The influence of affect on categorization.

Four studies with 256 undergraduates showed that positive affect, induced in any of 3 ways, influenced categorization of either of 2 types of stimuli—words or colors. As reflected by performance on 2 types of tasks (rating and sorting), Ss in whom positive affect had been induced tended to create and use categories more inclusively than did Ss in a control condition. On one task, they tended to group more stimuli together, and on the other task they tended to rate more low-prototypic exemplars of a category as members of the category. Results are interpreted in terms of an influence of affect on cognitive organization or on processes that might influence cognitive organization. It is suggested that borderline effects of negative affect on categorization, obtained in 2 of the studies, might result from normal people's attempts to cope with negative affect. (32 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Representing model theory in a type-theoretical logical framework

In a broad sense, logic is the field of formal languages for knowledge and truth that have a formal semantics. It tends to be difficult to give a narrower definition because very different kinds of logics exist. One of the most fundamental contrasts is between the different methods of assigning semantics. Here two classes can be distinguished: model theoretical semantics based on a foundation of mathematics such as set theory, and proof theoretical semantics based on an inference system possibly formulated within a type theory.Logical frameworks have been developed to cope with the variety of available logics unifying the underlying ontological notions and providing a meta-theory to reason abstractly about logics. While these have been very successful, they have so far focused on either model or proof theoretical semantics. We contribute to a unified framework by showing how the type/proof theoretical Edinburgh Logical Framework (LF) can be applied to the representation of model theoretical logics.We give a comprehensive formal representation of first-order logic, covering both its proof and its model theoretical semantics as well as its soundness in LF. For the model theory, we have to represent the mathematical foundation itself in LF, and we provide two solutions for that. Firstly, we give a meta-language that is strong enough to represent the model theory while being simple enough to be treated as a fragment of untyped set theory. Secondly, we represent Zermelo-Fraenkel set theory and show how it subsumes our meta-language. Specific models are represented as LF morphisms.All representations are given in and mechanically verified by the Twelf implementation of LF. Moreover, we use the Twelf module system to treat all connectives and quantifiers independently. Thus, individual connectives are available for reuse when representing other logics, and we obtain the first version of a feature library from which logics can be pieced together.Our results and methods are not restricted to first-order logic and scale to a wide variety of logical systems, thus demonstrating the feasibility of comprehensively formalizing large scale representation theorems in a logical framework.     

Algorithmic Computation and Approximation of Semantic Similarity

Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata—namely topical directories—to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.     

Influence of positive affect on the subjective utility of gains and losses: It is just not worth the risk.

A modification of the procedure originally used by D. Davidson et al (1956) to measure subjective utility was used to study the influence of positive affect on individuals' perceived value (utility) functions. Results indicated, as expected, that persons in whom positive affect had been induced showed a more negative subjective utility for losses than did controls. This indicates that losses seem worse to people who are feeling happy than to those in a control condition. The subjective utility functions of the two groups did not differ as much, however, when people were considering potential gain. Thus, at least in the situation tested in this study, potential gains did not seem to be more appealing (nor less so) for affect subjects than they did for controls. These findings are discussed in relation to theoretical issues in decision making and work suggesting that positive affect can promote increased sensitivity to losses in situations of potential meaningful loss. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Resin-loaded cationic hydrogel: A new sorbent for recovering of grapefruit polyphenols

This paper presents a study of the recovering of polyphenols from grapefruit biowastes through extraction and batch adsorption process using novel resin-loaded cationic hydrogel. To prepare adsorbent, 2-(dimethylamino) ethyl methacrylate (DMAEMA) monomer was polymerized by free radical mechanisms in presence of resin particles and then tertiary amino groups on PDMAEMA residue were quaternized using methyl iodide. The developed adsorbent was then characterized by FTIR, SEM, XRD, and Brunauer, Emmet, and Teller analysis. The resin-loaded cationic hydrogel displayed an enhanced affinity for binding with polyphenols at pH value of 10 due to the strong electrostatic attraction between positively charged adsorbent surface and solute molecules. The adsorption capacity of 60 (mg-naringin-g^?1-adsorbent) was obtained when the other experimental conditions were chosen as follows: initial polyphenol concentration; 0.13?g?mL^?1, contact time; 30?min, temperature; 25°C. On the other hand, the adsorbent consisting solely of resin particles shown lower affinity for polyphenols. The adsorption results revealed that the resin-loaded cationic hydrogels exhibited significant improvement in the recovery of polyphenols from grapefruit peels through integrated extraction–adsorption process.