A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

Temporal pattern recognition in video clips detection

Temporal representation and reasoning plays an important role in Data Mining and Knowledge Discovery, particularly, in mining and recognizing patterns with rich temporal information. Based on a formal characterization of time-series and state-sequences, this paper presents the computational technique and algorithm for matching state-based temporal patterns. As a case study of real-life applications, zone-defense pattern recognition in basketball games is specially examined as an illustrating example. Experimental results demonstrate that it provides a formal and comprehensive temporal ontology for research and applications in video events detection.

Synthesis and enzymatic evaluation of the guanosine analogue 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG): insights into the phosphorolysis reaction mechanism based on the blueprint transition state: SN1 or SN2?

A modified experimental procedure for the synthesis of MESG (2-amino-6-mercapto-7-methylpurine ribonucleoside) 1 has been successfully performed and its full characterization is presented. High resolution ESI(+)-MSMS indicates both the nucleoside bond cleavage as the main fragmentation in the gas phase and a possible SN1 mechanism. Ab initio transition state calculations based on the blue print transition state support this mechanistic rationale and discard an alternative SN2 mechanism. Assays using purine nucleoside phosphorylase (PNP) enzyme (human and M. tuberculosis sources) indicate its efficiency in the phosphorolysis of MESG and allow the quantitative determination of inorganic phosphate in real time assay.