12,212 research outputs found
Does the BICEP2 Observation of Cosmological Tensor Modes Imply an Era of Nearly Planckian Energy Densities?
BICEP2 observations, interpreted most simply, suggest an era of inflation
with energy densities of order (, not far below the
Planck density. However, models of TeV gravity with large extra dimensions
might allow a very different interpretation involving much more modest energy
scales. We discuss the viability of inflation in such models, and conclude that
existing scenarios do not provide attractive alternatives to single field
inflation in four dimensions. Because the detection of tensor modes strengthens
our confidence that inflation occurred, it disfavors models of large extra
dimensions, at least for the moment.Comment: 4 pages, v3: version to appear in JHE
Normal approximation for nonlinear statistics using a concentration inequality approach
Let be a general sampling statistic that can be written as a linear
statistic plus an error term. Uniform and non-uniform Berry--Esseen type bounds
for are obtained. The bounds are the best possible for many known
statistics. Applications to U-statistics, multisample U-statistics,
L-statistics, random sums and functions of nonlinear statistics are discussed.Comment: Published at http://dx.doi.org/10.3150/07-BEJ5164 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Determination of Nonlinear Genetic Architecture using Compressed Sensing
We introduce a statistical method that can reconstruct nonlinear genetic
models (i.e., including epistasis, or gene-gene interactions) from
phenotype-genotype (GWAS) data. The computational and data resource
requirements are similar to those necessary for reconstruction of linear
genetic models (or identification of gene-trait associations), assuming a
condition of generalized sparsity, which limits the total number of gene-gene
interactions. An example of a sparse nonlinear model is one in which a typical
locus interacts with several or even many others, but only a small subset of
all possible interactions exist. It seems plausible that most genetic
architectures fall in this category. Our method uses a generalization of
compressed sensing (L1-penalized regression) applied to nonlinear functions of
the sensing matrix. We give theoretical arguments suggesting that the method is
nearly optimal in performance, and demonstrate its effectiveness on broad
classes of nonlinear genetic models using both real and simulated human
genomes.Comment: 20 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1408.342
Instability of Quantum de Sitter Spacetime
Quantized fields (e.g., the graviton itself) in de Sitter (dS) spacetime lead
to particle production: specifically, we consider a thermal spectrum resulting
from the dS (horizon) temperature. The energy required to excite these
particles reduces slightly the rate of expansion and eventually modifies the
semiclassical spacetime geometry. The resulting manifold no longer has constant
curvature nor time reversal invariance, and back-reaction renders the classical
dS background unstable to perturbations. In the case of AdS, there exists a
global static vacuum state; in this state there is no particle production and
the analogous instability does not arise.Comment: 3 pages, v2: version to appear in JHE
SINR Analysis of Opportunistic MIMO-SDMA Downlink Systems with Linear Combining
Opportunistic scheduling (OS) schemes have been proposed previously by the
authors for multiuser MIMO-SDMA downlink systems with linear combining. In
particular, it has been demonstrated that significant performance improvement
can be achieved by incorporating low-complexity linear combining techniques
into the design of OS schemes for MIMO-SDMA. However, this previous analysis
was performed based on the effective signal-to-interference ratio (SIR),
assuming an interference-limited scenario, which is typically a valid
assumption in SDMA-based systems. It was shown that the limiting distribution
of the effective SIR is of the Frechet type. Surprisingly, the corresponding
scaling laws were found to follow with , rather
than the conventional form.
Inspired by this difference between the scaling law forms, in this paper a
systematic approach is developed to derive asymptotic throughput and scaling
laws based on signal-to-interference-noise ratio (SINR) by utilizing extreme
value theory. The convergence of the limiting distribution of the effective
SINR to the Gumbel type is established. The resulting scaling law is found to
be governed by the conventional form. These novel results are
validated by simulation results. The comparison of SIR and SINR-based analysis
suggests that the SIR-based analysis is more computationally efficient for
SDMA-based systems and it captures the asymptotic system performance with
higher fidelity.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
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