A Polyhedral Homotopy Algorithm For Real Zeros

We design a homotopy continuation algorithm, that is based on numerically tracking Viro's patchworking method, for finding real zeros of sparse polynomial systems. The algorithm is targeted for polynomial systems with coefficients satisfying certain concavity conditions. It operates entirely over the real numbers and tracks the optimal number of solution paths. In more technical terms; we design an algorithm that correctly counts and finds the real zeros of polynomial systems that are located in the unbounded components of the complement of the underlying A-discriminant amoeba.Comment: some cosmetic changes are done and a couple of typos are fixed to improve readability, mathematical contents remain unchange

Snapping Graph Drawings to the Grid Optimally

In geographic information systems and in the production of digital maps for small devices with restricted computational resources one often wants to round coordinates to a rougher grid. This removes unnecessary detail and reduces space consumption as well as computation time. This process is called snapping to the grid and has been investigated thoroughly from a computational-geometry perspective. In this paper we investigate the same problem for given drawings of planar graphs under the restriction that their combinatorial embedding must be kept and edges are drawn straight-line. We show that the problem is NP-hard for several objectives and provide an integer linear programming formulation. Given a plane graph G and a positive integer w, our ILP can also be used to draw G straight-line on a grid of width w and minimum height (if possible).Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Radio-quiet and radio-loud pulsars: similar in Gamma-rays but different in X-rays

We present new Chandra and XMM-Newton observations of a sample of eight radio-quiet Gamma-ray pulsars detected by the Fermi Large Area Telescope. For all eight pulsars we identify the X-ray counterpart, based on the X-ray source localization and the best position obtained from Gamma-ray pulsar timing. For PSR J2030+4415 we found evidence for an about 10 arcsec-long pulsar wind nebula. Our new results consolidate the work from Marelli et al. 2011 and confirm that, on average, the Gamma-ray--to--X-ray flux ratios (Fgamma/Fx) of radio-quiet pulsars are higher than for the radio-loud ones. Furthermore, while the Fgamma/Fx distribution features a single peak for the radio-quiet pulsars, the distribution is more dispersed for the radio-loud ones, possibly showing two peaks. We discuss possible implications of these different distributions based on current models for pulsar X-ray emission.Comment: Accepted for publication in The Astrophysical Journal; 12 pages, 3 figures, 2 table

The ecological approach to multimodal system design

Following the ecological approach to visual perception, this paper presents a framework that emphasizes the role of vision on referring actions. In particular, affordances are utilized to explain gestures variability in a multimodal human-computer interaction. Such a proposal is consistent with empirical findings obtained in different simulation studies showing how referring gestures are determined by the mutuality of information coming from the target and the set of movements available to the speaker. A prototype that follows anthropomorphic perceptual principles to analyze gestures has been developed and tested in preliminary computational validations

Sea ice working group (SIP)

The sea ice is a crucial component of the polar climate system, and has an impact on albedo, heat and gas ex- change, primary productivity and car- bon export, atmospheric and ocean circulation, freshwater budget, ocean stratification, and deep water mass for- mation. It is therefore critical that it is correctly specified as a forcing or pre- dicted as a feedback in modeling stud- ies

Learning Variational Models with Unrolling and Bilevel Optimization

In this paper we consider the problem of learning variational models in the context of supervised learning via risk minimization. Our goal is to provide a deeper understanding of the two approaches of learning of variational models via bilevel optimization and via algorithm unrolling. The former considers the variational model as a lower level optimization problem below the risk minimization problem, while the latter replaces the lower level optimization problem by an algorithm that solves said problem approximately. Both approaches are used in practice, but unrolling is much simpler from a computational point of view. To analyze and compare the two approaches, we consider a simple toy model, and compute all risks and the respective estimators explicitly. We show that unrolling can be better than the bilevel optimization approach, but also that the performance of unrolling can depend significantly on further parameters, sometimes in unexpected ways: While the stepsize of the unrolled algorithm matters a lot (and learning the stepsize gives a significant improvement), the number of unrolled iterations plays a minor role