Fault Isolation By Manifold Learning
, 1985 This thesis investigates the possibility of improving black box fault diagnosis by a process called manifold learning, which simply stated is a way of finding patterns in recorded sensor data.
Thurén, Mårten
core
Advanced Science, EarlyView.Antibody–drug conjugates (ADCs) transform breast cancer therapy, yet resistance limits their durability. Emerging evidence reveals that ADC failure is not solely tumor‐intrinsic but shaped by dynamic tumor–microenvironment interactions that alter drug delivery, processing, and response.
Minji Seo, Jangsoon Lee, Naoto T. Ueno
wiley +1 more source
A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric. [PDF]
Sci RepNadjafikhah M, Nasiri M.
europepmc +1 more source
Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data. [PDF]
BMC Bioinformatics, 2023 Wang Z +5 more
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A Perspective on Interactive Theorem Provers in Physics
Advanced Science, EarlyView.Into an interactive theorem provers (ITPs), one can write mathematical definitions, theorems and proofs, and the correctness of those results is automatically checked. This perspective goes over the best usage of ITPs within physics and motivates the open‐source community run project PhysLean, the aim of which is to be a library for digitalized physics
Joseph Tooby‐Smith
wiley +1 more source
Deep manifold learning reveals hidden developmental dynamics of a human embryo model. [PDF]
Sci AdvChen K +5 more
europepmc +1 more source
Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning. [PDF]
Int J Mol Sci, 2022 Wu Z, Chen E, Zhang S, Ma Y, Mao Y.
europepmc +1 more source
Non-Isometric Manifold Learning: Analysis and an Algorithm
, 2008 In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or ‘unrolling ’ of a manifold into a lower dimensional space.
Vincent Rabaud +2 more
core
Solid Harmonic Wavelet Bispectrum for Image Analysis
Advanced Science, EarlyView.The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown +3 more
wiley +1 more source
Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers. [PDF]
Biosensors (Basel)Kucukselbes H, Sayilgan E.
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