Results 181 to 190 of about 4,792,820 (293)
A New Hilbert's Hotel Argument Against Past‐Eternalism
ABSTRACT This paper offers a new formulation of the “Hilbert's Hotel Argument” (HHA) which is superior to existing formulations because it (1) demonstrates that HH is logically impossible in the concrete world, (2) takes into account the need to consider the assumptions of HHA, and (3) offers a reply to an important objection concerning the validity of
Andrew Ter Ern Loke, Eli Haitov
wiley +1 more source
A novel cryptographic framework and mathematical modeling for secure transmission of Parkinson's disease data using RSA and block-based secret sharing. [PDF]
Sambandham T +5 more
europepmc +1 more source
On goodness‐of‐fit testing for self‐exciting point processes
Abstract Despite the wide usage of parametric point processes in theory and applications, a sound goodness‐of‐fit procedure to test whether a given parametric model is appropriate for data coming from a self‐exciting point process has been missing in the literature.
José Carlos Fontanesi Kling +1 more
wiley +1 more source
A Dual Stream Deep Learning Framework for Alzheimer's Disease Detection Using MRI Sonification. [PDF]
Mohsin NA, Abdul Ameer MH.
europepmc +1 more source
Construction of fixed points of nonlinear mappings in Hilbert space
F., E., Browder, V. W., Petryshyn
semanticscholar +1 more source
Efficient multiple‐robust estimation for nonresponse data under informative sampling
Abstract Nonresponse in probability sampling presents a long‐standing challenge in survey sampling, often necessitating simultaneous adjustments to address sampling and selection biases. We develop a statistical framework that explicitly models sampling weights as random variables and establish the semiparametric efficiency bound for the parameter of ...
Kosuke Morikawa +2 more
wiley +1 more source
Quantum-like representation of neuronal networks' activity: modeling "mental entanglement". [PDF]
Khrennikov A, Yamada M.
europepmc +1 more source
Spatial depth for data in metric spaces
Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
Double BFV Quantisation of 3D Gravity. [PDF]
Canepa G, Schiavina M.
europepmc +1 more source

