Results 61 to 70 of about 43,664 (246)
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
On a coupled system of fractional sum-difference equations with p-Laplacian operator
In this paper, we propose a nonlocal fractional sum-difference boundary value problem for a coupled system of fractional sum-difference equations with p-Laplacian operator. The problem contains both Riemann–Liouville and Caputo fractional difference with
Pimchana Siricharuanun +2 more
doaj +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Positive solutions for eigenvalue problems of fractional q-difference equation with ϕ-Laplacian
The aim of this paper is to investigate the boundary value problem of a fractional q-difference equation with ϕ-Laplacian, where ϕ-Laplacian is a generalized p-Laplacian operator. We obtain the existence and nonexistence of positive solutions in terms of
Jufang Wang +3 more
doaj +1 more source
On fractional p-Laplacian parabolic problem with general data [PDF]
In this article the problem to be studied is the following $$ (P) \left\{ \begin{array}{rcll} u_t+(-\D^s_{p}) u & = & f(x,t) & \text{ in } Ø_{T}\equiv Ω\times (0,T), \\ u & = & 0 & \text{ in }(\ren\setminusØ) \times (0,T), \\ u & \ge & 0 & \text{ in }\ren \times (0,T),\\ u(x,0) & = & u_0(x) & \mbox{ in }Ø,
B. Abdellaoui +3 more
openaire +3 more sources
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Sobolev versus Hölder minimizers for the degenerate fractional p-Laplacian [PDF]
We consider a nonlinear pseudo-di erential equation driven by the fractional p-Laplacian (−∆)sp with s ∈ (0,1) and p ⩾ 2 (degenerate case), under Dirichlet type conditions in a smooth domain Ω.
MOSCONI, SUNRA JOHANNES NIKOLAJ +3 more
core +1 more source
Visual features, numerical descriptors, and controlled textual attributes extracted from smartphone images of Chenpi are integrated by VALIANT, a tailored multimodal framework for simultaneous storage‐age classification and authenticity verification. The workflow distinguishes genuine products from suspicious standard operating procedure mimics while ...
Simon C. K. Chan +5 more
wiley +1 more source
We are concerned with the uniqueness of solutions for a class of p-Laplacian fractional order nonlinear systems with nonlocal boundary conditions.
Jun-qi He, Xue-li Song
doaj +1 more source
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui +11 more
wiley +1 more source

