Why do He and She Disagree: The Role of Binary Morphological Features in Grammatical Gender Agreement in German.

In many languages, grammatical gender is an inherent property of nouns and, as such, forms a basis for agreement relations between nouns and their dependent elements (e.g., adjectives, determiners). Mental gender representation is traditionally assumed to be categorial, with categorial gender nodes corresponding to the given gender specifications in a certain language (e.g., [masculine], [feminine], [neuter] in German). In alternative models, inspired by accounts put forward in theoretical linguistics, it has been argued that mental gender representations consist of sets of binary features which might be fully specified (e.g., masc [+ m, - f], fem [- m, + f], neut [- m, - f]) or underspecified (e.g., masc [+ m], fem [+ f], neut [] or masc [+ m, - f], fem [], neut [- f]). We have conducted two experiments to test these controversial accounts. Native speakers of German were asked to decide on the (un-)grammaticality of gender agreement of visually presented combinations of I) definite determiners and nouns, and II) anaphoric personal pronouns and nouns in an implicit nominative singular setting. Overall, agreement violations with neuter das / es increased processing costs compared to violations with die / sie or der / er for masculine or feminine target nouns, respectively. The observed pattern poses a challenge for models involving categorial gender representation. Rather, it is consistent with feature-based representations of grammatical gender in the mental lexicon.

Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination.

Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against the state-of-the-art SeqSLAM algorithm.