Escaping the Trap of too Precise Topic Queries

At the very center of digital mathematics libraries lie controlled vocabularies which qualify the {\it topic} of the documents. These topics are used when submitting a document to a digital mathematics library and to perform searches in a library. The latter are refined by the use of these topics as they allow a precise classification of the mathematics area this document addresses. However, there is a major risk that users employ too precise topics to specify their queries: they may be employing a topic that is only "close-by" but missing to match the right resource. We call this the {\it topic trap}. Indeed, since 2009, this issue has appeared frequently on the i2geo.net platform. Other mathematics portals experience the same phenomenon. An approach to solve this issue is to introduce tolerance in the way queries are understood by the user. In particular, the approach of including fuzzy matches but this introduces noise which may prevent the user of understanding the function of the search engine. In this paper, we propose a way to escape the topic trap by employing the navigation between related topics and the count of search results for each topic. This supports the user in that search for close-by topics is a click away from a previous search. This approach was realized with the i2geo search engine and is described in detail where the relation of being {\it related} is computed by employing textual analysis of the definitions of the concepts fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath, U

Turing Instability in a Boundary-fed System

The formation of localized structures in the chlorine dioxide-idodine-malonic acid (CDIMA) reaction-diffusion system is investigated numerically using a realistic model of this system. We analyze the one-dimensional patterns formed along the gradients imposed by boundary feeds, and study their linear stability to symmetry-breaking perturbations (Turing instability) in the plane transverse to these gradients. We establish that an often-invoked simple local linear analysis which neglects longitudinal diffusion is inappropriate for predicting the linear stability of these patterns. Using a fully nonuniform analysis, we investigate the structure of the patterns formed along the gradients and their stability to transverse Turing pattern formation as a function of the values of two control parameters: the malonic acid feed concentration and the size of the reactor in the dimension along the gradients. The results from this investigation are compared with existing experiments.Comment: 41 pages, 18 figures, to be published in Physical Review

Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks

Zazo R, Lozano-Diez A, Gonzalez-Dominguez J, T. Toledano D, Gonzalez-Rodriguez J (2016) Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks. PLoS ONE 11(1): e0146917. doi:10.1371/journal.pone.0146917Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (similar to 3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.This work has been supported by project CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Señal de Voz (TEC2012-37585-C02-01), funded by Ministerio de Economia y Competitividad, Spain

Ann Oakley: new learning and global influence from working across conventional boundaries

Ann Oakley, pioneering social researcher for nearly 60 years, is Professor of Sociology and Social Policy at IOE (Institute of Education), UCL’s Faculty of Education and Society (University College London, UK). This article explores the innovation and influence of her work and the work of her close colleagues at the Social Science Research Unit (SSRU) and its Evidence for Policy and Practice Information and Coordinating Centre (EPPI-Centre). It describes advances in research and knowledge that have their roots in listening to what women have to say about their lives. The resulting novel research methods have straddled academic boundaries – between qualitative and quantitative methodologies, between disciplines, and between academia and wider society – to enhance understanding of complex social issues and approaches to addressing them within the public sector. The impact of this work is seen in terms of influencing science, knowledge management, policy decisions, professional practice and the general public. These achievements come from approaches that are outward looking and straddle academic disciplines to produce evidence that is relevant to policymaking and to practice, with the ultimate aim being to improve day-to-day life