865 resultados para Associative classifier


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Named entity recognizers are unable to distinguish if a term is a general concept as "scientist" or an individual as "Einstein". In this paper we explore the possibility to reach this goal combining two basic approaches: (i) Super Sense Tagging (SST) and (ii) YAGO. Thanks to these two powerful tools we could automatically create a corpus set in order to train the SuperSense Tagger. The general F1 is over 76% and the model is publicly available.

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Issue ownership is commonly conceptualized as multidimensional, consisting of a "competence" dimension and an "associative" dimension. Because existing operationalizations of issue ownership tap only the former dimension, we focus on associative issue ownership: the spontaneous identification between specific issues and specific parties in the minds of voters. Survey evidence from Belgium shows that the associative dimension of issue ownership can be measured, that it differs from competence issue ownership, and that it is an independent determinant of voting behavior.

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The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.

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Miniature diffusion size classifiers (miniDiSC) are novel handheld devices to measure ultrafine particles (UFP). UFP have been linked to the development of cardiovascular and pulmonary diseases; thus, detection and quantification of these particles are important for evaluating their potential health hazards. As part of the UFP exposure assessments of highwaymaintenance workers in western Switzerland, we compared a miniDiSC with a portable condensation particle counter (P-TRAK). In addition, we performed stationary measurements with a miniDiSC and a scanning mobility particle sizer (SMPS) at a site immediately adjacent to a highway. Measurements with miniDiSC and P-TRAK correlated well (correlation of r = 0.84) but average particle numbers of the miniDiSC were 30%âeuro"60% higher. This difference was significantly increased for mean particle diameters below 40 nm. The correlation between theminiDiSC and the SMPSduring stationary measurements was very high (r = 0.98) although particle numbers from the miniDiSC were 30% lower. Differences between the three devices were attributed to the different cutoff diameters for detection. Correction for this size dependent effect led to very similar results across all counters.We did not observe any significant influence of other particle characteristics. Our results suggest that the miniDiSC provides accurate particle number concentrations and geometric mean diameters at traffic-influenced sites, making it a useful tool for personal exposure assessment in such settings.

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Gas chromatography (GC) is an analytical tool very useful to investigate the composition of gaseous mixtures. However, hydrogen (H2) detection after a GC separation is only possible with a Thermal Conductivity Detector (TCD), a Helium Ionisation Detector (HID) or expensive Atomic Emission Detector (AED). Recently, indirect H2 detection by GC coupled to mass spectrometry (MS) was demonstrated but the mechanism of carrier gas protonation remained unclear. With electron impact as ionisation source of MS and helium (He) as GC carrier gas, H2 is not ionised according the expected Penning ionisation neither according to the Associative ionisation. Rearrangement ionisation (RI) was found to be the main channel for H2 and D2 ionisation under GC-MS conditions used in most of laboratories using GC-MS, leading to the formation of [He−H]+ and [He−D]+ ions.

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Campaigns raise public interest in politics and allow parties to convey their messages to voters. However, voters' exposure and attention during campaigns are biased towards parties and candidates they like. This hinders parties' ability to reach new voters. This paper theorises and empirically tests a simple way in which parties can break partisan selective attention: owning an issue. When parties own issues that are important for a voter, that voter is more likely to notice them. Using survey data collected prior to the 2009 Belgian regional elections it is shown that this effect exists independent of partisan preferences and while controlling for the absolute visibility of a party in the media. This indicates that issue ownership has an independent impact on voters' attention to campaigns. This finding shows that owning salient issues yields (potential) advantages for parties, since getting noticed is a prerequisite for conveying electoral messages and increasing electoral success.

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Whereas extant work on issue ownership treats voters' issue ownership perceptions as independent variables to explain electoral choice or party behaviour, this article examines whether parties can, by communicating on an issue, turn voters' perceptions of issue ownership to their advantage. In contrast to most previous studies that have focused on competence ownership - measured as a party's capacity to handle an issue - this article analyses the short-term and long-term impact of campaign messages on voters' perceptions of associative ownership, which refers to the voters' spontaneous party-issue association, regardless of whether or not voters consider the party as the most competent at dealing with the issue at hand. Based on an online experimental design in Belgium, we show that parties are unable to steal issues that are associated with another party. However, by communicating on their own issues, parties can reinforce their reputation as an associative owner - but only in the short run and only if their previous ownership reputation is not overly strong.

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The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

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We have studied the motor abilities and associative learning capabilities of adult mice placed in different enriched environments. Three-month-old animals were maintained for a month alone (AL), alone in a physically enriched environment (PHY), and, finally, in groups in the absence (SO) or presence (SOPHY) of an enriched environment. The animals' capabilities were subsequently checked in the rotarod test, and for classical and instrumental learning. The PHY and SOPHY groups presented better performances in the rotarod test and in the acquisition of the instrumental learning task. In contrast, no significant differences between groups were observed for classical eyeblink conditioning. The four groups presented similar increases in the strength of field EPSPs (fEPSPs) evoked at the hippocampal CA3-CA1 synapse across classical conditioning sessions, with no significant differences between groups. These trained animals were pulse-injected with bromodeoxyuridine (BrdU) to determine hippocampal neurogenesis. No significant differences were found in the number of NeuN/BrdU double-labeled neurons. We repeated the same BrdU study in one-month-old mice raised for an additional month in the above-mentioned four different environments. These animals were not submitted to rotarod or conditioned tests. Non-trained PHY and SOPHY groups presented more neurogenesis than the other two groups. Thus, neurogenesis seems to be related to physical enrichment at early ages, but not to learning acquisition in adult mice.

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In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.