A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier

be reanalyzed as well. In the present paper we show that the quark-level Linear Sigma Model (LoeM) is a non-trivial field theory in contrast with the possibly trivial pure boson (OE 4 ) 4 theory. Prior studies of OE 4 field theory using perturbative and partially nonperturbative methods [24 ] extracted

in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about

study is used to test and compare the program extraction machineries of the three proof assistants in a non-trivial setting. 1

Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm

powerful technique and reveals the integrability hidden in the usual CFT formulation. This thesis extends the matrix model description of 2D string theory to non-trivial backgrounds. We show how perturbations changing the background are incorporated into Matrix Quantum Mechanics. The perturbations

powerful technique and reveals the integrability hidden in the usual CFT formulation. This thesis extends the matrix model description of 2D string theory to non-trivial backgrounds. We show how perturbations changing the background are incorporated into Matrix Quantum Mechanics. The perturbations

systems. For different classes of such systems (e.g., hybrid automata, data-independent systems, relational automata, Petri nets, and lossy channel systems) this research has resulted in numerous highly nontrivial algorithms. As the interest in this area increases, it will be important to extract common

Singular spectrum analysis (SSA) is a useful tool for identification and extraction of oscillatory or other signals from noisy background. Its basic form, however, is reliable when a signal is embedded in white noise, while presence of "colored" noises could lead to spurious results

treatment and to discover strong decision rules representing classification patterns interesting for practitioners. The ESWL information system is an example of non-trivial medical data set where the use of a simple rough set model gives a high number of possible reducts which are impossible to interpret