6,830 research outputs found
Ray casting implicit fractal surfaces with reduced affine arithmetic
A method is presented for ray casting implicit surfaces defined by fractal combinations of procedural noise functions. The method is robust and uses affine arithmetic to bound the variation of the implicit function along a ray. The method is also efficient due to a modification in the affine arithmetic representation that introduces a condensation step at the end of every non-affine operation. We show that our method is able to retain the tight estimation capabilities of affine arithmetic for ray casting implicit surfaces made from procedural noise functions while being faster to compute and more efficient to store
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
JUNO Conceptual Design Report
The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine
the neutrino mass hierarchy using an underground liquid scintillator detector.
It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants
in Guangdong, China. The experimental hall, spanning more than 50 meters, is
under a granite mountain of over 700 m overburden. Within six years of running,
the detection of reactor antineutrinos can resolve the neutrino mass hierarchy
at a confidence level of 3-4, and determine neutrino oscillation
parameters , , and to
an accuracy of better than 1%. The JUNO detector can be also used to study
terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard
Model. The central detector contains 20,000 tons liquid scintillator with an
acrylic sphere of 35 m in diameter. 17,000 508-mm diameter PMTs with high
quantum efficiency provide 75% optical coverage. The current choice of
the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO
as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of
detected photoelectrons per MeV is larger than 1,100 and the energy resolution
is expected to be 3% at 1 MeV. The calibration system is designed to deploy
multiple sources to cover the entire energy range of reactor antineutrinos, and
to achieve a full-volume position coverage inside the detector. The veto system
is used for muon detection, muon induced background study and reduction. It
consists of a Water Cherenkov detector and a Top Tracker system. The readout
system, the detector control system and the offline system insure efficient and
stable data acquisition and processing.Comment: 328 pages, 211 figure
A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems
Among the algorithms that are likely to play a major role in future exascale
computing, the fast multipole method (FMM) appears as a rising star. Our
previous recent work showed scaling of an FMM on GPU clusters, with problem
sizes in the order of billions of unknowns. That work led to an extremely
parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This
paper reports on a a campaign of performance tuning and scalability studies
using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were
parallelized using OpenMP, and a test using 10^7 particles randomly distributed
in a cube showed 78% efficiency on 8 threads. Tuning of the
particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of
the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel
scalability was studied in both strong and weak scaling. The strong scaling
test used 10^8 particles and resulted in 93% parallel efficiency on 2048
processes for the non-SIMD code and 54% for the SIMD-optimized code (which was
still 2x faster). The weak scaling test used 10^6 particles per process, and
resulted in 72% efficiency on 32,768 processes, with the largest calculation
taking about 40 seconds to evaluate more than 32 billion unknowns. This work
builds up evidence for our view that FMM is poised to play a leading role in
exascale computing, and we end the paper with a discussion of the features that
make it a particularly favorable algorithm for the emerging heterogeneous and
massively parallel architectural landscape
Rule Of Thumb: Deep derotation for improved fingertip detection
We investigate a novel global orientation regression approach for articulated
objects using a deep convolutional neural network. This is integrated with an
in-plane image derotation scheme, DeROT, to tackle the problem of per-frame
fingertip detection in depth images. The method reduces the complexity of
learning in the space of articulated poses which is demonstrated by using two
distinct state-of-the-art learning based hand pose estimation methods applied
to fingertip detection. Significant classification improvements are shown over
the baseline implementation. Our framework involves no tracking, kinematic
constraints or explicit prior model of the articulated object in hand. To
support our approach we also describe a new pipeline for high accuracy magnetic
annotation and labeling of objects imaged by a depth camera.Comment: To be published in proceedings of BMVC 201
- …