Abstract. The paper presents INFOMIX a successful application of ASP tech-nology to the domain of Data Integration. INFOMIX is a novel system which supports powerful information integration, utilizing the ASP system DLV. While INFOMIX is based on solid theoretical foundations, it is a user

Abstract. Using a compilation of measurements of the stellar mass density as a function of redshift we can infer the cosmic star formation history. For z < 0.7 there is good agreement between the two star formation histories. At higher redshifts the instantaneous indicators suggest star

in HCGs most probably rotate around the gravitational center of the corresponding group. Other two possible mechanisms: flying apart of galaxies from the group in opposite directions, and infall of field galaxies upon the group are excluded. It follows that HCGs are more stable formations, than it has

Structural decomposition of galaxies into bulge, disk, and bar components is important to address a number of scientific problems. Measuring bulge, disk, and bar structural parameters will set constraints on the violent and secular processes of galaxy assembly and recurrent bar formation

inconsistencies in software and system requirements. Our assumption is that these requirements are given in a formal requirement specification language called Temporal Ac-tion Language (TeAL). [Contribution] We present a translation from TeAL to the ASP language format accepted by clingcon. We show that clingcon

Optical Character Recognition (OCR) is the process of translating images of handwritten, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and compression. The process can be broken down into 3 tightly coupled tasks: pre

is a polymorphic human enzyme with malate

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article pre-sents such a bioinformatics curric-ulum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. Online Learning Comes of Age Online academic ‘‘courseware’ ’ at the university level has now been available to the public for a decade, the earliest concerted effort having originated in 2002 with the Massachusetts Institute of Technology (MIT) and their OpenCour-seWare initiative

First, I would like to thank to Dimka Karastoyanova and Mariano Cilia for all the fruitful discussions, their guidance and support through the course of this project. Thanks also to Prof. Alejandro Buchmann who sponsored my stay at Technische Universität Darmstadt during the spring of 2004. The special thanks go to my advisor, Mariano Cilia, who has made all this possible. Finally, I’d like to thank to my family for all these years of support.

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement