Generalized second law of thermodynamics in scalar-tensor gravity

Within the context of scalar-tensor gravity, we explore the generalized second law (GSL) of gravitational thermodynamics. We extend the action of ordinary scalar-tensor gravity theory to the case in which there is a non-minimal coupling between the scalar field and the matter field (as chameleon field). Then, we derive the field equations governing the gravity and the scalar field. For a FRW universe filled only with ordinary matter, we obtain the modified Friedmann equations as well as the evolution equation of the scalar field. Furthermore, we assume the boundary of the universe to be enclosed by the dynamical apparent horizon which is in thermal equilibrium with the Hawking temperature. We obtain a general expression for the GSL of thermodynamics in the scalar-tensor gravity model. For some viable scalar-tensor models, we first obtain the evolutionary behaviors of the matter density, the scale factor, the Hubble parameter, the scalar field, the deceleration parameter as well as the effective equation of state (EoS) parameter. We conclude that in most of the models, the deceleration parameter approaches a de Sitter regime at late times, as expected. Also the effective EoS parameter acts like the LCDM model at late times. Finally, we examine the validity of the GSL for the selected models.Comment: 27 pages, 5 figure

Thermodynamics of apparent horizon in modified FRW universe with power-law corrected entropy

We derive the modified Friedmann equation corresponding to the power-law corrected entropy-area relation $S_{\rm A}=\frac{A}{4}[1-K_{\alpha} A^{1-\frac{\alpha}{2}}]$ which is motivated by the entanglement of quantum fields in and out of the apparent horizon. We consider a non-flat modified FRW universe containing an interacting viscous dark energy with dark matter and radiation. For the selected model, we study the effect of the power-law correction term to the entropy on the dynamics of dark energy. Furthermore, we investigate the validity of the generalized second law (GSL) of gravitational thermodynamics on the apparent horizon and conclude that the GSL is satisfied for $\alpha<2$.Comment: 12 pages, Accepted for Publication in JHE

Power Law Entropy Corrected New-Agegraphic Dark Energy in Ho\v{r}ava-Lifshitz Cosmology

We investigate the new agegraphic dark energy (NADE) model with power-law corrected entropy in the framework of Ho\v{r}ava-Lifshitz cosmology. For a non-flat universe containing the interacting power-law entropy-corrected NADE (PLECNADE) with dark matter, we obtain the differential equation of the evolution of density parameter as well as the deceleration parameter. To study parametric behavior, we used an interesting form of state parameter as function of redshift $\omega_{\Lambda}(z)=\omega_0+\omega_1 z$. We found that phantom crossing occurs for the state parameter for a non-zero coupling parameter, thus supporting interacting dark energy model.Comment: 13 pages, 2 figures, accepted for publication in 'Canadian J. Phys.

Non-parametric contextual stochastic search

Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks