30,427 research outputs found
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 NaFeAs
Specific heat, resistivity, susceptibility and Hall coefficient measurements
were performed on high-quality single crystalline NaFeAs. 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 CeOFFeAs
A series of layered CeOFFeAs 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
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