Partially obscured human detection based on component detectors using multiple feature descriptors

This paper presents a human detection system based on component detector using multiple feature descriptors. The contribution presents two issues for dealing with the problem of partially obscured human. First, it presents the extension of feature descriptors using multiple scales based Histograms of Oriented Gradients (HOG) and parallelogram based Haar-like feature (PHF) for improving the accuracy of the system. By using multiple scales based HOG, an extensive feature space allows obtaining high-discriminated features. Otherwise, the PHF is adaptive limb shapes of human in fast computing feature. Second, learning system using boosting classifications based approach is used for training and detecting the partially obscured human. The advantage of boosting is constructing a strong classification by combining a set of weak classifiers. However, the performance of boosting depends on the kernel of weak classifier. Therefore, the hybrid algorithms based on AdaBoost and SVM using the proposed feature descriptors is one of solutions for robust human detection.This paper presents a human detection system based on component detector using multiple feature descriptors. The contribution presents two issues for dealing with the problem of partially obscured human. First, it presents the extension of feature descriptors using multiple scales based Histograms of Oriented Gradients (HOG) and parallelogram based Haar-like feature (PHF) for improving the accuracy of the system. By using multiple scales based HOG, an extensive feature space allows obtaining high-discriminated features. Otherwise, the PHF is adaptive limb shapes of human in fast computing feature. Second, learning system using boosting classifications based approach is used for training and detecting the partially obscured human. The advantage of boosting is constructing a strong classification by combining a set of weak classifiers. However, the performance of boosting depends on the kernel of weak classifier. Therefore, the hybrid algorithms based on AdaBoost and SVM using the proposed feature descriptors is one of solutions for robust human detection

Color-stable, ITO-free white organic light-emitting diodes with enhanced efficiency using solution-processed transparent electrodes and optical outcoupling layers

In this work, we demonstrate color-stable, ITO-free white organic light-emitting diodes (WOLEDs) with enhanced efficiencies by combining the high-conductivity conducting polymer PEDOT:PSS as transparent electrode and a nanoparticle-based scattering layer (NPSL) as the effective optical out-coupling layer. In addition to efficiency enhancement, the NPSL is also beneficial to the stabilization of electroluminescent spectra/colors over viewing angles. Both the PEDOT:PSS and the NPSL can be fabricated by simple, low-temperature solution processing. The integration of both solution-processable transparent electrodes and light extraction structures into OLEDs is particularly attractive for applications since they simultaneously provide manufacturing, cost and efficiency advantages. PostprintPeer reviewe

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks

SVM Classifier – a comprehensive java interface for support vector machine classification of microarray data

MOTIVATION: Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. RESULTS: The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. CONCLUSION: We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at

TIM-2 is expressed on B cells and in liver and kidney and is a receptor for H-ferritin endocytosis

T cell immunoglobulin-domain and mucin-domain (TIM) proteins constitute a receptor family that was identified first on kidney and liver cells; recently it was also shown to be expressed on T cells. TIM-1 and -3 receptors denote different subsets of T cells and have distinct regulatory effects on T cell function. Ferritin is a spherical protein complex that is formed by 24 subunits of H- and L-ferritin. Ferritin stores iron atoms intracellularly, but it also circulates. H-ferritin, but not L-ferritin, shows saturable binding to subsets of human T and B cells, and its expression is increased in response to inflammation. We demonstrate that mouse TIM-2 is expressed on all splenic B cells, with increased levels on germinal center B cells. TIM-2 also is expressed in the liver, especially in bile duct epithelial cells, and in renal tubule cells. We further demonstrate that TIM-2 is a receptor for H-ferritin, but not for L-ferritin, and expression of TIM-2 permits the cellular uptake of H-ferritin into endosomes. This is the first identification of a receptor for ferritin and reveals a new role for TIM-2

Expression of CD80 and CD86 costimulatory molecules are potential markers for better survival in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>B7 Costimulatory signal is essential to trigger T-cell activation upon the recognition of tumor antigens. This study examined the expression of B7-1 (CD80) and B7-2 (CD86) costimulatory molecules along with HLA-DR and the presence of infiltrating lymphocytes and dendritic cells to assess their significance in patients with nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Expression of CD80, CD86, HLA-DR, S-100 protein and the presence of infiltrating lymphocytes and follicular dendritic reticulum cells were immunohistochemically examined on the paraffin-embedded tissue blocks from newly diagnosed NPC patients (n = 50). The results were correlated with clinical outcome of patients.</p> <p>Results</p> <p>CD80 and CD86 were each expressed in 10 of 50 cases in which they co-expressed in 9 cases. Univariate analysis revealed that patients with CD80/CD86 expression had significantly better overall survival than those without it (P = 0.017), but after adjustment for stage, nodal status, and treatment, the expression of CD80/CD86 did not significantly correlate with overall survival. Expression of HLA-DR and the presence of infiltrating lymphocytes and dendritic cells did not appear to have impact on the survival of patients.</p> <p>Conclusion</p> <p>Expression of CD80 and CD86 costimulatory molecules appears to be a marker of better survival in patient with NPC.</p