Gauge field fluctuation corrected QED3 effective action by fermionic particle-vortex duality

We develop a non-perturbative framework to incorporate gauge field fluctuations into QED3 effective actions in the infrared by fermionic particle-vortex duality. The utility is demonstrated by the application to models containing N species of 2-component Dirac fermions in a couple of solvable and interpretable electromagnetic backgrounds: N = 1 or 2. For the N = 1 model, we establish a correspondence between fermion Casimir energy at finite density and the magnetic Euler-Heisenberg Lagrangian, and we further evaluate the correction to their amplitudes. This in turn predicts the amplification of charge susceptibility and the reduction of magnetic permeability. We additionally supply physical interpretations to each component of our calculation as well as alternative derivations based on energy density measurements in different characteristic lengths. For N = 2, we show that the magnetic catalysis is erased in a U(1)$\times$U(1) QED3 and therefore there is no breakdown of chiral symmetry. Some reasoning is offered based on the properties of the lowest Landau level wave functions.Comment: 15 page

Geometrically constrained particle dynamics revisited: Equation of motion in terms of the normal curvature of the constraint manifold

We revisit the problem of the particle dynamics subject to a geometric holonomic constraint of codimension 1 in spatial dimensions d =2 and 3. In the absence of dissipation, we show that by solving the Lagrangian multiplier in a general fashion, the external potential independent part, the net normal force, of the equation of motion corresponds to precisely to the curvature of the trajectory on the constraint space multiplied by twice the kinetic energy. The tangent the trajectory is the instantaneous velocity. In d = 3, this term equals the second fundamental form II of the constraint surface evaluated on the unit tangent vector in the direction of velocity. Using these result we establish the relation between constrained particle dynamics with geodesic equations and derive intriguing kinematic implications using theorems from fundamental differential geometry.Comment: 7 pages, 1 figur

Internet Privacy Information Propagation Model

With the rapid growth of information and communication technology (ICT), the violation of information privacy has increased in recent years. The privacy concerns now re-emerge right because people perceives a threat from new ICT that are equipped with enhanced capabilities for surveillance, storage, retrieval, and diffusion of personal information. With the trend in the prevalence and the easy use of ICT, it is of necessary to pay much attention to the issue how the ICT can threaten the privacy of individuals on the Internet. While the Email and P2P tools are the most popular ICT, this paper aims at understanding their respectively dissemination patterns in spreading of personal private information. To this purpose, this paper using dynamic model technique to simulate the pattern of sensitive or personal private information propagating situation. In this study, an Email propagation model and a Susceptible-Infected-Removed (SIR) model are proposed to simulate the propagation patterns of Email and P2P network respectively. Knowing their dissemination patterns would be helpful for system designers, ICT manager, corporate IT personnel, educators, policy makers, and legislators to incorporate consciousness of social and ethical information issues into the protection of information privacy

Predicting The Helpfulness Of Online Product Reviewers: A Data Mining Approach

The purpose of this study is to propose a data mining approach to predict the helpfulness scores of online product reviewers. Such prediction can facilitate consumers to judge whether to believe or disbelieve reviews written by different reviewers and can help e-stores or third-party product review websites to target and retain quality reviewers. In this study, we identify eight independent variables from the perspectives of reviewers’ review behavior and trust network to predict the helpfulness scores for these reviewers. We adopt M5 and SVM Regression as our underlying learning algorithms. Our empirical evaluation results on the basis of two product categories (i.e., Car and Computer) suggest that our proposed helpfulness prediction technique can predict the helpfulness scores of online product reviewers