Enhancing urban analysis through lacunarity multiscale measurement

Urban spatial configurations in most part of the developing countries showparticular urban forms associated with the more informal urban development ofthese areas. Latin American cities are prime examples of this sort, butinvestigation of these urban forms using up to date computational and analyticaltechniques are still scarce. The purpose of this paper is to examine and extendthe methodology of multiscale analysis for urban spatial patterns evaluation. Weexplain and explore the use of Lacunarity based measurements to follow a lineof research that might make more use of new satellite imagery information inurban planning contexts. A set of binary classifications is performed at differentthresholds on selected neighbourhoods of a small Brazilian town. Theclassifications are appraised and lacunarity measurements are compared in faceof the different geographic referenced information for the same neighbourhoodareas. It was found that even with the simple image classification procedure, animportant amount of spatial configuration characteristics could be extracted withthe analytical procedure that, in turn, may be used in planning and other urbanstudies purposes

Hierarchical modeling of molecular energies using a deep neural network

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network--a composition of many nonlinear transformations--acting on a representation of the molecule. HIP-NN achieves state-of-the-art performance on a dataset of 131k ground state organic molecules, and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty

New planetary and EB candidates from Campaigns 1-6 of the K2 mission

With only two functional reaction wheels, Kepler cannot maintain stable pointing at its original target field and entered a new mode of observation called K2. Our method is based on many years of experience in planet hunting for the CoRoT mission. Due to the unstable pointing, K2 light curves present systematics that are correlated with the target position in the CCD. Therefore, our pipeline also includes a decorrelation of this systematic noise. Our pipeline is optimised for bright stars for which spectroscopic follow-up is possible. We achieve a maximum precision on 6 hours of 6 ppm. The decorrelated light curves are searched for transits with an adapted version of the CoRoT alarm pipeline. We present 172 planetary candidates and 327 eclipsing binary candidates from campaigns 1, 2, 3, 4, 5 and 6 of K2. Both the planetary candidates and eclipsing binary candidates lists are made public to promote follow-up studies. The light curves will also be available to the community.Comment: 22 pages. 5 figures, 4 tables, Accepted for publication in A&

A new algorithm for generalized fractional programs

A new dual problem for convex generalized fractional programs with no duality gap is presented and it is shown how this dual problem can be efficiently solved using a parametric approach. The resulting algorithm can be seen as “dual†to the Dinkelbach-type algorithm for generalized fractional programs since it approximates the optimal objective value of the dual (primal) problem from below. Convergence results for this algorithm are derived and an easy condition to achieve superlinear convergence is also established. Moreover, under some additional assumptions the algorithm also recovers at the same time an optimal solution of the primal problem. We also consider a variant of this new algorithm, based on scaling the “dual†parametric function. The numerical results, in case of quadratic-linear ratios and linear constraints, show that the performance of the new algorithm and its scaled version is superior to that of the Dinkelbach-type algorithms. From the computational results it also appears that contrary to the primal approach, the “dual†approach is less influenced by scaling.fractional programming;generalized fractional programming;Dinkelbach-type algorithms;quasiconvexity;Karush-Kuhn-Tucker conditions;duality

Physical-Layer Security over Correlated Erasure Channels

We explore the additional security obtained by noise at the physical layer in a wiretap channel model setting. Security enhancements at the physical layer have been proposed recently using a secrecy metric based on the degrees of freedom that an attacker has with respect to the sent ciphertext. Prior work focused on cases in which the wiretap channel could be modeled as statistically independent packet erasure channels for the legitimate receiver and an eavesdropper. In this paper, we go beyond the state-of-the-art by addressing correlated erasure events across the two communication channels. The resulting security enhancement is presented as a function of the correlation coefficient and the erasure probabilities for both channels. It is shown that security improvements are achievable by means of judicious physical-layer design even when the eavesdropper has a better channel than the legitimate receiver. The only case in which this assertion may not hold is when erasures are highly correlated across channels. However, we are able to prove that correlation cannot nullify the expected security enhancement if the channel quality of the legitimate receiver is strictly better than that of the eavesdropper.Comment: 5 pages, 4 figures, submitted to ISIT 201