi-core and many-core processors, and graphical processing units (GPUs). However, scientists who need to leverage such platforms are usually not parallel or distributed programming experts. Thus, they face numerous challenges when implementing and porting their software-based experimental tools to such platforms. In this paper, we introduce a sequential-to-parallel engineering approach to help scientists in engineering their scientific applications. Our approach is based on capturing sequential program details, planned parallelization aspects, and program deployment details using a set of domain-specific visual languages (DSVLs). Then, using code generation, we generate the corresponding parallel program using necessary parallel and distributed programming models (MPI, Open CL, or Open MP). We summarize three case studies (matrix multiplication, N-Body simulation, and digital signal processing) to evaluate our approach.

关键词： Computational modeling Instruction sets Graphics processing units Kernel Data structures Parallel programming Data models

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