Results 51 to 60 of about 121,870 (266)
Unified spectral bounds on the chromatic number
, 2014 One of the best known results in spectral graph theory is the following lower bound on the chromatic number due to Alan Hoffman, where mu_1 and mu_n are respectively the maximum and minimum eigenvalues of the adjacency matrix: chi >= 1 + mu_1 / (- mu_n).
Elphick, Clive, Wocjan, Pawel
core +2 more sources
Flexible Dielectric Acoustic Resonator Patch for Tissue Regeneration
Advanced Materials, EarlyView.A flexible dielectric acoustic resonator patch enables MHz‐range ultrasound generation through resonance amplification without using piezoelectric materials. Conformal integration on a curved substrate allows efficient acoustic delivery to tissue‐mimicking environments.
Donyoung Kang +7 more
wiley +1 more source
Advanced Robotics Research, EarlyView.Embedded flexible sensing technologies advance underwater soft robotics, yet most systems still suffer from hysteresis and limited perceptiveness. Instead, vision‐based tactile sensors provide reliable and rapid feedback essential for complex underwater tasks.
Qiyi Zhang +5 more
wiley +1 more source
Advanced Science, EarlyView.Geometry and connectivity are complementary structures, which have demonstrated their ability to represent the brain's functional activity. This study evaluates geometric and connectome eigenmodes as biologically informed constraints for EEG source localization.
Pok Him Siu +6 more
wiley +1 more source
Communications in Advanced Mathematical Sciences, 2018 The signless Laplacian eigenvalues of a graph $G$ are eigenvalues of the matrix $Q(G) = D(G) + A(G)$, where $D(G)$ is the diagonal matrix of the degrees of the vertices in $G$ and $A(G)$ is the adjacency matrix of $G$.
Rao Li
doaj +1 more source
Laplacian matrices of weighted digraphs represented as quantum states
, 2017 Representing graphs as quantum states is becoming an increasingly important approach to study entanglement of mixed states, alternate to the standard linear algebraic density matrix-based approach of study.
Adhikari, Bibhas +3 more
core +1 more source
Laplacian Matrix for Dimensionality Reduction and Clustering [PDF]
, 2020 Many problems in machine learning can be expressed by means of a graph with nodes representing training samples and edges representing the relationship between samples in terms of similarity, temporal proximity, or label information. Graphs can in turn be represented by matrices. A special example is the Laplacian matrix, which allows us to assign each
Laurenz Wiskott, Fabian Schönfeld
openaire +2 more sources
Advanced Science, EarlyView.Prolonged exposure to stiff extracellular matrix drives cancer‐associated fibroblasts into a persistently activated myofibroblast state. Two parallel pathways are identified: β1 integrin activation smoothens the nuclear lamina to reduce lamin–chromatin contacts, while the formin mDia2 regulates nuclear actin to alter chromatin organization.
Swathi Packirisamy +4 more
wiley +1 more source
Network Coherence in a Family of Book Graphs
Frontiers in Physics, 2020 In this paper, we study network coherence characterizing the consensus behaviors with additive noise in a family of book graphs. It is shown that the network coherence is determined by the eigenvalues of the Laplacian matrix.
Jing Chen, Yifan Li, Weigang Sun
doaj +1 more source
Advanced Science, EarlyView.INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv +11 more
wiley +1 more source