Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function ...

In this paper, an efficient method is presented for solving two dimensional Fredholm and Volterra integral equations of the second kind. Chebyshev polynomials are applied to approximate a solution for ...

A variety of linear models are available to represent common active electronic devices such as transistors and vacuum tubes. Devices operating under large-signal conditions often require nonlinear ...

Phase II randomized clinical trial comparing albumin-bound docetaxel vs. docetaxel in patients with previously treated advanced gastric or gastroesophageal junction adenocarcinoma. This is an ASCO ...

A python implementation of the algorithm used to generate optimal piecewise linear approximations of convex functions proposed by Imamoto and Tang [1]. The algorithm uses an iterative search to find ...

Deep Reinforcement Learning (RL) powered by neural net approximation of the Q function has had enormous empirical success. While the theory of RL has traditionally focused on linear function ...

The state of a molecular system can be described in terms of collective variables. These low-dimensional descriptors of molecular structure can be used to monitor the state of the simulation, to ...

Assessing and modeling precipitation in mountainous areas remains a major challenge in glacier mass balance modeling. Observations are typically scarce and reanalysis data and similar climate products ...