Results 211 to 220 of about 38,023 (256)

Characterizing the spatial patterns and determinants of cerebrospinal fluid pseudorandom flow in the human brain with low b-value diffusion MRI. [PDF]

Imaging Neurosci (Camb)
Nazeri A, Hosseini H, Dehkharghanian T, Lindsay KE, LaMontagne P, Shimony JS, Benzinger TLS, Sotiras A.   +7 more
europepmc   +1 more source

Direct Gravity‐Induced Modulation of Cardiac Conduction Pathways Evidenced Through Propagation Features in Electrophysiological Mapping

Advanced Materials Technologies, EarlyView.
A high‐density wearable body‐surface potential mapping array reveals how gravity reshapes cardiac conduction in real time. By resolving spatiotemporal delay patterns invisible to conventional ECG, the platform uncovers posture‐dependent electrophysiological adaptations across the thorax.
Ruben Ruiz‐Mateos Serrano, Matias L. Picchio, Daniele Mantione, David Mecerreyes, George G. Malliaras   +4 more
wiley   +1 more source

SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition. [PDF]

BioData Min
Huo Y, Chan WH, Amerhaider Nuar ANB, Gao H.   +3 more
europepmc   +1 more source

The structure is the message: Preserving experimental context through tensor decomposition. [PDF]

Cell Syst
Tan ZC, Meyer AS.
europepmc   +1 more source

Tensor decomposition of transportation temporal and spatial big data: A brief review. [PDF]

Fundam Res
Li L, Lin X, Ran B, Du B.
europepmc   +1 more source

ChromaFactor: Deconvolution of single-molecule chromatin organization with non-negative matrix factorization. [PDF]

PLoS Comput Biol
Gunsalus LM, Keiser MJ, Pollard KS.
europepmc   +1 more source

A data-driven AI framework for personalized diagnosis, prognosis, and therapeutic optimization in chronic disease management using multimodal big data analytics. [PDF]

Front Mol Biosci
Zhang Y, Song Z, Cai Q.
europepmc   +1 more source

Tucker Decomposition-Based Feature Selection and SSA-Optimized Multi-Kernel SVM for Transformer Fault Diagnosis. [PDF]

Sensors (Basel)
Wang L, Liu X.
europepmc   +1 more source

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VARIATIONAL BAYESIAN PARTIALLY OBSERVED NON-NEGATIVE TENSOR FACTORIZATION

2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018
Non-negative matrix and tensor factorization (NMF/NTF) have become important tools for extracting part based representations in data. It is however unclear when an NMF or NTF approach is most suited for data and how reliably the models predict when trained on partially observed data.
Jesper L. Hinrich, Soren F. V. Nielsen, Kristoffer H. Madsen, Morten Morup   +3 more
openaire   +2 more sources