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arXiv 2017年

作者： Milz, Simon Pollock, Felix A. Le, Thao P. Chiribella, Giulio Modi, Kavan School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Dept. of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Computer Science University of Oxford Wolfson Building Parks Road United Kingdom Canadian Institute for Advanced Research CIFAR Program in Quantum Information Science TorontoONM5G 1Z8 Canada

Quantum mechanics, in principle, allows for processes with indefinite causal order. However, most of these causal anomalies have not yet been detected experimentally. We show that every such process can be simulated experimentally by means of non-Markovian dynamics with a measurement on additional degrees of freedom. Explicitly, we provide a constructive scheme to implement arbitrary acausal processes. Furthermore, we give necessary and sufficient conditions for open system dynamics with measurement to yield processes that respect causality locally, and find that tripartite entanglement and nonlocal unitary transformations are crucial requirements for the simulation of causally indefinite processes. These results show a direct connection between three counter-intuitive concepts: non-Markovianity, entanglement, and causal indefiniteness. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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Envelope Approximation on Doppler Ultrasound Spectrogram for Estimating Flow Speed in Carotid Artery

Envelope Approximation on Doppler Ultrasound Spectrogram for...

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International computer Symposium (ICS)

作者： Wei-Che Chien Wei-Min Liu An-Bang Liu Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Neuroscience Center Hualien Tzu Chi Hospital Hualien Taiwan

ISBN: (纸本)9781509034390

The development of Doppler ultrasound allows us to convert the distribution of reflective ultrasound energy into colored blood flow imaging. The Doppler spectrogram also reveals the change of flow velocity over time when specifying an observation window inside the vessel wall. With the information doctors can visually search for abnormalities in blood vessels or valves from irregular patterns in the Doppler spectrogram within several cardiac cycles. Currently there are very few automatic analysis that can be applied to either color flow imaging or Doppler spectrogram. We hypothesize that the dynamics of flow speed in carotid could be an indicator to abnormalities on vascular wall like arteriosclerosis and stenosis. In this work the time-varying Doppler spectrogram during a carotid ultrasound exam was recorded as a video clip, and the image series were concatenated as a long spectrogram waveform image. To assess the carotid stiffness with previously developed nonlinear analysis and measures, the waveform image was further converted to 1-D signal by several proposed methods. Among them we found one that generated the most reasonable and less noisy curve, and expressed the dynamics of flow speed with minimal distortion.

关键词： Doppler effect Ultrasonic imaging Spectrogram Imaging Carotid arteries Blood flow Two dimensional displays

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Data-driven human grasp movement analysis 47

Data-driven human grasp movement analysis

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47th International Symposium on Robotics, ISR 2016

作者： Marino, Hamal Gabiccini, Marco Leonardis, Aleš Bicchi, Antonio DICI and Centro di Ricerca E. Piaggio University of Pisa Pisa56122 Italy Dept. of Advanced Robotics Istituto Italiano di Tecnologia Genova16163 Italy School of Computer Science and Centre for Computational Neuroscience and Cognitive Robotics University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9783800742318

Humans tend to simplify the space of possible grasps they can perform. Yet, the description of human hand motions is very complex, and methods to reduce this complexity have attracted much attention in the motor control literature. Important implications in robot hand design and programming have also generated a wide interest in the robotics research community. Early studies prevalently used direct analysis methods such as visual inspection to define grasp taxonomies. More recently, analytical methods have been employed to perform grasping data dimensionality reduction. In this paper, we present a methodology to reconcile these two distinct and apparently incompatible approaches under a unified framework: this allows us to obtain a data-generated grasp taxonomy along with low-dimensional representations which could be used for human grasping data classification and posture reconstruction, as well as for simplifying grasp planning algorithms and robotic hands programming. © 2016 VDE VERLAG GMBH Berlin Offenbach.

关键词： Taxonomies

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Cluster analysis for SME risk analysis documents based on pillar K-means

Telkomnika (Telecommunication Computing Electronics and Cont...

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Telkomnika (Telecommunication Computing Electronics and Control) 2016年第2期14卷 674-683页

作者： Wahyudin, Irfan Djatna, Taufik Kusuma, Wisnu Ananta Computer Science Mathematic and Natural Science Bogor Agricultural University Indonesia Postgraduate Program in Dept. Agro-industrial Technology Bogor Agricultural University Kampus IPB Darmaga P.O. Box 220 Bogor Indonesia

In Small Medium Enterprise's (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative ***, there is another problem that the decision makers have difficulity to quantify the risk's weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal. © 2016 Universitas Ahmad Dahlan.

关键词： Sentiment analysis

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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SourceSeer: Forecasting rare disease outbreaks using multiple data sources

SourceSeer: Forecasting rare disease outbreaks using multipl...

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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Rekatsinas, Theodoras Ghosh, Saurav Mekaru, Sumiko R. Nsoesie, Elaine O. Brownstein, John S. Getoor, Lise Ramakrishnan, Naren Dept. of Computer Science University of Maryland College Park United States Dept. of Computer Science Virginia Tech. United States Children's Hospital Informatics Program Boston Children's Hospital United States Dept. of Computer Science University of California Santa Cruz United States

ISBN: (纸本)9781510811522

Rapidly increasing volumes of news feeds from diverse data sources, such as online newspapers, Twitter and online blogs are proving to be extremely valuable resources in helping anticipate, detect, and forecast outbreaks of rare diseases. This paper presents SourceSeer, a novel algorithmic framework that combines spatio-temporal topic models with source-based anomaly detection techniques to effectively forecast the emergence and progression of infectious rare diseases. SourceSeer is capable of discovering the location focus of each source allowing sources to be used as experts with varying degrees of authoritativeness. To fuse the individual source predictions into a final outbreak prediction we employ a multiplicative weights algorithm taking into account the accuracy of each source. We evaluate the performance of SourceSeer using incidence data for hantavirus syndromes in multiple countries of Latin America provided by HealthMap over a timespan of fifteen months. We demonstrate that SourceSeer makes predictions of increased accuracy compared to several baselines and is capable of forecasting disease outbreaks in a timely manner even when no outbreaks were previously reported. Copyright © SIAM.

关键词： Hantavirus

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26th Annual Computational neuroscience Meeting (CNS*2017) of the Organization for Computational neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC neuroscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Pervasive Neuroimaging with Fog Computing and Linked Data

Pervasive Neuroimaging with Fog Computing and Linked Data

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International computer Symposium (ICS)

作者： John Kar-Kin Zao Tchin-Tze Gan Chun-Kai You Sergio Jose Rodreguez Mendez Chieh You Cheng-En Chung Yu-Te Wang Tim Mullen Christian Kothe Ching-Teng Hsiao Tzyy-Ping Jung Computer Science Department NCTU Hsinchu Taiwan Comput. Sci. Dept. NCTU Hsinchu Taiwan Swartz Center for Computational Neuroscience UCSD San Diego California USA Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

ISBN: (纸本)9781509034390

A pilot system for performing real-time brain imaging in naturalistic environments have been developed using wireless EEG headsets, motion sensors, smart telephones and ubiquitous computing servers. This paper described its pervasive architecture and introduced its enabling technologies, which include machine-to-machine publish/subscribe protocols, interoperable data/meta-data formats, multi-tier fog/cloud computing infrastructure and semantic linked data web. A live demonstration of this system was first performed at the US Army Research Lab meeting in March 2013. An expanded system capable of supporting real-time brain state classification and continuous model calibration will soon be made available as a web services.

关键词： Servers Electroencephalography Sensors Ontologies Real-time systems Semantics Brain modeling

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Effectiveness of subject specific instruction on mu-based brain-computer interface training

Effectiveness of subject specific instruction on mu-based br...

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Annual Northeast Bioengineering Conference

作者： Alexandria Battison Maura Schlussel Tom Fuller Yih-Choung Yu Lisa Gabel Neuroscience Program Dept. of Electrical and Computer Engineering Dept. of Psychology Lafayette College Easton PA USA

Brain-computer interface (BCI) technology is a growing field, becoming a more viable aid for individuals who have lost voluntary motor control. The mu rhythm, a sensorimotor rhythm that is suppressed when one imagines motor activity (Event Related Desychronization [ERD]), has been particularly utilized in BCIs because of its potential for diverse applications. This study aimed to improve participants' BCI performance by giving them specific instructions for imagine motor behavior to improve their control over sustained mu power. Participants were provided with instructions that were either non-specific (NS), specific (SP), or participant specific (PS) to imagine motor behavior. The ability to control the strength of the mu rhythm was studied by using a simple feedback based paradigm and a novel BCI targeting game. The results showed that utilizing specific instructions for mu-based BCI systems can lead to decreased training time, increased BCI literacy, and enhanced control over mu-based BCI devices. These results suggest that instruction type can make mu-based BCI devices more accessible to individuals with impaired motor behavior.

关键词：

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Systems for spectrum forensics 2

Systems for spectrum forensics

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2nd ACM International Workshop on Hot Topics in Wireless, HotWireless 2015

作者： Anderson, Austin Wang, Xiang Baker, Kenneth R. Grunwald, Dirk Dept. of Aerospace Engineering Univ. of Colorado Boulder United States Dept. of Electrical Energy and Computer Engineering Univ. of Colorado Boulder United States Interdisciplinary Telecommunications Program Univ. of Colorado Boulder United States Dept. of Computer Science Univ. of Colorado Boulder United States

ISBN: (纸本)9781450336994

Historically, spectrum allocations were assigned to a single user by regulation and spectrum interference was relatively easy to identify. Enforcement was simplified by having a small set of known licensees. Spectrum regulation is increasingly moving toward dynamic spectrum access mediated by databases, exclusion zones, sensors and other methods intended to improve spectrum efficiency. To have meaning, these spectrum sharing mechanisms must have some form of enforcement. While the spectrum policy community has consider ex-post (preventative) and ex-ante (punitive) enforcement mechanisms, enforcement will rely on spectrum forensics - the scientific gathering of information to inform an enforcement action. In this paper, we initiate exploration of a system for spectrum forensics that combines crowd-sourced interference detection, fixed sensors and aerial measurement as well as a system for minimizing the data that needs to be collected. As part of a multi-tiered approach, the highest tier will be enabled by the equivalent of an "SDR in the sky" for self-directed localization and identification of interference sources.

关键词： Antennas

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