Meaning of the λ parameter of skew–normal and log–skew normal distributions in fluid geochemistry

The literature related to skew–normal distributions has grown rapidly in recent years but at the moment few applications concern the description of natural phenomena with this type of probability models, as well as the interpretation of their parameters. The skew–normal distributions family represents an extension of the normal family to which a parameter (λ) has been added to regulate the skewness. The development of this theoretical field has followed the general tendency in Statistics towards more flexible methods to represent features of the data, as adequately as possible, and to reduce unrealistic assumptions as the normality that underlies most methods of univariate and multivariate analysis. In this paper an investigation on the shape of the frequency distribution of the logratio ln(Cl−/Na+) whose components are related to waters composition for 26 wells, has been performed. Samples have been collected around the active center of Vulcano island (Aeolian archipelago, southern Italy) from 1977 up to now at time intervals of about six months. Data of the logratio have been tentatively modeled by evaluating the performance of the skew–normal model for each well. Values of the λ parameter have been compared by considering temperature and spatial position of the sampling points. Preliminary results indicate that changes in λ values can be related to the nature of environmental processes affecting the data

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Procedural Meaning Explicatures in the Development of New Subordination Strategies in Romance

The sociocultural changes that led to the genesis of Romance languages widened the gap between oral and written patterns, which display different discoursive and linguistic devices. In early documents, discoursive implicatures connecting propositions were not generally codified, so that the reader should furnish the correct interpretation according to his own perception of real facts; which can still be attested in current oral utterances. Once Romance languages had undergone several levelling processes which concluded in the first standardizations, implicatures became explicatures and were syntactically codified by means of univocal new complex conjunctions. As a consequence of the emergence of these new subordination strategies, a freer distribution of the information conveyed by the utterances is allowed. The success of complex structural patterns ran alongside of the genesis of new narrative genres and the generalization of a learned rhetoric. Both facts are a spontaneous effect of new approaches to the act of reading. Ancient texts were written to be read to a wide audience, whereas those printed by the end of the XV th century were conceived to be read quietly, in a low voice, by a private reader. The goal of this paper is twofold, since we will show that: a) The development of new complex conjunctions through the history of Romance languages accommodates to four structural patterns that range from parataxis to hypotaxis. b) This development is a reflex of the well known grammaticalization path from discourse to syntax that implies the codification of discoursive strategies (Givón 2 1979, Sperber and Wilson 1986, Carston 1988, Grice 1989, Bach 1994, Blackemore 2002, among others]