Results 131 to 140 of about 93,156 (306)

Facial Shape Space using Statistical Models from Surface Normal. [PDF]

, 2012
The analysis of shape-variations due to changes in facial expression and gender difference is a key problem in face recognition. In this thesis, we use statistical shape analysis to construct shape-spaces that span facial expressions and gender, and use ...
Ceolin, Simone Regina
core  

stMixer for Scalable Mosaic Integration and Label Transfer in Spatial Histology and Multi‐Omics

Advanced Science, EarlyView.
stMixer is an unsupervised framework for scalable integration and label transfer across spatial histology and multi‐slide multi‐omics data with incomplete modality overlap. It combines self‐looped cross‐attention, multimodal metric learning, and graph‐guided cluster voting to align heterogeneous sections, correct batch effects, and propagate ...
Qixing Yang, Yan Wang, Luonan Chen, Chunman Zuo   +3 more
wiley   +1 more source

Euclidean N-space

, 1962
This study of the Euclidean N-space looks at some definitions and their characteristics, some comparisons, boundedness and compactness, and transformations and ...
Horner, Donald R.
core  

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

Framed osculating and rectifying curves in 4-dimensional Euclidean space

, 2022
Bu çalışma dört bölümden oluşmuştur. Birinci bölümde giriş kısmına yer verilmiştir. İkinci bölümde Öklid uzayında temel kavramlara ayrılmıştır. Ayrıca bu bölümde 4-boyutlu Öklid uzayında oskülatör ve rektifiyan eğriler hakkında temel tanım ve teoremlere,
Şavlı, Merve Nur
core  

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

Advanced 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, Jialuo Xu, Gaoyuan Du, Xiangting Jia, Dongmin Zhao, Chunyan Zhou, Kexin Xiao, Jia Gu, Junnan Zhu, Xuequn Shang   +9 more
wiley   +1 more source

A Study on Euclidean Space and Affine Space

, 2019
In this thesis, a Euclidean space is a finite-dimensional real vector space with an inner product.But when we analysed deeply, they are quite different as discussed in this paper.
K. Backia Lakshmi, Dr. K. Selvaraj
core  

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

Advanced 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, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Sphere packings in Euclidean space with forbidden distances

Forum of Mathematics, Sigma
In this paper, we study the sphere packing problem in Euclidean space where we impose additional constraints on the separations of the center points. We prove that any sphere packing in dimension $48$ , with spheres of radii r, such that no two ...
Felipe Gonçalves, Guilherme Vedana
doaj   +1 more source

Searching in Euclidean Spaces with Predictions

We study the problem of searching for a target at some unknown location in $\mathbb{R}^d$ when additional information regarding the position of the target is available in the form of predictions. In our setting, predictions come as approximate distances to the target: for each point $p\in \mathbb{R}^d$ that the searcher visits, we obtain a value $λ(p)$
Cabello, S., Giannopoulos, P.
openaire   +3 more sources