The Differences of Star Formation History Between Merging Galaxies and Field Galaxies in the EDR of the SDSS

Based on the catalog of merging galaxies in the Early Data Release (EDR) of the Sloan Digital Sky Survey (SDSS), the differences of star formation history between merging galaxies and field galaxies are studied statistically by means of three spectroscopic indicators the 4000-\r{A} break strength, the Balmer absorption-line index, and the specific star formation rate. It is found that for early-type merging galaxies the interactions will not induce significant enhancement of the star-formation activity because of its stability and lack of cool gas. On the other hand, late-type merging galaxies always in general display more active star formation than field galaxies on different timescales within about 1Gyr. We also conclude that the mean stellar ages of late-type merging galaxies are younger than those of late-type field galaxies.Comment: 9 pages, 4 figures, accepted for publication in PAS

On defining partition entropy by inequalities

Partition entropy is the numerical metric of uncertainty within a partition of a finite set, while conditional entropy measures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies, are actually additional constraints on its entropy. Thus, in this paper partition entropy is defined as a function of probability distribution, satisfying all the inequalities of not only partition entropy itself but also its conditional counterpart. These inequality postulates formalize the intuitive understandings of uncertainty contained in partitions of finite sets.We study the relationships between these inequalities, and reduce the redundancies among them. According to two different definitions of conditional entropy from its partition entropy, the convenient and unified checking conditions for any partition entropy are presented, respectively. These properties generalize and illuminate the common nature of all partition entropies

Multiple phase transitions in single-crystalline Na1−δ_{1-\delta}FeAs

Specific heat, resistivity, susceptibility and Hall coefficient measurements were performed on high-quality single crystalline Na$_{1-\delta}$FeAs. This compound is found to undergo three successive phase transitions at around 52, 41, and 23 K, which correspond to structural, magnetic and superconducting transitions, respectively. The Hall effect result indicates the development of energy gap at low temperature due to the occurrence of spin-density-wave instability. Our results provide direct experimental evidence of the magnetic ordering in the nearly stoichiometric NaFeAs.Comment: 4 pages, 4 figure

Superconductivity at 41 K and its competition with spin-density-wave instability in layered CeO1−x_{1-x}Fx_xFeAs

A series of layered CeO$_{1-x}$F$_x$FeAs compounds with x=0 to 0.20 are synthesized by solid state reaction method. Similar to the LaOFeAs, the pure CeOFeAs shows a strong resistivity anomaly near 145 K, which was ascribed to the spin-density-wave instability. F-doping suppresses this instability and leads to the superconducting ground state. Most surprisingly, the superconducting transition temperature could reach as high as 41 K. The very high superconducting transition temperature strongly challenges the classic BCS theory based on the electron-phonon interaction. The very closeness of the superconducting phase to the spin-density-wave instability suggests that the magnetic fluctuations play a key role in the superconducting paring mechanism. The study also reveals that the Ce 4f electrons form local moments and ordered antiferromagnetically below 4 K, which could coexist with superconductivity.Comment: 4 pages, 5 figure

Recommendation using DMF-based fine tuning method

© 2016, Springer Science+Business Media New York. Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence between item and user factors. A basic MF model starts with an object function, which is consisted of the squared error between original training matrix and predicted matrix as well as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. However, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore, we propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method