Pere Vieta (1779–1856), promoter of free public teaching of physics in Catalonia

Free public teaching of physics in Catalonia started in the early 19th century, even if the interest in experimental physics goes back to the 18th century, where this discipline was discussed at various learned societies. The first chair of Physics in Barcelona was not a university chair but that of the Junta de Comerç de Barcelona (Trade Board of Barcelona), which had several scientific-technical Schools. In fact, at that time, Barcelona had no university, because it had been supressed by King Felipe V after the War of the Spanish Succession (ended in 1714). The promoter of free public teaching of experimental physics was Pere (Pedro) Vieta i Gibert (17791856), who was the first professor of that subject both at the School of the Trade Board and at the University of Barcelona, once it was restored in 1842. Vieta, who was a surgeon in the Army, combined his two professions and his interest in meteorology, he having recorded meteorological observations in Barcelona for many years. Many of his students were influential people in the scientific, intellectual, political and economic history of the 19th century in Catalonia and Spain. [Contrib Sci 11:237-247 (2015)]Postprint (published version

Regular triangulations of dynamic sets of points

The Delaunay triangulations of a set of points are a class of triangulations which play an important role in a variety of different disciplines of science. Regular triangulations are a generalization of Delaunay triangulations that maintain both their relationship with convex hulls and with Voronoi diagrams. In regular triangulations, a real value, its weight, is assigned to each point. In this paper a simple data structure is presented that allows regular triangulations of sets of points to be dynamically updated, that is, new points can be incrementally inserted in the set and old points can be deleted from it. The algorithms we propose for insertion and deletion are based on a geometrical interpretation of the history data structure in one more dimension and use lifted flips as the unique topological operation. This results in rather simple and efficient algorithms. The algorithms have been implemented and experimental results are given.Postprint (published version

Interprocess data transfer in ATLAS, a platform for distributed applications

The ATLAS platform strives to make several useful but technically involved mechanisms available to the programmer building applications over it with the least possible effort. These mechanisms include network distribution of cooperating processes, a powerful macro language, a journaling system and fault tolerance in the presence of network failures or node crashes. In this paper we discuss the techniques used in ATLAS to implement data transfer over a network between different machines with the least hassle to the programmer.Postprint (published version

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.Comment: published, 15 page

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part II: Proximal Optimization and Performance Evaluation

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this second part, we focus on our algorithmic contributions. We provide an algorithm for functional inverse diffusion that solves the variational problem we posed in Part I. As part of the derivation of this algorithm, we present the proximal operator for the non-negative group-sparsity regularizer, which is a novel result that is of interest in itself, also in comparison to previous results on the proximal operator of a sum of functions. We then present a discretized approximated implementation of our algorithm and evaluate it both in terms of operational cell-detection metrics and in terms of distributional optimal-transport metrics.Comment: published, 16 page