Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI

Impact of Temperament Types and Anger Intensity on Drivers\u27 EEG Power Spectrum and Sample Entropy: An On-road Evaluation Toward Road Rage Warning

"Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less consideration of drivers\u27 individual differences. This study aims to explore the impact of temperament types and anger intensity on drivers\u27 EEG characteristics. Thirty-two drivers with valid license were enrolled to perform on-road experiments on a particularly busy route on which a variety of provoking events like cutting in line of surrounding vehicle, jaywalking, occupying road of non-motor vehicle and traffic congestion frequently happened. Then, muti-factor analysis of variance (ANOVA) and post hoc analysis were utilized to study the impact of temperament types and anger intensity on drivers\u27 power spectrum and sample entropy of θ and β waves extracted from EEG signals. The study results firstly indicated that right frontal region of the brain has close relationship with driving anger. Secondly, there existed significant main effects of temperament types on power spectrum and sample entropy of β wave while significant main effects of anger intensity on power spectrum and sample entropy of θ and β wave were all observed. Thirdly, significant interactions between temperament types and anger intensity for power spectrum and sample entropy of β wave were both noted. Fourthly, with the increase of anger intensity, the power spectrum and sample entropy both decreased sufficiently for θ wave while increased remarkably for β wave. The study results can provide a theoretical support for designing a personalized and hierarchical warning system for road rage

Specific-Heat Measurement of Residual Superconductivity in the Normal State of Underdoped Cuprate Superconductors

We have measured the magnetic field and temperature dependence of specific heat on $Bi_2Sr_{2-x}La_xCuO_{6+\delta}$ single crystals in wide doping and temperature regions. The superconductivity related specific heat coefficient $\gamma_{sc}$ and entropy $S_{sc}$ are determined. It is found that $\gamma_{sc}$ has a hump-like anomaly at $T_c$ and behaves as a long tail which persists far into the normal state for the underdoped samples, but for the heavily overdoped samples the anomaly ends sharply just near $T_c$. Interestingly, we found that the entropy associated with superconductivity is roughly conserved when and only the long tail part in the normal state is taken into account for the underdoped samples, indicating the residual superconductivity above T$_c$.Comment: 4 pages, 4 figures, Accepted for publication in Physical Review Letter