BTZ Black Hole Entropy in Loop Quantum Gravity and in Spin Foam Models

We present a comparison of the calculation of BTZ black hole entropy in loop quantum gravity and in spin foam models. We see that both give the same answer.Comment: 10 pages, 3 figures, Final version, improve

Black Hole Entropy in Loop Quantum Gravity and Number Theory

We show that counting different configurations that give rise to black hole entropy in loop quantum gravity is related to partitions in number theory.Comment: 6 page

Authority of State and Local Police to Enforce Federal Immigration Law

[Excerpt] This report discusses the authority of state and local law enforcement to assist in the enforcement of federal immigration law through the investigation and arrest of persons believed to have violated such laws. It describes current provisions in federal law that permit state and local police to enforce immigration law directly, analyzes major cases concerning the ability of states and localities to assist in immigration enforcement, and briefly examines opinions on the issue by the Office of Legal Counsel (OLC) within the Department of Justice. This report does not discuss legal issues raised by states and localities enacting their own immigration-related laws, including measures intended to supplement federal law through the imposition of additional criminal or civil penalties

Entropy in Spin Foam Models: The Statistical Calculation

Recently an idea for computing the entropy of black holes in the spin foam formalism has been introduced. Particularly complete calculations for the three dimensional euclidean BTZ black hole were done. The whole calculation is based on observables living at the horizon of the black hole universe. Departing from this idea of observables living at the horizon, we now go further and compute the entropy of BTZ black hole in the spirit of statistical mechanics. We compare both calculations and show that they are very interrelated and equally valid. This latter behaviour is certainly due to the importance of the observables.Comment: 11 pages, 1 figur

Human and Object Recognition with a High-resolution tactile sensor

This paper 1 describes the use of two artificial intelligence methods for object recognition via pressure images from a high-resolution tactile sensor. Both meth- ods follow the same procedure of feature extraction and posterior classification based on a supervised Supported Vector Machine (SVM). The two approaches differ on how features are extracted: while the first one uses the Speeded-Up Robust Features (SURF) descriptor, the other one employs a pre-trained Deep Convolutional Neural Network (DCNN). Besides, this work shows its applica- tion to object recognition for rescue robotics, by distinguishing between differ- ent body parts and inert objects. The performance analysis of the proposed methods is carried out with an experiment with 5-class non-human and 3-class human classification, providing a comparison in terms of accuracy and compu-tational load. Finally, it is discussed how feature-extraction based on SURF can be obtained up to five times faster compared to DCNN. On the other hand, the accuracy achieved using DCNN-based feature extraction can be 11.67% superior to SURF.Proyecto DPI2015-65186-R European Commission under grant agreement BES-2016-078237. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech