Aspect-oriented programming (AOP) has been designed to provide a better separation of concerns at development level by modularizing concerns that would otherwise be tangled and scattered across other concerns. Current mainstream AOP techniques separate crosscutting concerns on a syntactic basis whereas a concern is more a semantic matter. Therefore, a different, more semanticoriented, approach ...

The Neumann system on the 2-dimensional sphere is used as a tool to convey some ideas on the bi-Hamiltonian point of view on separation of variables. It is shown that, from this standpoint, its separation coordinates and its integrals of motion can be found in a systematic way.

In this paper, we prove a nonlinear separation associated with a constrained multiobjective optimization problem in the image space, as well as the equivalence between the existence of nonlinear separation function and a saddle point condition for a generalized Lagrangian function associated with the given problem. As applications, we obtain some equivalent conditions on exact penalty methods f...

a new approach for a cloud point extraction-electrothermal atomic absorption spectrometric method was used for determining bismuth. the aqueous analyte was acidified with sulfuric acid (ph 3.0–3.5). triton x-114 was added as a surfactant and 2-amino-cyclopentene-1-dithiocarboxylic acid was used as a complexing agent. after phase separation at 50 ◦ c based on the cloud point separation of the mi...

We consider and analyze properties of large sets of randomly selected (i.i.d.) points in high dimensional spaces. In particular, we consider the problem of whether a single data point that is randomly chosen from a finite set of points can be separated from the rest of the data set by a linear hyperplane. We formulate and prove stochastic separation theorems, including: 1) with probability clos...

It has recently been shown that the logarithmic barrier method for solving nite-dimensional, linearly constrained quadratic optimization problems can be extended to an innnite-dimensional setting with complexity estimates similar to the nite dimensional case. As a consequence, an eecient computational method for solving the linearly constrained LQ control problem is now a vailable. In this pape...

We examine optimal Linear Quadratic Gaussian control for a system in which communication between the sensor (output of the plant) and the controller occurs across a packet-dropping link. We extend the familiar LQG separation principle to this problem that allows us to solve this problem using a standard LQR state-feedback design, along with an optimal algorithm for propagating and using the inf...

Machine learning systems are increasingly used to make decisions about people’s lives, such as whether to give someone a loan or whether to interview someone for a job. This has led to considerable interest in making such machine learning systems fair. One approach is to transform the input data used by the algorithm. This can be achieved by passing each input data point through a representatio...

In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian distribution and the von Mises distribution, proposed to model sparse directional data. The distribution’s parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Di...