The Daya Bay Reactor Neutrino Experiment has measured the last unknown neutrino mixing angle, {theta}13, to be non-zero at the 7.7{sigma} level. This is the most precise measurement to {theta}13 to date. To further enhance the understanding of the re
sponse of the antineutrino detectors (ADs), a detailed calibration of an AD with the Manual Calibration System (MCS) was undertaken during the summer 2012 shutdown. The MCS is capable of placing a radioactive source with a positional accuracy of 25 mm in R direction, 20 mm in Z axis and 0.5{deg} in {Phi} direction. A detailed description of the MCS is presented followed by a summary of its performance in the AD calibration run.
Sun et al. provided an insightful comment arXiv:1108.5739v1 on our manuscript entitled Controllability of Complex Networks with Nonlinear Dynamics on arXiv. We agree on their main point that linearization about locally desired states can be violated
in general by the breakdown of local control of the linearized complex network with nonlinear state. Therefore, we withdraw our manuscript. However, other than nonlinear dynamics, our claim that a single-node-control can fully control the general bidirectional/undirected linear network with 1D self-dynamics is still valid, which is similar to (but different from) the conclusion of arXiv:1106.2573v3 that all-node-control with a single signal can fully control any direct linear network with nodal-dynamics (1D self-dynamics).
We first review the related works on the observable consequence of landscape and the regulation of e-foldings during inflation. We focus on a branch of observable consequence of landscape which predicts an open universe with negative curvature if e-f
oldings $N>62$. After discussing the observable regulation from the aspect by Kaloper, Kleban and Sorbo, we make an argument that in the non-flat background the observable $N$ is suppressed by a factor $k/rho_{0}$. We point out that this seems to detect the information where e-foldings $N>62$ possibly. Finally, we discuss our outcomes with the recent work by Arkani-Hamed et al.
We explore the noncommutative effect on single field inflation and compare with WMAP five-year data. First, we calculate the noncommutative effect from the potential and dynamical terms, and construct the general form of modified power spectrum. Seco
nd, we consider the leading order modification of slow-roll, DBI and K-inflation and unite the modification, which means the modification is nearly model independent at this level. Finally, comparing with the WMAP5 data, we find that the modified can be well realized as the origin of the relative large spectral index and the quite small running.
We investigate an exact solution that describes the embedding of the four-dimensional (4D) perfect fluid in a five-dimensional (5D) Einstein spacetime. The effective metric of the 4D perfect fluid as a hypersurface with induced matter is equivalent t
o the Robertson-Walker metric of cosmology. This general solution shows interconnections among many 5D solutions, such as the solution in the braneworld scenario and the topological black hole with cosmological constant. If the 5D cosmological constant is positive, the metric periodically depends on the extra dimension. Thus we can compactify the extra dimension on $S^1$ and study the phenomenological issues. We also generalize the metric ansatz to the higher-dimensional case, in which the 4D part of the Einstein equations can be reduced to a linear equation.
Recommender systems are significant to help people deal with the world of information explosion and overload. In this Letter, we develop a general framework named self-consistent refinement and implement it be embedding two representative recommendat
ion algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods.
Information overload in the modern society calls for highly efficient recommendation algorithms. In this letter we present a novel diffusion based recommendation model, with users ratings built into a transition matrix. To speed up computation we int
roduce a Green function method. The numerical tests on a benchmark database show that our prediction is superior to the standard recommendation methods.
We propose a Hamiltonian formalism for a generalized Friedmann-Roberson-Walker cosmology model in the presence of both a variable equation of state (EOS) parameter $w(a)$ and a variable cosmological constant $Lambda(a)$, where $a$ is the scale factor
. This Hamiltonian system containing 1 degree of freedom and without constraint, gives Friedmann equations as the equation of motion, which describes a mechanical system with a variable mass object moving in a potential field. After an appropriate transformation of the scale factor, this system can be further simplified to an object with constant mass moving in an effective potential field. In this framework, the $Lambda$ cold dark matter model as the current standard model of cosmology corresponds to a harmonic oscillator. We further generalize this formalism to take into account the bulk viscosity and other cases. The Hamiltonian can be quantized straightforwardly, but this is different from the approach of the Wheeler-DeWitt equation in quantum cosmology.
The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original informatio
n. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?
