There is increasing experimental evidence that suggests the dynamics of glass forming liquids do not diverge at finite temperature above zero Kelvin, same time there has been recent progress in development non-diverging transition models. In this work we examine two models: elastically collective nonlinear Langevin equation theory (ECNLE) and shoving model, both which relate an energy barrier f...

In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which ...

We present new achievements on the use of deep convolutional neural networks (CNN) in the problem of pedestrian detection (PD). In this paper, we aim to address the following questions: (i) Given non-deep state-of-the-art pedestrian detectors (e.g. ACF, LDCF), is it possible to improve their top performances ?; (ii) is it possible to apply a pre-trained deep model to these detectors to boost th...

The importance of state estimation in fluid mechanics is well-established; it required for accomplishing several tasks including design/optimization, active control, and future prediction. A common tactic this regards to rely on reduced order models. Such approaches, general, use measurement data one-time instance. However, oftentimes available from sensors sequential ignoring results informati...