Results 81 to 90 of about 33,661 (311)
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Horizontal Grid Spacing Convergence of Aquaplanet Experiments Using a Global‐Storm Resolving Model
Aquaplanet experiments are used to investigate the statistical convergence of the Global Storm‐Resolving model (GSRM) ICOsahedral Nonhydrostatic (ICON) model, under successive, two‐fold horizontal grid spacing refinements from 160 to 1.25 km.
A. Peinado Bravo, D. Klocke, B. Stevens
doaj +1 more source
Regression Asymptotics Using Martingale Convergence Methods [PDF]
Weak convergence of partial sums and multilinear forms in independent random variables and linear processes to stochastic integrals now plays a major role in nonstationary time series and has been central to the development of unit root econometrics. The
Peter C.B. Phillips, Rustam Ibragimov
core
This work presents a bio‐inspired computing framework for Parkinson's disease analog recognition using electroencephalogram signals. Temporally encoded EEG features stimulate a mycelium‐inspired memristive reservoir, where disease‐related patterns emerge through physical spatiotemporal dynamics.
Ioannis K. Chatzipaschalis +5 more
wiley +1 more source
We give some improved convergence results about the smoothing-regularization approach to mathematical programs with vanishing constraints (MPVC for short), which is proposed in Achtziger et al. (2013).
Qingjie Hu +3 more
doaj +1 more source
Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation
A soft-constrained neural network for density estimation (SC-NN-4pdf) has recently been introduced to tackle the issues arising from the application of neural networks to density estimation problems (in particular, the satisfaction of the second ...
Edmondo Trentin
doaj +1 more source
Convergence and asymptotic variance of bootstrapped finite-time ruin probabilities with partly shifted risk processes. [PDF]
The classical risk model is considered and a sensitivity analysis of finite-time ruin probabilities is carried out. We prove the weak convergence of a sequence of empirical finite-time ruin probabilities.
Stéphane Loisel +2 more
core
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng +4 more
wiley +1 more source
On the Asymptotic Convergence of Subgraph Generated Models
We study a family of random graph models - termed subgraph generated models (SUGMs) - initially developed by Chandrasekhar and Jackson in which higher-order structures are explicitly included in the network formation process. We use matrix concentration inequalities to show convergence of the adjacency matrix of networks realized from such SUGMs to the
Xinchen Xu, Francesca Parise
openaire +2 more sources
Accounting for animal health in efficiency analysis: An application to Swedish dairy farms
Abstract Poor animal health is a central concern in modern livestock production. Despite the necessity to incorporate animal health in efficiency analysis, the theoretical and empirical developments are limited on this subject. This article appropriately characterizes the axiomatic properties of animal health within a production framework.
Frederic Ang +3 more
wiley +1 more source

