Improving face recognition with multispectral fusion and support vector machines

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudos de imagens provenientes de membranas de borracha natural com aditivos metálicos utilizando técnicas de Fourier, Gabor e Wavelets

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For t (t) over bar events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p(T) > 0.9GeV is 94% for pseudorapidities of vertical bar eta vertical bar < 0.9 and 85% for 0.9 < vertical bar eta vertical bar < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p(T) = 100GeV emitted at vertical bar eta vertical bar < 1.4, the resolutions are approximately 2.8% in p(T), and respectively, 10 m m and 30 mu m in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10-12 mu m in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung.

Multi-q pattern analysis: A case study in image classification

This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shannon entropy for general pattern recognition, and proposes a multi-q approach to improve pattern analysis using entropy. A series of experiments were carried out for the problem of classifying image patterns. Given a dataset of 40 pattern classes, the goal of our image case study is to assess how well the different entropies can be used to determine the class of a newly given image sample. Our experiments show that the Tsallis entropy using the proposed multi-q approach has great advantages over the Boltzmann-Gibbs-Shannon entropy for pattern classification, boosting image recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy and the multi-q approach. (C) 2012 Elsevier B.V. All rights reserved.

Joint NOD2/RIPK2 Signaling Regulates IL-17 Axis and Contributes to the Development of Experimental Arthritis

Intracellular pattern recognition receptors such as the nucleotide-binding oligomerization domain (NOD)-like receptors family members are key for innate immune recognition of microbial infection and may play important roles in the development of inflammatory diseases, including rheumatic diseases. In this study, we evaluated the role of NOD1 and NOD2 on development of experimental arthritis. Ag-induced arthritis was generated in wild-type, NOD1(-/-)!, NOD2(-/-), or receptor-interacting serine-threonine kinase 2(-/-) (RIPK2(-/-)) immunized mice challenged intra-articularly with methylated BSA. Nociception was determined by electronic Von Frey test. Neutrophil recruitment and histopathological analysis of proteoglycan lost was evaluated in inflamed joints. Joint levels of inflammatory cytokine/chemokine were measured by ELISA. Cytokine (IL-6 and IL-23) and NOD2 expressions were determined in mice synovial tissue by RT-PCR. The NOD2(-/-) and RIPK2(-/-), but not NOD1(-/-), mice are protected from Ag-induced arthritis, which was characterized by a reduction in neutrophil recruitment, nociception, and cartilage degradation. NOD2/RIPK2 signaling impairment was associated with a reduction in proinflammatory cytokines and chemokines (TNF, IL-1 beta, and CXCL1/KC). IL-17 and IL-17 triggering cytokines (IL-6 and IL-23) were also reduced in the joint, but there is no difference in the percentage of CD4(+) IL-17(+) cells in the lymph node between arthritic wild-type and NOD2(-/-) mice. Altogether, these findings point to a pivotal role of the NOD2/RIPK2 signaling in the onset of experimental arthritis by triggering an IL-17-dependent joint immune response. Therefore, we could propose that NOD2 signaling is a target for the development of new therapies for the control of rheumatoid arthritis. The Journal of Immunology, 2012, 188: 5116-5122.

Murine Dendritic Cells Transcriptional Modulation upon Paracoccidioides brasiliensis Infection

Limited information is available regarding the modulation of genes involved in the innate host response to Paracoccidioides brasiliensis, the etiologic agent of paracoccidioidomycosis. Therefore, we sought to characterize, for the first time, the transcriptional profile of murine bone marrow-derived dendritic cells (DCs) at an early stage following their initial interaction with P. brasiliensis. DCs connect innate and adaptive immunity by recognizing invading pathogens and determining the type of effector T-cell that mediates an immune response. Gene expression profiles were analyzed using microarray and validated using real-time RT-PCR and protein secretion studies. A total of 299 genes were differentially expressed, many of which are involved in immunity, signal transduction, transcription and apoptosis. Genes encoding the cytokines IL-12 and TNF-alpha, along with the chemokines CCL22, CCL27 and CXCL10, were up-regulated, suggesting that P. brasiliensis induces a potent proinflammatory response in DCs. In contrast, pattern recognition receptor (PRR)-encoding genes, particularly those related to Toll-like receptors, were down-regulated or unchanged. This result prompted us to evaluate the expression profiles of dectin-1 and mannose receptor, two other important fungal PRRs that were not included in the microarray target cDNA sequences. Unlike the mannose receptor, the dectin-1 receptor gene was significantly induced, suggesting that this beta-glucan receptor participates in the recognition of P. brasiliensis. We also used a receptor inhibition assay to evaluate the roles of these receptors in coordinating the expression of several immune-related genes in DCs upon fungal exposure. Altogether, our results provide an initial characterization of early host responses to P. brasiliensis and a basis for better understanding the infectious process of this important neglected pathogen.

An evolutive approach for smile recognition in video sequences

Facial expression recognition is one of the most challenging research areas in the image recognition ¯eld and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future

On using periocular biometric for gender classification in the wild

[EN]Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem.

An analysis of automatic gender classification

[EN]Different researches suggest that inner facial features are not the only discriminative features for tasks such as person identification or gender classification. Indeed, they have shown an influence of features which are part of the local face context, such as hair, on these tasks. However, object-centered approaches which ignore local context dominate the research in computational vision based facial analysis. In this paper, we performed an analysis to study which areas and which resolutions are diagnostic for the gender classification problem. We first demonstrate the importance of contextual features in human observers for gender classification using a psychophysical ”bubbles” technique.

Aspects of poriferan (Suberites domuncula) apoptosis and innate immune system and evolutionary implications

Survivin, a unique member of the family of inhibitors of apoptosis (IAP) proteins, orchestrates intracellular pathways during cell division and apoptosis. Its central regulatory function in vertebrate molecular pathways as mitotic regulator and inhibitor of apoptotic cell death has major implications for tumor cell proliferation and viability, and has inspired several approaches that target survivin for cancer therapy. Analyses in early-branching Metazoa so far propose an exclusive role of survivin as a chromosomal passenger protein, whereas only later during evolution the second, complementary antiapoptotic function might have arisen, concurrent with increased organismal complexity. To lift the veil on the ancestral function(s) of this key regulatory molecule, a survivin homologue of the phylogenetically oldest extant metazoan taxon (phylum Porifera) was identified and functionally characterized. SURVL of the demosponge Suberites domuncula shares significant similarities with its metazoan homologues, ranging from conserved exon/intron structures to the presence of localization signal and protein-interaction domains, characteristic of IAP proteins. Whereas sponge tissue displayed a very low steady-state level, SURVL expression was significantly up-regulated in rapidly proliferating primmorph cells. In addition, challenge of sponge tissue and primmorphs with cadmium and the lipopeptide Pam3Cys-Ser-(Lys)4 stimulated SURVL expression, concurrent with the expression of newly discovered poriferan caspases (CASL and CASL2). Complementary functional analyses in transfected HEK-293 revealed that heterologous expression of poriferan survivin in human cells not only promotes cell proliferation but also augments resistance to cadmium-induced cell death. Taken together, these results demonstrate both a deep evolutionary conserved and fundamental dual role of survivin, and an equally conserved central position of this key regulatory molecule in interconnected pathways of cell cycle and apoptosis. Additionally, SDCASL, SDCASL2, and SDTILRc (TIR-LRR containing protein) may represent new components of the innate defense sentinel in sponges. SDCASL and SDCASL2 are two new caspase-homolog proteins with a singular structure. In addition to their CASc domains, SDCASL and SDCASL2 feature a small prodomain NH2-terminal (effector caspases) and a remarkably long COOH-terminal domain containing one or several functional double stranded RNA binding domains (dsrm). This new caspase prototype can characterize a caspase specialization coupling pathogen sensing and apoptosis, and could represent a very efficient defense mechanism. SDTILRc encompasses also a unique combination of domains: several leucine rich repeats (LRR) and a Toll/IL-1 receptor (TIR) domain. This unusual domain association may correspond to a new family of intracellular sensing protein, forming a subclass of pattern recognition receptors (PRR).

Role of αvβ3 – Integrin and TLR2 in the innate response to Herpes Simplex Virus infection and Delivery of retargeted oncolytic Herpes Simplex via carrier cells

In the first part of my thesis I studied the mechanism of initiation of the innate response to HSV-1. Innate immune response is the first line of defense set up by the cell to counteract pathogens infection and it is elicited by the activation of a number of membrane or intracellular receptors and sensors, collectively indicated as PRRs, Patter Recognition Receptors. We reported that the HSV pathogen-associated molecular patterns (PAMP) that activate Toll-like receptor 2 (TLR2) and lead to the initiation of innate response are the virion glycoproteins gH/gL and gB, which constitute the conserved fusion core apparatus across the Herpesvirus. Specifically gH/gL is sufficient to initiate a signaling cascade which leads to NF-κB activation. Then, by gain and loss-of-function approaches, we found that αvβ3-integrin is a sensor of and plays a crucial role in the innate defense against HSV-1. We showed that αvβ3-integrin signals through a pathway that concurs with TLR2, affects activation/induction of interferons type 1, NF-κB, and a polarized set of cytokines and receptors. Thus, we demonstrated that gH/gL is sufficient to induce IFN1 and NF-κB via this pathway. From these data, we proposed that αvβ3-integrin is considered a class of non-TLR pattern recognition receptors. In the second part of my thesis I studied the capacity of human mesenchymal stromal cells isolated by fetal membranes (FM-hMSCs) to be used as carrier cells for the delivery of retargeted R-LM249 virus. The use of systemically administrated carrier cells to deliver oncolytic viruses to tumoral targets is a promising strategy in oncolytic virotherapy. We observed that FM-hMSCs can be infected by R-LM249 and we optimized the infection condition; then we demonstrate that stromal cells sustain the replication of retargeted R-LM249 and spread it to target tumoral cells. From these preliminary data FM-hMSCs resulted suitable to be used as carrier cells

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Pathogen recognition by the long pentraxin PTX3

Innate immunity represents the first line of defence against pathogens and plays key roles in activation and orientation of the adaptive immune response. The innate immune system comprises both a cellular and a humoral arm. Components of the humoral arm include soluble pattern recognition molecules (PRMs) that recognise pathogen-associated molecular patterns (PAMPs) and initiate the immune response in coordination with the cellular arm, therefore acting as functional ancestors of antibodies. The long pentraxin PTX3 is a prototypic soluble PRM that is produced at sites of infection and inflammation by both somatic and immune cells. Gene targeting of this evolutionarily conserved protein has revealed a nonredundant role in resistance to selected pathogens. Moreover, PTX3 exerts important functions at the cross-road between innate immunity, inflammation, and female fertility. Here, we review the studies on PTX3, with emphasis on pathogen recognition and cross-talk with other components of the innate immune system.