相关论文: Solving the $H_{0}$ tension in $f(T)$ Gravity thro…

Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expert information on robot dynamics. Bayesian reinforcement…

Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed to address the problem at hand. We introduce a…

Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their…

The exponential $f(R)$ gravity model provides a theoretically well-motivated extension of General Relativity, introducing a modified gravitational dynamics at late times consistent with a dynamical dark energy scenario, while recovering the…

Despite increasingly precise observations and sophisticated theoretical models, the discrepancy between measurements of H0 from the cosmic microwave background or from Baryon Acoustic Oscillations combined with Big-Bang Nucleosynthesis…

Lithium-ion batteries are a key energy storage technology driving revolutions in mobile electronics, electric vehicles and renewable energy storage. Capacity retention is a vital performance measure that is frequently utilized to assess…

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks…

We present a unified Bayesian framework to jointly constrain the Hubble constant $H_0$ and the post-Newtonian parameter $\gamma$, a key probe of deviations from general relativity, using the population characteristics of strongly lensed…

Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore…

The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining…

Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted by a expert designer and represent heuristics for relatively…

A well-known approach to describe the dynamics of an open quantum system is to compute the master equation evolving the reduced density matrix of the system. This approach plays an important role in describing excitation transfer through…

Measured time delays between the images of a gravitationally lensed source can lead to a determination of the Hubble constant ($H_o$), but only if the lensing mass distribution is well understood. The inability to sufficiently constrain…

This study investigates the evolution of Friedmann-Robertson-Walker (FRW) cosmological models within the $f(Q)$ gravity framework, utilizing a specific $f(Q)$ formulation and a novel Hubble parameter $H(z)$ parameterization to probe the…

We report how to alleviate both the $H_0$ and $\sigma_8$ tensions simultaneously within $f(T)$ gravity. In particular, we consider the parametrization $f(T)=-T-2\Lambda/M_P^2+\alpha T^\beta$, where two out of the three parameters are…

This study proposes a unified framework comprising two complementary approaches to constrain three functional forms of $f(T,B)$ gravity, namely the linear, quadratic, and general power law models, by jointly utilizing early and late…

We present, to our knowledge, the first systematic study of early-late cosmic evolution and acceleration in the framework of $f(T,\mathcal{L}_m)$ gravity, an extension of teleparallel theories coupling torsion with the matter Lagrangian. By…

To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are then selected through best-of-$n$ (BoN) sampling or the LLM…

Modified theories of gravity encompass a class of $f(R)$-models that seek to elucidate the observed late time accelerated expansion of the universe. In this study, we examine a set of viable $f(R)$ models (Hu-Sawicki: two cases,…

Efficiently tackling multiple tasks within complex environment, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions, such as reinforcement learning (RL).…