We simulate and experimentally demonstrate the optical single-pixel identification of two-dimensional (2D) objects by analyzing corresponding complex-valued (i.e, amplitude phase) Laguerre–Gaussian​ (LGl,p) spectra containing both azimuthal (i.e., l) radial p) indices. A sequence beams, each carrying a different superposition one LG mode fundamental Gaussian mode, propagate through objects. The...

We consider the problem of sequential estimation unknowns state-space and deep models that include functions latent processes models. The proposed approach relies on Gaussian are implemented via random feature-based processes. In these models, we have two sets unknowns, highly nonlinear (the values processes) conditionally linear constant parameters processes). present a method based particle f...

In this paper, a fast and parallelizable method based on Gaussian processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs) through evolution pairs massive stars. Two obstacles arise in application are priori unknown conditions BBH large scale simulation data. We address them by proposing local emulator which combines GP classifier regress...

Enterprises currently employ Cloud services to improve the scalability of their services and resource providers strategically price resources to maximize their utilities. While Nash equilibrium is the dominant concept for studying such kind of interaction, evolutionary game theory seems more appropriate for modeling agents’ strategic interactions as it relaxes many strong assumptions. This pape...

This paper proposes the use of Gaussian Mixture Models to estimate conditional probability density functions in an environmental risk mapping context. A conditional Gaussian Mixture Model has been compared to the geostatistical method of Sequential Gaussian Simulations and shows good performances in reconstructing local PDF. The data sets used for this comparison are parts of the digital elevat...