Mobslinger: The Fastest Mobile in the West.

Whilst there is a number of location sensing games emerging for mobile phones, from both commercial and academic sectors, there are few examples of social proximity based games that are effectively position independent. Bluetooth would seem an obvious choice for proximity based games, although the majority of games produced to-date simply uses it to provide a quasi peer to peer connection between users of multiplayer games. This is no-doubt due to the fact that proximity can often be implied from other location sensing technologies and that Bluetooth is often perceived as difficult to employ. In this paper we will show that Bluetooth can provide exciting game scenarios that can enable spontaneous stimulated social interaction using only proximity information. We illustrate this through the design rationale and subsequent implementation of ‘mobslinger’ which is a wild west, quick draw, ‘shoot-em-up’ game using mobile phones

Cosmological Simulations on a Grid of Computers

The work presented in this paper aims at restricting the input parameter values of the semi-analytical model used in GALICS and MOMAF, so as to derive which parameters influence the most the results, e.g., star formation, feedback and halo recycling efficiencies, etc. Our approach is to proceed empirically: we run lots of simulations and derive the correct ranges of values. The computation time needed is so large, that we need to run on a grid of computers. Hence, we model GALICS and MOMAF execution time and output files size, and run the simulation using a grid middleware: DIET. All the complexity of accessing resources, scheduling simulations and managing data is harnessed by DIET and hidden behind a web portal accessible to the users.Comment: Accepted and Published in AIP Conference Proceedings 1241, 2010, pages 816-82

Detecting stars, galaxies, and asteroids with Gaia

(Abridged) Gaia aims to make a 3-dimensional map of 1,000 million stars in our Milky Way to unravel its kinematical, dynamical, and chemical structure and evolution. Gaia's on-board detection software discriminates stars from spurious objects like cosmic rays and Solar protons. For this, parametrised point-spread-function-shape criteria are used. This study aims to provide an optimum set of parameters for these filters. We developed an emulation of the on-board detection software, which has 20 free, so-called rejection parameters which govern the boundaries between stars on the one hand and sharp or extended events on the other hand. We evaluate the detection and rejection performance of the algorithm using catalogues of simulated single stars, double stars, cosmic rays, Solar protons, unresolved galaxies, and asteroids. We optimised the rejection parameters, improving - with respect to the functional baseline - the detection performance of single and double stars, while, at the same time, improving the rejection performance of cosmic rays and of Solar protons. We find that the minimum separation to resolve a close, equal-brightness double star is 0.23 arcsec in the along-scan and 0.70 arcsec in the across-scan direction, independent of the brightness of the primary. We find that, whereas the optimised rejection parameters have no significant impact on the detectability of de Vaucouleurs profiles, they do significantly improve the detection of exponential-disk profiles. We also find that the optimised rejection parameters provide detection gains for asteroids fainter than 20 mag and for fast-moving near-Earth objects fainter than 18 mag, albeit this gain comes at the expense of a modest detection-probability loss for bright, fast-moving near-Earth objects. The major side effect of the optimised parameters is that spurious ghosts in the wings of bright stars essentially pass unfiltered.Comment: Accepted for publication in A&

Spartan Daily, March 19, 1997

Volume 108, Issue 39https://scholarworks.sjsu.edu/spartandaily/9114/thumbnail.jp

Diamond Dicing

In OLAP, analysts often select an interesting sample of the data. For example, an analyst might focus on products bringing revenues of at least 100 000 dollars, or on shops having sales greater than 400 000 dollars. However, current systems do not allow the application of both of these thresholds simultaneously, selecting products and shops satisfying both thresholds. For such purposes, we introduce the diamond cube operator, filling a gap among existing data warehouse operations. Because of the interaction between dimensions the computation of diamond cubes is challenging. We compare and test various algorithms on large data sets of more than 100 million facts. We find that while it is possible to implement diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a hundred times faster than popular database engines (including a row-store and a column-store).Comment: 29 page