Linguística funcional: princípios, temas, objetos e conexões

This paper aims at offering an overview of the different functionalist approaches in all their programmes, by verifying which functionalist premises would be relevant in each case. This may be a way to elucidate the differentiated approach of certain themes and of certain objects of analysis, and especially, to contribute to a better account of certain relations between each proposal and others realms of knowledge.

Combined analysis of categorical and numerical descriptors of Australian groundnut accessions using nonlinear principal component analysis

For users of germplasm collections, the purpose of measuring characterization and evaluation descriptors, and subsequently using statistical methodology to summarize the data, is not only to interpret the relationships between the descriptors, but also to characterize the differences and similarities between accessions in relation to their phenotypic variability for each of the measured descriptors. The set of descriptors for the accessions of most germplasm collections consists of both numerical and categorical descriptors. This poses problems for a combined analysis of all descriptors because few statistical techniques deal with mixtures of measurement types. In this article, nonlinear principal component analysis was used to analyze the descriptors of the accessions in the Australian groundnut collection. It was demonstrated that the nonlinear variant of ordinary principal component analysis is an appropriate analytical tool because subspecies and botanical varieties could be identified on the basis of the analysis and characterized in terms of all descriptors. Moreover, outlying accessions could be easily spotted and their characteristics established. The statistical results and their interpretations provide users with a more efficient way to identify accessions of potential relevance for their plant improvement programs and encourage and improve the usefulness and utilization of germplasm collections.

A perfusion fMRI investigation of thematic and categorical context effects in the spoken production of object names

The context in which objects are presented influences the speed at which they are named. We employed the blocked cyclic naming paradigm and perfusion functional magnetic resonance imaging (fMRI) to investigate the mechanisms responsible for interference effects reported for thematicallyand categorically related compared to unrelated contexts. Naming objects in categorically homogeneous contexts induced a significant interference effect that accumulated from the second cycle onwards. This interference effect was associated with significant perfusion signal decreases in left middle and posterior lateral temporal cortex and the hippocampus. By contrast, thematically homogeneous contexts facilitated naming latencies significantly in the first cycle and did not differ from heterogeneous contexts thereafter, nor were they associated with any perfusion signal changes compared to heterogeneous contexts. These results are interpreted as being consistent with an account in which the interference effect both originates and has its locus at the lexical level, with an incremental learning mechanism adapting the activation levels of target lexical representations following access. We discuss the implications of these findings for accounts that assume thematic relations can be active lexical competitors or assume mandatory involvement of top-down control mechanisms in interference effects during naming.

Differential processing of thematic and categorical conceptual relations in spoken word production

Studies of semantic context effects in spoken word production have typically distinguished between categorical (or taxonomic) and associative relations. However, associates tend to confound semantic features or morphological representations, such as whole-part relations and compounds (e.g., BOAT-anchor, BEE-hive). Using a picture-word interference paradigm and functional magnetic resonance imaging (fMRI), we manipulated categorical (COW-rat) and thematic (COW-pasture) TARGET-distractor relations in a balanced design, finding interference and facilitation effects on naming latencies, respectively, as well as differential patterns of brain activation compared with an unrelated distractor condition. While both types of distractor relation activated the middle portion of the left middle temporal gyrus (MTG) consistent with retrieval of conceptual or lexical representations, categorical relations involved additional activation of posterior left MTG, consistent with retrieval of a lexical cohort. Thematic relations involved additional activation of the left angular gyrus. These results converge with recent lesion evidence implicating the left inferior parietal lobe in processing thematic relations and may indicate a potential role for this region during spoken word production.

New insights from IODP Expedition 340 offshore Montserrat: First drilling of large volcanic island landslides

Montserrat now provides one of the most complete datasets for understanding the character and tempo of hazardous events at volcanic islands. Much of the erupted material ends up offshore, and this offshore record may be easier to date due to intervening hemiplegic sediments between event beds. The offshore dataset includes the first scientific drilling of volcanic island landslides during IODP Expedition 340, together with an unusually comprehensive set of shallow sediment cores and 2-D and 3-D seismic surveys. Most recently in 2013, Remotely Operated Vehicle (ROV) dives mapped and sampled the surface of the main landslide deposits. This contribution aims to provide an overview of key insights from ongoing work on IODP Expedition 340 Sites offshore Montserrat.Key objectives are to understand the composition (and hence source), emplacement mechanism (and hence tsunami generation) of major landslides, together with their frequency and timing relative to volcanic eruption cycles. The most recent major collapse event is Deposit 1, which involved ~1.8 km cubed of material and produced a blocky deposit at ~12-14ka. Deposit 1 appears to have involved not only the volcanic edifice, but also a substantial component of a fringing bioclastic shelf, and material locally incorporated from the underlying seafloor. This information allows us to test how first-order landslide morphology (e.g. blocky or elongate lobes) is related to first-order landslide composition. Preliminary analysis suggests that Deposit 1 occurred shortly before a second major landslide on the SW of the island (Deposit 5). It may have initiated English's Crater, but was not associated with a major change in magma composition. An associated turbidite-stack suggests it was emplaced in multiple stages, separated by at least a few hours and thus reducing the tsunami magnitude. The ROV dives show that mega-blocks in detail comprise smaller-scale breccias, which can travel significant distances without complete disintegration. Landslide Deposit 2 was emplaced at ~130ka, and is more voluminous (~8.4km cubed). It had a much more profound influence on the magmatic system, as it was linked to a major explosive mafic eruption and formation of a new volcanic centre (South Soufriere Hills) on the island. Site U1395 confirms a hypothesis based on the site survey seismic data that Deposit 2 includes a substantial component of pre-existing seafloor sediment. However, surprisingly, this pre-existing seafloor sediment in the lower part of Deposit 2 at Site U1395 is completely undeformed and flat lying, suggesting that Site U1395 penetrated a flat lying block. Work to date material from the upper part of U1396, U1395 and U1394 will also be summarised. This work is establishing a chronostratigraphy of major events over the last 1 Ma, with particularly detailed constraints during the last ~250ka. This is helping us to understand whether major landslides are related to cycles of volcanic eruptions.

Submarine record of volcanic island construction and collapse in the Lesser Antilles arc: First scientific drilling of submarine volcanic island landslides by IODP Expedition 340

IODP Expedition 340 successfully drilled a series of sites offshore Montserrat, Martinique and Dominica in the Lesser Antilles from March to April 2012. These are among the few drill sites gathered around volcanic islands, and the first scientific drilling of large and likely tsunamigenic volcanic island-arc landslide deposits. These cores provide evidence and tests of previous hypotheses for the composition and origin of those deposits. Sites U1394, U1399, and U1400 that penetrated landslide deposits recovered exclusively seafloor sediment, comprising mainly turbidites and hemipelagic deposits, and lacked debris avalanche deposits. This supports the concepts that i/ volcanic debris avalanches tend to stop at the slope break, and ii/ widespread and voluminous failures of preexisting low-gradient seafloor sediment can be triggered by initial emplacement of material from the volcano. Offshore Martinique (U1399 and 1400), the landslide deposits comprised blocks of parallel strata that were tilted or microfaulted, sometimes separated by intervals of homogenized sediment (intense shearing), while Site U1394 offshore Montserrat penetrated a flat-lying block of intact strata. The most likely mechanism for generating these large-scale seafloor sediment failures appears to be propagation of a decollement from proximal areas loaded and incised by a volcanic debris avalanche. These results have implications for the magnitude of tsunami generation. Under some conditions, volcanic island landslide deposits composed of mainly seafloor sediment will tend to form smaller magnitude tsunamis than equivalent volumes of subaerial block-rich mass flows rapidly entering water. Expedition 340 also successfully drilled sites to access the undisturbed record of eruption fallout layers intercalated with marine sediment which provide an outstanding high-resolution data set to analyze eruption and landslides cycles, improve understanding of magmatic evolution as well as offshore sedimentation processes.

A quasi-likelihood approach for ordered categorical data with overdispersion

Quasi-likelihood (QL) methods are often used to account for overdispersion in categorical data. This paper proposes a new way of constructing a QL function that stems from the conditional mean-variance relationship. Unlike traditional QL approaches to categorical data, this QL function is, in general, not a scaled version of the ordinary log-likelihood function. A simulation study is carried out to examine the performance of the proposed QL method. Fish mortality data from quantal response experiments are used for illustration.

Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Categorical Meteorological Products: Evaluation and Analysis

In meteorology, observations and forecasts of a wide range of phenomena for example, snow, clouds, hail, fog, and tornados can be categorical, that is, they can only have discrete values (e.g., "snow" and "no snow"). Concentrating on satellite-based snow and cloud analyses, this thesis explores methods that have been developed for evaluation of categorical products and analyses. Different algorithms for satellite products generate different results; sometimes the differences are subtle, sometimes all too visible. In addition to differences between algorithms, the satellite products are influenced by physical processes and conditions, such as diurnal and seasonal variation in solar radiation, topography, and land use. The analysis of satellite-based snow cover analyses from NOAA, NASA, and EUMETSAT, and snow analyses for numerical weather prediction models from FMI and ECMWF was complicated by the fact that we did not have the true knowledge of snow extent, and we were forced simply to measure the agreement between different products. The Sammon mapping, a multidimensional scaling method, was then used to visualize the differences between different products. The trustworthiness of the results for cloud analyses [EUMETSAT Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS)] compared with ceilometers of the Helsinki Testbed was estimated by constructing confidence intervals (CIs). Bootstrapping, a statistical resampling method, was used to construct CIs, especially in the presence of spatial and temporal correlation. The reference data for validation are constantly in short supply. In general, the needs of a particular project drive the requirements for evaluation, for example, for the accuracy and the timeliness of the particular data and methods. In this vein, we discuss tentatively how data provided by general public, e.g., photos shared on the Internet photo-sharing service Flickr, can be used as a new source for validation. Results show that they are of reasonable quality and their use for case studies can be warmly recommended. Last, the use of cluster analysis on meteorological in-situ measurements was explored. The Autoclass algorithm was used to construct compact representations of synoptic conditions of fog at Finnish airports.

Algorithm for mining outliers in categorical data through ranking

The rapid growth in the field of data mining has lead to the development of various methods for outlier detection. Though detection of outliers has been well explored in the context of numerical data, dealing with categorical data is still evolving. In this paper, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase, this algorithm explores a clustering of the given data, followed by the ranking phase for determining the set of most likely outliers. The proposed algorithm is expected to perform better as it can identify different types of outliers, employing two independent ranking schemes based on the attribute value frequencies and the inherent clustering structure in the given data. Unlike some existing methods, the computational complexity of this algorithm is not affected by the number of outliers to be detected. The efficacy of this algorithm is demonstrated through experiments on various public domain categorical data sets.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.