Results 71 to 80 of about 4,460,603 (181)
Quantum‐Like Dynamics in Whole‐Brain Models of the Human Connectome
Advanced Science, Volume 13, Issue 51, 14 September 2026.Quantum‐like dynamics in the human brain. The level of quantum‐like behavior in a non‐quantum system of coupled oscillators is regulated by the spectral gap of the coupling graph. Whole‐brain modelling using QL fits the empirical data significantly better and has a lower model‐derived energy cost than the non‐QL model.
Gustavo Deco +5 more
wiley +1 more source
Coupled Dynamics Between Networks and Fields in Physical Space: A Theoretical Perspective
Annalen der Physik, Volume 538, Issue 9, September 2026.Networks embedded in physical space often communicate through dynamical fields that both mediate and respond to collective behavior. A unified theoretical perspective is presented for these network‐field systems, linking established mathematical frameworks and revealing how feedback between discrete interactions and continuous spatial processes can ...
Alex Arenas +4 more
wiley +1 more source
Semi reproducing kernel hilbert spaces and mixed precision computation. [PDF]
, 2018 Positive definite and conditionally positive definite functions are widely used in interpolation and smoothing problems, particularly when the data is scattered.
Garing, Ronald
core +1 more source
Finite‐Time Protocols Stabilize Charging in Noisy Ising Quantum Batteries
Advanced Quantum Technologies, Volume 9, Issue 9, September 2026.A transverse‐field quantum Ising chain is charged as a quantum battery using finite‐time ramps, which suppress energy oscillations and stabilize charging compared to sudden quenches. When time‐correlated noise is introduced, the outcome depends entirely on the protocol: noise can either boost stored energy or improve efficiency, never both ...
Riccardo Grazi +3 more
wiley +1 more source
The Cross‐Kernel Margin: A Robustness Measure for Quantum Kernel Methods
Advanced Quantum Technologies, Volume 9, Issue 9, September 2026.The cross‐kernel margin is introduced as a robustness measure for Quantum Kernel‐Assisted Support Vector Machines. This metric evaluates a classifier learned from a perturbed kernel within the ideal, unperturbed kernel geometry. Derived stability bounds quantify the corresponding inverse squared‐margin deviation and are numerically tested under local ...
S. Govender, I. Sinayskiy
wiley +1 more source
High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space. [PDF]
Math Geosci, 2020 Yao L, Dimitrakopoulos R, Gamache M.
europepmc +1 more source
Full Details of Solving Initial Value Problems by Reproducing Kernel Hilbert Space Method [PDF]
, 2015 In this paper we solve in full details an initial value problem by reproducing kernel Hilbert space method and we notice that this solution is close to the exact solution.
AL- Azzawi, Saad N. +2 more
core +1 more source
Some Lemmas on Reproducing Kernel Hilbert Spaces [PDF]
, 2002 Reproducing kernel Hilbert spaces (RKHS) provides a framework for approximation from finite data using the idea of bounded linear functionals. The approximation problem in this case can be viewed as the inverse problem of finding the optimum operator from the Euclidean space of observations to some subspace of the RKHS.
Dodd, T.J., Harrison, R.F.
openaire
Safe exploration in reproducing kernel Hilbert spaces
CoRRAccepted to AISTATS ...
Abdullah Tokmak +3 more
openaire +4 more sources
An Introduction to Stochastic Deep Learning
WIREs Computational Statistics, Volume 18, Issue 3, September 2026.The stochastic neural network, formulated as a composition of linear, logistic, and nonlinear regression modules, serves both as a deep learning model and as an analytical device for studying the properties of deep learning. It broadens deep learning beyond prediction‐oriented function approximation into a richer framework for statistical inference ...
Faming Liang
wiley +1 more source