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379 results on '"Richman R"'

1. Examination of the Correlation Between Physical and Psychological Measures in Community-Dwelling Older Adults

Author

Staples WH, Kays A, and Richman R

Subjects
mood, mobility, older adults, falls, Geriatrics, RC952-954.6
Abstract

William H Staples, Adam Kays, Rachel Richman Krannert School of Physical Therapy, University of Indianapolis, Indianapolis, IN, USACorrespondence: William H StaplesKrannert School of Physical Therapy, University of Indianapolis, 1400 East Hanna Avenue, Indianapolis, IN 46227, USATel +1 317-788-2112Fax +1 317-788-3542Email stapleswh@uindy.eduIntroduction: The purpose of this study was to determine if correlations exist between strength and mobility and psychological measures of anxiety and depression in community-dwelling older adults.Methods: One hundred and eleven participants randomly completed trials of grip strength (GS), the Timed Up and Go (TUG), the 10-meter walk test (10MWT), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS) in a prospective, correlational study.Results: This study found significant correlations between and within physical measures of strength and mobility and psychological measures. Age, GS, GDS, and education were significant predictors of gait speed (10MWT). Age, GS, and GDS were predictors of TUG scores. Grip strength was found to be a significant predictor of fall status; fallers had significantly weaker GS than non-fallers. Symptoms of anxiety (GAS) were predictive of symptoms of depression.Discussion: Objective measures of physical performance can provide information regarding an individual’s symptoms of anxiety and depression. Health professionals should understand the correlations between mood and physical ability to better treat their patients.Keywords: mood, mobility, older adults, falls

Published
2020

2. Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles

Author

Merz, M., Richman, R., Tsanakas, T., and Wüthrich, M. V.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Finance - Statistical Finance, Statistics - Applications, 68T07
Abstract

A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method, which we call Marginal Attribution by Conditioning on Quantiles (MACQ). Our approach is based on analyzing the marginal attribution of predictions (outputs) to individual features (inputs). Specificalllly, we consider variable importance by mixing (global) output levels and, thus, explain how features marginally contribute across different regions of the prediction space. Hence, MACQ can be seen as a marginal attribution counterpart to approaches such as accumulated local effects (ALE), which study the sensitivities of outputs by perturbing inputs. Furthermore, MACQ allows us to separate marginal attribution of individual features from interaction effect, and visually illustrate the 3-way relationship between marginal attribution, output level, and feature value.

Published
2021

3. Developing a Novel 1D-HAM Numerical Modelling Tool for Assessing the Hygrothermal Properties of Cross-Laminated Timber (CLT) Including Adhesive Layers

Author

Vyas, Y., Johns, D., Richman, R., Liao, Z., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Walbridge, Scott, editor, Nik-Bakht, Mazdak, editor, Ng, Kelvin Tsun Wai, editor, Shome, Manas, editor, Alam, M. Shahria, editor, el Damatty, Ashraf, editor, and Lovegrove, Gordon, editor

Published
2023
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4. Hygrothermal Analysis of Cross-Laminated Timber (CLT) in Canadian Climates With and Without Adhesive Layers Using a 1D-HAM Numerical Modelling Tool

Author

Johns, D., Vyas, Y., Richman, R., Liao, Z., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Walbridge, Scott, editor, Nik-Bakht, Mazdak, editor, Ng, Kelvin Tsun Wai, editor, Shome, Manas, editor, Alam, M. Shahria, editor, el Damatty, Ashraf, editor, and Lovegrove, Gordon, editor

Published
2023
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8. A multi-task network approach for calculating discrimination-free insurance prices

Author

Lindholm, M., Richman, R., Tsanakas, A., and Wüthrich, M. V.

Subjects
HF
Abstract

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders’ protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.

Published
2022

14. Catching Element Formation In The Act - The Case for a New MeV Gamma-Ray Mission: Radionuclide Astronomy in the 2020s

Author

Timmes, Fryer, F., Hungerford, C., Couture, A. L., Adams, A., Aoki, F., Arcones, W., Arnett, A., Auchettl, D., Avila, K., Badenes, M., Baron, C., Bauswein, E., Beacom, A., Blackmon, J., Blondin, J., Bloser, S., Boggs, P., Boss, S., Brandt, A., Bravo, T., Brown, E., Bruenn, P., Budtz-Jørgensen, S., Burns, C., Calder, E., Caputo, A., Champagne, R., Chevalier, A., Chieffi, R., Chipps, A., Cinabro, K., Clarkson, D., Clayton, O., Coc, D., Connolly, A., Conroy, D., Côté, C., Couch, B., Dauphas, S., Boer, N., Deibel, R. J., Denisenkov, C., Desch, P., Dessart, S., Diehl, L., Doherty, R., Domínguez, C., Dong, I., Dwarkadas, S., Fan, V., Fields, D., Fields, B., Filippenko, C., Fisher, A., Foucart, R., Fransson, F., Fröhlich, C., Fuller, C., Gibson, G., Giryanskaya, B., Görres, V., Goriely, J., Grebenev, S., Grefenstette, S., Grohs, B., Guillochon, E., Harpole, J., Harris, A., Harris, C., Harrison, J. A., Hartmann, F., Hashimoto, D., Heger, M., Hernanz, A., Herwig, M., Hirschi, F., Hix, R., Höflich, R. W., Hoffman, P., Holcomb, R., Hsiao, C., Iliadis, E., Janiuk, C., Janka, A., Jerkstrand, T., Johns, A., Jones, L., José, S., Kajino, J., Karakas, T., Karpov, A., Kasen, P., Kierans, D., Kippen, C., Korobkin, M., Kobayashi, O., Kozma, C., Krot, C., Kumar, S., Kuvvetli, P., Laird, I., Laming, A., Larsson, J. M., Lattanzio, J., Lattimer, J., Leising, J., Lennarz, M., Lentz, A., Limongi, E., Lippuner, M., Livne, J., Lloyd-Ronning, E., Longland, N., Lopez, R., Lugaro, L. A., Lutovinov, M., Madsen, A., Malone, K., Matteucci, C., Mcenery, F., Meisel, J., Messer, Z., Metzger, B., Meyer, B., Meynet, B., Mezzacappa, G., Miller, A., Miller, J., Milne, R., Misch, P., Mitchell, W., Mösta, L., Motizuki, P., Müller, Y., Mumpower, B., Murphy, M., Nagataki, J., Nakar, S., Nomoto, E., Nugent, K., Nunes, P., Shea, F. O., Oberlack, B., Pain, U., Parker, S., Perego, L., Pignatari, A., Pinedo, M., Plewa, G. M., Simon Portegies Zwart, Poznanski, S. F., Priedhorsky, D., Pritychenko, W., Radice, B., Ramirez-Ruiz, D., Rauscher, E., Reddy, T., Rehm, S., Reifarth, E., Richman, R., Ricker, D., Rijal, P., Roberts, N., Röpke, L., Rosswog, F., Ruiter, S., Ruiz, A. J., Savin, C., Schatz, D. W., Schneider, H., Schwab, D., Seitenzahl, J., Shen, I., Siegert, K., Sim, T., Smith, S., Smith, D., Smith, K., Sollerman, M., Sprouse, J., Spyrou, T., Starrfield, A., Steiner, S., Strong, A., Sukhbold, A. W., Suntzeff, T., Surman, N., Tanimori, R., The, T., S Thielemann, L., K Tolstov, F., Tominaga, A., Tomsick, N., Townsley, J., Tsintari, D., Tsygankov, P., Vartanyan, S., Venters, D., Vestrand, T., Vink, T., Waldman, J., Wang, R., Wang, L., Warren, X., West, M., Wheeler, C., Wiescher, J. C., Winkler, M., Winter, C., Wolf, L., Woolf, B., Woosley, R., Wu, S., Wrede, J., Yamada, C., Young, S., Zegers, P., and Zingale, R.

Subjects
Physics, education.field_of_study, 010308 nuclear & particles physics, Astrophysics::High Energy Astrophysical Phenomena, Population, Gamma ray, Astronomy, Cosmic ray, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Galaxy, Interstellar medium, Supernova, Neutron star, Nucleosynthesis, 0103 physical sciences, education, 010303 astronomy & astrophysics, Astrophysics::Galaxy Astrophysics
Abstract

Gamma-ray astronomy explores the most energetic photons in nature to address some of the most pressing puzzles in contemporary astrophysics. It encompasses a wide range of objects and phenomena: stars, supernovae, novae, neutron stars, stellar-mass black holes, nucleosynthesis, the interstellar medium, cosmic rays and relativistic-particle acceleration, and the evolution of galaxies. MeV gamma-rays provide a unique probe of nuclear processes in astronomy, directly measuring radioactive decay, nuclear de-excitation, and positron annihilation. The substantial information carried by gamma-ray photons allows us to see deeper into these objects, the bulk of the power is often emitted at gamma-ray energies, and radioactivity provides a natural physical clock that adds unique information. New science will be driven by time-domain population studies at gamma-ray energies. This science is enabled by next-generation gamma-ray instruments with one to two orders of magnitude better sensitivity, larger sky coverage, and faster cadence than all previous gamma-ray instruments. This transformative capability permits: (a) the accurate identification of the gamma-ray emitting objects and correlations with observations taken at other wavelengths and with other messengers; (b) construction of new gamma-ray maps of the Milky Way and other nearby galaxies where extended regions are distinguished from point sources; and (c) considerable serendipitous science of scarce events -- nearby neutron star mergers, for example. Advances in technology push the performance of new gamma-ray instruments to address a wide set of astrophysical questions.

Published
2019

23. Inorganic and Organometallic Photochemistry

Author

MARK S. WRIGHTON, NORMAN SUTIN, CAROL CREUTZ, PATRICIA J. DELAIVE, J. T. LEE, H. ABRUÑA, H. W. SPRINTSCHNIK, T. J. MEYER, DAVID G. WHITTEN, HARRY B. GRAY, KENT R. MANN, NATHAN S. LEWIS, JOHN A. THICH, ROBERT M. RICHMAN, R. J. WATTS, J. S. HARRINGTON, J. VAN HOUTEN, PETER C. FORD, MARIAN S. HENRY, MO and MARK S. WRIGHTON, NORMAN SUTIN, CAROL CREUTZ, PATRICIA J. DELAIVE, J. T. LEE, H. ABRUÑA, H. W. SPRINTSCHNIK, T. J. MEYER, DAVID G. WHITTEN, HARRY B. GRAY, KENT R. MANN, NATHAN S. LEWIS, JOHN A. THICH, ROBERT M. RICHMAN, R. J. WATTS, J. S. HARRINGTON, J. VAN HOUTEN, PETER C. FORD, MARIAN S. HENRY, MO

Published
1978

33. 425

Author

Richman, R., primary and Triner, W., additional

Published
2006
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