Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

Accurate and reliable tracking of the 3D position of human heads is a continuing research problem in computer vision. This paper addresses the specific problem of model-based tracking with a generic deformable 3D head model. Following the work of Vetter and Blanz, a collection of head models is obtained from a 3D scanner, registered and parameterized to give a generic head model which is linear...

We consider the classical LINEAR OPTIMIZATION problem, but in the Turing rather than the REAL-RAM model. Asking for mere computability of a function’s maximum over some closed domain, we show that the common presumptions ‘full-dimensional’ and ‘bounded’ in fact cannot be omitted: The sound framework of Recursive Analysis enables us to rigorously prove this folkloristic observation! On the other...

Ultrasonic vibration assisted single point incremental forming (UVaSPIF) is based on localized plastic deformation in a sheet metal blank. It consists to deform gradually and locally the sheet metal using vibrating hemispherical-head tool controlled by a CNC milling machine. The ultrasonic excitation of forming tool reduces the vertical component of forming force. In addition, application of ul...

We introduce new parametrized classes of shape admissible domains in $\mathbb{R}^n$, $n\geq 2$, and prove that they are compact with respect to the convergence sense characteristic functions, Hausdorff sense, compacts, weak their boundary volumes. The these bounded $(\varepsilon,\infty)$-domains possibly fractal boundaries can have parts any nonuniform dimension greater than or equal $n-1$ less...

Local search techniques have proved to be very efficient in evolutionary multi-objective optimization(MOO). However, the reasons behind the success of local search in MOO have not yet been well discussed. This paper attempts to investigate empirically the main factors that may have contributed significantly to the success of local search in MOO. It is found that for many widely used test proble...

Computing a bijective spherical parametrization of a genus-0 surface with low distortion is a fundamental task in geometric modeling and processing. Current methods for spherical parametrization cannot, in general, control the worst case distortion of all triangles nor guarantee bijectivity. Given an initial bijective spherical parametrization, with high distortion, we develop a non-linear cons...

In this paper, an algorithm for sparse channel estimation, called ‘1-regularized leastabsolutes (‘1-LA), and an algorithm for equalization, called linear least-absolutes (LLA), in non-Gaussian impulsive noise are proposed. The proposed approaches are based on the minimization of the absolute error function, rather than the squared error function. By replacing the standard modulus with the ‘1-mo...

This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm can handle both general convex (possibly non-smooth) regularizers and general convex constraints. When the empirical data loss is strongly convex, we establish linear convergence rate, give explicit expr...