1,151 research outputs found
Removing EU milk quotas, soft landing versus hard landing
This paper analyses EU dairy policy reforms and mainly focus on EU milk quota removal scenarios. The model used to evaluate the scenario is a spatial equilibrium model of the dairy sector. It integrates the main competitor of the EU on world markets, Oceania, as well as the main importing regions in the rest of the world. The paper first assesses the impact of the Luxembourg scenario in the prospect of a new WTO agreement in the future. It then provide a quantitative assessment of the impact of the abolition of EU milk quotas on the EU dairy sector either through a gradual phasing out or through an abrupt abolition of milk quotas. Compared to a status-quo policy, the Luxembourg policy leads to a 7.6 percent milk price decrease and a 1.9 percent milk production increase. A gradual increase of milk quotas as recently proposed by the European Commission (+ 7% over 6 years) generate a 9% drop in the EU milk price (compared to the Luxembourg scenario) and an increase in production by 3.5%. A complete elimination of quotas leads to an additional 1% increase in production and an additional 3% drop in the EU milk price. As compared to the baseline scenario, in the Luxembourg scenario in 2014-15, producers gain 1.3 billion ¿, whereas in the same year they lose 2.6 billion ¿ in the soft landing scenario. As such the direct payments are more than sufficient to compensate producers for the loss of producer surplus in the Luxembourg scenario, but fall short to achieve full compensation in the soft landing scenario
SpECTRE: A Task-based Discontinuous Galerkin Code for Relativistic Astrophysics
We introduce a new relativistic astrophysics code, SpECTRE, that combines a
discontinuous Galerkin method with a task-based parallelism model. SpECTRE's
goal is to achieve more accurate solutions for challenging relativistic
astrophysics problems such as core-collapse supernovae and binary neutron star
mergers. The robustness of the discontinuous Galerkin method allows for the use
of high-resolution shock capturing methods in regions where (relativistic)
shocks are found, while exploiting high-order accuracy in smooth regions. A
task-based parallelism model allows efficient use of the largest supercomputers
for problems with a heterogeneous workload over disparate spatial and temporal
scales. We argue that the locality and algorithmic structure of discontinuous
Galerkin methods will exhibit good scalability within a task-based parallelism
framework. We demonstrate the code on a wide variety of challenging benchmark
problems in (non)-relativistic (magneto)-hydrodynamics. We demonstrate the
code's scalability including its strong scaling on the NCSA Blue Waters
supercomputer up to the machine's full capacity of 22,380 nodes using 671,400
threads.Comment: 41 pages, 13 figures, and 7 tables. Ancillary data contains
simulation input file
Massively parallel approximate Gaussian process regression
We explore how the big-three computing paradigms -- symmetric multi-processor
(SMC), graphical processing units (GPUs), and cluster computing -- can together
be brought to bare on large-data Gaussian processes (GP) regression problems
via a careful implementation of a newly developed local approximation scheme.
Our methodological contribution focuses primarily on GPU computation, as this
requires the most care and also provides the largest performance boost.
However, in our empirical work we study the relative merits of all three
paradigms to determine how best to combine them. The paper concludes with two
case studies. One is a real data fluid-dynamics computer experiment which
benefits from the local nature of our approximation; the second is a synthetic
data example designed to find the largest design for which (accurate) GP
emulation can performed on a commensurate predictive set under an hour.Comment: 24 pages, 6 figures, 1 tabl
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an important
pre-processing step for analytic procedures and subsequent inferences about
longitudinal tissue changes. Manual segmentation defines the current gold
standard in quality but is prohibitively expensive. Automatic approaches are
computationally intensive, incredibly slow at scale, and error prone due to
usually involving many potentially faulty intermediate steps. In order to
streamline the segmentation, we introduce a deep learning model that is based
on volumetric dilated convolutions, subsequently reducing both processing time
and errors. Compared to its competitors, the model has a reduced set of
parameters and thus is easier to train and much faster to execute. The contrast
in performance between the dilated network and its competitors becomes obvious
when both are tested on a large dataset of unprocessed human brain volumes. The
dilated network consistently outperforms not only another state-of-the-art deep
learning approach, the up convolutional network, but also the ground truth on
which it was trained. Not only can the incredible speed of our model make large
scale analyses much easier but we also believe it has great potential in a
clinical setting where, with little to no substantial delay, a patient and
provider can go over test results.Comment: Published as a conference paper at IJCNN 2017 Preprint versio
Upscaling and Development of Linear Array Focused Laser Differential Interferometry for Simultaneous 1D Velocimetry and Spectral Profiling in High-Speed Flows
In this research a new configuration of linear array-focused laser differential interferometry (LA-FLDI) is described. This measurement expands on previous implementations of LA-FLDI through the use of an additional Wollaston prism. This additional prism expands the typical single LA-FLDI column into two columns of FLDI point pairs. The additional column of probed locations allows for increased spatial sampling of frequency spectra as well as the addition of simultaneous wall normal velocimetry measurements. The new configuration is used to measure the velocity profile and frequency content across a Mach 2 turbulent boundary layer at six wall normal locations simultaneously. Features of the measured spectra are shown to agree with expectations and the obtained boundary layer velocity profile is compared with previously obtained PIV measurements. The increase in simultaneously probed points provided by LA-FLDI is ideal for impulse facilities where spatial scanning via measurement system translation is not possible for a single run and techniques such as PIV may not be feasible. Initial testing was also carried out to determine if FLDI-based velocimetry can provide reasonable velocity profiles for adverse pressure gradients and over distributed roughness. Finally, a prototype photodiode array is proposed to simplify the optical setup for LA-FLDI and initial test results are provided comparing the impulse response of the prototype array to that of the amplified photodetectors currently in use
Fourier analysis of finite element preconditioned collocation schemes
The spectrum of the iteration operator of some finite element preconditioned Fourier collocation schemes is investigated. The first part of the paper analyses one-dimensional elliptic and hyperbolic model problems and the advection-diffusion equation. Analytical expressions of the eigenvalues are obtained with use of symbolic computation. The second part of the paper considers the set of one-dimensional differential equations resulting from Fourier analysis (in the tranverse direction) of the 2-D Stokes problem. All results agree with previous conclusions on the numerical efficiency of finite element preconditioning schemes
Toward connecting core-collapse supernova theory with observations: I. Shock revival in a 15 Msun blue supergiant progenitor with SN 1987A energetics
We study the evolution of the collapsing core of a 15 Msun blue supergiant
supernova progenitor from the core bounce until 1.5 seconds later. We present a
sample of hydrodynamic models parameterized to match the explosion energetics
of SN 1987A.
We find the spatial model dimensionality to be an important contributing
factor in the explosion process. Compared to two-dimensional simulations, our
three-dimensional models require lower neutrino luminosities to produce equally
energetic explosions. We estimate that the convective engine in our models is
4% more efficient in three dimensions than in two dimensions. We propose that
the greater efficiency of the convective engine found in three-dimensional
simulations might be due to the larger surface-to-volume ratio of convective
plumes, which aids in distributing energy deposited by neutrinos.
We do not find evidence of the standing accretion shock instability nor
turbulence being a key factor in powering the explosion in our models. Instead,
the analysis of the energy transport in the post-shock region reveals
characteristics of penetrative convection. The explosion energy decreases
dramatically once the resolution is inadequate to capture the morphology of
convection on large scales. This shows that the role of dimensionality is
secondary to correctly accounting for the basic physics of the explosion.
We also analyze information provided by particle tracers embedded in the
flow, and find that the unbound material has relatively long residency times in
two-dimensional models, while in three dimensions a significant fraction of the
explosion energy is carried by particles with relatively short residency times.Comment: accepted for publication in Astrophysical Journa
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