Inconsistences in Interacting Agegraphic Dark Energy Models

It is found that the origin agegraphic dark energy tracks the matter in the matter-dominated epoch and then the subsequent dark-energy-dominated epoch becomes impossible. It is argued that the difficulty can be removed when the interaction between the agegraphic dark energy and dark matter is considered. In the note, by discussing three different interacting models, we find that the difficulty still stands even in the interacting models. Furthermore, we find that in the interacting models, there exists the other serious inconsistence that the existence of the radiation/matter-dominated epoch contradicts the ability of agegraphic dark energy in driving the accelerated expansion. The contradiction can be avoided in one of the three models if some constraints on the parameters hold.Comment: 12 pages, no figure; analysis is added; conclusion is unchange

On the Relation of Hard X-ray Peak Flux and Outburst Waiting Time in the Black Hole Transient GX 339-4

Aims. In this work we re-investigated the empirical relation between the hard X-ray peak flux and the outburst waiting time found previously in the black hole transient GX 339-4. We tested the relation using the observed hard X-ray peak flux of the 2007 outburst of GX 339-4, clarified issues about faint flares, and estimated the lower limit of hard X-ray peak flux for the next outburst. Methods. We included Swift/BAT data obtained in the past four years. Together with the CGRO/BATSE and RXTE/HEXTE light curves, the observations used in this work cover a period of 18 years. Results. The observation of the 2007 outburst confirms the empirical relation discovered before. This strengthens the apparent link between the mass in the accretion disk and the peak luminosity of the brightest hard state that the black hole transient can reach. We also show that faint flares with peak fluxes smaller than about 0.12 crab do not affect the empirical relation. We predict that the hard X-ray peak flux of the next outburst should be larger than 0.65 crab, which will make it at least the second brightest in the hard X-ray since 1991.Comment: 4 pages, 3 figures, accepted by A&

Toward precision mass measurements of neutron-rich nuclei relevant to rr-process nucleosynthesis

The open question of where, when, and how the heavy elements beyond iron enrich our Universe has triggered a new era in nuclear physics studies.\ Of all the relevant nuclear physics inputs, the mass of very neutron-rich nuclides is a key quantity for revealing the origin of heavy elements beyond iron.\ Although the precise determination of this property is a great challenge, enormous progress has been made in recent decades, and it has contributed significantly to both nuclear structure and astrophysical nucleosynthesis studies.\ In this review, we first survey our present knowledge of the nuclear mass surface, emphasizing the importance of nuclear mass precision in $r$-process calculations.\ We then discuss recent progress in various methods of nuclear mass measurement with a few selected examples.\ For each method, we focus on recent breakthroughs and discuss possible ways of improving the weighing of $r$-process nuclides.Comment: 10 figures, review articles in Frontiers of Physic

Efficient processing node proximity via random walk with restart

Graph is a useful tool to model complicated data structures. One important task in graph analysis is assessing node proximity based on graph topology. Recently, Random Walk with Restart (RWR) tends to pop up as a promising measure of node proximity, due to its proliferative applications in e.g. recommender systems, and image segmentation. However, the best-known algorithm for computing RWR resorts to a large LU matrix factorization on an entire graph, which is cost-inhibitive. In this paper, we propose hybrid techniques to efficiently compute RWR. First, a novel divide-and-conquer paradigm is designed, aiming to convert the large LU decomposition into small triangular matrix operations recursively on several partitioned subgraphs. Then, on every subgraph, a “sparse accelerator” is devised to further reduce the time of RWR without any sacrifice in accuracy. Our experimental results on real and synthetic datasets show that our approach outperforms the baseline algorithms by at least one constant factor without loss of exactness