Cognitive Deficit of Deep Learning in Numerosity

Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the CNN computational machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful for visual numerosity represents a minimum level of human intelligence.Comment: Accepted for presentation at the AAAI-1

Linearly Typed Dyadic Group Sessions for Building Multiparty Sessions

Traditionally, each party in a (dyadic or multiparty) session implements exactly one role specified in the type of the session. We refer to this kind of session as an individual session (i-session). As a generalization of i-session, a group session (g-session) is one in which each party may implement a group of roles based on one channel. In particular, each of the two parties involved in a dyadic g-session implements either a group of roles or its complement. In this paper, we present a formalization of g-sessions in a multi-threaded lambda-calculus (MTLC) equipped with a linear type system, establishing for the MTLC both type preservation and global progress. As this formulated MTLC can be readily embedded into ATS, a full-fledged language with a functional programming core that supports both dependent types (of DML-style) and linear types, we obtain a direct implementation of linearly typed g-sessions in ATS. The primary contribution of the paper lies in both of the identification of g-sessions as a fundamental building block for multiparty sessions and the theoretical development in support of this identification.Comment: This paper can be seen as the pre-sequel to classical linear multirole logic (CLML). arXiv admin note: substantial text overlap with arXiv:1603.0372