Results 111 to 120 of about 101,961 (306)

Accurately Deciphering Tissue Heterogeneity From Spatial Multi‐Modal and Multi‐Omics With STransformer

open access: yesAdvanced Science, EarlyView.
STransformer is a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity.
Xingyi Li   +9 more
wiley   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

open access: yesAdvanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio   +6 more
wiley   +1 more source

One-loop quantization of Euclidean D3-branes in holographic backgrounds

open access: yesJournal of High Energy Physics
In this note we analyze the semi-classical quantization of D3-branes in three different holographic backgrounds in type IIB string theory. The first background is Euclidean AdS5 with S 1 × S 3 boundary accompanied with a twist to preserve supersymmetry ...
Friðrik Freyr Gautason   +1 more
doaj   +1 more source

A Safety Study for Dynamical Systems on Heisenberg Lie Group of dimension 4 [PDF]

open access: yesModeling, Identification and Control
The safety property of dynamical systems has typically been studied in Euclidean spaces. In this work, we extend the notion of safety to a non-Euclidean geometry.
Sultan Selcuk Sutlu   +3 more
doaj   +1 more source

Topics in Non-Euclidean geometry

open access: yes, 1959
Thesis (M.A.)-- University of Wichita, College of Liberal Arts and Sciences, Dept. of MathematicsIt is difficult for a high school student to fully appreciate the study of Euclidean geometry without some notion of its relation to other geometries and ...
Carter, Martin
core  

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

open access: yesAdvanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

open access: yesAdvanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan   +12 more
wiley   +1 more source

The discovery of non-Euclidean geometry

open access: yes, 2014
This project gives a basic understanding of the difference between the Euclidean geometry and the non-Euclidean geometries (elliptic and hyperbolic). Furthermore the focus of the project is to describe the discovery of non-Euclidean geometry in the view ...
Salnaja, Alma, Rho, Aran
core  

Eighteen Essays in Non-Euclidean Geometry

open access: yes, 2019
International audienceThis book consists of a series of self-contained essays on non-Euclidean geometry in a broad sense, including the classical geometries of constant curvature (spherical and hyperbolic), de Sitter, anti-de Sitter, co-Euclidean, co ...
A'Campo, Norbert   +2 more
core   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

open access: yesAIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim   +2 more
wiley   +1 more source

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