Results 131 to 140 of about 6,215,042 (159)

Shaping dynamical neural computations using spatiotemporal constraints. [PDF]

Biochem Biophys Res Commun
Kim JZ, Larsen B, Parkes L.
europepmc   +1 more source

Bipolar complex q-rung orthopair fuzzy aggregation operators for enhanced decision-making in uncertain environments. [PDF]

Sci Rep
Alshammari I.
europepmc   +1 more source

"In Mathematical Language": On Mathematical Foundations of Quantum Foundations. [PDF]

Entropy (Basel)
Plotnitsky A.
europepmc   +1 more source

A Hyperbolic Sum Rule for Probability: Solving Recursive ("Chicken and Egg") Problems. [PDF]

Entropy (Basel)
Parker MC, Jeynes C, Walker SD.
europepmc   +1 more source

Theories of Relativistic Dissipative Fluid Dynamics. [PDF]

Entropy (Basel)
Rocha GS, Wagner D, Denicol GS, Noronha J, Rischke DH.   +4 more
europepmc   +1 more source

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis. [PDF]

Appl Intell (Dordr)
Petkov H, MacLellan C, Dong F.
europepmc   +1 more source

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. [PDF]

Front Neurosci
Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Lifespan Brain Chart Consortium (LBCC), Global Brain Consortium (GBC), Cuban Human Brain Mapping Project (CHBMP), Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA.   +17 more
europepmc   +1 more source

Machine learning for estimation and control of quantum systems. [PDF]

Natl Sci Rev
Ma H, Qi B, Petersen IR, Wu RB, Rabitz H, Dong D.   +5 more
europepmc   +1 more source

The Invariance Principle for Vector Valued Random Variables with Applications to Functional Random Limit Theorems

Lecture Notes in Statistics, 1981
1.Introduction. Let ξ1, ξ2,… be a sequence of independent real valued random variables with the same distribution with mean 0 and variance 1. Define the stochastic process ζn by $$\zeta _n \left( t \right) = S_{\left[ {nt} \right]} /n^{1/2} = \left( {\xi _1 + \cdots \xi _{nt} } \right)/n^{1/2} ,$$ where 0⩽t⩽1 and n = 1, 2,… The classical ...
T. Byczkowski, T. Inglot
openaire   +3 more sources