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Kinetic information extracted from biochemical methane potential (BMP) tests is often reported but its value is unclear. Inter-laboratory reproducibility provides a useful indication of its value. Here we extracted estimates of the first-order rate constan ...
Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...
In this paper, the charged polymer escapement phenomenon, via a little hole of nano-metric dimensions arranged in a constitutive biological membrane, is studied. We will present the case of the transport process of an ideal polymer in a 3-dimensional exten ...
We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully ...
This paper presents the open-source Python-based parameter extractor (SEKV-E) for the simplified EKV (sEKV) model, which enables the modern low-power circuit designs with the inversion coefficient design methodology. The tool extracts the essential sEKV pa ...
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...
Crack formation and development in reinforced concrete structures is a complex mechanism affected by a large number of factors. Typically, the estimation of the crack width as a function of the reinforce-ment stress state is based on simplified assumptions ...
A method is proposed to reconstruct the 3D molecular structure from micrographs collected at just one sample tilt angle in the random conical tilt scheme in cryo-electron microscopy. The method uses autocorrelation analysis on the micrographs to estimate f ...
Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) ...
Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...
