Results 261 to 270 of about 163,993 (328)

Algebraic cycles and crystalline cohomology [PDF]

open access: green, 2015
Amalendu Krishna, Jinhyun Park
openalex  

Differentiable River Routing for End‐to‐End Learning of Hydrological Processes

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Deep Learning (DL) approaches have shown high accuracy in rainfall runoff modeling. Currently, however, large‐scale DL hydrological simulations at national and global scales still rely on external routing schemes to propagate runoff outputs through river networks, preventing them from leveraging the benefits of end‐to‐end learning of ...
Tristan Hascoet   +3 more
wiley   +1 more source

Comparing Dynamic Versus Pre‐Planned Combinations of Individual and Collaborative Learning in a Classroom Experiment

open access: yesJournal of Computer Assisted Learning, Volume 42, Issue 1, February 2026.
ABSTRACT Background Traditionally, combining individual and collaborative learning happens in a pre‐planned and synchronised manner where the whole class switches between activities at the same time. However, in an era where personalised learning is showing great promise, a more dynamic way of combining the two activities may lead to better learning ...
Kexin Bella Yang   +4 more
wiley   +1 more source

Looking Back to 1991 Economic Forecasting: Introducing Cointegration

open access: yesOxford Bulletin of Economics and Statistics, Volume 88, Issue 1, Page 1-21, February 2026.
ABSTRACT Originally written in 1991 to advance the formal analysis of macroeconomic forecasting models and methods following the development of cointegration, alternative forecasting devices, conditional and unconditional forecasts, and data accuracy are considered.
David F. Hendry
wiley   +1 more source

Robust estimation of a Markov chain transition matrix from multiple sample paths

open access: yesStatistica Neerlandica, Volume 80, Issue 1, February 2026.
Markov chains are fundamental models for stochastic dynamics, with applications in a wide range of areas such as population dynamics, queueing systems, reinforcement learning, and Monte Carlo methods. Estimating the transition matrix and stationary distribution from observed sample paths is a core statistical challenge, particularly when multiple ...
Lasse Leskelä, Maximilien Dreveton
wiley   +1 more source

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