Electron Spin Resonance of defects in the Haldane System Y(2)BaNiO(5)

We calculate the electron paramagnetic resonance (EPR) spectra of the antiferromagnetic spin-1 chain compound Y(2)BaNi(1-x)Mg(x)O(5) for different values of x and temperature T much lower than the Haldane gap (~100K). The low-energy spectrum of an anisotropic Heisenberg Hamiltonian, with all parameters determined from experiment, has been solved using DMRG. The observed EPR spectra are quantitatively reproduced by this model. The presence of end-chain S=1/2 states is clearly observed as the main peak in the spectrum and the remaining structure is completely understood.Comment: 5 pages, 4 figures include

Electron-Doped Manganese Perovskites: The Polaronic State

Using the Lanczos method in linear chains we study the ground state of the double exchange model including an antiferromagnetic super-exchange in the low concentration limit. We find that this ground state is always inhomogeneous, containig ferromagnetic polarons. The extention of the polaron spin distortion, the dispersion relation and their trapping by impurities, are studied for diferent values of the super exchange interaction and magnetic field. We also find repulsive polaron polaron interaction.Comment: 4 pages, 6 embedded figure

Vortex Viscosity in Magnetic Superconductors Due to Radiation of Spin Waves

In type-II superconductors that contain a lattice of magnetic moments, vortices polarize the magnetic system inducing additional contributions to the vortex mass, vortex viscosity, and vortex-vortex interaction. Extra magnetic viscosity is caused by radiation of spin waves by a moving vortex. Like in the case of Cherenkov radiation, this effect has a characteristic threshold behavior and the resulting vortex viscosity may be comparable to the well-known Bardeen-Stephen contribution. The threshold behavior leads to an anomaly in the current-voltage characteristics, and a drop in dissipation for a current interval that is determined by the magnetic excitation spectrum.Comment: 4 pages, 1 figur

Discovering trends in brand interest through topic models

Topic Modeling is a well-known unsupervised learning technique used when dealing with text data. It is used to discover latent patterns, called topics, in a collection of documents (corpus). This technique provides a convenient way to retrieve information from unclassified and unstructured text. Topic Modeling tasks have been performed for tracking events/topics/trends in different domains such as academic, public health, marketing, news, and so on. In this paper, we propose a framework for extracting topics from a large dataset of short messages, for brand interest tracking purposes. The framework consists training LDA topic models for each brand using time intervals, and then applying the model on aggregated documents. Additionally, we present a set of preprocessing tasks that helped to improve the topic models and the corresponding outputs. The experiments demonstrate that topic modeling can successfully track people’s discussions on Social Networks even in massive datasets, and ca pture those topics spiked by real-life events.info:eu-repo/semantics/acceptedVersio

Spatio-temporal analysis of brand interest using social networks

Social Networks have become part of many people's lives, and applications like Facebook and Twitter are used on a daily basis by millions of users. In such applications, users share their feelings, opinions, and experiences. Twitter in particular, is used to talk about diverse topics, including brands and their products and services. In this paper, we analyze how brand interest is reflected on Twitter and how this platform can be used to monitor mentions of specific brands, as an indicator of brand interest. Our methodology is based on time, location, and the number of brand-related tweets to perform a spatio-temporal analysis. This type of analysis can provide relevant insights into how brand interest evolves over the time and how it might differ from one country to another. We have collected four years' worth of data and report trends, differences, and similarities in terms of brand interest for each brand and for each country.info:eu-repo/semantics/acceptedVersio