On magnetic pair production above fast pulsar polar caps

Magnetic pair production is one of high-energy electromagnetic conversion processes important to the development of pair-photon cascades in pulsars. On the basis of current polar cap models, the properties of magnetic pair production in fast pulsars are discussed. Suppose there is a roughly dipole magnetic field at the stellar surface, the author estimate the effects on non-zero curvature of magnetic field lines upon curvature radiation from primary particles and pair production rate near the surface of pulsars

Relativistic electron in curved magnetic fields

Making use of the perturbation method based on the nonlinear differential equation theory, the author investigates the classical motion of a relativistic electron in a class of curved magnetic fields which may be written as B=B(O,B sub phi, O) in cylindrical coordinates (R. phi, Z). Under general astrophysical conditions the author derives the analytical expressions of the motion orbit, pitch angle, etc., of the electron in their dependence upon parameters characterizing the magnetic field and electron. The effects of non-zero curvature of magnetic field lines on the motion of electrons and applicabilities of these results to astrophysics are also discussed

Strangeness magnetic form factor of the proton in the extended chiral quark model

Background: Unravelling the role played by nonvalence flavors in baryons is crucial in deepening our comprehension of QCD. Strange quark, a component of the higher Fock states in baryons, is an appropriate tool to investigate nonperturbative mechanisms generated by the pure sea quark. Purpose: Study the magnitude and the sign of the strangeness magnetic moment $\mu_s$ and the magnetic form factor ($G_M^s$) of the proton. Methods: Within an extended chiral constituent quark model, we investigate contributions from all possible five-quark components to $\mu_s$ and $G_M^s (Q^2)$ in the four-vector momentum range $Q^2 \leq 1$ (GeV/c)$^2$. Probability of the strangeness component in the proton wave function is calculated employing the $^3 P_0$ model. Results: Predictions are obtained without any adjustable parameters. Observables $\mu_s$ and $G_M^s (Q^2)$ are found to be small and negative, consistent with the lattice-QCD findings as well as with the latest data released by the PVA4 and HAPPEX Collaborations. Conclusions: Due to sizeable cancelations among different configurations contributing to the strangeness magnetic moment of the proton, it is indispensable to (i) take into account all relevant five-quark components and include both diagonal and non-diagonal terms, (ii) handle with care the oscillator harmonic parameter $\omega_5$ and the ${s \bar s}$ component probability.Comment: References added, typos corrected, accepted for publication by Phys. Rev.

Conformal Transformations and Weak Field Limit of Scalar-Tensor Gravity

The weak field limit of scalar tensor theories of gravity is discussed in view of conformal transformations. Specifically, we consider how physical quantities, like gravitational potentials derived in the Newtonian approximation for the same scalar-tensor theory, behave in the Jordan and in the Einstein frame. The approach allows to discriminate features that are invariant under conformal transformations and gives contributions in the debate of selecting the true physical frame. As a particular example, the case of $f(R)$ gravity is considered.Comment: 11 pages, preliminary versio

Person re-identification by robust canonical correlation analysis

Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry