Results 71 to 80 of about 5,677,932 (246)

An inverse three spectra problem for Sturm–Liouville operators

open access: yesBoundary Value Problems, 2018
In this paper, we consider the inverse three spectra problems of recovering the Sturm–Liouville equation by the spectra of the Neumann–Dirichlet boundary value problem on [0,1] $[0,1]$, the Neumann–Robin problem on [0,1/2] $[0,1/2]$, and the Robin ...
Yongxia Guo, Guangsheng Wei, Ruoxia Yao
doaj   +1 more source

Reflecting Brownian Snake and a Neumann-Dirichlet Problem

open access: yes, 2007
: The paper deals with a Markov path-valued process : the reflecting Brownian snake. It is a particular case of the path-valued process former introduced by Le Gall.
Rue Des Saints-p`eres   +2 more
core  

Who Are the Consumers of European Farmers' Markets? A Cross‐Country Analysis

open access: yesAgribusiness, EarlyView.
ABSTRACT With substantial growth in the number of farmers' markets (FMs) in developed countries, the number of consumers visiting FMs is also increasing. This study comparatively assesses the consumers of FMs in three European countries where FMs traditionally play a distinctive role in food supply chains.
Áron Török   +6 more
wiley   +1 more source

W1,p versus C1: The nonsmooth case involving critical growth

open access: yesBulletin of Mathematical Sciences, 2020
In this paper, we study a class of generalized and not necessarily differentiable functionals of the form J(u) =∫ΩG(x,∇u)dx −∫Ωj1(x,u)dx −∫∂Ωj2(x,u)dσ with functions j1: Ω × ℝ → ℝ, j2: ∂Ω × ℝ → ℝ that are only locally Lipschitz in the second ...
Yunru Bai   +3 more
doaj   +1 more source

Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks

open access: yesAdvanced Intelligent Discovery, EarlyView.
Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç   +2 more
wiley   +1 more source

Solutions to nonlocal Neumann boundary value problems

open access: yesElectronic Journal of Qualitative Theory of Differential Equations, 2018
In this paper we study the nonlocal Neumann boundary value problem of the following form $$ u'' =f(t,u,u'),\quad u'(0)=0, \quad u'(1)=\int_{0 }^{1}u'(s)dg(s), $$ where $f:[0,1]\times\mathbb R^n\times\mathbb R^n\to\mathbb R^n$ and $g=\mbox{diag}(g_1 ...
Katarzyna Szymanska-Debowska
doaj   +1 more source

Mean Curvature Flow with a Neumann Boundary Condition in Flat Spaces [PDF]

open access: yes, 2012
In this thesis I study mean curvature flow in both Euclidean and Minkowski space with a Neumann boundary condition. In Minkowski space I show that for a convex timelike cone boundary condition, with compatible spacelike initial data, mean curvature flow
LAMBERT, BENJAMIN,STEPHEN
core  

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The systematic design of memristor‐based neural network is provided by analog conductance state parameters to accurately emulate the software‐based high‐resolution weight at discrete device level. The requirement of discrete analog conductance of memristor device is measured as ≈50 states with nonlinearity value of ≈0.142 within the deviation range of ...
Jingon Jang, Yoonseok Song, Sungjun Park
wiley   +1 more source

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

open access: yesAdvanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison   +4 more
wiley   +1 more source

Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks

open access: yesAdvanced Intelligent Systems, EarlyView.
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu   +6 more
wiley   +1 more source

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