"Out of sync": The breakdown of economic sentiment cycles in the E.U.

We present empirical evidence about the properties of economic sentiment cycle synchronization for Germany, France and the UK and compare them with the `crisis' countries Italy, Spain, Portugal and Greece. Instead of using output data we prefer to focus on the economic sentiment indicator (ESI), a forward-looking, survey-based variable consistently available from 1985. The cyclical nature of the ESI allows us to analyze the presence or not of synchronicity among country pairs before and after the onset of the financial crisis. Our results show that ESI movements were mostly synchronous before 2008 but they exhibit a breakdown after 2008, with this feature being more prominent in Greece. We also find that, after the political manoeuvring of the past two years, a cycle re-integration or re-synchronization is on the way. An analysis of the evolution of the synchronicity measures indicates that they can potentially be used to identify sudden phase breaks in ESI co-movement and they can offer a signal as to when the EU economies are getting “in” or “out of sync”.

Fit to Burst

A single drama for BBC Radio 3 The Wire: Andy has hit a mid-life crisis. His career is a mess, his relationship is falling apart and despite, or perhaps because of this, he seems intent on eating himself into an early grave. The Voice in his head has warned him, clearly, and a nightmarish tour of his inner organs has left him in no doubt that he is headed for, at best, a coronary arrest; at worst, something that he can't bear to think about it.

And yet he persists. Drowning in despair he grasps at crumbs of comfort, ingesting enough food to support a small country and doubling his waist and his weight in just one month, terrifying his wife and his work colleagues and rendering the average doorway inadequate for his desperate attempts at escape.

Speed of processing in the primary motor cortex: A continuous theta burst stimulation study

‘Temporally urgent’ reactions are extremely rapid, spatially precise movements that are evoked following discrete stimuli. The involvement of primary motor cortex (M1) and its relationship to stimulus intensity in such reactions is not well understood. Continuous theta burst stimulation (cTBS) suppresses focal regions of the cortex and can assess the involvement of motor cortex in speed of processing. The primary objective of this study was to explore the involvement of M1 in speed of processing with respect to stimulus intensity. Thirteen healthy young adults participated in this experiment. Behavioral testing consisted of a simple button press using the index finger following median nerve stimulation of the opposite limb, at either high or low stimulus intensity. Reaction time was measured by the onset of electromyographic activity from the first dorsal interosseous (FDI) muscle of each limb. Participants completed a 30 min bout of behavioral testing prior to, and 15 min following, the delivery of cTBS to the motor cortical representation of the right FDI. The effect of cTBS on motor cortex was measured by recording the average of 30 motor evoked potentials (MEPs) just prior to, and 5 min following, cTBS. Paired t-tests revealed that, of thirteen participants, five demonstrated a significant attenuation, three demonstrated a significant facilitation and five demonstrated no significant change in MEP amplitude following cTBS. Of the group that demonstrated attenuated MEPs, there was a biologically significant interaction between stimulus intensity and effect of cTBS on reaction time and amplitude of muscle activation. This study demonstrates the variability of potential outcomes associated with the use of cTBS and further study on the mechanisms that underscore the methodology is required. Importantly, changes in motor cortical excitability may be an important determinant of speed of processing following high intensity stimulation.

Experimental and Computational Approach Investigating Burst Fracture Augmentation Using PMMA and Calcium Phosphate Cements

The aim of the study was to use a computational and experimental approach to evaluate, compare and predict the ability of calcium phosphate (CaP) and poly (methyl methacrylate) (PMMA) augmentation cements to restore mechanical stability to traumatically fractured vertebrae, following a vertebroplasty procedure. Traumatic fractures (n = 17) were generated in a series of porcine vertebrae using a drop-weight method. The fractured vertebrae were imaged using μCT and tested under axial compression. Twelve of the fractured vertebrae were randomly selected to undergo a vertebroplasty procedure using either a PMMA (n = 6) or a CaP cement variation (n = 6). The specimens were imaged using μCT and re-tested. Finite element models of the fractured and augmented vertebrae were generated from the μCT data and used to compare the effect of fracture void fill with augmented specimen stiffness. Significant increases (p <0.05) in failure load were found for both of the augmented specimen groups compared to the fractured group. The experimental and computational results indicated that neither the CaP cement nor PMMA cement could completely restore the vertebral mechanical behavior to the intact level. The effectiveness of the procedure appeared to be more influenced by the volume of fracture filled rather than by the mechanical properties of the cement itself.

‘Who Ate All the Pride?’:Patriotic Sentiment and English National Football Support.

The growing popularity of English national insignia in international football tournaments has been widely interpreted as evidence of the emergence of a renewed English national consciousness. However, little empirical research has considered how people in England actually understand football support in relation to national identity. Interview data collected around the time of the Euro 2000 and the 2002 World Cup tournaments fail to substantiate the presumption that support for the England football team maps onto claims to patriotic sentiment in any straightforward way. People with far-right political affiliations did generally use national football support to symbolise a general pride in English national identity. However, other people either claimed not to support the England national team precisely because of its associations with nationalism, or else bracketed the domain of football support from more general connotations of English patriotism.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Selection of burst-like transients and stochastic variables using multi-brand image differencing in the Pan-STARRS1 Medium Deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands gP1, rP1, iP1, and zP1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Twitter Sentiment Analysis for Security-Related Information Gathering

Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.

Quality of service in optical burst switching networks

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009

Évolution et sources du sentiment d'efficacité personnelle des enseignantes et des enseignants débutants du secondaire au Québec

Depuis une trentaine d' années, les nouveaux enseignants sont confrontés à des conditions d'insertion difficiles, notamment en raison de la précarité professionnelle. Ce contexte peut affecter de différentes manières le développement et la mobilisation des compétences, processus qui sont en relation avec le développement du sentiment d'efficacité personnelle (SEP) face à l'enseignement. Le SEP étant considéré comme une ressource essentielle pour agir avec compétence, s'engager dans les tâches et persévérer dans la profession enseignante, nous avons consacré notre recherche doctorale à ce sujet. Pour la méthodologie, la recherche a fait appel à des entrevues semi-structurées auprès de 15 enseignantes et enseignants, à une liste de contrôle permettant aux participants de cibler les sources de leur SEP, et enfin à la technique des incidents critiques pour faire évoquer des expériences mettant en relief le déploiement du SEP en contexte de pratique. Les résultats de la recherche montrent que le SEP se développe premièrement, au cours de la formation initiale grâce à la formation théorique et surtout aux stages. Deuxièmement, durant la phase d'insertion professionnelle par la mobilisation réussie des compétences acquises en formation initiale, par l'expérience d'enseignement, les conditions d'insertion facilitantes, la formation continue et l'apport des différents acteurs du milieu scolaire et universitaire, notamment à travers le soutien social, la rétroaction, la collaboration, la reconnaissance et la confiance témoignées. Les résultats indiquent également que ceux et celles qui se sentent efficaces surmontent mieux les difficultés de l'insertion, prennent davantage d'initiatives, innovent dans leur pratiques et obtiennent de meilleures performances dans les tâches attribuées. Notre étude contribue à apporter une nouvelle perspective dans la compréhension du SEP basée sur de véritables expériences d'enseignants débutants du secondaire, fait découvrir les possibilités sur lesquelles les programmes de formation et les mesures d' insertion pourraient s'appuyer et enfin, permet d'enrichir la problématique de l'insertion en enseignement.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.