P.V. Golubtsov

The Effects of Incomplete Information in Stochastic Common-Stock Harvesting Games

Here the dynamic fishery harvesting game is generalized to a stochastic environment in order to examine the implications of incomplete and asymmetric information. The main emphasis is on a split stream version of the game: At the beginning of each harvest season the initial fish stock (or “recruitment”) divides into two streams, each one accessible to harvest by just one of the two competing fishing fleets. The fleets simultaneously harvest down their streams, achieving net seasonal payoffs for the catch. After harvest, the residual sub-stocks reunite to form the broodstock for the subsequent generation. The strength of this subsequent generation is determined by a specified “stock-recruitment relation,” and the cycle repeats. In this cyclic process, both natural environmental factors (stream-split proportions and stock-recruitment relation) and economic factors (harvest costs and benefits) will incorporate Markovian stochastic elements. At the beginning of each season, both fleets know the current recruitment and also have some (generally incomplete or delayed, and often asymmetric) knowledge of the current values of the stochastic elements. The knowledge structure of each specific game version is held in common by the competitors. In the dynamic game each fleet sets its harvest policy with the objective of maximizing the expected discounted sum of seasonal payoffs, and conditional on the extent of its current knowledge and of the anticipated policy of its competitor.

The Concept of Information in Big Data Processing

The need to transform existing algorithms in Big Data Systems is considered. The transformation must allow independent and parallel processing of separate fragments of data. The characteristic aspects of a well-organized intermediate compact form of information and its natural algebraic properties are studied and an illustrative example is provided.

The Linear Estimation Problem and Information in Big-Data Systems

This paper addresses the problem of transforming the optimal linear estimation procedure in such a way that separate fragments of initial data are processed individually and concurrently. A representation of intermediate information is proposed that allows an algorithm to concurrently extract this information from each initial data set, combine it, and use it for estimation. It is shown that, on an information space constructed, an ordering is induced that reflects the concept of information quality.

Mapping wafer flatness changes in chemical mechanical planarization

This paper discusses the feasibility of using non-contact capacitive gauging technology for wafer flatness characterization in Chemical Mechanical Planarization (CMP). A simple but efficient method is introduced which provides guidance for CMP wafer flatness qualification per advanced lithography requirements. The statistical analysis quantitatively explains why CMP is an enabling technology for surface planarization in sub-half micron or less technologies. A gain of approximate 7% in terms of site flatness improvement at 0.18 /spl mu/m technology is observed which represents a significant yield improvement from a lithographic perspective.

Invariance considerations in design of image-formation-measurement computer systems

Image registration measurement systems (MSs) often possess the following specific properties: the measurement results of these systems demand for further complex processing; these MSs deal with large amounts of data; and they possess a high level of invariance. In this paper a general description of measurement systems invariant with respect to a given group of transformations (e.g., translations, reflections, rotations of field of view) is considered. Then the problem of an optimum measurement computer system (MCS) synthesis for a given invariant MS is stated. It consists in obtaining a mapping (describing processing algorithm), delivering an optimum to the MCS as a whole. It is shown that proper allowance for invariance can significantly simplify MCSs synthesis problem solution and, as a consequence, appreciably cut down the computational costs involved both for constructing an optimum computational component of MCS and for its subsequent functioning.