6,285 research outputs found
Probabilistic modeling and statistical inference for random fields and space-time processes
Author from publisher's list. Cover title.Final report for ONR Grant N00014-91-J-100
Final report on estimation and statistical analysis of spatially distributed random processes
Includes bibliographical references.Final report;Supported by the NSF. ECS-8312921prepared by Alan S. Willsky, Bernard C. Levy, George C. Verghese
On the Inversion of High Energy Proton
Inversion of the K-fold stochastic autoconvolution integral equation is an
elementary nonlinear problem, yet there are no de facto methods to solve it
with finite statistics. To fix this problem, we introduce a novel inverse
algorithm based on a combination of minimization of relative entropy, the Fast
Fourier Transform and a recursive version of Efron's bootstrap. This gives us
power to obtain new perspectives on non-perturbative high energy QCD, such as
probing the ab initio principles underlying the approximately negative binomial
distributions of observed charged particle final state multiplicities, related
to multiparton interactions, the fluctuating structure and profile of proton
and diffraction. As a proof-of-concept, we apply the algorithm to ALICE
proton-proton charged particle multiplicity measurements done at different
center-of-mass energies and fiducial pseudorapidity intervals at the LHC,
available on HEPData. A strong double peak structure emerges from the
inversion, barely visible without it.Comment: 29 pages, 10 figures, v2: extended analysis (re-projection ratios,
2D
The Tightness of the Kesten-Stigum Reconstruction Bound of Symmetric Model with Multiple Mutations
It is well known that reconstruction problems, as the interdisciplinary
subject, have been studied in numerous contexts including statistical physics,
information theory and computational biology, to name a few. We consider a
-state symmetric model, with two categories of states in each category,
and 3 transition probabilities: the probability to remain in the same state,
the probability to change states but remain in the same category, and the
probability to change categories. We construct a nonlinear second order
dynamical system based on this model and show that the Kesten-Stigum
reconstruction bound is not tight when .Comment: Accepted, to appear Journal of Statistical Physic
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
Magnetic resonance (MR) image acquisition is an inherently prolonged process,
whose acceleration by obtaining multiple undersampled images simultaneously
through parallel imaging has always been the subject of research. In this
paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable
of learning multi-scale spatial-frequency features from both real and imaginary
components, for fast parallel MR image reconstruction. By reformulating the
complex operations using octave convolutions, our model shows a strong ability
to capture richer representations of MR images, while at the same time greatly
reducing the spatial redundancy. More specifically, the input feature maps and
convolutional kernels are first split into two components (i.e., real and
imaginary), which are then divided into four groups according to their spatial
frequencies. Then, our Dual-OctConv conducts intra-group information updating
and inter-group information exchange to aggregate the contextual information
across different groups. Our framework provides two appealing benefits: (i) it
encourages interactions between real and imaginary components at various
spatial frequencies to achieve richer representational capacity, and (ii) it
enlarges the receptive field by learning multiple spatial-frequency features of
both the real and imaginary components. We evaluate the performance of the
proposed model on the acceleration of multi-coil MR image reconstruction.
Extensive experiments are conducted on an {in vivo} knee dataset under
different undersampling patterns and acceleration factors. The experimental
results demonstrate the superiority of our model in accelerated parallel MR
image reconstruction. Our code is available at:
github.com/chunmeifeng/Dual-OctConv.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence
(AAAI) 202
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