Quintessence reconstructed: new constraints and tracker viability

We update and extend our previous work reconstructing the potential of a quintessence field from current observational data. We extend the cosmological data set to include new supernova data, plus information from the cosmic microwave background and from baryon acoustic oscillations. We extend the modeling by considering Padé approximant expansions as well as Taylor series, and by using observations to assess the viability of the tracker hypothesis. We find that parameter constraints have improved by a factor of 2, with a strengthening of the preference of the cosmological constant over evolving quintessence models. Present data show some signs, though inconclusive, of favoring tracker models over nontracker models under our assumptions

Extension of Information Geometry to Non-statistical Systems: Some Examples

Our goal is to extend information geometry to situations where statistical modeling is not obvious. The setting is that of modeling experimental data. Quite often the data are not of a statistical nature. Sometimes also the model is not a statistical manifold. An example of the former is the description of the Bose gas in the grand canonical ensemble. An example of the latter is the modeling of quantum systems with density matrices. Conditional expectations in the quantum context are reviewed. The border problem is discussed: through conditioning the model point shifts to the border of the differentiable manifold.Comment: 8 pages, to be published in the proceedings of GSI2015, Lecture Notes in Computer Science, Springe

3D Character Modeling in Virtual Reality

The paper presents a virtual reality modeling system based on interactive web technologies. The system's goal is to provide a user-friendly virtual environment for the development of 3D characters with an articulated structure. The interface allows the modeling of both the character's joint structure (the hierarchy) and its segment geometry (the skin). The novelty of the system consists of (1) the combination of web technologies used (VRML, Java and EAI) which provides the possibility of online modeling, (2) rules and constraints based operations and thus interface elements, (3) vertices and sets of vertices used as graphics primitives and (4) the possibility to handle and extend hierarchies based on the H-anim structure elements

The importance of better models in stochastic optimization

Standard stochastic optimization methods are brittle, sensitive to stepsize choices and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives. To address these challenges, we investigate models for stochastic minimization and learning problems that exhibit better robustness to problem families and algorithmic parameters. With appropriately accurate models---which we call the aProx family---stochastic methods can be made stable, provably convergent and asymptotically optimal; even modeling that the objective is nonnegative is sufficient for this stability. We extend these results beyond convexity to weakly convex objectives, which include compositions of convex losses with smooth functions common in modern machine learning applications. We highlight the importance of robustness and accurate modeling with a careful experimental evaluation of convergence time and algorithm sensitivity