Segmentation and detection of cattle branding images using CNN and SVM classification

This article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93% in the first experiment with 39 brandings and 1,950 sample images, and 95% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 32s and 42s, respectively

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum