Results 81 to 90 of about 117,362 (280)

Distance-Regular Graphs and Halved Graphs

open access: yesEuropean Journal of Combinatorics, 1986
Let G be a bipartite distance-regular graph with bipartition \(V(G)=X\cup Y\). Let \(V(G')=X\) and, for x and y in X, let x be adjacent to y in G' if and only if x is of distance two from y in G. Then G' is called a halved graph of G, and is distance-regular. This paper discusses whether G' is one of the known, large-diameter, distance-regular graphs.
openaire   +2 more sources

Edge-regular graphs with regular cliques [PDF]

open access: yesEuropean Journal of Combinatorics, 2018
We exhibit infinitely many examples of edge-regular graphs that have regular cliques and that are not strongly regular. This answers a question of Neumaier from 1981.
Gary R. W. Greaves, Jack H. Koolen
openaire   +3 more sources

Long‐Tea‐CLIP: An Expert‐Level Multimodal AI Framework for Fine‐Grained Green Tea Grading Across Five Sensory Dimensions

open access: yesAdvanced Science, EarlyView.
Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu   +9 more
wiley   +1 more source

Deep Domain Adaptation Based on Adversarial Network With Graph Regularization

open access: yesIEEE Access, 2020
Although most transfer learning methods can reduce the difference of the feature distributions between the source and target domains effectively, some classes in the two domains may still be misaligned after domain adaptation, especially for the classes ...
Xu Jia, Na Ma, Fuming Sun
doaj   +1 more source

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models [PDF]

open access: yes, 2010
A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include $K$-fold cross-validation ($K$-CV), Akaike information criterion (AIC), and Bayesian ...
Liu, Han   +2 more
core   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao   +11 more
wiley   +1 more source

Semi-supervised learning by constructing query-document heterogeneous information network

open access: yesTongxin xuebao, 2014
Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored ...
Yu-feng LIU, Ren-fa LI
doaj   +2 more sources

Manifold Regularization Graph Structure Auto-Encoder to Detect Loop Closure for Visual SLAM

open access: yesIEEE Access, 2019
Loop closure detection plays a vital role in the visual simultaneous localization and mapping (SLAM) systems. In order to overcome the shortcomings of the artificial design algorithm to extract insufficient features, this paper proposes a graph ...
Zhonghua Wang   +3 more
doaj   +1 more source

On End-regular graphs

open access: yesDiscrete Mathematics, 1996
A monoid which is von Neumann regular is called orthodox if its idempotents form a submonoid. A graph is called (End)-regular or (End)-orthodox if its monoid of graph endomorphisms is a (von Neumann) regular or orthodox monoid. Here graph endomorphisms are mappings of the vertex set which preserve edges.
openaire   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

open access: yesAdvanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju   +5 more
wiley   +1 more source

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