Results 31 to 40 of about 8,432 (220)

On Metric Choice in Dimension Reduction for Fréchet Regression

open access: yesInternational Statistical Review, EarlyView.
Fréchet regression is becoming a mainstay in modern data analysis for analysing non-traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous ...
Soale, Abdul Nasah   +2 more
core   +2 more sources

Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations

open access: yesAdvanced Electronic Materials, EarlyView.
This work presents a throat‐mounted mechanoelectric sensor integrated with an ion‐gel/graphene reservoir device for on‐device speech recognition. The system converts raw biomechanical vibrations into rich nonlinear current dynamics, enabling efficient classification through a simple linear readout. The approach highlights a compact and tunable physical‐
Daiki Nishioka   +5 more
wiley   +1 more source

Classification of lactose and mandelic acid THz spectra using subspace and wavelet-packet algorithms [PDF]

open access: yes, 2007
This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their respective THz transients.
Walker, G.   +26 more
core   +1 more source

A Kinetic–Energetic Bottleneck of Charge‐Transfer Injection Governs Energy Loss in Organic Solar Cells

open access: yesAdvanced Energy Materials, EarlyView.
Kinetic–energetic projection of time‐resolved photoluminescence reveals that charge‐transfer injection acts as a universal bottleneck in organic solar cells. A physics‐constrained Bayesian framework identifies an emergent effective CT injection rate governing the trade‐off between charge generation and nonradiative energy loss.
Rong Wang   +16 more
wiley   +1 more source

The ensemble conditional variance estimator for sufficient dimension reduction

open access: yes, 2022
Ensemble Conditional Variance Estimation (ECVE) is a novel sufficient dimension reduction (SDR) method in regressions with continuous response and predictors.
Fertl, Lukas, Bura, Efstathia
core   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah   +3 more
wiley   +1 more source

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

open access: yesAdvanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows   +7 more
wiley   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

open access: yesAdvanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez   +4 more
wiley   +1 more source

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