Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Simulação tridimensional adaptativa da separação das fases de uma mistura bifásica usando a equação de Cahn-Hilliard

Simulamos a separação dos componentes de uma mistura bifásica com a equação de Cahn-Hilliard. Esta equação contém intrincados termos não lineares e derivadas de alta ordem. Além disso, a delgada região de transição entre os componentes da mistura requer muita resolução. Assim, determinar a solução numérica da equação de Cahn-Hilliard não é uma tarefa fácil, principalmente em três dimensões. Conseguimos a resolução exigida no tempo usando uma discretização semi-implícita de segunda ordem. No espaço, obtemos a precisão requerida utilizando malhas refinadas localmente com a estratégia AMR. Essas malhas se adaptam dinamicamente para recobrir a região de transição. O sistema linear proveniente da discretização é solucionado por intermédio de técnicas multinível-multigrid.

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.

Food Competition in a Semi-Free-Ranging Cebus apella Group

The competitive regime faced by individuals is fundamental to modelling the evolution of social organization. In this paper, we assess the relative importance of contest and scramble food competition on the social dynamics of a provisioned semi-free-ranging Cebus apella group (n=18). Individuals competed directly for provisioned and clumped foods. Effects of indirect competition were apparent with individuals foraging in different areas and with increased group dispersion during periods of low food abundance. We suggest that both forms of competition can act simultaneously and to some extent synergistically in their influence on social dynamics; the combination of social and ecological opportunities for competition and how those opportunities are exploited both influence the nature of the relationships within social groups of primates and underlie the evolved social structure. Copyright (c) 2008 S. Karger AG, Basel

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Describing the Situational Contexts of Sweetened Product Consumption in a Middle Eastern Canadian Community: Application of a Mixed Method Design

Little is known about the situational contexts in which individuals consume processed sources of dietary sugars. This study aimed to describe the situational contexts associated with the consumption of sweetened food and drink products in a Catholic Middle Eastern Canadian community. A two-stage exploratory sequential mixed-method design was employed with a rationale of triangulation. In stage 1 (n = 62), items and themes describing the situational contexts of sweetened food and drink product consumption were identified from semi-structured interviews and were used to develop the content for the Situational Context Instrument for Sweetened Product Consumption (SCISPC). Face validity, readability and cultural relevance of the instrument were assessed. In stage 2 (n = 192), a cross-sectional study was conducted and exploratory factor analysis was used to examine the structure of themes that emerged from the qualitative analysis as a means of furthering construct validation. The SCISPC reliability and predictive validity on the daily consumption of sweetened products were also assessed. In stage 1, six themes and 40-items describing the situational contexts of sweetened product consumption emerged from the qualitative analysis and were used to construct the first draft of the SCISPC. In stage 2, factor analysis enabled the clarification and/or expansion of the instrument's initial thematic structure. The revised SCISPC has seven factors and 31 items describing the situational contexts of sweetened product consumption. Initial validation of the instrument indicated it has excellent internal consistency and adequate test-retest reliability. Two factors of the SCISPC had predictive validity for the daily consumption of total sugar from sweetened products (Snacking and Energy demands) while the other factors (Socialization, Indulgence, Constraints, Visual Stimuli and Emotional needs) were rather associated to occasional consumption of these products.

AN EFFICIENT HPLC-UV METHOD FOR THE QUANTITATIVE DETERMINATION OF CEFADROXIL IN HUMAN PLASMA AND ITS APPLICATION IN PHARMACOKINETIC STUDIES

Cefadroxil is a semi-synthetic first-generation oral cephalosporin used in the treatment of mild to moderate infections of the respiratory and urinary tracts, skin and soft tissue infections. In this work a simple, rapid, economic and sensitive HPLC-UV method is described for the quantitative determination of cefadroxil in human plasma samples using lamivudine as internal standard. Sample pre-treatment was accomplished through protein precipitation with acetonitrile and chromatographic separation was performed with a mobile phase consisting of a mixture of sodium dihydrogen phosphate monohydrate solution, methanol and acetonitrile in the ratio of 90:8:2 (v/v/v) at a flow rate of 1.0mL/min. The proposed method is linear between 0.4 to 40.0 mu g/mL and its average recovery is 102.21% for cefadroxil and 97.94% for lamivudine. The method is simple, sensitive, reproducible, less time consuming for determination of cefadroxil in human plasma. The method can therefore be recommended for pharmacokinetics studies, including bioavailability and bioequivalence studies.

BEM modeling of saturated porous media susceptible to damage

This paper deals with the numerical analysis of saturated porous media, taking into account the damage phenomena on the solid skeleton. The porous media is taken into poro-elastic framework, in full-saturated condition, based on Biot's Theory. A scalar damage model is assumed for this analysis. An implicit boundary element method (BEM) formulation, based on time-independent fundamental solutions, is developed and implemented to couple the fluid flow and two-dimensional elastostatic problems. The integration over boundary elements is evaluated using a numerical Gauss procedure. A semi-analytical scheme for the case of triangular domain cells is followed to carry out the relevant domain integrals. The non-linear problem is solved by a Newton-Raphson procedure. Numerical examples are presented, in order to validate the implemented formulation and to illustrate its efficacy. (C) 2011 Elsevier Ltd. All rights reserved.

Utilization of fringe projection technique for evaluation of wound dimensions and of healing progress.

Background: The methods used for evaluating wound dimensions, especially the chronic ones, are invasive and inaccurate. The fringe projection technique with phase shift is a non-invasive, accurate and low-cost optical method. Objective: The aim is to validate the technique through the determination of dimensions of objects of known topography and with different geometries and colors to simulate the wounds and tones of skin color. Taking into account the influence of skin wound optical factors, the technique will be used to evaluate actual patients’ wound dimensions and to study its limitations in this application. Methods: Four sinusoidal fringe patterns, displaced ¼ of period each, were projected onto the objects surface. The object dimensions were obtained from the unwrapped phase map through the observation of the fringe deformations caused by the object topography and using phase shift analysis. An object with simple geometry was used for dimensional calibration and the topographic dimensions of the others were determined from it. After observing the compatibility with the data and validating the method, it was used for measuring the dimensions of real patients’ wounds. Results and Conclusions: The discrepancies between actual topography and dimensions determined with Fringe Projection Technique and for the known object were lower than 0.50 cm. The method was successful in obtaining the topography of real patient’s wounds. Objects and wounds with sharp topographies or causing shadow or reflection are difficult to be evaluated with this technique.