Von Mises-Fisher models in the total variability subspace for language recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I. Lopez-Moreno, D. Ramos, J. Gonzalez-Dominguez, and J. Gonzalez-Rodriguez, "Von Mises-Fisher models in the total variability subspace for language recognition", IEEE Signal Processing Letters, vol. 18, no. 12, pp. 705-708, October 2011This letter proposes a new modeling approach for the Total Variability subspace within a Language Recognition task. Motivated by previous works in directional statistics, von Mises-Fisher distributions are used for assigning language-conditioned probabilities to language data, assumed to be spherically distributed in this subspace. The two proposed methods use Kernel Density Functions or Finite Mixture Models of such distributions. Experiments conducted on NIST LRE 2009 show that the proposed techniques significantly outperform the baseline cosine distance approach in most of the considered experimental conditions, including different speech conditions, durations and the presence of unseen languages.This work was supported by the Ministerio de Ciencia e Innovación under FPI Grant TEC2009-14719-C02-01 and cátedra UAM-Telefónic

Integration strategies for the success of mergers and acquisitions in financial services companies

The research shows how managers can plan a successful integration process following a merger and acquisition. Presents a series of frameworks which discuss understanding value creation in mergers and acquisitions, selecting the right strategy and managing the integration process; drawn largely from research studies and interviews made to managers with experience in leading integration processes of financial services companies in Europe, Latin America and USA. Concludes that, by following the key drivers framework described, managers can turn the integration process into a successful project, and academics can focus their post-merger research having into account the opinion of managers

Frame-by-frame language identification in short utterances using deep neural networks

This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 64, (2015) DOI 10.1016/j.neunet.2014.08.006This work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 s task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i-vector system of 40% in LRE09 3 second task and 76% in Google 5M LID

Implicaciones anestésicas en la enfermedad de Von Recklinghausen

ResumenLa enfermedad de Von Recklinghausen (EVR) o neurofibromatosis tipoi (NF1) es una enfermedad con herencia autosómica dominante con un amplio espectro de manifestaciones clínicas. Los neurofibromas son las lesiones características. Este trastorno se asocia con importantes consideraciones anestésicas, principalmente cuando los neurofibromas aparecen en la orofaringe y laringe, produciendo dificultades en la laringoscopia y en la intubación endotraqueal. Describimos el manejo anestésico de un paciente con NF1 bajo anestesia general para extirpación de neurofibromas faciales. Hemos realizado un breve repaso de la literatura existente para optimizar el manejo anestésico y reducir el número de complicaciones asociadas con las manifestaciones sistémicas de este síndrome.AbstractVon Recklinghausen disease or neurofibromatosis Type I (NF1) is an autosomal dominant disease with a wide spectrum of clinical manifestations. Neurofibromas are the characteristic lesions. This disorder is associated with important anaesthetic considerations, mainly when neurofibromas occur in the oropharnyx and larynx, leading to difficult laryngoscopy and tracheal intubation. We describe the anaesthetic management of a patient with NF1 under general anaesthesia for facial neurofibroma excision. We performed a brief review of the literature with the aim of optimizing the anaesthetic management and reduce the number of complications associated with the systemic manifestations of this syndrome

Reconstruction of drought episodes for central Spain from rogation ceremonies recorded at the Toledo Cathedral from 1506 to 1900: A methodological approach

Rogation (ceremonies to ask God for rain: pro-pluvia, or to stop raining: pro-serenitate) analysis is an effective method to derive information about climate extremes from documentary data. Weighted annual sum by levels has been a widespread technique to analyze such data but this analysis is liable to be biased to spring values as these ceremonies are strongly related to farming activities. The analysis of the length of propluvia periods (the time span during which rogations are carried out in relation to a drought event) and the combination of annual and seasonal information offers a more objective criterion for the analysis of the drought periods and an increase in the resolution of the study. Analysis by the pro-pluvia periods method of the rogation series from the Toledo (central Spain) Cathedral Chapter allows a good characterization of the droughts during the 1506–1900 period. Two drought maxima appear during the 1600–1675 and 1711–1775 periods, characterized by rogations during almost all the year, with a middle stage (1676–1710) when droughts were less frequent and their length shortened. Sea level pressure patterns for the instrumental and documentary periods show that droughts were mostly related to a north-eastern position of the Azores High that displaced the Atlantic lowpressure systems towards a northern position. There is a weak relation with the North Atlantic Oscillation but this fact is related to the local character of the series that increases the weight of the local factors. Comparison of rainfall/drought records around Spain and theWestern Mediterranean reveals the heterogeneity of their distribution in time and space as well as stresses the need of more and longer reconstructions. Better knowledge of drought variability would help to improve regional models of climate extremes and the understanding of the atmospheric patterns related to their development

Evolución de eventos climáticos extremos (inundaciones y sequías) para la zona central de la Península Ibérica desde el siglo XVI a partir del registro de rogativas e inundaciones históricas.

En este trabajo se presenta la evolución desde 1500 a 1900 de dos tipos de eventos climáticos extremos característicos de la Península Ibérica, las inundaciones y las sequías. Este estudio se ha llevado a cabo en la meseta sur de la Península. Aprovechando la continuidad del registro documental desde el s. XVI hasta nuestros días para la zona de estudio, hemos utilizado registros de rogativas e inundaciones históricas del rio Tajo, acaecidas en Aranjuez, Toledo y Talavera. En los cuatro siglos estudiados, parece que los periodos en los que hay una alta frecuencia de sequías también existe una alta frecuencia de inundaciones, aunque estos eventos raramente coinciden en un mismo año. En función de la frecuencia y la intensidad de los eventos, se han distinguido seis periods, dos con una alta frecuencia de eventos (1557-1623), (1717-1798), uno con frencuencia media (1624-1716), dos con frecuencias bajas (1500-1556) y (1798-1850), debido probablemente a un aumento de la presión antrópica sobre los cauces y una disminución en la frecuencia de rogativas por motivos sociopolíticos

La enseñanza de la Transición democrática española: una comparativa entre libros de texto de bachillerato (1979-1996)

From the History teaching the analysis of textbooks is one of the most deeply rooted lines of research in recent years, so this article analyzes the way in which the Democratic Transition has been taught, through high school textbooks corresponding to the subject of History of Spain, from different publishers, both confessional and secular.Desde la didáctica de la Historia, el análisis de los libros de texto es una de las líneas de investigación más arraigadas en los últimos años. Este trabajo analiza la forma en que se ha enseñado la Transición democrática a través de libros de texto de Bachillerato correspondientes a la asignatura de Historia de España, de diferentes editoriales, tanto confesionales como laicas

Automatic language identification using deep neural networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I. López-Moreno, J. González-Domínguez, P. Oldrich, D. R. Martínez, J. González-Rodríguez, "Automatic language identification using deep neural networks", IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP, Florence (Italy), 2014This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based acoustic systems on two different datasets: Google 5M LID corpus and NIST LRE 2009. Results show how LID can largely benefit from using DNNs, especially when a large amount of training data is available. We found relative improvements up to 70%, in Cavg, over the baseline system

Dynamic analysis of office lighting smart controls management based on user requirements

Daylight dynamic metrics provide an alternative approach for the assessment of the energy savings promoted by lighting control systems. This research aims to quantify the energy savings allowed by lighting smart controls using continuous and overcast daylight autonomy, novel metrics tested monitoring a mesh of illuminance-meters in test cells over a one-year period. Three types of smart controls are proposed, based on switches and dimmers, some of which were managed by illuminance-meters and irradiance detectors. Energy savings are assessed according to weather data, room dimensions, inner reflectances, window size and user requirements—illuminance needs and working hours. The results show a reduction in the average energy consumption of electric lighting of up to 23%, suggesting the suitability of the smart controls proposed. Smart controls without illuminance-meter feedback are only recommended for shallow rooms with low requirements, while dark deep rooms demand a complex dimming system managed by external illuminance-meters

A Deep Learning Approach for Fusing Sensor Data from Screw Compressors

[EN] Chillers are commonly used for thermal regulation to maintain indoor comfort in medium and large buildings. However, inefficiencies in this process produce significant losses, and optimization tasks are limited because of accessibility to the system. Data analysis techniques transform measurements coming from several sensors into useful information. Recent deep learning approaches have achieved excellent results in many applications. These techniques can be used for computing new data representations that provide comprehensive information from the device. This allows real-time monitoring, where information can be checked with current working operation to detect any type of anomaly in the process. In this work, a model based on a 1D convolutional neural network is proposed for fusing data in order to predict four different control stages of a screw compressor in a chiller. The evaluation of the method was performed using real data from a chiller in a hospital building. Results show a satisfactory performance and acceptable training time in comparison with other recent methods. In addition, the model is capable of predicting control states of other screw compressors different than the one used in the training. Furthermore, two failure cases are simulated, providing an early alarm detection when a continuous wrong classification is performed by the model.SIThis research was funded by the Spanish Ministry of Science and Innovation and the European Regional Development Fund under project DPI2015-69891-C2-1-R/2-R.Ministerio de Economía y Competitivida