Thermodynamics of AdS Black Holes in Einstein-Scalar Gravity

We study the thermodynamics of $n$-dimensional static asymptotically AdS black holes in Einstein gravity coupled to a scalar field with a potential admitting a stationary point with an AdS vacuum. Such black holes with non-trivial scalar hair can exist provided that the mass-squared of the scalar field is negative, and above the Breitenlohner-Freedman bound. We use the Wald procedure to derive the first law of thermodynamics for these black holes, showing how the scalar hair (or "charge") contributes non-trivially in the expression. We show in general that a black hole mass can be deduced by isolating an integrable contribution to the (non-integrable) variation of the Hamiltonian arising in the Wald construction, and that this is consistent with the mass calculated using the renormalised holographic stress tensor and also, in those cases where it is defined, with the mass calculated using the conformal method of Ashtekar, Magnon and Das. Similar arguments can also be given for the smooth solitonic solutions in these theories. Neither the black hole nor the soliton solutions can be constructed explicitly, and we carry out a numerical analysis to demonstrate their existence and to provide approximate checks on some of our thermodynamic results.Comment: 42 pages, 2 figures. Version published in JHEP, plus a "Note Added" expanding on our definition of "mass" via the first la

"Teleparallel" Dark Energy

Using the "teleparallel" equivalent of General Relativity as the gravitational sector, which is based on torsion instead of curvature, we add a canonical scalar field, allowing for a nonminimal coupling with gravity. Although the minimal case is completely equivalent to standard quintessence, the nonminimal scenario has a richer structure, exhibiting quintessence-like or phantom-like behavior, or experiencing the phantom-divide crossing. The richer structure is manifested in the absence of a conformal transformation to an equivalent minimally-coupled model.Comment: 5 pages, 1 figure, Version published in PLB704 (2011) 384-38

Information filtering via biased heat conduction

Heat conduction process has recently found its application in personalized recommendation [T. Zhou \emph{et al.}, PNAS 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction (BHC), which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix and Delicious datasets could be improved by 43.5%, 55.4% and 19.2% compared with the standard heat conduction algorithm, and the diversity is also increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.Comment: 4 pages, 3 figure