X-ray Scattered Halo around IGR J17544-2619

X-ray photons coming from an X-ray point source not only arrive at the detector directly, but also can be strongly forward-scattered by the interstellar dust along the line of sight (LOS), leading to a detectable diffuse halo around the X-ray point source. The geometry of small angle X-ray scattering is straightforward, namely, the scattered photons travel longer paths and thus arrive later than the unscattered ones; thus the delay time of X-ray scattered halo photons can reveal information of the distances of the interstellar dust and the point source. Here we present a study of the X-ray scattered around IGR J17544-2619, which is one of the so-called supergiant fast X-ray transients. IGR J17544-2619 underwent a striking outburst when observed with Chandra on 2004 July 3, providing a near delta-function lightcurve. We find that the X-ray scattered halo around IGR J17544-2619 is produced by two interstellar dust clouds along the LOS. The one which is closer to the observer gives the X-ray scattered at larger observational angles; whereas the farther one, which is in the vicinity of the point source, explains the halo with a smaller angular size. By comparing the observational angle of the scattered halo photons with that predicted by different dust grain models, we are able to determine the normalized dust distance. With the delay times of the scattered halo photons, we can determine the point source distance, given a dust grain model. Alternatively we can discriminate between the dust grain models, given the point source distance.Comment: Accepted for publication in ApJ. 25 pages, 9 figures, 6 table

Fast Min-Sum Algorithms for Decoding of LDPC over GF(q)

In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier in ISIT 05 only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200

How Cooperation and Competition Affect Student Academic Performance and Wellbeing

Due to the emergence of positive psychology and education, increasing attention has been paid to student physical and mental development and character building in addition to their academic performance. Schools have made efforts to encourage cooperative learning behavior in students. Research also shows that students display better academic performance, more positive peer relationships, and stronger senses of belonging to the school in a cooperative learning environment. On the other hand, there are intense competitions among students in a school setting. A reasonable amount of competition is seen as a motivational factor in student learning, with positive effects on student academic achievements. Also, competitions with explicit, proper goals may bring excitements and enjoyment to individuals