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Manifold-adaptive dimension estimation revisited

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arXiv 2020年

作者： Benkő, Zsigmond Stippinger, Marcell Rehus, Roberta Bencze, Attila Fabó, Dániel Hajnal, Boglárka Erőss, Loránd Telcs, András Somogyvári, Zoltán Department of Computational Sciences Wigner Research Centre for Physics H-1121 Hungary János Szentágothai Doctoral School of Neurosciences Semmelweis University H-1085 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BudapestH-1111 Hungary Epilepsy Center Department of Neurology National Institute of Clinical Neurosciences BudapestH-1145 Hungary Department of Functional Neurosurgery National Institute of Clinical Neurosciences BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University BudapestH-1083 Hungary Department of Quantitative Methods University of Pannonia Faculty of Business and Economics VeszprémH-8200 Hungary Neuromicrosystems ltd. BudapestH-1113 Hungary

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i.d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected-median-FSA estimator with kNN estimators: maximum likelihood (ML, Levina-Bickel) and two implementations of DANCo (R and matlab). We show that corrected-median-FSA estimator beats the ML estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

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Environment International 2024年 187卷 108712页

作者： Wen, Bo Wu, Yao Guo, Yuming Gasparrini, Antonio Tong, Shilu Overcenco, Ala Urban, Aleš Schneider, Alexandra Entezari, Alireza Vicedo-Cabrera, Ana Maria Zanobetti, Antonella Analitis, Antonis Zeka, Ariana Tobias, Aurelio Nunes, Baltazar Alahmad, Barrak Armstrong, Ben Forsberg, Bertil Pan, Shih-Chun Íñiguez, Carmen Ameling, Caroline Valencia, César De la Cruz Åström, Christofer Houthuijs, Danny Van Dung, Do Royé, Dominic Indermitte, Ene Lavigne, Eric Mayvaneh, Fatemeh Acquaotta, Fiorella de'Donato, Francesca Rao, Shilpa Sera, Francesco Carrasco-Escobar, Gabriel Kan, Haidong Orru, Hans Kim, Ho Holobaca, Iulian-Horia Kyselý, Jan Madureira, Joana Schwartz, Joel Jaakkola, Jouni J.K. Katsouyanni, Klea Diaz, Magali Hurtado Ragettli, Martina S. Hashizume, Masahiro Pascal, Mathilde Coélho, Micheline de Sousa Zanotti Stagliorio Ortega, Nicolás Valdés Ryti, Niilo Scovronick, Noah Michelozzi, Paola Matus Correa, Patricia Goodman, Patrick Saldiva, Paulo Hilario Nascimento Raz, Raanan Abrutzky, Rosana Osorio, Samuel Dang, Tran Ngoc Colistro, Valentina Huber, Veronika Lee, Whanhee Seposo, Xerxes Honda, Yasushi Kim, Yoonhee Guo, Yue Leon Bell, Michelle L. Li, Shanshan Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Methodology London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane Australia National Agency for Public Health of the Ministry of Health Labour and Social Protection of the Republic of Moldova Moldova Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Institute of Epidemiology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Research University of Bern Bern Switzerland Department of Environmental Health Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Hygiene Epidemiology and Medical Statistics National and Kapodistrian University of Athens Athens Greece Institute for Environment Health and Societies Brunel University London London United Kingdom Institute of Environmental Assessment and Water Research Spanish Council for Scientific Research Barcelona Spain School of Tropical Medicine and Global Health Nagasaki University Naga

Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health. © 2024

关键词： Location

An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes

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arXiv 2018年

作者： Speicher, Nora K. Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Methods in Medical Informatics Department of Computer Science University of Tübingen Germany

Due to the complexity of cancer, clustering algorithms have been used to disentangle the observed heterogeneity and identify cancer subtypes that can be treated specifically. While kernel based clustering approaches allow the use of more than one input matrix, which is an important factor when considering a multidimensional/manifold disease like cancer, the clustering results remain hard to evaluate and, in many cases, it is unclear which piece of information had which impact on the final result. In this paper, we propose an extension of multiple kernel learning clustering that enables the characterization of each identified patient cluster based on the features that had the highest impact on the result. To this end, we combine feature clustering with multiple kernel dimensionality reduction and introduce FIPPA, a score which measures the feature cluster impact on a patient cluster. Results: We applied the approach to different cancer types described by four different data types with the aim of identifying integrative patient subtypes and understanding which features were most important for their identification. Our results show that our method does not only have state-of-the-art performance according to standard measures (e.g., survival analysis), but, based on the high impact features, it also produces meaningful explanations for the molecular bases of the subtypes. This could provide an important step in the validation of potential cancer subtypes and enable the formulation of new hypotheses concerning individual patient groups. Similar analysis are possible for other disease phenotypes. Availability: Source code for rMKL-LPP with feature clustering and the calculation of fFIPPA scores is available at https://***/nora/fFIPPA. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

Mobile Health–Supported Virtual Reality and Group Problem Management Plus: Protocol for a Cluster Randomized Trial Among Urban Refugee and Displaced Youth in Kampala, Uganda (Tushirikiane4MH, Supporting Each Other for Mental Health)

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JMIR Research Protocols 2022年第12期11卷 e42342页

作者： Logie, Carmen H. Okumu, Moses Kortenaar, Jean-Luc Gittings, Lesley Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Khan, Torsum Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada Institute for Water Environment & Health United Nations University Hamilton ON Canada Centre for Gender & Sexual Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign IL United States School of Social Sciences Uganda Christian University Mukono Uganda Dalla Lana School of Public Health University of Toronto Toronto ON Canada School of Health Studies Faculty of Health Sciences Western University London ON Canada Centre for Social Science Research University of Cape Town Cape Town South Africa Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada Department of Pediatrics McMaster University Hamilton ON Canada Biostatistics Unit Father Sean O'Sullivan Research Centre St Joseph's Healthcare Hamilton ON Canada Centre for Development of Best Practices in Health Yaoundé Central Hospital Yaoundé Cameroon Division of Epidemiology and Biostatistics Department of Global Health Stellenbosch University Cape Town South Africa

Background: Although mental health challenges disproportionately affect people in humanitarian contexts, most refugee youth do not receive the mental health support needed. Uganda is the largest refugee-hosting nation in Africa, hosting over 1.58 million refugees in 2022, with more than 111,000 living in the city of Kampala. There is limited information about effective and feasible interventions to improve mental health outcomes and mental health literacy, and to reduce mental health stigma among urban refugee adolescents and youth in low- and middle-income countries (LMICs). Virtual reality (VR) is a promising approach to reduce stigma and improve mental health and coping, yet such interventions have not yet been tested in LMICs where most forcibly displaced people reside. Group Problem Management Plus (GPM+) is a scalable brief psychological transdiagnostic intervention for people experiencing a range of adversities, but has not been tested with adolescents and youth to date. Further, mobile health (mHealth) strategies have demonstrated promise in promoting mental health literacy. Objective: The aim of this study is to evaluate the feasibility and effectiveness of two youth-tailored mental health interventions (VR alone and VR combined with GMP+) in comparison with the standard of care in improving mental health outcomes among refugee and displaced youth aged 16-24 years in Kampala, Uganda. methods: A three-arm cluster randomized controlled trial will be implemented across five informal settlements grouped into three sites, based on proximity, and randomized in a 1:1:1 design. Approximately 330 adolescents (110 per cluster) are enrolled and will be followed for approximately 16 weeks. Data will be collected at three time points: baseline enrollment, 8 weeks following enrollment, and 16 weeks after enrollment. Primary (depression) and secondary outcomes (mental health literacy, attitudes toward mental help–seeking, adaptive coping, mental health stigma, mental well

关键词： adolescents and youth implementation research mental health mobile health refugee Uganda urban virtual reality

Dynamics of neural fields with exponential temporal kernel

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arXiv 2019年

作者： Shamsara, Elham Yamakou, Marius E. Atay, Fatihcan M. Jost, Jürgen Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen72076 Germany Department of Data Science Friedrich-Alexander-Universität Erlangen-Nürnberg Cauerstr. 11 Erlangen91058 Germany Department of Mathematics Bilkent University Ankara06800 Turkey Max-Planck-Institut für Mathematik in den Naturwissenschaften Inselstr. 22 Leipzig04103 Germany Santa Fe Institute for the Sciences of Complexity Santa FeNM87501 United States ScaDS.AI Dresden Leipzig Germany

We consider the standard neural field equation with an exponential temporal kernel. We analyze the time-independent (static) and time-dependent (dynamic) bifurcations of the equilibrium solution and the emerging spatiotemporal wave patterns. We show that an exponential temporal kernel does not allow static bifurcations such as saddle-node, pitchfork, and in particular, static Turing bifurcations. However, the exponential temporal kernel possesses the important property that it takes into account the finite memory of past activities of neurons, which Green’s function does not. Through a dynamic bifurcation analysis, we give explicit bifurcation conditions. Hopf bifurcations lead to temporally non-constant, but spatially constant solutions, but Turing-Hopf bifurcations generate spatially and temporally non-constant solutions, in particular, traveling waves. Bifurcation parameters are the coefficient of the exponential temporal kernel, the transmission speed of neural signals, the time delay rate of synapses, and the ratio of excitatory to inhibitory synaptic *** Codes 92C20, 37N25, 37G10 Copyright © 2019, The Authors. All rights reserved.

关键词： Hopf bifurcation

Data-driven approach to human motion modeling with Lua and gesture description language 9

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Data-driven approach to human motion modeling with Lua and g...

9th International Conference on Machine Vision, ICMV 2016

作者： Hachaj, Tomasz Koptyra, Katarzyna Ogiela, Marek R. Pedagogical Univ. of Krakow Pedagogical University of Krakow Institute of Computer Science and Computer Methods 2 Podchorazych Ave Krakow30-084 Poland AGH University of Science and Technology Cryptography and Cognitive Informatics Research Group 30 Mickiewicza Ave Krakow30-059 Poland

ISBN: (纸本)9781510611313

The aim of this paper is to present the novel proposition of the human motion modelling and recognition approach that enables real time MoCap signal evaluation. By motions (actions) recognition we mean classification. The role of this approach is to propose the syntactic description procedure that can be easily understood, learnt and used in various motion modelling and recognition tasks in all MoCap systems no matter if they are vision or wearable sensor based. To do so we have prepared extension of Gesture Description Language (GDL) methodology that enables movements description and real-time recognition so that it can use not only positional coordinates of body joints but virtually any type of discreetly measured output MoCap signals like accelerometer, magnetometer or gyroscope. We have also prepared and evaluated the cross-platform implementation of this approach using Lua scripting language and Java technology. This implementation is called Data Driven GDL (DD-GDL). In tested scenarios the average execution speed is above 100 frames per second which is an acquisition time of many popular MoCap solutions. © 2017 SPIE.

关键词： Modeling languages

Machine discovery of partial differential equations from spatiotemporal data

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

Estimating Flow Rates through Fracture Networks using Combinatorial Optimization

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arXiv 2018年

作者： Hobé, A. Vogler, D. Seybold, M.P. Ebigbo, A. Settgast, R.R. Saar, M.O. ETH Zurich Geothermal Energy and Geofluids Institute of Geophysics Zurich Switzerland ETH Zurich Transport Processes and Reactions Laboratory Institute of Process Engineering Zurich Switzerland University of Stuttgart Institute of Formal Methods in Computer Science Stuttgart Germany Lawrence Livermore National Laboratory Atmospheric Earth and Energy Division LivermoreCA United States

MSC Codes 8608 (Primary) 8604 (Secondary)To enable fast uncertainty quantification of fluid flow in a discrete fracture network (DFN), we present two approaches to quickly compute fluid flow in DFNs using combinatorial optimization algorithms. Specifically, the presented Hanan Shortest Path Maxflow (HSPM) and Intersection Shortest Path Maxflow (ISPM) methods translate DFN geometries and properties to a graph on which a max flow algorithm computes a combinatorial flow, from which an overall fluid flow rate is estimated using a shortest path decomposition of this flow. The two approaches are assessed by comparing their predictions with results from explicit numerical simulations of simple test cases as well as stochastic DFN realizations covering a range of fracture densities. Both methods have a high accuracy and very low computational cost, which can facilitate much-needed in-depth analyses of the propagation of uncertainty in fracture and fracture-network properties to fluid flow rates. Copyright © 2018, The Authors. All rights reserved.

关键词： Numerical methods

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality (vol 12, 5178, 2022)

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SCIENTIFIC REPORTS 2022年第1期12卷 1-14页

作者： Mistry, Malcolm N. Schneider, Rochelle Masselot, Pierre Roye, Dominic Armstrong, Ben Kysely, Jan Orru, Hans Sera, Francesco Tong, Shilu Lavigne, Eric Urban, Ales Madureira, Joana Garcia-Leon, David Ibarreta, Dolores Ciscar, Juan-Carlos Feyen, Luc de Schrijver, Evan de Sousa Zanotti Stagliorio Coelho, Micheline Pascal, Mathilde Tobias, Aurelio Guo, Yuming Vicedo-Cabrera, Ana M. Gasparrini, Antonio Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK Department of Economics Ca’ Foscari University of Venice Venice Italy The Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine London UK Forecast Department European Centre for Medium-Range Weather Forecast (ECMWF) Reading UK Ф-Lab European Space Agency (ESA-ESRIN) Frascati Italy Department of Statistics Computer Science and Applications ‘G. Parenti’ University of Florence Florence Italy Centre for Statistical Methodology London School of Hygiene & Tropical Medicine London UK Department of Geography University of Santiago de Compostela Santiago de Compostela Spain CIBER de Epidemiología y Salud Pública (CIBERESP) Madrid Spain Institute of Atmospheric Physics of the Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Department of Family Medicine and Public Health University of Tartu Tartu Estonia Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia Center for Global Health School of Public Health Nanjing Medical University Nanjing China Air Health Science Division Health Canada Ottawa ON Canada School of Epidemiology and Public Health University of Ottawa Ottawa ON Canada Department of Environmental Health Instituto Nacional de Saúde Dr Ricardo Jorge Porto Portugal EPIUnit—Instituto de Saúde Pública Universidade do Porto Porto Portugal Centro de Investigação em Saúde Pública Escola Nacional de Saúde Pública Universidade NOVA de Lisboa Lisbon Portugal The Joint Research Center (JRC) European Commission Seville Spain The Joint Research Center (JRC) European Commission

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.

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