Type Reconstruction in the Presence of Polymorphic Recursion and the Recursive Types

We establish the equivalence of type reconstruction with polymorphic recursion and recursive types is equivalent to regular semi-unification which proves the undecidability of the corresponding type reconstruction problem. We also establish the equivalence of type reconstruction with polymorphic recursion and positive recursive types to a special case of regular semi-unification which we call positive regular semi-unification. The decidability of positive regular semi-unification is an open problem.

A Fully Distributed Location Management Scheme for Large PCS

In [previous papers] we presented the design, specification and proof of correctness of a fully distributed location management scheme for PCS networks and argued that fully replicating location information is both appropriate and efficient for small PCS networks. In this paper, we analyze the performance of this scheme. Then, we extend the scheme in a hierarchical environment so as to scale to large PCS networks. Through extensive numerical results, we show the superiority of our scheme compared to the current IS-41 standard.

Recursive Estimation of Motion and Planar Structure

A specialized formulation of Azarbayejani and Pentland's framework for recursive recovery of motion, structure and focal length from feature correspondences tracked through an image sequence is presented. The specialized formulation addresses the case where all tracked points lie on a plane. This planarity constraint reduces the dimension of the original state vector, and consequently the number of feature points needed to estimate the state. Experiments with synthetic data and real imagery illustrate the system performance. The experiments confirm that the specialized formulation provides improved accuracy, stability to observation noise, and rate of convergence in estimation for the case where the tracked points lie on a plane.

Efficient Hash-Consing of Recursive Types

Efficient storage of types within a compiler is necessary to avoid large blowups in space during compilation. Recursive types in particular are important to consider, as naive representations of recursive types may be arbitrarily larger than necessary through unfolding. Hash-consing has been used to efficiently store non-recursive types. Deterministic finite automata techniques have been used to efficiently perform various operations on recursive types. We present a new system for storing recursive types combining hash-consing and deterministic finite automata techniques. The space requirements are linear in the number of distinct types. Both update and lookup operations take polynomial time and linear space and type equality can be checked in constant time once both types are in the system.

Type Inference For Recursive Definitions

We consider type systems that combine universal types, recursive types, and object types. We study type inference in these systems under a rank restriction, following Leivant's notion of rank. To motivate our work, we present several examples showing how our systems can be used to type programs encountered in practice. We show that type inference in the rank-k system is decidable for k ≤ 2 and undecidable for k ≥ 3. (Similar results based on different techniques are known to hold for System F, without recursive types and object types.) Our undecidability result is obtained by a reduction from a particular adaptation (which we call "regular") of the semi-unification problem and whose undecidability is, interestingly, obtained by methods totally different from those used in the case of standard (or finite) semi-unification.

A Neural Theory of Visual Search: Recursive Attention to Segmentations and Surfaces

A neural theory is proposed in which visual search is accomplished by perceptual grouping and segregation, which occurs simultaneous across the visual field, and object recognition, which is restricted to a selected region of the field. The theory offers an alternative hypothesis to recently developed variations on Feature Integration Theory (Treisman, and Sato, 1991) and Guided Search Model (Wolfe, Cave, and Franzel, 1989). A neural architecture and search algorithm is specified that quantitatively explains a wide range of psychophysical search data (Wolfe, Cave, and Franzel, 1989; Cohen, and lvry, 1991; Mordkoff, Yantis, and Egeth, 1990; Treisman, and Sato, 1991).