30,880 research outputs found
SIMULATION STUDY ON WATERFLOOD FRONT: BLOCK HADE OF TARIM OILFIELD IN NORTHWEST CHINA
Block Hade consist of a deep thin sandstone reservoir of two sub-layer reservoirs. The thickness is
about 1.5 m for each layer. The two-layer “staircase” horizontal well is used for recovery. In order
to determine water displacement front and edge water movement, tracer test is conducted in the
reservoir. But the cycle of field tracer monitoring is about 150-360 days. This prevented the efficient
monitoring of waterflood swept area and waterflood advance direction and velocity, after the cycle
of tracer monitoring. Conservation of mass with respect to tracer flow and history performance
matching of tracer enabled the study of water-flood front and edge-water advance. The simulation result
is basically consistent with the monitored field tracer results. Therefore, numerical model can be used to
conduct a longer monitoring period. It can make up for the disadvantage of the complexity of the
tracer monitoring setup, its implementation, and time-consuming monitoring cycle. The water-flood
front, water-flood swept area, advancing velocity and the predominant water injection direction can be
obtained. Furthermore, it is possible to evaluate and predict the injection-production well interaction and
can also provide a reliable basis to deploy reasonable flood patterns to enhance oil recovery
Universally Decodable Matrices for Distributed Matrix-Vector Multiplication
Coded computation is an emerging research area that leverages concepts from
erasure coding to mitigate the effect of stragglers (slow nodes) in distributed
computation clusters, especially for matrix computation problems. In this work,
we present a class of distributed matrix-vector multiplication schemes that are
based on codes in the Rosenbloom-Tsfasman metric and universally decodable
matrices. Our schemes take into account the inherent computation order within a
worker node. In particular, they allow us to effectively leverage partial
computations performed by stragglers (a feature that many prior works lack). An
additional main contribution of our work is a companion matrix-based embedding
of these codes that allows us to obtain sparse and numerically stable schemes
for the problem at hand. Experimental results confirm the effectiveness of our
techniques.Comment: 6 pages, 1 figur
Posterior propriety and admissibility of hyperpriors in normal hierarchical models
Hierarchical modeling is wonderful and here to stay, but hyperparameter
priors are often chosen in a casual fashion. Unfortunately, as the number of
hyperparameters grows, the effects of casual choices can multiply, leading to
considerably inferior performance. As an extreme, but not uncommon, example use
of the wrong hyperparameter priors can even lead to impropriety of the
posterior. For exchangeable hierarchical multivariate normal models, we first
determine when a standard class of hierarchical priors results in proper or
improper posteriors. We next determine which elements of this class lead to
admissible estimators of the mean under quadratic loss; such considerations
provide one useful guideline for choice among hierarchical priors. Finally,
computational issues with the resulting posterior distributions are addressed.Comment: Published at http://dx.doi.org/10.1214/009053605000000075 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A High-Throughput Solver for Marginalized Graph Kernels on GPU
We present the design and optimization of a linear solver on General Purpose GPUs for the efficient and high-throughput evaluation of the marginalized graph kernel between pairs of labeled graphs. The solver implements a preconditioned conjugate gradient (PCG) method to compute the solution to a generalized Laplacian equation associated with the tensor product of two graphs. To cope with the gap between the instruction throughput and the memory bandwidth of current generation GPUs, our solver forms the tensor product linear system on-the-fly without storing it in memory when performing matrix-vector dot product operations in PCG. Such on-the-fly computation is accomplished by using threads in a warp to cooperatively stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. Besides, we propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to improve the efficiency of the sparse format.We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales
Comment on ``A New Symmetry for QED'' and ``Relativistically Covariant Symmetry in QED''
We show that recently found symmetries in QED are just non-local versions of
standard BRST symmetry.Comment: 4 pages, revte
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
Estimation of the Kronecker Covariance Model by Quadratic Form
We propose a new estimator, the quadratic form estimator, of the Kronecker product model for covariance matrices. We show that this estimator has good properties in the large dimensional case (i.e., the cross-sectional dimension n is large relative to th
Analysis of complex contagions in random multiplex networks
We study the diffusion of influence in random multiplex networks where links
can be of different types, and for a given content (e.g., rumor, product,
political view), each link type is associated with a content dependent
parameter in that measures the relative bias type- links
have in spreading this content. In this setting, we propose a linear threshold
model of contagion where nodes switch state if their "perceived" proportion of
active neighbors exceeds a threshold \tau. Namely, a node connected to
active neighbors and inactive neighbors via type- links will turn
active if exceeds its threshold \tau. Under this
model, we obtain the condition, probability and expected size of global
spreading events. Our results extend the existing work on complex contagions in
several directions by i) providing solutions for coupled random networks whose
vertices are neither identical nor disjoint, (ii) highlighting the effect of
content on the dynamics of complex contagions, and (iii) showing that
content-dependent propagation over a multiplex network leads to a subtle
relation between the giant vulnerable component of the graph and the global
cascade condition that is not seen in the existing models in the literature.Comment: Revised 06/08/12. 11 Pages, 3 figure
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