Chaoticity and Dissipation of Nuclear Collective Motion in a Classical Model

We analyze the behavior of a gas of classical particles moving in a two-dimensional "nuclear" billiard whose multipole-deformed walls undergo periodic shape oscillations. We demonstrate that a single particle Hamiltonian containing coupling terms between the particles' motion and the collective coordinate induces a chaotic dynamics for any multipolarity, independently on the geometry of the billiard. The absence of coupling terms allows us to recover qualitatively the "wall formula" predictions. We also discuss the dissipative behavior of the wall motion and its relation with the order-to-chaos transition in the dynamics of the microscopic degrees of freedom.Comment: LateX, 11 pages, 7 figures available on request, to appear in the Proceedings of XXXIV Winter Meeting on Nuclear Physics, Bormio 22-27 January, 199

Hybrid neutron stars within the Nambu-Jona-Lasinio model and confinement

Recently, it has been shown that the standard Nambu-Jona-Lasinio (NJL) model is not able to reproduce the correct QCD behavior of the gap equation at large density, and therefore a different cutoff procedure at large momenta has ben proposed. We found that, even with this density dependent cutoff procedure, the pure quark phase in neutron stars (NS) interiors is unstable, and we argue that this could be related to the lack of confinement in the original NJL model.Comment: 2 pages, 1 figure, to be published in the proceedings of the conference EXOCT07, Catania, 11-15 June, 200

An open source research framework for IoT-capable smart traffic lights

Recent technological advances are completely reshaping the way we build our cities, and the way we enjoy them. Future smart cities will employ a number of smart sensors, which cooperatively work to deliver advanced services that improve security and quality of life. The capability of deploying and testing such technologies directly on-the-field is paramount to research, however comes with a significant effort in terms of time and price. For this reason, we introduce an opensource design framework for highly-connected smart sensors, and we implemented it in an advanced controller for traffic light, providing a single component to support researchers and engineers from the earliest stages of development in laboratories till on-the-field research and testing

Time development of a density perturbation in the unstable nuclear matter

We present the solution of the time development of an unstable initial density perturbation in the linearized Vlasov equation, completing the previous analysis in the literature. The additional contributions found are usually damped and can be neglected at large times in the unstable region. The work clarifies also the problem of the normalization of the solution with respect to the initial perturbation of the density.Comment: revision of the discussion, different initial perturbation, 9 pages, 4 figures included, uses epsfi

Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy

Artificial Neural Networks, as the name itself suggests, are biologically inspired algorithms designed to simulate the way in which the human brain processes information. Like neurons, which consist of a cell nucleus that receives input from other neurons through a web of input terminals, an Artificial Neural Network includes hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), and are organized in layers. The power of neural computations comes from connecting neurons in a network: in fact, in an Artificial Neural Network it is possible to manage a different number of information at the same time. What is not fully understood is which is the most efficient way to train an Artificial Neural Network, and in particular what is the best mini-batch size for maximize accuracy while minimizing training time. The idea that will be developed in this study has its roots in the biological world, that inspired the creation of Artificial Neural Network in the first place. Humans have altered the face of the world through extraordinary adaptive and technological advances: those changes were made possible by our cognitive structure, particularly the ability to reasoning and build causal models of external events. This dynamism is made possible by a high degree of curiosity. In the biological world, and especially in human beings, curiosity arises from the constant search of knowledge and information: behaviours that support the information sampling mechanism range from the very small (initial mini-batch size) to the very elaborate sustained (increasing mini-batch size). The goal of this project is to train an Artificial Neural Network by increasing dynamically, in an adaptive manner (with validation set), the mini-batch size; our hypothesis is that this training method will be more efficient (in terms of time and costs) compared to the ones implemented so far

Motion Sickness Minimization Alerting System Using The Next Curvature Topology

Current intelligent car prototypes increasingly move to become autonomous where no driver is required. If an automated vehicle has rearward and forward facing seats and none of the passengers pay attention to the road, they increasingly experience the motion sickness because of the inability of passengers to anticipate the future motion trajectory. In this paper, we focus on anticipatory audio and video cues using pleasant sounds and a Human Machine Interface to display and inform the passengers about the upcoming trajectories that may lead to make the passengers sick. To be able to anticipate the next moves, we require an evaluation system of the next 1 kilometer of the road using the map. The road is investigated based on the amount of the turns and the maximum speed allowed that lead to lateral accelerations that is high enough based on Motion Sickness Dose Value to make the passengers sick. The system alerts the passengers through a Human Machine Interface to focus on the road for prevention of the Motion Sickness. We evaluate our method by using Motion Sickness Dose Value. Based on this work, we can prevent the sickness due to lateral accelerations by making the passengers to focus on the road and decrease the vestibular conflict

Heavy Ion Dynamics and Neutron Stars

Some considerations are reported, freely inspired from the presentations and discussions during the Beijing Normal University Workshop on the above Subject, held in July 2007. Of course this cannot be a complete summary but just a collection of personal thougths aroused during the meeting.Comment: 11 pages, no figures, Summary Talk, Int.Workshop on "Nuclear Dynamics in Heavy Ion Collisions and Neutron Stars", Beijing Normal Univ. July 07, to appear in Int.Journ.Modern Physics E (2008