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Cell Genomics 2025年 100876页

作者： Cunningham, Chelsea E. Vizeacoumar, Frederick S. Zhang, Yue Kyrylenko, Liliia Both, Simon Maranda, Vincent Dong, He Price, Jared D.W. Gao, Peng Wagner, Konrad Wu, Yingwen Lazell-Wright, Mary Ganapathysamy, Ashtalakshmi Hari, Rithik Bhanumathy, Kalpana K. Denomy, Connor Saxena, Anjali Vizeacoumar, Jeff P. Morales, Alain Morejon Khan, Faizaan Mosley, Shayla Chen, Angie Katrii, Tetiana Zoller, Ben G.E. Rajamanickam, Karthic Walke, Prachi Gong, Lihui Patel, Hardikkumar Elhasasna, Hussain Dahiya, Renuka Abuhussein, Omar Dmitriev, Anton Freywald, Tanya Munhoz, Erika Prando Ruppin, Eytan Lee, Joo Sang Rox, Katharina Koebel, Martin Hopkins, Laura Lee, Cheng Han Yadav, Sunil Gasparoni, Gilles Walter, Jörn Krishnan, Anand Datla, Raju Toosi, Behzad Baker, Kristi Meens, Jalna Cescon, David W. Ailles, Laurie Leary, Scot C. Wu, Yuliang Empting, Martin Kiemer, Alexandra K. Freywald, Andrew Vizeacoumar, Franco J. Department of Oncology College of Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pathology and Laboratory Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pharmacy Pharmaceutical Biology Saarland University PharmaScienceHub Saarbrücken 66123 Germany Global Institute for Food Security University of Saskatchewan Saskatoon S7N 4L8 SK Canada Agriculture and Agri-Food Canada Saskatoon Research and Development Centre 107 Science Place Saskatoon S7N 0X2 SK Canada Antiviral & Antivirulence Drugs (AVID) Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) Helmholtz Centre for Infection Research (HZI) and Department of Pharmacy Saarland University Saarbrücken 66123 Germany Department of Oncology University of Alberta Edmonton T6G 1Z2 AB Canada Department of Anatomy Physiology and Pharmacology University of Saskatchewan and Cameco MS Neuroscience Research Centre 701 Queen St. Saskatoon S7K 0M7 SK Canada Cancer Data Science Laboratory National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Center for Bioinformatics and Computational Biology and Department of Computer Sciences University of Maryland College Park 20742 MD United States Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 South Korea Department of Chemical Biology (CBIO) Helmholtz Center for Infection Research (HZI) Inhoffenstrasse 7 Braunschweig 38124 Germany Department of Pathology University of Calgary Calgary AB Canada Department of Laboratory Medicine and Pathology University of Alberta Edmonton AB Canada Department of Genetics Saarland University Saarbrücken Germany Western College of Veterinary Medicine University of Saskatchewan Room 2343 52 Campus Drive Saskatoon S7N 5B4 Canada Princess Margaret Cancer Centre University Health Network Toronto ON Canada Department of Biochemistry Microb

Chromosomal instability (CIN) drives tumor heterogeneity, complicating cancer therapy. Although Polo-like kinase 1 (PLK1) overexpression induces CIN, direct inhibition of PLK1 has shown limited clinical benefits. We therefore performed a genome-wide synthetic dosage lethality (SDL) screen to identify effective alternative targets and validated over 100 candidates using in vivo and in vitro secondary CRISPR screens. We employed direct-capture Perturb-seq to assess the transcriptional consequences and viability of each SDL perturbation at a single-cell resolution. This revealed IGF2BP2 as a critical genetic dependency that, when targeted, downregulated PLK1 and significantly restricted tumor growth. Mechanistic analyses showed that IGF2BP2 loss disrupted cellular energy metabolism and mitochondrial ATP production by downregulating PLK1 levels as well as genes associated with oxidative phosphorylation. Consistent with this, pharmacological inhibition of IGF2BP2 severely impacts the viability of PLK1-overexpressing cancer cells addicted to higher metabolic rates. Our work offers a novel therapeutic strategy against PLK1-driven heterogeneous malignancies. © 2025 The Author(s)

关键词： chromosomal instability IGF2BP2 IMP2 in vivo CRISPR screen perturb-seq PLK1 single-cell CRISPR screening synthetic dosage lethality tumor heterogeneity

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CovidCTNet: An open-source deep learning approach to identify Covid-19 using CT image

arXiv

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arXiv 2020年

作者： Javaheri, Tahereh Homayounfar, Morteza Amoozgar, Zohreh Reiazi, Reza Homayounieh, Fatemeh Abbas, Engy Laali, Azadeh Radmard, Amir Reza Gharib, Mohammad Hadi Mousavi, Seyed Ali Javad Ghaemi, Omid Babaei, Rosa Mobin, Hadi Karimi Hosseinzadeh, Mehdi Jahanban-Esfahlan, Rana Seidi, Khaled Kalra, Mannudeep K. Zhang, Guanglan Chitkushev, L.T. Haibe-Kains, Benjamin Malekzadeh, Reza Rawassizadeh, Reza Health Informatics Lab Metropolitan College Boston University Boston United States Department of Biomedical Engineering Amirkabir University of Technology Tehran Iran Department of Radiation Oncology Massachusetts General Hospital Harvard Medical School Boston United States Princess Margaret Cancer Centre University of Toronto Toronto Canada Department of Medical Biophysics University of Toronto Toronto Canada Department of Medical Physics School of Medicine Iran university of Medical Sciences Department of Radiology Massachusetts General Hospital Harvard Medical School Boston United States Joint Department of Medical Imaging University of Toronto Toronto Canada Department of Infectious Diseases Firoozgar Hospital Iran University of Medical Sciences Tehran Iran Department of Radiology Shariati Hospital Tehran University of Medical Sciences Tehran Iran Department of Radiology Gloestan Rheumatology Research Center Golestan University of Medical Sciences Gorgan Iran Department of Internal Medicine Iran University of Medical Sciences Tehran Iran Department of Radiology Iran University of Medical Sciences Tehran Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Medical Biotechnology School of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz Iran Department of Computer Science Metropolitan College Boston University Boston United States Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Digestive Disease Research Center Tehran University of Medical Sciences Tehran Iran

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria D'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén David Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn Rochester Claudia Mazzà Silvia Del Din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien

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Understanding and comparing scalable Gaussian process regression for big data

arXiv

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arXiv 2018年

作者： Liua, Haitao Cai, Jianfei Ong, Yew-Soon Wang, Yi Rolls-Royce@NTU Corporate Lab Nanyang Technological University Singapore637460 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Data Science and Artificial Intelligence Research Center Nanyang Technological University Singapore639798 Singapore Central Technology Group Rolls-Royce Singapore 1 Seletar Aerospace Crescent Singapore797565 Singapore

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hence, various scalable GPs have been developed in the literature in order to improve the scalability while retaining desirable prediction accuracy. This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness. The numerical experiments on two toy examples and five real-world datasets with up to 250K points offer the following findings. In terms of scalability, most of the scalable GPs own a time complexity that is linear to the training size. In terms of capability, the sparse approximations capture the long-term spatial correlations, the local aggregations capture the local patterns but suffer from over-fitting in some scenarios. In terms of controllability, we could improve the performance of sparse approximations by simply increasing the inducing size. But this is not the case for local aggregations. In terms of robustness, local aggregations are robust to various initializations of hyperparameters due to the local attention mechanism. Finally, we highlight that the proper hybrid of global and local scalable GPs may be a promising way to improve both the model capability and scalability for big data. Copyright © 2018, The Authors. All rights reserved.

关键词： Scalability

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Unsupervised feature learning architecture with multi-clustering integration RBM

arXiv

引用

arXiv 2018年

作者： Chu, Jielei Wang, Hongjun Liu, Jing Gong, Zhiguo Li, Tianrui Institute of Artificial Intelligence School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China School of Business Sichuan University ChengduSichuan610065 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three unsupervised K-means, affinity propagation and spectral clustering algorithms to obtain three different clustering partitions (CPs) without any background knowledge or label. Then, an unanimous voting strategy is used to generate a local clustering partition (LCP). The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into one step contrastive divergence (CD1) learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. The experiments demonstrate that the proposed unsupervised feature learning architecture has more powerful feature representation and generalization capability than the state-of-the-art graph regularized RBM (GraphRBM) for clustering tasks in the Microsoft Research Asia Multimedia (MSRA-MM)2.0 dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Integration

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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

Event reconstruction using pattern spectra and convolutional...

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7th Heidelberg International Symposium on High-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Breen, S. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

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Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study

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JMIR formative research 2025年 9卷 e67792页

作者： Martin A Berge Anisoara Paraschiv-Ionescu Cameron Kirk Arne Küderle Encarna Micó-Amigo Clemens Becker Andrea Cereatti Silvia Del Din Monika Engdal Judith Garcia-Aymerich Karoline B Grønvik Clint Hansen Jeffrey M Hausdorff Jorunn L Helbostad Carl-Philipp Jansen Lars Gunnar Johnsen Jochen Klenk Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Müller Lynn Rochester Lars Schwickert Kristin Taraldsen Beatrix Vereijken Department of Neuromedicine and Movement Science Norwegian University of Science and Technology Trondheim Norway. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom. Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Geriatric Center Medical Faculty Heidelberg Heidelberg University Heidelberg Germany. Department of Geriatrics and Rehabilitation Robert Bosch Hospital Stuttgart Germany. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research Newcastle Biomedical Research Centre Newcastle University and the Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom. Barcelona Institute for Global Health Barcelona Spain. Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública Madrid Spain. Department of Neurology University Hospital Schleswig-Holstein and Kiel University Kiel Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Medical Center Tel Aviv Israel. Department of Physical Therapy Faculty of Medical & Health Sciences Tel Aviv University Tel Aviv Israel. Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel. Rush Alzheimer's Disease Center Rush University Medical Center Chicago IL United States. Department of Orthopedic Surgery Rush Medical College Rush University Chicago IL United States. Department of Orthopaedic Surgery St. Olav's Hospital Trondheim Norway. Institute of Epidemiology and Medical Biometry Ulm University Ulm Germany. IB University of Health and Social Sciences Study Centre Stuttgart Stuttgart Ger

BACKGROUND:Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored. OBJECTIVE:This study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use. METHODS:In total, 19 community-dwelling participants were real-world monitored for 2.5 hours using a lower back wearable device and a reference system combining inertial modules, distance sensors, and pressure insoles. A total of 6 DMO estimates from 164 WBs from 58% (11/19) of the participants (aged 71-90 years; assessed 32-390 days after surgery; Short Physical Performance Battery [SPPB] scores of 3-12; gait speed range 0.39-1.34 m/s) were assessed against the reference system at the WB and participant level. We stratified by WB duration (all WBs, WBs of >10 seconds, WBs of 10-30 seconds, and WBs of >30 seconds) and lower versus higher SPPB scores and observed whether days since surgery and walking aid use affected DMO accuracy and reliability. RESULTS:Across WBs, walking speed and distance ranged from 0.25 to 1.29 m/s and from 1.7 to 436.5 m, respectively. Estimation of walking speed, cadence, stride duration, number of steps, and distance stratified by WB duration showed intraclass correlation coefficients (ICCs) ranging from 0.50 to 0.99 and mean relative errors (MREs) from -6.9% to 12.8%. Stride length estimation showed poor reliability, with ICCs ranging from 0.30 to 0.49 and MREs from 6.1% to 13.2%. Walking speed and distance ICCs in the higher-SPPB score group ranged from 0.85 to 0.99, and MREs ranged from -10.1% to -1.7%. In the lower-SPPB score group, walking speed and distance ICCs ranged from 0.17 to 0.99, and MREs ranged from 13.5% to 32.6%. There was no discernible effect of time since su

关键词： active aging cadence digital mobility assessment distance free living gait walking speed wearable device

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Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications

arXiv

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arXiv 2018年

作者： Zhang, Chao Gao, Xianjie Hsieh, Min-Hsiu Hang, Hanyuan Tao, Dacheng School of Mathematical Sciences Dalian University of Technology Liaoning Dalian116024 China Centre for Quantum Software and Information University of Technology Sydney SydneyNSW2007 Australia AI Lab Samsung Research China - Beijing Beijing100028 China UBTECH Sydney Artificial Intelligence Centre The School of Computer Science Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random variables. We obtain several types of tail inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound the expectation of the spectral norm of a matrix i.d. series. Moreover, by developing a new lower-bound function for Q(s) = (s + 1) log(s + 1) − s that appears in the Bennett-type inequality, we derive a tighter tail inequality of the largest eigenvalue of the matrix i.d. series than the Bernstein-type inequality when the matrix dimension is high. The resulting lower-bound function is of independent interest and can improve any Bennett-type concentration inequality that involves the function Q(s). The class of i.d. probability distributions is large and includes Gaussian and Poisson distributions, among many others. Therefore, our results encompass the existing work [1] on matrix Gaussian series as a special case. Lastly, we show that the tail inequalities of a matrix i.d. series have applications in several optimization problems including the chance constrained optimization problem and the quadratic optimization problem with orthogonality constraints. In addition, we also use the resulting tail bounds to show that random matrices constructed from i.d. random variables satisfy the restricted isometry property (RIP) when it acts as a measurement matrix in compressed sensing. © 2018, CC BY-NC-ND.

关键词： Compressed sensing

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Study of Clinical Staging and Classification of Retinal Images for Retinopathy of Prematurity (ROP) Screening

Study of Clinical Staging and Classification of Retinal Imag...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Deepthi Badarinath S. Chaitra Neha Bharill M. Tanveer Mukesh Prasad H.N. Suma Abhishek M. Appaji Anand Vinekar Department of Medical Electronics B M S College of Engineering Bangalore India Department of computer Science and Engineering Indian Institute of Information Technology Dharwad India Discipline of Mathematics Indian Institute of Technology Indore India Centre for Artificial Intelligence School of Software FEIT University of Technology Sydney Australia Narayana Nethralaya Postgraduate Institute of Ophthalmology Bangalore India

Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in infants can be diagnosed easily. This tool takes the input from the Retcam and detects the stage, zone, and gives a rating of 1 to 9 for classifying the severity of the disease in the infants. This is achieved by extracting the optic disc, marking the ridge, and the distance of the optic nerve. This tool can be easily used by nurses and paramedics, unlike the existing technologies which require the guidance of a specialist to come to a conclusion.

关键词： Retina Retinopathy Optical imaging Tools Optical sensors Biomedical imaging

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Forecasting: theory and practice

arXiv

引用

arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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