Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception.

In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage.

Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks.

Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules.

A Bayesian estimation method for variational phase-field fracture problems.

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach.

A multilevel Monte Carlo finite element method for the stochastic Cahn-Hilliard-Cook equation.

In this paper, we employ the multilevel Monte Carlo finite element method to solve the stochastic Cahn-Hilliard-Cook equation. The Ciarlet-Raviart mixed finite element method is applied to solve the fourth-order equation. In order to estimate the mild solution, we use finite elements for space discretization and the semi-implicit Euler-Maruyama method in time.

A new method for selective functionalization of silicon nanowire sensors and Bayesian inversion for its parameters.

In this work, a modification procedure for the functionalization of silicon nanowire (SiNW) is applied in biological field effect transistor (BioFET) system. The proposed method precedes the silanization reaction in a manner that the only SiNW and not its SiO substrate is functionalized by (3-Aminopropyl) triethoxysilane (APTES) initiators. This method has an effective role in increasing the sensitivity of BioFET sensors and can be applied in commercial ones.

Optimal design of nanowire field-effect troponin sensors.

We propose a design strategy for affinity-based biosensors using nanowires for sensing and measuring biomarker concentration in biological samples. Such sensors have been shown to have superior properties compared to conventional biosensors in terms of LOD (limit of detection), response time, cost, and size. However, there are several parameters affecting the performance of such devices that must be determined.