Results 181 to 190 of about 137,581,306 (207)

PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks. [PDF]

Acta Crystallogr D Struct Biol
Lombard V, Van JN, Grudinin S, Laine E.
europepmc   +1 more source

Differential effects of visual and tactile deprivation on spatially modulated cells in the medial entorhinal cortex. [PDF]

Nat Commun
Tian J, Wen S, Zhou Y, Hu N, Yao S, Zhang X, Liu Y, He Y, Wang Z, Miao C.   +9 more
europepmc   +1 more source

An emergent disease-associated motor neuron state precedes cell death in ALS. [PDF]

Cell
Gautier O, Blum JA, Nguyen TP, Cao S, Klemm S, Yamakawa M, Huh D, Hurt JA, Sinnott-Armstrong N, Zeng Y, Davis CO, Bombosch J, Liu C, Encarnacion LN, Guttenplan KA, Chen D, Kathiria A, Zhao L, Moore S, Meng A, Ong K, Cleveland DW, Ravits J, Rexach JE, Greenleaf WJ, Gitler AD.   +25 more
europepmc   +1 more source

Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus

de Vicente A, Mitelut C, Mendes RV, Marianelli L, Rosell MC, Bruckner D, Bardella G, Donato F.   +7 more
europepmc   +1 more source

A Sequential Semismooth Newton Method for the Nearest Low-Rank Correlation Matrix Problem

SIAM Journal on Optimization, 2011
Based on the well known result that the sum of largest eigenvalues of a symmetric matrix can be represented as a semidefinite programming problem (SDP), we formulate the nearest low-rank correlation matrix problem as a nonconvex SDP and propose a ...
Hou-Duo Qi
exaly   +2 more sources

Efficient Rank Reduction of Correlation Matrices

Linear Algebra and Its Applications, 2007
Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show, in numerical tests, that our methods compare favourably with the existing methods in the literature.
Patrick J. F. Groenen, Raoul Pietersz
exaly   +1 more source