Results 121 to 130 of about 9,473 (177)

Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models. [PDF]

PLoS Comput Biol
Dommanget-Kott M, Fernandez-de-Cossio-Diaz J, Coraggioso M, Bormuth V, Monasson R, Debrégeas G, Cocco S.   +6 more
europepmc   +1 more source

Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice

Biological Reviews, Volume 101, Issue 5, Page 2379-2407, October 2026.
ABSTRACT Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip‐dating approaches, including fossil data, for inference of time‐scaled trees ...
Melanie J. Hopkins, Mark C. Nikolic, James D. Holmes, Daniela S. Monti, Ernesto E. Vargas‐Parra, Russell D.C. Bicknell, Gregory D. Edgecombe, Katherine Jordan‐Burmeister, John R. Paterson, Shravya Srivastava‐Losey   +9 more
wiley   +1 more source

Adaptive Phoneme State Learning Architecture for Enhanced Speech Recognition Using Backpropagation Neural Network and Hidden Markov Model. [PDF]

F1000Res
Siddalingappa R, S D, Savitha M, P K, Stella Mary I P, Gornale S, B A L, Li K, Wen Goh K.   +8 more
europepmc   +1 more source

Stochastic Gradient Descent in High Dimensions for Multi‐Spiked Tensor PCA

Communications on Pure and Applied Mathematics, Volume 79, Issue 10, Page 2291-2369, October 2026.
ABSTRACT We study the high‐dimensional dynamics of online stochastic gradient descent (SGD) for the multi‐spiked tensor model. This multi‐index model arises from the tensor principal component analysis (PCA) problem with multiple spikes, where the goal is to estimate the unknown signal vectors within the N$N$‐dimensional unit sphere through maximum ...
Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo   +2 more
wiley   +1 more source

Evolution of Methods for the Quantitative Assessment of Inbreeding in Livestock. [PDF]

Biology (Basel)
Getmantseva L, Bakoev S, Kolosova M, Usatov A, Amerkhanov K, Lukonina O.   +5 more
europepmc   +1 more source

Emergent Learner Agency in Implicit Human–AI Collaboration: How Supportive and Contrarian AI Personas Reshape Interaction

Journal of Computer Assisted Learning, Volume 42, Issue 5, October 2026.
ABSTRACT Background As agentic artificial intelligence (AI) systems move beyond tool‐like support toward more autonomous, team‐like roles, a central question concerns the extent to which such systems can meaningfully participate in collaborative learning.
Yueqiao Jin, Roberto Martinez‐Maldonado, Dragan Gašević, Xibin Han, Lixiang Yan   +4 more
wiley   +1 more source

Information Design for Early‐Stage Dose‐Finding Trials

Naval Research Logistics (NRL), Volume 73, Issue 7, Page 1141-1158, October 2026.
ABSTRACT To enhance enrollment rates in early‐stage dose‐finding clinical trials, we propose an information design approach, where the clinical investigator (CI) commits to an information releasing mechanism (IRM) based on the treatment's uncertain efficacy and toxicity to encourage patients to participate in the trial.
Amin Khademi, Ningyuan Chen
wiley   +1 more source

Adaptive Cognitive Intervention Architecture: An Exploratory Computational Framework for Precision Reading Comprehension in Higher Education. [PDF]

J Intell
Valentín Melgarejo TF, Oscátegui Nájera GJ, Hachoque Aguirre DM, Espinoza Apolinario U, Cruz Quinto IS, García Yale FA, Bernaldo Faustino LK, Ramos Pando C, Chacón Leandro J, Rivas Meza A, La Madrid Vivar PL, Alvarez Lopez JR, Valentín Melgarejo PL, Zenteno Ruiz FA.   +13 more
europepmc   +1 more source

Multi‐Objective Bayesian Co‐Optimization of Parameterized Moving Horizon Estimation and Model Predictive Control

International Journal of Robust and Nonlinear Control, Volume 36, Issue 15, Page 7193-7213, October 2026.
ABSTRACT This paper proposes a Machine Learning (ML)‐enabled estimator‐controller design framework, in which a parameterized Model Predictive Controller (MPC) and a parameterized Moving Horizon Estimator (MHE) are jointly refined using Bayesian Optimization (BO).
Hossein Nejatbakhsh Esfahani, Javad Mohammadpour Velni   +1 more
wiley   +1 more source