Revisiting the Rigidly Rotating Magnetosphere model for sigma Ori E. I. Observations and Data Analysis

We have obtained 18 new high-resolution spectropolarimetric observations of the B2Vp star sigma Ori E with both the Narval and ESPaDOnS spectropolarimeters. The aim of these observations is to test, with modern data, the assumptions of the Rigidly Rotating Magnetosphere (RRM) model of Townsend & Owocki (2005), applied to the specific case of sigma Ori E by Townsend et al. (2005). This model includes a substantially offset dipole magnetic field configuration, and approximately reproduces previous observational variations in longitudinal field strength, photometric brightness, and Halpha emission. We analyze new spectroscopy, including H I, He I, C II, Si III and Fe III lines, confirming the diversity of variability in photospheric lines, as well as the double S-wave variation of circumstellar hydrogen. Using the multiline analysis method of Least-Squares Deconvolution (LSD), new, more precise longitudinal magnetic field measurements reveal a substantial variance between the shapes of the observed and RRM model time-varying field. The phase resolved Stokes V profiles of He I 5876 A and 6678 A lines are fit poorly by synthetic profiles computed from the magnetic topology assumed by Townsend et al. (2005). These results challenge the offset dipole field configuration assumed in the application of the RRM model to sigma Ori E, and indicate that future models of its magnetic field should also include complex, higher-order components.Comment: 13 pages, 8 figures. Accepted for publication in MNRA

Simulating agent personality changes over time in interactive game environments

Designing agents which exhibit believable behaviors by implementing personality that changes over time is the main goal of this research. The changes in personality arise from internal agent conditions as well as agent interactions in the environment, so the conditions local to agents and the interactions and relationships between such agents and with elements in the environment were explored as the major influential factors. In order to evaluate the implementation of agent personality, a computational model based on psychological theories was adopted. The interplay of agent personality, behavior and interaction in agent adaptation was the point of focus and subsequently tackled in the context of a game environment

Mood recognition using combined algorithms and methods (MR CAM)

This paper explores the study of mood recognition in order to aid in activities that concern human-computer interaction. This study is relevant to empathic computing as it should be capable of continuously recognize the emotion and automatically recognize the mood of its users. A lot of existing emotion recognition techniques has been developed to solve the problem of human-computer interaction, however, these emotions only show the feeling of a user in a given instant and not that of the whole time the user has been using the system. This research aims to explore and contribute to this field of study by recognizing the emotion and mood through the use of facial expressions. This research focuses on studying existing techniques on emotion recognition from facial expressions with the use of active shape models. The people exhibit specific emotions in frontal position so as to maximize the observation of facial expressions. Features generated are formed in the 2-D model are then utilized for emotion classification techniques such as naive bayees and sequential minimal optimization. The results of the emotion classifiers play a major role in finding the mood of the person in the video as these results are the factors being considered for the mood recognition algorithm applied in the research