This paper reviews some of the basic premises of Quantal-Enhancement Theory as developed by K.N. Stevens and his colleagues. Quantal theory seeks to explain why some articulatory and acoustic dimensions are favored over others in distinctive feature contrasts across languages. In this paper, after a review of basic concepts, a protocol for quantal feature definitions is proposed and problems in...

B Y THE CLOSE of World War II, a syndrome of "wet lung" had been identified in which life-threatening respiratory distress unexpectedly interrupted convalescence from hemorrhagic and traumatic shock incurred during combat. During the recent war in Vietnam, as the salvage rate after circulatory collapse on the battlefield increased, the syndrome became even more familiar, but under new eponyms. ...

Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions. In this paper, we propose a close yet discriminative domain adaptation metho...

We continue the review of some of the basic premises of QuantalEnhancement Theory (Stevens 1972, 1989, etc.) initiated in Clements and Ridouane (2006). While Quantal Theory proposes to account for similarities in feature realisation across speakers and languages, Enhancement Theory proposes to account for regular patterns of cross-linguistic variation. In this sense these two theories may be re...

Visual Attributes are properties observable in images that have human-designated names ( e.g., smiling, natural) and they are valuable as a new semantic cue in various vision problems like facial verification, object recognition, generating description of unfamiliar objects and to facilitate zero shot transfer learning etc. While most of the work on attributes focuses on binary attributes (indi...