Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

The pattern recognition receptors expressed on neutrophils and the associated cytokine profile from different aged patients with Candida-related denture stomatitis

This study evaluated the expression of pattern recognition receptors (PRRs) and activation factors associated with salivary and blood neutrophils from different aged patients diagnosed with Candida-related denture stomatitis (DS). Expression of neutrophil PRRs was determined by flow cytometry and immunofluorescence, and the levels of selected cytokines that influence immune activation were determined by ELISA. The salivary (but not the serum derived) neutrophils of individuals with DS were found to have an increased expression of CD69 regardless of the age of the patient compared to patients without DS. However, these salivary neutrophils had a lower expression of CD66b and CD64. Expression of TLR2 was lower on the salivary-and serum-derived neutrophils from elderly individuals compared to the neutrophils of younger subjects, regardless of whether the individual had DS. Salivary interleukin (IL)-4 was elevated in both of the elderly subject groups (with or without DS). Only elderly DS patients were observed to have increased serum IL-4 levels and reduced salivary IL-12 levels. Younger DS patients showed an increase in salivary IL-10 levels, and both the saliva and the serum levels of IFN-gamma were increased in all of the younger subjects. Our data demonstrated that changes in both the oral immune cells and the protein components could be associated with DS. Furthermore, changes in the blood-derived factors were more associated with age than DS status. (C) 2012 Elsevier Inc. All rights reserved.

Expression of non-TLR pattern recognition receptors in the spleen of BALB/c mice infected with Plasmodium yoelii and Plasmodium chabaudi chabaudi AS

The spleen plays a crucial role in the development of immunity to malaria, but the role of pattern recognition receptors (PRRs) in splenic effector cells during malaria infection is poorly understood. In the present study, we analysed the expression of selected PRRs in splenic effector cells from BALB/c mice infected with the lethal and non-lethal Plasmodium yoelii strains 17XL and 17X, respectively, and the non-lethal Plasmodium chabaudi chabaudi AS strain. The results of these experiments showed fewer significant changes in the expression of PRRs in AS-infected mice than in 17X and 17XL-infected mice. Mannose receptor C type 2 (MRC2) expression increased with parasitemia, whereas Toll-like receptors and sialoadhesin (Sn) decreased in mice infected with P. chabaudi AS. In contrast, MRC type 1 (MRC1), MRC2 and EGF-like module containing mucin-like hormone receptor-like sequence 1 (F4/80) expression decreased with parasitemia in mice infected with 17X, whereas MRC1 an MRC2 increased and F4/80 decreased in mice infected with 17XL. Furthermore, macrophage receptor with collagenous structure and CD68 declined rapidly after initial parasitemia. SIGNR1 and Sn expression demonstrated minor variations in the spleens of mice infected with either strain. Notably, macrophage scavenger receptor (Msr1) and dendritic cell-associated C-type lectin 2 expression increased at both the transcript and protein levels in 17XL-infected mice with 50% parasitemia. Furthermore, the increased lethality of 17X infection in Msr1 -/- mice demonstrated a protective role for Msr1. Our results suggest a dual role for these receptors in parasite clearance and protection in 17X infection and lethality in 17XL infection.

Pattern recognition analysis on heavy ion reaction data

One of the problems in the analysis of nucleus-nucleus collisions is to get information on the value of the impact parameter b. This work consists in the application of pattern recognition techniques aimed at associating values of b to groups of events. To this end, a support vec- tor machine (SVM) classifier is adopted to analyze multifragmentation reactions. This method allows to backtracing the values of b through a particular multidimensional analysis. The SVM classification con- sists of two main phase. In the first one, known as training phase, the classifier learns to discriminate the events that are generated by two different model:Classical Molecular Dynamics (CMD) and Heavy- Ion Phase-Space Exploration (HIPSE) for the reaction: 58Ni +48 Ca at 25 AMeV. To check the classification of events in the second one, known as test phase, what has been learned is tested on new events generated by the same models. These new results have been com- pared to the ones obtained through others techniques of backtracing the impact parameter. Our tests show that, following this approach, the central collisions and peripheral collisions, for the CMD events, are always better classified with respect to the classification by the others techniques of backtracing. We have finally performed the SVM classification on the experimental data measured by NUCL-EX col- laboration with CHIMERA apparatus for the previous reaction.

Non-evolutive pattern recognition techniques: An application in medical image diagnostics

Lo studio dell’intelligenza artificiale si pone come obiettivo la risoluzione di una classe di problemi che richiedono processi cognitivi difficilmente codificabili in un algoritmo per essere risolti. Il riconoscimento visivo di forme e figure, l’interpretazione di suoni, i giochi a conoscenza incompleta, fanno capo alla capacità umana di interpretare input parziali come se fossero completi, e di agire di conseguenza. Nel primo capitolo della presente tesi sarà costruito un semplice formalismo matematico per descrivere l’atto di compiere scelte. Il processo di “apprendimento” verrà descritto in termini della massimizzazione di una funzione di prestazione su di uno spazio di parametri per un ansatz di una funzione da uno spazio vettoriale ad un insieme finito e discreto di scelte, tramite un set di addestramento che descrive degli esempi di scelte corrette da riprodurre. Saranno analizzate, alla luce di questo formalismo, alcune delle più diffuse tecniche di artificial intelligence, e saranno evidenziate alcune problematiche derivanti dall’uso di queste tecniche. Nel secondo capitolo lo stesso formalismo verrà applicato ad una ridefinizione meno intuitiva ma più funzionale di funzione di prestazione che permetterà, per un ansatz lineare, la formulazione esplicita di un set di equazioni nelle componenti del vettore nello spazio dei parametri che individua il massimo assoluto della funzione di prestazione. La soluzione di questo set di equazioni sarà trattata grazie al teorema delle contrazioni. Una naturale generalizzazione polinomiale verrà inoltre mostrata. Nel terzo capitolo verranno studiati più nel dettaglio alcuni esempi a cui quanto ricavato nel secondo capitolo può essere applicato. Verrà introdotto il concetto di grado intrinseco di un problema. Verranno inoltre discusse alcuni accorgimenti prestazionali, quali l’eliminazione degli zeri, la precomputazione analitica, il fingerprinting e il riordino delle componenti per lo sviluppo parziale di prodotti scalari ad alta dimensionalità. Verranno infine introdotti i problemi a scelta unica, ossia quella classe di problemi per cui è possibile disporre di un set di addestramento solo per una scelta. Nel quarto capitolo verrà discusso più in dettaglio un esempio di applicazione nel campo della diagnostica medica per immagini, in particolare verrà trattato il problema della computer aided detection per il rilevamento di microcalcificazioni nelle mammografie.

The therapeutic potential of the humoral pattern recognition molecule PTX3 in chronic lung infection caused by Pseudomonas aeruginosa

Chronic lung infections by Pseudomonas aeruginosa strains are a major cause of morbidity and mortality in cystic fibrosis (CF) patients. Although there is no clear evidence for a primary defect in the immune system of CF patients, the host is generally unable to clear P. aeruginosa from the airways. PTX3 is a soluble pattern recognition receptor that plays nonredundant roles in the innate immune response to fungi, bacteria, and viruses. In particular, PTX3 deficiency is associated with increased susceptibility to P. aeruginosa lung infection. To address the potential therapeutic effect of PTX3 in P. aeruginosa lung infection, we established persistent and progressive infections in mice with the RP73 clinical strain RP73 isolated from a CF patient and treated them with recombinant human PTX3. The results indicated that PTX3 has a potential therapeutic effect in P. aeruginosa chronic lung infection by reducing lung colonization, proinflammatory cytokine levels (CXCL1, CXCL2, CCL2, and IL-1β), and leukocyte recruitment in the airways. In models of acute infections and in in vitro assays, the prophagocytic effect of PTX3 was maintained in C1q-deficient mice and was lost in C3- and Fc common γ-chain-deficient mice, suggesting that facilitated recognition and phagocytosis of pathogens through the interplay between complement and FcγRs are involved in the therapeutic effect mediated by PTX3. These data suggested that PTX3 is a potential therapeutic tool in chronic P. aeruginosa lung infections, such as those seen in CF patients.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Analysis of pattern recognition techniques for in-air signature biometrics

As a result of advances in mobile technology, new services which benefit from the ubiquity of these devices are appearing. Some of these services require the identification of the subject since they may access private user information. In this paper, we propose to identify each user by drawing his/her handwritten signature in the air (in-airsignature). In order to assess the feasibility of an in-airsignature as a biometric feature, we have analysed the performance of several well-known patternrecognitiontechniques—Hidden Markov Models, Bayes classifiers and dynamic time warping—to cope with this problem. Each technique has been tested in the identification of the signatures of 96 individuals. Furthermore, the robustness of each method against spoofing attacks has also been analysed using six impostors who attempted to emulate every signature. The best results in both experiments have been reached by using a technique based on dynamic time warping which carries out the recognition by calculating distances to an average template extracted from several training instances. Finally, a permanence analysis has been carried out in order to assess the stability of in-airsignature over time.

Damage detection by using FBGs and strain field pattern recognition techniques

A novel methodology for damage detection and location in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (principal component analysis) and damage indices (T 2 and Q). We propose the use of fiber Bragg gratings (FBGs) as strain sensors

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.