86,823 research outputs found
On a Conjecture of Givental
These brief notes record our puzzles and findings surrounding Givental's
recent conjecture which expresses higher genus Gromov-Witten invariants in
terms of the genus-0 data. We limit our considerations to the case of a
projective line, whose Gromov-Witten invariants are well-known and easy to
compute. We make some simple checks supporting his conjecture.Comment: 13 pages, no figures; v.2: new title, minor change
An asymptotic sampling formula for the coalescent with Recombination
Ewens sampling formula (ESF) is a one-parameter family of probability
distributions with a number of intriguing combinatorial connections. This
elegant closed-form formula first arose in biology as the stationary
probability distribution of a sample configuration at one locus under the
infinite-alleles model of mutation. Since its discovery in the early 1970s, the
ESF has been used in various biological applications, and has sparked several
interesting mathematical generalizations. In the population genetics community,
extending the underlying random-mating model to include recombination has
received much attention in the past, but no general closed-form sampling
formula is currently known even for the simplest extension, that is, a model
with two loci. In this paper, we show that it is possible to obtain useful
closed-form results in the case the population-scaled recombination rate
is large but not necessarily infinite. Specifically, we consider an asymptotic
expansion of the two-locus sampling formula in inverse powers of and
obtain closed-form expressions for the first few terms in the expansion. Our
asymptotic sampling formula applies to arbitrary sample sizes and
configurations.Comment: Published in at http://dx.doi.org/10.1214/09-AAP646 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Intrinsic Charm Flavor and Helicity Content in the Proton
Contributions to the quark flavor and spin observables from the intrinsic
charm in the proton are discussed in the SU(4) quark meson fluctuation model.
Our results suggest that the probability of finding the intrinsic charm in the
proton is less than 1%. The intrinsic charm helicity is small and negative,
. The fraction of the total quark helicity
carried by the intrinsic charm is less than 2%, and c_\up/c_\dw=35/67.Comment: 4 pages, 2 tables (revised version
Symbol error rate analysis for M-QAM modulated physical-layer network coding with phase errors
Recent theoretical studies of physical-layer network coding (PNC) show much interest on high-level modulation, such as M-ary quadrature amplitude modulation (M-QAM), and most related works are based on the assumption of phase synchrony. The possible presence of synchronization error and channel estimation error highlight the demand of analyzing the symbol error rate (SER) performance of PNC under different phase errors. Assuming synchronization and a general constellation mapping method, which maps the superposed signal into a set of M coded symbols, in this paper, we analytically derive the SER for M-QAM modulated PNC under different phase errors. We obtain an approximation of SER for general M-QAM modulations, as well as exact SER for quadrature phase-shift keying (QPSK), i.e. 4-QAM. Afterwards, theoretical results are verified by Monte Carlo simulations. The results in this paper can be used as benchmarks for designing practical systems supporting PNC. © 2012 IEEE
Tractable diffusion and coalescent processes for weakly correlated loci
Widely used models in genetics include the Wright-Fisher diffusion and its
moment dual, Kingman's coalescent. Each has a multilocus extension but under
neither extension is the sampling distribution available in closed-form, and
their computation is extremely difficult. In this paper we derive two new
multilocus population genetic models, one a diffusion and the other a
coalescent process, which are much simpler than the standard models, but which
capture their key properties for large recombination rates. The diffusion model
is based on a central limit theorem for density dependent population processes,
and we show that the sampling distribution is a linear combination of moments
of Gaussian distributions and hence available in closed-form. The coalescent
process is based on a probabilistic coupling of the ancestral recombination
graph to a simpler genealogical process which exposes the leading dynamics of
the former. We further demonstrate that when we consider the sampling
distribution as an asymptotic expansion in inverse powers of the recombination
parameter, the sampling distributions of the new models agree with the standard
ones up to the first two orders.Comment: 34 pages, 1 figur
Simultaneous Multiple Surface Segmentation Using Deep Learning
The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical imaging applications. Despite their widespread use, these require human
experts to design transformations, image features, surface smoothness priors,
and re-design for a different tissue, organ or imaging modality. Here, we
propose a Deep Learning based approach for segmentation of the surfaces in
volumetric medical images, by learning the essential features and
transformations from training data, without any human expert intervention. We
employ a regional approach to learn the local surface profiles. The proposed
approach was evaluated on simultaneous intraretinal layer segmentation of
optical coherence tomography (OCT) images of normal retinas and retinas
affected by age related macular degeneration (AMD). The proposed approach was
validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The
experiments showed statistically significant improvement in accuracy for our
approach compared to state-of-the-art graph based optimal surface segmentation
with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used
to learn the surfaces for both normal and diseased images. The mean unsigned
surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI
2.02-2.60 voxels) was improved to voxels (95% CI 1.14-1.40 voxels) using
our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB
memory, which is much faster than the 2837.46 s and 6.87 GB memory required by
the G-OSC method on the same computer system.Comment: 8 page
Determining SUSY Parameters in Chargino Pair-Production in Collisions
In most supersymmetric theories, charginos , mixtures
of charged color-neutral gauginos and higgsinos, belong to the class of the
lightest supersymmetric particles. They are easy to observe at
colliders. By measuring the total cross sections and the left-right asymmetries
with polarized electron beams in , the chargino masses and the gaugino-higgsino mixing angles can be
determined. From these observables the fundamental SUSY parameters can be
derived: the SU(2) gaugino mass , the modulus and
of the higgsino mass parameter, and , the ratio of the
vacuum expectation values of the two neutral Higgs doublet fields. The
solutions are unique; the CP-violating phase can be determined
uniquely by analyzing effects due to the normal polarization of the charginos.Comment: 20 pages, 4 figures, uses axodraw.st
Custodial bulk Randall-Sundrum model and B->K* l+ l'-
The custodial Randall-Sundrum model based on SU(2)_L X SU(2)_R X U(1)_(B-L)
generates new flavor-changing-neutral-current (FCNC) phenomena at tree level,
mediated by Kaluza-Klein neutral gauge bosons. Based on two natural assumptions
of universal 5D Yukawa couplings and no-cancellation in explaining the observed
standard model fermion mixing matrices, we determine the bulk Dirac mass
parameters. Phenomenological constraints from lepton-flavor-violations are also
used to specify the model. From the comprehensive study of B->K* l+ l'-, we
found that only the B->K*ee decay has sizable new physics effects. The zero
value position of the forward-backward asymmetry in this model is also
evaluated, with about 5% deviation from the SM result. Other effective
observables are also suggested such as the ratio of two differential (or
partially integrated) decay rates of B->K*ee and B->K*mu mu. For the first KK
gauge boson mass of M_A^(1)=2-4 TeV, we can have about 10-20% deviation from
the SM results.Comment: references added with minor change
Decoding coalescent hidden Markov models in linear time
In many areas of computational biology, hidden Markov models (HMMs) have been
used to model local genomic features. In particular, coalescent HMMs have been
used to infer ancient population sizes, migration rates, divergence times, and
other parameters such as mutation and recombination rates. As more loci,
sequences, and hidden states are added to the model, however, the runtime of
coalescent HMMs can quickly become prohibitive. Here we present a new algorithm
for reducing the runtime of coalescent HMMs from quadratic in the number of
hidden time states to linear, without making any additional approximations. Our
algorithm can be incorporated into various coalescent HMMs, including the
popular method PSMC for inferring variable effective population sizes. Here we
implement this algorithm to speed up our demographic inference method diCal,
which is equivalent to PSMC when applied to a sample of two haplotypes. We
demonstrate that the linear-time method can reconstruct a population size
change history more accurately than the quadratic-time method, given similar
computation resources. We also apply the method to data from the 1000 Genomes
project, inferring a high-resolution history of size changes in the European
population.Comment: 18 pages, 5 figures. To appear in the Proceedings of the 18th Annual
International Conference on Research in Computational Molecular Biology
(RECOMB 2014). The final publication is available at link.springer.co
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