Enhanced thermoelectric figure of merit in vertical graphene junctions

In this work, we investigate thermoelectric properties of junctions consisting of two partially overlapped graphene sheets coupled to each other in the cross-plane direction. It is shown that because of the weak van-der Waals interactions between graphene layers, the phonon conductance in these junctions is strongly reduced, compared to that of single graphene layer structures, while their electrical performance is weakly affected. By exploiting this effect, we demonstrate that the thermoelectric figure of merit can reach values higher than 1 at room temperature in junctions made of gapped graphene materials, for instance, graphene nanoribbons and graphene nanomeshes. The dependence of thermoelectric properties on the junction length is also discussed. This theoretical study hence suggests an efficient way to enhance thermoelectric efficiency of graphene devices.Comment: 6 pages, 4 figures, submitte

A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well defined parameter space. In some problems, such parameter spaces are defined using function approximation in which feature functions are manually defined. Therefore, the performance of those techniques strongly depends on the quality of chosen features. Hence, enabling CMA-ES to optimize on a more complex and general function class of the objective has long been desired. Specifically, we consider modeling the input space for black-box optimization in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a functional optimization problem whose domain is a function space that enables us to optimize in a very rich function class. In addition, we propose CMA-ES-RKHS, a generalized CMA-ES framework, that performs black-box functional optimization in the RKHS. A search distribution, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive representation of the function and covariance operator is achieved with sparsification techniques. We evaluate CMA-ES-RKHS on a simple functional optimization problem and bench-mark reinforcement learning (RL) domains. For an application in RL, we model policies for MDPs in RKHS and transform a cumulative return objective as a functional of RKHS policies, which can be optimized via CMA-ES-RKHS. This formulation results in a black-box functional policy search framework

Coverage-Validity-Aware Algorithmic Recourse

Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models. Existing algorithmic recourse approaches often assume an invariant predictive model; however, the predictive model is usually updated upon the arrival of new data. Thus, a recourse that is valid respective to the present model may become invalid for the future model. To resolve this issue, we propose a novel framework to generate a model-agnostic recourse that exhibits robustness to model shifts. Our framework first builds a coverage-validity-aware linear surrogate of the nonlinear (black-box) model; then, the recourse is generated with respect to the linear surrogate. We establish a theoretical connection between our coverage-validity-aware linear surrogate and the minimax probability machines (MPM). We then prove that by prescribing different covariance robustness, the proposed framework recovers popular regularizations for MPM, including the $\ell_2$-regularization and class-reweighting. Furthermore, we show that our surrogate pushes the approximate hyperplane intuitively, facilitating not only robust but also interpretable recourses. The numerical results demonstrate the usefulness and robustness of our framework

Groundwater Exploitation Zoning Aiming at Management of Sustainable Groundwater Exploitation and Use in Ca Mau Peninsula, Vietnam

The research is financed by KC08.08/16-20: Study of measures for mitigating and adapting to drought and salinity intrusion as natural hazards in Camau peninsula, Ministry of Science and Technology, Vietnam Abstract Groundwater system in Camau Peninsula has 6 main aquifers (not including very poorly productive qh aquifer), of which 4 aquifers are predominantly exploited, namely qp2-3, qp1, n22 and n21; 2 minor aquifers are qp3 and n13. Although the aquifers are located over the area, due to complicated fresh/saline interfaces in sections, exploitation and protection of groundwater sources is dealing with many problems. In the paper, information of aquifers is systematized into a map of groundwater exploitation zoning on scale 1:200,000 for the purpose of supplying essential information of water sources management in each socio-economical zone. Keywords: Camau peninsula, potential exploitable groundwater reserve. DOI: 10.7176/JEES/10-4-04 Publication date: April 30th 202