Kinematic Evolution of a Slow CME in Corona Viewed by STEREO-B on 8 October 2007

We studied the kinematic evolution of the 8 October 2007 CME in the corona based on Sun-Earth Connection Coronal and Heliospheric Investigation (SECCHI) onboard satellite B of Solar TErrestrial RElations Observatory (STEREO). The observational results show that this CME obviously deflected to a lower latitude region for about 30$^\circ$ at the beginning. After this, the CME propagated radially. We also analyze the influence of the background magnetic field on the deflection of this CME. We find that the deflection of this CME at an early stage may be caused by the nonuniform distribution of the background magnetic field energy density and that the CME tended to propagate to the region with lower magnetic energy density. In addition, we found that the velocity profile of this gradual CME shows multiphased evolution during its propagation in COR1-B FOV. The CME velocity first kept at a constant of 23.1km.s-1. Then, it accelerated continuously with a positive acceleration of 7.6m.s-2.Comment: 10 pages, 7 figure

Quantitative Analysis of CME Deflections in the Corona

In this paper, ten CME events viewed by the STEREO twin spacecraft are analyzed to study the deflections of CMEs during their propagation in the corona. Based on the three-dimensional information of the CMEs derived by the graduated cylindrical shell (GCS) model [Thernisien et al., 2006], it is found that the propagation directions of eight CMEs had changed. By applying the theoretical method proposed by Shen et al. [2011] to all the CMEs, we found that the deflections are consistent, in strength and direction, with the gradient of the magnetic energy density. There is a positive correlation between the deflection rate and the strength of the magnetic energy density gradient and a weak anti-correlation between the deflection rate and the CME speed. Our results suggest that the deflections of CMEs are mainly controlled by the background magnetic field and can be quantitatively described by the magnetic energy density gradient (MEDG) model.Comment: 19 pages, 20 figure

Statistical Study of Coronal Mass Ejection Source Locations: Understanding CMEs Viewed in Coronagraphs

How to properly understand coronal mass ejections (CMEs) viewed in white-light coronagraphs is crucial to many relative researches in solar and space physics. The issue is now particularly addressed in this paper through studying the source locations of all the 1078 LASCO CMEs listed in CDAW CME catalog during 1997 -- 1998 and their correlation with CMEs' apparent parameters. By manually checking LASCO and EIT movies of these CMEs, we find that, except 231 CMEs whose source locations can not be identified due to poor data, there are 288 CMEs with location identified on the front-side solar disk, 234 CMEs appearing above solar limb, and 325 CMEs without evident eruptive signatures in the field of view of EIT. Based on the statistical results of CMEs' source locations, four physical issues, including (1) the missing rate of CMEs by SOHO LASCO and EIT, (2) the mass of CMEs, (3) the causes of halo CMEs and (4) the deflections of CMEs in the corona, are exhaustively analyzed. It is found that (1) about 32% of front-side CMEs can not be recognized by SOHO, (2) the brightness of a CME at any heliocentric distance is roughly positively correlated with its speed, and the CME mass derived from the brightness is probably overestimated, (3) both projection effect and violent eruption are the major causes of halo CMEs, and especially for limb halo CMEs, the latter is the primary one, (4) most CMEs deflected towards equator near the solar minimum, and these deflections can be classified into three types, the asymmetrical expansion, non-radial ejection, and the deflected propagation.Comment: 15 pages, 14 figure

Meta Clustering for Collaborative Learning

An emerging number of learning scenarios involve a set of learners/analysts each equipped with a unique dataset and algorithm, who may collaborate with each other to enhance their learning performance. From the perspective of a particular learner, a careless collaboration with task-irrelevant other learners is likely to incur modeling error. A crucial problem is to search for the most appropriate collaborators so that their data and modeling resources can be effectively leveraged. Motivated by this, we propose to study the problem of `meta clustering', where the goal is to identify subsets of relevant learners whose collaboration will improve the performance of each individual learner. In particular, we study the scenario where each learner is performing a supervised regression, and the meta clustering aims to categorize the underlying supervised relations (between responses and predictors) instead of the raw data. We propose a general method named as Select-Exchange-Cluster (SEC) for performing such a clustering. Our method is computationally efficient as it does not require each learner to exchange their raw data. We prove that the SEC method can accurately cluster the learners into appropriate collaboration sets according to their underlying regression functions. Synthetic and real data examples show the desired performance and wide applicability of SEC to a variety of learning tasks