Search for exclusive or semi-exclusive gamma gamma production and observation of exclusive and semi-exclusive e(+)e(-) production in pp collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis.

Feature extraction in pressure signals for leak detection in water networks

Techniques based on signal analysis for leak detection in water supply systems typically use long pressure and/or flow data series of variable length. This paper presents the feature extraction from pressure signals and their application to the identification of changes related to the onset of a leak. Example signals were acquired from an experimental laboratory circuit, and features were extracted from temporal domain and from transformed signals. Statistical analysis of features values and a classification method were applied. It was verified the feasibility of using feature vectors for distinguish data acquired in the absence or presence of a leak.

Luminescent solar concentrators: challenges for lanthanide-based organic-inorganic hybrid materials

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Tailoring the refractive index of Ge-S based glass for 3D embedded waveguides operating in the mid-IR region

The photosensitivity of GeSx binary glasses in response to irradiation to femtosecond pulses at 800 nm is investigated. Samples with three different molecular compositions were irradiated under different exposure conditions. The material response to laser exposure was characterized by both refractometry and micro-Raman spectroscopy. It is shown that the relative content of sulfur in the glass matrix influences the photo-induced refractive index modification. At low sulfur content, both positive and negative index changes can be obtained while at high sulfur content, only a positive index change can be reached. These changes were correlated with variations in the Raman response of exposed glass which were interpreted in terms of structural modifications of the glass network. Under optimized exposure conditions, waveguides with positive index changes of up to 7.8x10−3 and a controllable diameter from 14 to 25 μm can be obtained. Direct inscription of low insertion losses (IL = 3.1 – 3.9 dB) waveguides is demonstrated in a sample characterized by a S/Ge ratio of 4. The current results open a pathway towards the use of Ge-S binary glasses for the fabrication of integrated mid-infrared photonic components.

O amanhã de quem cresce: influências e valores presentes na constituição de projetos de vida de jovens adolescentes de um curso técnico de uma instituição estadual

Pós-graduação em Educação - FFC

Restauración onlay usando técnica semi-directa con resina compuesta: reporte de un caso

Posterior teeth restorations have changed the contemporary treatment philosophy influenced by the aesthetic demand of patients, progress of adhesive material science and techniques for preservation and rehabilitation of affected teeth by dental caries and trauma. The development of Onlay restorations with semi-direct technique in endodontically teeth treated aims to preserve the remaining surfaces, to reduce the possibility of fracture and polymerization shrinkage. In addition, better restoration adaptation and marginal seal, resistance to wear and dimensional stability are achieved. This case reports the rehabilitation of an endodontically treated permanent maxillary first molar in a 13 years old- patient who attended the Araraquara School of Dentistry, Brazil, using Miris 2 Composite resin with semi-direct technique and obtaining an aesthetic and functional restoration in a single appointment. The fundaments and clinical guidelines of the procedure are detailed, based on the review of the literature that supports this conservative treatment.

Chromosome evolution in lycosoid spiders (Araneomorphae): a scenario based on analysis of seven species of the families Lycosidae, Senoculidae and Trechaleidae

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Locating control points in aerial images with a multi-scale approach based on terrestrial image patches

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Patch-based local histograms and contour estimation for static foreground classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.