Starving the Dark Markets: International Injunctions as a Means to Curb Small Arms and Light Weapons Trafficking Note

International arms sales are a big business. This understatement fails to elucidate the extensive industrial and economic impact of the weapons trade between nations. One of the most influential portions of is the production and sale of Small Arms and Light Weapons (SALW). Yet despite SALW sales valued at billions of dollars per year, little international regulation exists to control these sales. While most SALW sales occur within the legitimate sphere of business, a large number of SALW are sold and resold through the “grey” and “black” markets: illegal methods of sale that do not conform to any international norms. Combating the growth of these markets has been frustrating not only due to the lack of comprehensive regulation but also because the markets are designed to be as difficult to detect as possible. This Note sets out an alternative path of preventing SALW from entering the grey and black markets. After engaging in an analysis of existing international law, this Note suggests adoption of stronger export controls and an injunctive mechanism to aid in their enforcement. It draws inspiration from the British Mareva injunction, and after suggesting modifications to international export regulations this Note demonstrates how an injunctive process similar to the Mareva injunction may be used at the International Court of Justice to prevent weapons from entering the grey and black markets

Vertex similarity in networks

We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks

Electronic Quantum Monte Carlo Calculations of Atomic Forces, Vibrations, and Anharmonicities

Atomic forces are calculated for first-row monohydrides and carbon monoxide within electronic quantum Monte Carlo (QMC). Accurate and efficient forces are achieved by using an improved method for moving variational parameters in variational QMC. Newton's method with singular value decomposition (SVD) is combined with steepest descent (SD) updates along directions rejected by the SVD, after initial SD steps. Dissociation energies in variational and diffusion QMC agree well with experiment. The atomic forces agree quantitatively with potential energy surfaces, demonstrating the accuracy of this force procedure. The harmonic vibrational frequencies and anharmonicity constants, derived from the QMC energies and atomic forces, also agree well with experimental values.Comment: 6 pages, 2 figures; updated conten

Can a workspace help to overcome the query formulation problem in image retrieval?

We have proposed a novel image retrieval system that incorporates a workspace where users can organise their search results. A task-oriented and user-centred experiment has been devised involving design professionals and several types of realistic search tasks. We study the workspace’s effect on two aspects: task conceptualisation and query formulation. A traditional relevance feedback system serves as baseline. The results of this study show that the workspace is more useful with respect to both of the above aspects. The proposed approach leads to a more effective and enjoyable search experience

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Evaluating a workspace's usefulness for image retrieval

Image searching is a creative process. We have proposed a novel image retrieval system that supports creative search sessions by allowing the user to organise their search results on a workspace. The workspace’s usefulness is evaluated in a task-oriented and user-centred comparative experiment, involving design professionals and several types of realistic search tasks. In particular, we focus on its effect on task conceptualisation and query formulation. A traditional relevance feedback system serves as a baseline. The results of this study show that the workspace is more useful in terms of both of the above aspects and that the proposed approach leads to a more effective and enjoyable search experience. This paper also highlights the influence of tasks on the users’ search and organisation strategy

The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page