Reconstructing (h,v)-convex 2-dimensional patterns of objects from approximate horizontal and vertical projections

The problem of reconstructing a pattern of an object from its approximate discrete orthogonal projections in a 2-dimensional grid, may have no solution because the inaccuracy in the measurements of the projections may generate an inconsistent problem. To attempt to overcome this difficulty, one seeks to reconstruct a pattern with projection values having possibly some bounded differences with the given projection values and minimizing the sum of the absolute differences.

This paper addresses the problem of reconstructing a pattern with a difference at most equal to +1 or −1 between each of its projection values and the corresponding given projection value. We deal with the case of patterns which have to be horizontally and vertically convex and the case of patterns which have to be moreover connected, the so-called convex polyominoes. We show that in both cases, the problem of reconstructing a pattern can be transformed into a Satisfiability (SAT) Problem. This is done in order to take advantage of the recent advances in the design of solvers for the SAT Problem. We show, experimentally, that by adding two important features to CSAT (an efficient SAT solver), optimal patterns can be found if there exist feasible ones. These two features are: first, a method that extracts in linear time an optimal pattern from a set of feasible patterns grouped in a generic pattern (obtaining a generic pattern may be exponential in the worst case) and second, a method that computes actively a lower bound of the sum of absolute differences that can be obtained from a partially defined pattern. This allows to prune the search tree if this lower bound exceeds the best sum of absolute differences found so far.     

Recognition of Simple Enlarged Horn Formulas and Simple Extended Horn Formulas

The enlarged Horn formulas generalize the extended Horn formulas introduced by Chandru and Hooker (1991). Their satisfying truth assignments can be generated with polynomial delay. Unfortunately no polynomial algorithm is known for recognizing enlarged Horn formulas or extended Horn formulas. In this paper we define the class of simple enlarged Horn formulas, a subclass of the enlarged Horn formulas, that contains the simple extended Horn formulas introduced by Swaminathan and Wagner (1995). We present recognition algorithms for the simple enlarged Horn formulas and the simple extended Horn formulas whose complexity is bounded by the complexity of the arborescence-realization problem.     

Direction relations and two-dimensional range queries: optimisation techniques

This paper defines direction relations (e.g., north, northeast) between two-dimensional objects and shows how they can be efficiently retrieved using B-, KDB- and R- tree-based data structures. Essentially, our work studies optimisation techniques for 2D range queries that arise during the processing of direction relations. We test the efficiency of alternative indexing methods through extensive experimentation and present analytical models that estimate their performance. The analytical estimates are shown to be very close to the actual results and can be used by spatial query optimizers in order to predict the retrieval cost. In addition, we implement modifications of the existing structures that yield better performance for certain queries. We conclude the paper by discussing the most suitable method depending on the type of the range and the properties of the data.     

Optimal Neural Network Model for Characterization of Process-Induced Damage in Drilling Carbon Fiber Reinforced Epoxy Composites

In this paper, a method for robust design of a neural network (NN) model for prediction of delamination (Da), damage width (Dw), and hole surface roughness (Ra) during drilling in carbon fiber reinforced epoxy (BMS 8-256) is presented. This method is based on a parametric analysis of neural network models using a design of experiments approach. The effects of number of neurons (N), hidden layers (L), activation function (AF), and learning algorithm (LA) on the mean square error (MSE) of model prediction are quantified. Using the aforementioned method, a robust NN model was developed that predicted process-induced damage with high accuracy.     

Differential Expression of Proteins in an Atypical Presentation of Autoimmune Lymphoproliferative Syndrome

Autoimmune lymphoproliferative syndrome (ALPS) is a rare disease defined as a defect in the lymphocyte apoptotic pathway. Currently, the diagnosis of ALPS is based on clinical aspects, defective lymphocyte apoptosis and mutations in Fas, FasL and Casp 10 genes. Despite this, ALPS has been misdiagnosed. The aim of this work was to go one step further in the knowledge of the disease, through a molecular and proteomic analysis of peripheral blood mononuclear cells (PBMCs) from two children, a 13-year-old girl and a 6-year-old boy, called patient 1 and patient 2, respectively, with clinical data supporting the diagnosis of ALPS. Fas, FasL and Casp10 genes from both patients were sequenced, and a sample of the total proteins from patient 1 was analyzed by label-free proteomics. Pathway analysis of deregulated proteins from PBMCs was performed on the STRING and PANTHER bioinformatics databases. A mutation resulting in an in-frame premature stop codon and protein truncation was detected in the Fas gene from patient 2. From patient 1, the proteomic analysis showed differences in the level of expression of proteins involved in, among other processes, cell cycle, regulation of cell cycle arrest and immune response. Noticeably, the most down-regulated protein is an important regulator of the cell cycle process. This could be an explanation of the disease in patient 1.     

Polymer Layered Silicate Nanocomposites

Polymer nanocomposites with layered silicates as the inorganic phase (reinforcement) are discussed. The materials design and synthesis rely on the ability of layered silicates to intercalate in the galleries between their layers a wide range of monomers and polymers. Special emphasis is placed on a new, versatile and environmentally benign synthesis approach by polymer melt intercalation. In contrast to in-situ polymerization and solution intercalation, melt intercalation involves mixing the layered silicate with the polymer and heating the mixture above the softening point of the polymer. Compatibility with various polymers is accomplished by derivatizing the silicates with alkyl ammonium cations via an ion exchange reaction. By fine-tuning the surface characteristics nanodispersion (i. e. intercalation or delamination) can be accomplished. The resulting polymer layered silicate (PLS) nanocomposites exhibit properties dramatically different from their more conventional counterparts. For example, PLS nanocomposites can attain a particular degree of stiffness, strength and barrier properties with far less inorganic content than comparable glass- or mineral reinforced polymers and, therefore, they are far lighter in weight. In addition, PLS nanocomposites exhibit significant increase in thermal stability as well as self-extinguishing characteristics. The combination of improved properties, convenient processing and low cost has already led to a few commercial applications with more currently under development.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Towards a Possibilistic Logic Handling of Preferences

The classical way of encoding preferences in decision theory is by means of utility or value functions. However agents are not always able to deliver such a function directly. In this paper, we relate three different ways of specifying preferences, namely by means of a set of particular types of constraints on the utility function, by means of an ordered set of prioritized goals expressed by logical propositions, and by means of an ordered set of subsets of possible choices reaching the same level of satisfaction. These different expression modes can be handled in a weighted logical setting, here the one of possibilistic logic. The aggregation of preferences pertaining to different criteria can then be handled by fusing sets of prioritized goals. Apart from a better expressivity, the benefits of a logical representation of preferences are to put them in a suitable format for reasoning purposes, or for modifying them.     

A noise-resistant algorithm for grid finding in microarray image analysis

We present an algorithm for automatic spot localization for microarray images with rectangular spot and block packing. As an input, the algorithm requires only the common array design parameters: number of block rows and columns and number of spot rows and columns within each block. It proved to be robust with respect to different types of contamination and can tolerate a high percentage of the missing spots. The validity of the developed algorithm has been tested and confirmed using a large set of images of various designs from different microarray platforms. Comparison with academic and commercial packages has shown that for uncontaminated images our algorithm performs similarly, whereas for certain problematic images it outperforms the other packages.Patent pending.     

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decision-maker's attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.