Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

CONTEXT TREE SELECTION AND LINGUISTIC RHYTHM RETRIEVAL FROM WRITTEN TEXTS

The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet they are conjectured, on linguistic grounds, to implement different rhythms. We show that this linguistic question can be formulated as a problem of model selection in the class of variable length Markov chains. To carry on this approach, we compare texts from European and Brazilian Portuguese. These texts are previously encoded according to some basic rhythmic features of the sentences which can be automatically retrieved. This is an entirely new approach from the linguistic point of view. Our statistical contribution is the introduction of the smallest maximizer criterion which is a constant free procedure for model selection. As a by-product, this provides a solution for the problem of optimal choice of the penalty constant when using the BIC to select a variable length Markov chain. Besides proving the consistency of the smallest maximizer criterion when the sample size diverges, we also make a simulation study comparing our approach with both the standard BIC selection and the Peres-Shields order estimation. Applied to the linguistic sample constituted for our case study, the smallest maximizer criterion assigns different context-tree models to the two dialects of Portuguese. The features of the selected models are compatible with current conjectures discussed in the linguistic literature.

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

Mechanical thrombectomy with solitaire stent retrieval for acute ischemic stroke in a Brazilian population

OBJECTIVE: Large vessel occlusion in acute ischemic stroke is associated with low recanalization rates under intravenous thrombolysis. We evaluated the safety and efficacy of the Solitaire AB stent in treating acute ischemic stroke. METHODS: Patients presenting with acute ischemic stroke were prospectively evaluated. The neurological outcomes were assessed using the National Institutes of Health Stroke Scale and the modified Rankin Scale. Time was recorded from the symptom onset to the recanalization and procedure time. Recanalization was assessed using the thrombolysis in cerebral infarction score. RESULTS: Twenty-one patients were evaluated. The mean patient age was 65, and the National Institutes of Health Stroke Scale scores ranged from 7 to 28 (average 17+/-6.36) at presentation. The vessel occlusions occurred in the middle cerebral artery (61.9%), distal internal carotid artery (14.3%), tandem carotid occlusion (14.3%), and basilar artery (9.5%). Primary thrombectomy, rescue treatment and a bridging approach represented 66.6%, 28.6%, and 4.8% of the performed procedures, respectively. The mean time from symptom onset to recanalization was 356.5+/-107.8 minutes (range, 80-586 minutes). The mean procedure time was 60.4+/-58.8 minutes (range, 14-240 minutes). The overall recanalization rate (thrombolysis in cerebral infarction scores of 3 or 2b) was 90.4%, and the symptomatic intracranial hemorrhage rate was 14.2%. The National Institutes of Health Stroke Scale scores at discharge ranged from 0 to 25 (average 6.9+/-7). At three months, 61.9% of the patients had a modified Rankin Scale score of 0 to 2, with an overall mortality rate of 9.5%. CONCLUSIONS: Intra-arterial thrombectomy with the Solitaire AB device appears to be safe and effective. Large randomized trials are necessary to confirm the benefits of this approach in acute ischemic stroke.

Acceptance Threshold Hypothesis is Supported by Chemical Similarity of Cuticular Hydrocarbons in a Stingless Bee, Melipona asilvai

The ability to discriminate nestmates from non-nestmates in insect societies is essential to protect colonies from conspecific invaders. The acceptance threshold hypothesis predicts that organisms whose recognition systems classify recipients without errors should optimize the balance between acceptance and rejection. In this process, cuticular hydrocarbons play an important role as cues of recognition in social insects. The aims of this study were to determine whether guards exhibit a restrictive level of rejection towards chemically distinct individuals, becoming more permissive during the encounters with either nestmate or non-nestmate individuals bearing chemically similar profiles. The study demonstrates that Melipona asilvai (Hymenoptera: Apidae: Meliponini) guards exhibit a flexible system of nestmate recognition according to the degree of chemical similarity between the incoming forager and its own cuticular hydrocarbons profile. Guards became less restrictive in their acceptance rates when they encounter non-nestmates with highly similar chemical profiles, which they probably mistake for nestmates, hence broadening their acceptance level.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Phylogenetic and Similarity Analysis of HTLV-1 Isolates from HIV-Coinfected Patients from the South and Southeast Regions of Brazil

HTLV-1 is endemic in Brazil and HIV/ HTLV-1 coinfection has been detected, mostly in the northeast region. Cosmopolitan HTLV-1a is the main subtype that circulates in Brazil. This study characterized 17 HTLV-1 isolates from HIV coinfected patients of southern (n = 7) and southeastern (n = 10) Brazil. HTLV-1 provirus DNA was amplified by nested PCR (env and LTR) and sequenced. Env sequences (705 bp) from 15 isolates and LTR sequences (731 bp) from 17 isolates showed 99.5% and 98.8% similarity among sequences, respectively. Comparing these sequences with ATK (HTLV-1a) and Mel5 (HTLV-1c) prototypes, similarities of 99% and 97.4%, respectively, for env and LTR with ATK, and 91.6% and 90.3% with Mel5, were detected. Phylogenetic analysis showed that all sequences belonged to the transcontinental subgroup A of the Cosmopolitan subtype, clustering in two Latin American clusters.