Resolving Conflict through Explicit Bargaining

This article analyzes the impact of conciliatory initiatives on conflict resolution in two-party bargaining. It specifically develops and tests a theory of unilateral initiatives derived from Osgood\u27s (1962) notion of Graduated and Reciprocated Initiatives in Tension Reduction (GRIT). The major propositions of the theory indicate that, given a pattern of mutual resistance or hostility, unilateral initiatives and tit-for-tat retaliation in response to punitive action will produce more conciliation and less hostility by an opponent. To test the theory, a bargaining setting was created in a laboratory experiment in which parties exchanged offers and counteroffers on an issue across a number of rounds while also having the option to engage in punitive action against one another. The results indicated that (1) unilateral initiatives produced more concession making and less hostility than a reciprocity strategy, and (2) tit-for-tat retaliation heightened hostility initially but reduced it over time. The article suggests some general, abstract conditions under which two parties in conflict can produce conciliation and reach agreements without the intervention of third parties

On Dimer Models and Closed String Theories

We study some aspects of the recently discovered connection between dimer models and D-brane gauge theories. We argue that dimer models are also naturally related to closed string theories on non compact orbifolds of \BC^2 and \BC^3, via their twisted sector R charges, and show that perfect matchings in dimer models correspond to twisted sector states in the closed string theory. We also use this formalism to study the combinatorics of some unstable orbifolds of \BC^2.Comment: 1 + 25 pages, LaTeX, 11 epsf figure

Comment on the Generation Number in Orbifold Compactifications

There has been some confusion concerning the number of $(1,1)$-forms in orbifold compactifications of the heterotic string in numerous publications. In this note we point out the relevance of the underlying torus lattice on this number. We answer the question when different lattices mimic the same physics and when this is not the case. As a byproduct we classify all symmetric $Z_N$-orbifolds with $(2,2)$ world sheet supersymmetry obtaining also some new ones.Comment: 28 pages, 9 figures not included, available in postscript at reques

Semantic indeterminacy in object relative clauses

This article examined whether semantic indeterminacy plays a role in comprehension of complex structures such as object relative clauses. Study 1 used a gated sentence completion task to assess which alternative interpretations are dominant as the relative clause unfolds; Study 2 compared reading times in object relative clauses containing different animacy configurations to unambiguous passive controls; and Study 3 related completion data and reading data. The results showed that comprehension difficulty was modulated by animacy configuration and voice (active vs. passive). These differences were well correlated with the availability of alternative interpretations as the relative clause unfolds, as revealed by the completion data. In contrast to approaches arguing that comprehension difficulty stems from syntactic complexity, these results suggest that semantic indeterminacy is a major source of comprehension difficulty in object relative clauses. Results are consistent with constraint-based approaches to ambiguity resolution and bring new insights into previously identified sources of difficulty. (C) 2007 Elsevier Inc. All rights reserved

Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces