Approximately optimal experimental design for heteroscedastic Gaussian process models

This paper presents a greedy Bayesian experimental design criterion for heteroscedastic Gaussian process models. The criterion is based on the Fisher information and is optimal in the sense of minimizing parameter uncertainty for likelihood based estimators. We demonstrate the validity of the criterion under different noise regimes and present experimental results from a rabies simulator to demonstrate the effectiveness of the resulting approximately optimal designs.

An unusual ruthenium(II) complex of 2-(2-pyridyl)benzothiazole

The ligand 2-(2-pyridyl)benzothiazole (L) can act both as an N-N and an N-S chelating donor. The latter coordination mode is expected to be preferred when it is involved in coordination to Ru(II) which is a soft acceptor centre However, in the title compound, chlorobis(acetonitrile)triphenylphosphino-2-(2-pyridyl)benzothiazole-N,N-ruthenium(II) chlride, [Ru(L)(PPh3(CH3CN)2Cl]Cl, the ligand acts in N,N-bidentate manner and the Ru(II) ion is found to be present in an N4PCl coordination environment. PPh3 and Cl are trans to each other and the two CH3CN ligands occupy cis positions facing the NN donor atoms of ligand L.

Assessing the value relevance of accounting data after the introduction of IFRS in Europe

Since 2005, European-listed companies have been required to prepare their consolidated financial statements in accordance with the International Financial Reporting Standards (IFRS). We examine whether value relevance increased following the introduction of IFRS, using a sample of 3,721 companies listed on five European stock exchanges: Frankfurt, Madrid, Paris, London, and Milan. We find mixed evidence of an increase in value relevance. However, the influence of earnings on share price increased following the introduction of IFRS in Germany, France, and the United Kingdom, while the influence of book value of equity decreased (except for the United Kingdom). © 2010 Blackwell Publishing Ltd.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.