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arXiv 2017年

作者： Cho, Yeong-Jun Kim, Su-A Park, Jae-Han Lee, Kyuewang Yoon, Kuk-Jin School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of Intel Visual Computing Institute Saarland Informatics Campus Germany ASRI Department of Electrical and Computer Engineering Seoul National University

Person re-identification is the task of recognizing or identifying a person across multiple views in multi-camera networks. Although there has been much progress in person reidentification, person re-identification in large-scale multi-camera networks still remains a challenging task because of the large spatio-temporal uncertainty and high complexity due to a large number of cameras and people. To handle these difficulties, additional information such as camera network topology should be provided, which is also difficult to automatically estimate, unfortunately. In this study, we propose a unified framework which jointly solves both person re-identification and camera network topology inference problems with minimal prior knowledge about the environments. The proposed framework takes general multi-camera network environments into account and can be applied to online person re-identification in large-scale multi-camera networks. In addition, to effectively show the superiority of the proposed framework, we provide a new person re-identification dataset with full annotations, named SLP, captured in the multi-camera network consisting of nine non-overlapping cameras. Experimental results using our person re-identification and public datasets show that the proposed methods are promising for both person re-identification and camera topology inference tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

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Next generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning approaches

arXiv

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arXiv 2019年

作者： Shiri, Isaac Maleki, Hassan Hajianfar, Ghasem Abdollahi, Hamid Ashrafinia, Saeed Hatt, Mathieu Oveisi, Mehrdad Rahmim, Ph.D. Arman Department of Biomedical and Health Informatics Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Research Center for Molecular and Cellular Imaging Tehran University of Medical Sciences Tehran Iran Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Medical Physics School of Medicine Iran University of Medical Science Tehran Iran Department of Radiology and Radiological Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States INSERM UMR 1101 LaTIM Univ Brest BrestF-29238 France Department of Computer Science University of British Columbia VancouverBC Canada Departments of Radiology and Physics & Astronomy University of British Columbia VancouverBC Canada Department of Integrative Oncology BC Cancer Research Centre VancouverBC Canada Department of Biomedical and Health Informatics Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Radiology and Physics University of British Columbia Senior Scientist & Provincial Medical Imaging Physicist BC Cancer Agency BC Cancer Research Center 675 West 10th Ave Office 5-114 VancouverBCV5Z 1L3 Canada

Purpose:Considerable progress has been made in assessment and management of NSCLC patients based on mutation status in the EGFR and KRAS. At the same time, NSCLC management through KRAS and EGFR mutation profiling faces some challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on low dose CT, diagnostic CT, PET modalities radiomic features and machine learning (ML) ***:Our study involved NSCLC cancer patient with 186 PET, and 175 low dose CT and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted. Conventional clinically used standard uptake value (SUV) parameters were also obtained for PET images. Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed and q-value were reported for univariate analysis. Six feature selection methods and twelve classifiers were used to predict gene status in patient. We performed 10-fold cross-validation for model tuning to improve robustness and all model evaluation was reported on independent validation sets (68 patients). The mean area under the receiver operator characteristic (ROC) curve (AUC) was obtained for performance ***:The best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore,

关键词： Forecasting

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Relationships between measures of neurovascular integrity and fluid transport in aging: a multi-modal neuroimaging study

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Fluids and barriers of the CNS 2025年第1期22卷 59页

作者： Ella Rowsthorn Lachlan Cribb Benjamin Sinclair William Pham Trevor T-J Chong Stephanie Yiallourou Marina Cavuoto Lucy Vivash Terence J O'Brien Xingfeng Shao Danny J J Wang Meng Law Matthew P Pase Ian H Harding Department of Neuroscience School of Translational Medicine Monash University 99 Commercial Road Melbourne VIC 3004 Australia. School of Psychological Sciences and Turner Institute for Brain and Mental Health Monash University 18 Innovation Walk Clayton VIC 3168 Australia. Department of Neurology Alfred Health 99 Commercial Road Melbourne VIC 3004 Australia. Department of Clinical Neurosciences St Vincent's Hospital 41 Victoria Parade Fitzroy VIC 3065 Australia. National Ageing Research Institute The Royal Melbourne Hospital 34-54 Poplar Road Parkville VIC 3052 Australia. Laboratory of FMRI Technology (LOFT) Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA 90033 USA. Department of Radiology Alfred Health 99 Commercial Road Melbourne VIC 3004 Australia. Department of Electrical and Computer Systems Engineering Monash University 14 Alliance Lane Clayton VIC 3168 Australia. School of Psychological Sciences and Turner Institute for Brain and Mental Health Monash University 18 Innovation Walk Clayton VIC 3168 Australia. matthew.pase@monash.edu. Department of Neuroscience School of Translational Medicine Monash University 99 Commercial Road Melbourne VIC 3004 Australia. ian.harding@qimrberghofer.edu.au. QIMR Berghofer Medical Research Institute 300 Herston Road Herston QLD 4006 Australia. ian.harding@qimrberghofer.edu.au.

Fluid transport in the neurovascular unit is essential for maintaining brain health through nutrient delivery and waste clearance. However, these systems are complex and the inter-dependencies between elements of these systems and how they may change through aging is not well understood. MRI outcomes provide insight into the underlying biological mechanisms of these systems in vivo, including water exchange rate through the neurovascular unit (BBB k), enlarged perivascular spaces (ePVS), cerebral blood flow (CBF), free water (FW), and white matter hyperintensities (WMH). To explore the relationships between functional elements of the neurovascular unit, this study investigated relationships between these MRI measures using Bayesian mixed models, and their variation with chronological age or atrophy-related brain age (brainageR) using linear regression. In 132 non-clinical older adults (mean age = 67 years; 68% female), BBB k positively associated with CBF (β^ = 0.08, 95% credible interval (CI) = [0.02, 0.15]). FW positively associated with both ePVS (β^ = 0.44, CI = [0.30, 0.63]) and WMH (β^ = 0.13, CI = [0.04, 0.21]). BBB k, CBF and ePVS decreased with age, while FW and WMH increased (all p < 0.05). There were no associations with atrophy-related brain age (all p > 0.05). Relationships between FW, ePVS and WMH likely reflect interconnectivity of fluid regulation within different compartments, while the relationship between BBB k and CBF indicates a link between neurovascular fluid flow and vessel function. While individual metrics of neurovascular integrity are associated with age, their inter-relationships appear stable, providing a baseline for future research in fluid transport and vascular health in neurodegenerative disease.

关键词： Aging Fluid transport Glymphatic clearance Multi-modal MRI Neurovascular unit Vascular health

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Secure and Efficient RNS Software Implementation for Elliptic Curve Cryptography

Secure and Efficient RNS Software Implementation for Ellipti...

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IEEE European Symposium on Security and Privacy Workshops

作者： Apostolos P. Fournaris Louiza Papachristodoulou Nicolas Sklavos Electrical and Computer Engineering Dpt. University of Patras Greece Digital Security Group Radboud University Nijmegen The Netherlands Computer Engineering & Informatics Dpt. University of Patras Greece

ISBN: (纸本)9781538622452

Elliptic Curve Cryptography operations rely heavily on the strong security of scalar multiplication. However, this operation is vulnerable to side channel (SCA) and fault injection (FA) attacks. The use of alternative arithmetic systems like Residue Number System (RNS) for all scalar multiplication underline operations has been proposed as an efficient countermeasure approach for the above attacks. In RNS, a number is represented as a set of smaller numbers, where each one is the result of the modular reduction with a given moduli basis. Under certain requirements, a number can be uniquely transformed from the integers to the RNS domain (and vice versa) and all arithmetic operations can be performed in RNS. This representation provides an inherent SCA and FA resistance to many attacks and can be further enhanced by additional RNS arithmetic manipulations or more traditional algorithmic countermeasures. In this paper, extending our previous work, we explore the potentials of RNS as an SCA and FA countermeasure. A description of RNS based SCA and FA resistance means is provided through appropriate scalar multiplication algorithmic variations, traces of the proposed algorithm are collected and the results are analyzed regarding the RNS countermeasure strength. More specifically, in this paper, a secure RNS based Montgomery Power Ladder based scalar multiplication algorithm is provided and is implemented on an ARM Cortex A7 processor. The implementation SCA-FA resistance is evaluated by collecting preliminary leakage trace results that validate our initial assumptions.

关键词： Resistance Manganese Elliptic curve cryptography Algorithm design and analysis Europe Privacy

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A Genetic Algorithm to Determine Research Consultation Schedules in Campus Environment

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IOP Conference Series: Materials Science and engineering 2020年第1期796卷

作者： R Idroes A Maulana T R Noviandy R Suhendra N R Sasmita A Lala Irvanizam Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia Department of Pharmacy Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia Department of Electrical and Computer Engineering Faculty of Engineering Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia Computational and Applied Statistics Research Group Department of Statistics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia

Scheduling algorithm is one of computation problem with high complexity. One of the problems is a difficulty to determine an optimal scheduling alternative that is adjusted with the constraints needed. This study aims to test whether the Genetic Algorithm (GA) program that was built was successful for maximizing the value of fitness. The data used in this study is data simulation from the GA Program. Based on the result of this study, we can concluded that the GA program can be used for scheduling because it has good accuracy. This can be seen from the fitness value of 1 and there is no clash between one schedule and another schedule and there are no restrictions that are violated.

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SMT-based synthesis of safe and robust pid controllers for stochastic hybrid systems 13th

SMT-based synthesis of safe and robust pid controllers for s...

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13th International Haifa Verification Conference, HVC 2017

作者： Shmarov, Fedor Paoletti, Nicola Bartocci, Ezio Lin, Shan Smolka, Scott A. Zuliani, Paolo School of Computing Newcastle University Newcastle upon Tyne United Kingdom Department of Computer Science Stony Brook University Stony BrookNY United States Faculty of Informatics TU Wien Wien Austria Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States

ISBN: (纸本)9783319703886

We present a new method for the automated synthesis of safe and robust Proportional-Integral-Derivative (PID) controllers for stochastic hybrid systems. Despite their widespread use in industry, no automated method currently exists for deriving a PID controller (or any other type of controller, for that matter) with safety and performance guarantees for such a general class of systems. In particular, we consider hybrid systems with nonlinear dynamics (Lipschitz-continuous ordinary differential equations) and random parameters, and we synthesize PID controllers such that the resulting closed-loop systems satisfy safety and performance constraints given as probabilistic bounded reachability properties. Our technique leverages SMT solvers over the reals and nonlinear differential equations to provide formal guarantees that the synthesized controllers satisfy such properties. These controllers are also robust by design since they minimize the probability of reaching an unsafe state in the presence of random disturbances. We apply our approach to the problem of insulin regulation for type 1 diabetes, synthesizing controllers with robust responses to large random meal disturbances, thereby enabling them to maintain blood glucose levels within healthy, safe ranges. © Springer International Publishing AG 2017.

关键词： Controllers

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Explaining the unique nature of individual gait patterns with deep learning

arXiv

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arXiv 2018年

作者： Horst, Fabian Lapuschkin, Sebastian Samek, Wojciech Müller, Klaus-Robert Schöllhorn, Wolfgang I. Department of Training and Movement Science Institute of Sport Science Johannes Gutenberg-University Mainz Mainz Rhineland-Palatinate Germany Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Department of Electrical Engineering & Computer Science Technical University Berlin Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Saarland Germany

Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait. Copyright © 2018, The Authors. All rights reserved.

关键词： Gait analysis

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Deep feature transfer between localization and segmentation tasks

arXiv

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arXiv 2018年

作者： Hu, Szu-Yeu Beers, Andrew Chang, Ken Höbel, Kathi Peter Campbell, J. Erdoguus, Deniz Ioannidis, Stratis Dy, Jennifer Chiang, Michael F. Kalpathy-Cramer, Jayashree Brown, James M. Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Department of Ophthalmology Casey Eye Institute Oregon Health and Science University Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University Portland United States

In this paper, we propose a new pre-training scheme for U-net based image segmentation. We first train the encoding arm as a localization network to predict the center of the target, before extending it into a U-net architecture for segmentation. We apply our proposed method to the problem of segmenting the optic disc from fundus photographs. Our work shows that the features learned by encoding arm can be transferred to the segmentation network to reduce the annotation burden. We propose that an approach could have broad utility for medical image segmentation, and alleviate the burden of delineating complex structures by pre-training on annotations that are much easier to acquire. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Tissue disorder for label-free diagnosis of biopsies using quantitative phase imaging

Tissue disorder for label-free diagnosis of biopsies using q...

引用

Optics and Photonics Japan, OFJ 2017

作者： Takabayashi, Masanori Majeed, Hassaan Kajdacsy-Balla, Andre Popescu, Gabriel Department of Systems Design and Informatics Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Japan Department of Bioengineering University of Illinois at Urbana-Champaign United States Department of Pathology University of Illinois at Chicago United States

ISBN: (纸本)9781557528209

Using quantitative phase imaging, we measured the tissue disorder strength, which is known as one of the effective markers of early carcinogenesis. We retrieved the disorder parameter from local refractive index fluctuation map obtained by spatial light interference microscopy (SLIM). We show that SLIM imaging combined with the disorder analysis is a valuable approach for screening of benign and malignant breast tissue biopsies. © 2017 OSA - The Optical Society. All rights reserved.

关键词： Tissue

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Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures

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European journal of nuclear medicine and molecular imaging 2021年第5期48卷 1697页

作者： Hongmei Wang Lu Wang Edward H Lee Jimmy Zheng Wei Zhang Safwan Halabi Chunlei Liu Kexue Deng Jiangdian Song Kristen W Yeom Department of Radiology The First Affiliated Hospital of University of Science and Technology of China (USTC) Division of Life Sciences and Medicine USTC Hefei 230036 Anhui China. School of Medical Informatics China Medical University Shenyang 110122 Liaoning China. Department of Radiology School of Medicine Stanford University 725 Welch Rd MC 5654 Palo Alto CA 94305 USA. Department of Radiology The Lu'an Affiliated Hospital Anhui Medical University Luan 237000 Anhui China. Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 USA. Helen Wills Neuroscience Institute University of California Berkeley CA 94720 USA. College of Medical Informatics China Medical University Shenyang 110122 Liaoning China. song.jd0910@***. Department of Radiology School of Medicine Stanford University 1201 Welch Rd Lucas Center PS055 Stanford CA 94305 USA. song.jd0910@***.

A Correction to this paper has been published: https://***/10.1007/s00259-021-05268-5

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