Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Bijral P"'
Autor:
Bonnie Grant, Joseph Kean, Naim Vali, John Campbell, Lorraine Maden, Prun Bijral, Waljit S. Dhillo, James McVeigh, Richard Quinton, Channa N. Jayasena
Publikováno v:
Substance Abuse Treatment, Prevention, and Policy, Vol 18, Iss 1, Pp 1-8 (2023)
Abstract Background Anabolic–androgenic steroids (AAS) mimic the effects of testosterone and may include testosterone itself; they are used for body enhancement within the general population. AAS use has been linked with increased mortality, cardio
Externí odkaz:
https://doaj.org/article/019245d9c84f48bf9d7fcf26ce9754c4
Publikováno v:
Archives of Public Health, Vol 81, Iss 1, Pp 1-7 (2023)
Abstract Background In 1991, Halpern and Coren claimed that left-handed people die nine years younger than right-handed people. Most subsequent studies did not find support for the difference in age of death or its magnitude, primarily because of the
Externí odkaz:
https://doaj.org/article/8951f3b09efa4461b7d0b13da65fc0d3
Autor:
Yan, Xiaohan, Bijral, Avleen S.
We present a dynamic prediction framework for binary sequences that is based on a Bernoulli generalization of the auto-regressive process. Our approach lends itself easily to variants of the standard link prediction problem for a sequence of time dep
Externí odkaz:
http://arxiv.org/abs/2007.11811
Autor:
Wojcik, Stefan, Bijral, Avleen, Johnston, Richard, Lavista, Juan Miguel, King, Gary, Kennedy, Ryan, Vespignani, Alessandro, Lazer, David
While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, m
Externí odkaz:
http://arxiv.org/abs/2003.13822
Autor:
Bijral, Avleen S.
We extend the feature selection methodology to dependent data and propose a novel time series predictor selection scheme that accommodates statistical dependence in a more typical i.i.d sub-sampling based framework. Furthermore, the machinery of mixi
Externí odkaz:
http://arxiv.org/abs/1905.07659
We present online prediction methods for time series that let us explicitly handle nonstationary artifacts (e.g. trend and seasonality) present in most real time series. Specifically, we show that applying appropriate transformations to such time ser
Externí odkaz:
http://arxiv.org/abs/1611.02365
Autor:
Bijral, Avleen S.
In this dissertation we propose alternative analysis of distributed stochastic gradient descent (SGD) algorithms that rely on spectral properties of the data covariance. As a consequence we can relate questions pertaining to speedups and convergence
Externí odkaz:
http://arxiv.org/abs/1608.08337
In this paper we apply a time series based Vector Auto Regressive (VAR) approach to the problem of predicting unemployment insurance claims in different census regions of the United States. Unemployment insurance claims data, reported weekly, are a l
Externí odkaz:
http://arxiv.org/abs/1605.05784
We study a distributed consensus-based stochastic gradient descent (SGD) algorithm and show that the rate of convergence involves the spectral properties of two matrices: the standard spectral gap of a weight matrix from the network topology and a ne
Externí odkaz:
http://arxiv.org/abs/1603.04379
Autor:
Mel Ramasawmy, Lydia Poole, Zareen Thorlu-Bangura, Aneesha Chauhan, Mayur Murali, Parbir Jagpal, Mehar Bijral, Jai Prashar, Abigail G-Medhin, Elizabeth Murray, Fiona Stevenson, Ann Blandford, Henry W W Potts, Kamlesh Khunti, Wasim Hanif, Paramjit Gill, Madiha Sajid, Kiran Patel, Harpreet Sood, Neeraj Bhala, Shivali Modha, Manoj Mistry, Vinod Patel, Sarah N Ali, Aftab Ala, Amitava Banerjee
Publikováno v:
JMIR Cardio, Vol 6, Iss 2, p e37360 (2022)
BackgroundDigital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the
Externí odkaz:
https://doaj.org/article/69eed878c11c47e2b60753c57c5d55f4