Results 131 to 140 of about 6,366,089 (278)
Augmentation invariant manifold learning
Journal of the Royal Statistical Society Series B: Statistical MethodologyAbstract Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications.
openaire +3 more sources
Advanced Materials, EarlyView.The point‐of‐care diagnosis of Clostridioides difficile infection remains challenging due to reliance on slow culture and amplification‐based methods. A lab‐in‐a‐tube electrochemical biosensor integrating DNAzymes, antifouling magnetic beads, and a hierarchically structured flow cell enables amplification‐ and culture‐free detection directly in stool ...
Survanshu Saxena +10 more
wiley +1 more source
Advanced Materials Interfaces, EarlyView.Interfacial charge transfer and low‐resistance interphase formation between PEO‐based polymer and Li10GeP2S12 solid electrolytes are investigated using multi‐electrode impedance spectroscopy and advanced analytical techniques such as XPS and ToF‐SIMS.
Ujjawal Sigar +6 more
wiley +1 more source
Manifold Learning From Time Series [PDF]
, 2006 This thesis addresses the problem of learning manifold from time series. We use the mixtures of probabilistic principal component analyzers (MPPCA) to model the nonliner manifold. In addition, we extend the MPPCA model by aligning the PCA coe.cients from
Lin, Ruei-Sung
core
Advanced Materials Technologies, EarlyView.A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn +2 more
wiley +1 more source
Advanced Materials Technologies, EarlyView.An O radical‐induced n‐to‐p transition in PbS quantum dots (QDs) is achieved via atmospheric Ar/O2 treatment, enabling favorable band alignment and stable photogating in PbS QD/IGZO phototransistors. Interfacial defect passivation and Fermi‐level modulation, confirmed by XPS and UPS analyses, effectively suppress dark current, induce a positive VTH ...
MD Redowan Mahmud Arnob +4 more
wiley +1 more source
Estimation of smooth vector fields on manifolds by optimization on Stiefel group
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 Real data are usually characterized by high dimensionality. However, real data obtained from real sources, due to the presence of various dependencies between data points and limitations on their possible values, form, as a rule, form a small part of the
E.N. Abramov, Yu.A. Yanovich
doaj
Programmable Pneumatic Actuator System for a Bioinspired Artificial Colon
Advanced Materials Technologies, EarlyView.A modular soft robotic colon simulator driven by programmable pneumatic actuation is developed to mimic physiological and pathological‐like motility patterns of the large intestine. Finite element–guided design and distributed control enable anatomically realistic deformation for peristalsis, segmentation, and mass movements, supporting capsule ...
Andrew Bickerdike +6 more
wiley +1 more source
Low‐Pressure Plasma‐Based Wrinkling of PDMS and Machine Learning‐Driven Property Engineering
Advanced Materials Technologies, EarlyView.Wrinkled surfaces are well‐suited for controlled surface deformations in the µm range. The key challenge is the relation between the resulting wrinkle features and the necessary process conditions. Machine learning techniques have solved the prediction and inverse design problems for various preparation conditions, opening a precisely controlled ...
Fabian Kopsch +7 more
wiley +1 more source
Enhancing cluster analysis via topological manifold learning [PDF]
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: we show that clustering embedding vectors representing the inherent ...
Scheipl, Fabian +3 more
core +1 more source