Results 261 to 270 of about 163,993 (328)
LWLCM: A novel lightweight stream cipher using logistic chaos function and multiplexer for IoT communications. [PDF]
Afzal S +5 more
europepmc +1 more source
Differentiable River Routing for End‐to‐End Learning of Hydrological Processes
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
Global stabilization of Boolean networks with applications to biomolecular network control. [PDF]
Rafimanzelat MR.
europepmc +1 more source
Coexistence of algebraic and non-algebraic limit cycles for quintic polynomial differential systems
Ahmed Bendjeddou, Rachid Cheurfa
openalex +1 more source
The Spectral Theory of Motives: Algebraic Cycles as Standing Waves in the Roughness Spectrum
Lee, Sunggil
openalex +1 more source
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
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
Simplex polynomial in complex networks and its applications to compute the Euler characteristic. [PDF]
Wang Z, Fu X, Deng B, Chen Y, Zhao H.
europepmc +1 more source
Robust estimation of a Markov chain transition matrix from multiple sample paths
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

