9,535 research outputs found
Measurement of Event Plane Correlations in Pb-Pb Collisions at =2.76 TeV with the ATLAS Detector
A measurement of correlations between event-plane angles is
presented as a function of centrality for Pb-Pb collisions at
TeV. These correlations are estimated from
observed event-plane angles obtained from charged particle or
transverse energy flow measured over a large pseudorapidity range ,
followed by a resolution correction that accounts for the dispersion of
relative to . Various correlators involving two or three event
planes with acceptable resolution are measured. Significant positive
correlations are observed for , ,
, , and
. However, the measured correlations for
are negative. These results may shed light on the
patterns of the fluctuation of the created matter in the initial state as well
as the subsequent hydrodynamic evolution.Comment: 5 pages, 4 figures. Proceedings for Hard Probes 2012, Cagliari, Ital
Bayesian Estimation of White Matter Atlas from High Angular Resolution Diffusion Imaging
We present a Bayesian probabilistic model to estimate the brain white matter
atlas from high angular resolution diffusion imaging (HARDI) data. This model
incorporates a shape prior of the white matter anatomy and the likelihood of
individual observed HARDI datasets. We first assume that the atlas is generated
from a known hyperatlas through a flow of diffeomorphisms and its shape prior
can be constructed based on the framework of large deformation diffeomorphic
metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape
space in a linear space of initial momentum uniquely determining diffeomorphic
geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI
atlas can be modeled using a centered Gaussian random field (GRF) model of the
initial momentum. In order to construct the likelihood of observed HARDI
datasets, it is necessary to study the diffeomorphic transformation of
individual observations relative to the atlas and the probabilistic
distribution of orientation distribution functions (ODFs). To this end, we
construct the likelihood related to the transformation using the same
construction as discussed for the shape prior of the atlas. The probabilistic
distribution of ODFs is then constructed based on the ODF Riemannian manifold.
We assume that the observed ODFs are generated by an exponential map of random
tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs
can be modeled using a GRF of their tangent vectors in the ODF Riemannian
manifold. We solve for the maximum a posteriori using the
Expectation-Maximization algorithm and derive the corresponding update
equations. Finally, we illustrate the HARDI atlas constructed based on a
Chinese aging cohort of 94 adults and compare it with that generated by
averaging the coefficients of spherical harmonics of the ODF across subjects
Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions
In this paper, we propose a novel large deformation diffeomorphic
registration algorithm to align high angular resolution diffusion images
(HARDI) characterized by orientation distribution functions (ODFs). Our
proposed algorithm seeks an optimal diffeomorphism of large deformation between
two ODF fields in a spatial volume domain and at the same time, locally
reorients an ODF in a manner such that it remains consistent with the
surrounding anatomical structure. To this end, we first review the Riemannian
manifold of ODFs. We then define the reorientation of an ODF when an affine
transformation is applied and subsequently, define the diffeomorphic group
action to be applied on the ODF based on this reorientation. We incorporate the
Riemannian metric of ODFs for quantifying the similarity of two HARDI images
into a variational problem defined under the large deformation diffeomorphic
metric mapping (LDDMM) framework. We finally derive the gradient of the cost
function in both Riemannian spaces of diffeomorphisms and the ODFs, and present
its numerical implementation. Both synthetic and real brain HARDI data are used
to illustrate the performance of our registration algorithm
Methods for Nonparametric and Semiparametric Regressions with Endogeneity: a Gentle Guide
This paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric instrumental variables regression (NPIV), nonparametric quantile IV regression and many more semi-nonparametric structural models. Asymptotic properties of the sieve estimators and the sieve Wald, quasi-likelihood ratio (QLR) hypothesis tests of functionals with nonparametric endogeneity are presented. For sieve NPIV estimation, the rate-adaptive data-driven choices of sieve regularization parameters and the sieve score bootstrap uniform confidence bands are described. Finally, simple sieve variance estimation and over-identification test for semiparametric two-step GMM are reviewed. Monte Carlo examples are included
Probing High Parton Densities at Low- in d+Au Collisions at PHENIX Using the New Forward and Backward Muon Piston Calorimeters
The new forward Muon Piston Calorimeters allow PHENIX to explore low-
parton distributions in d+Au collisions with hopes of observing gluon
saturation. We present a two-particle azimuthal correlation
measurement made between a mid-rapidity particle () and a
forward () wherein we compare correlation widths in
d+Au to p+p and compute .Comment: 4 pages, 3 figures - To appear in the conference proceedings for
Quark Matter 2009, March 30 - April 4, Knoxville, Tennesse
Effects of antipsychotics on bone mineral density and prolactin levels in patients with schizophrenia: a 12-month prospective study
Objective: Effects of conventional and atypical antipsychotics on bone mineral density (BMD) and serum prolactin levels (PRL) were examined in patients with schizophrenia.Methods: One hundred and sixty-three first-episode inpatients with schizophrenia were recruited, to whom one of three conventional antipsychotics (perphenazine, sulpiride, and chlorpromazine) or one of three atypical antipsychotics (clozapine, quetiapine, and aripiprazole)was prescribed for 12 months as appropriate. BMD and PRL were tested before and after treatment. Same measures were conducted in 90 matched healthy controls.Results Baseline BMD of postero-anterior L1–L4 range from 1.04 ± 0.17 to 1.42 ± 1.23, and there was no significant difference between the patients group and healthy control group. However, post-treatment BMD values in patients (ranging from 1.02 ± 0.15 to 1.23 ± 0.10) were significantly lower than that in healthy controls (ranging from 1.15 ± 0.12 to 1.42 ± 1.36). The BMD values after conventional antipsychotics were significantly lower than that after atypical antipsychotics. The PRL level after conventional antipsychotics (53.05 ± 30.25 ng/ml) was significantly higher than that after atypical antipsychotics (32.81 ± 17.42 ng/ml). Conditioned relevance analysis revealed significant negative correlations between the PRL level and the BMD values after conventional antipsychotics.Conclusion The increase of PRL might be an important risk factor leading to a high prevalence of osteoporosis in patients with schizophrenia on long-term conventional antipsychotic medication.<br/
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning
A key assumption in most existing works on FL algorithms' convergence
analysis is that the noise in stochastic first-order information has a finite
variance. Although this assumption covers all light-tailed (i.e.,
sub-exponential) and some heavy-tailed noise distributions (e.g., log-normal,
Weibull, and some Pareto distributions), it fails for many fat-tailed noise
distributions (i.e., ``heavier-tailed'' with potentially infinite variance)
that have been empirically observed in the FL literature. To date, it remains
unclear whether one can design convergent algorithms for FL systems that
experience fat-tailed noise. This motivates us to fill this gap in this paper
by proposing an algorithmic framework called FAT-Clipping (\ul{f}ederated
\ul{a}veraging with \ul{t}wo-sided learning rates and \ul{clipping}), which
contains two variants: FAT-Clipping per-round (FAT-Clipping-PR) and
FAT-Clipping per-iteration (FAT-Clipping-PI). Specifically, for the largest
such that the fat-tailed noise in FL still has a bounded
-moment, we show that both variants achieve
and
convergence rates in the
strongly-convex and general non-convex settings, respectively, where and
are the numbers of clients and communication rounds. Moreover, at the
expense of more clipping operations compared to FAT-Clipping-PR,
FAT-Clipping-PI further enjoys a linear speedup effect with respect to the
number of local updates at each client and being lower-bound-matching (i.e.,
order-optimal). Collectively, our results advance the understanding of
designing efficient algorithms for FL systems that exhibit fat-tailed
first-order oracle information.Comment: Published as a conference paper at NeurIPS 202
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