31,576 research outputs found
Error-Correcting Codes for Automatic Control
Systems with automatic feedback control may consist of several remote devices, connected only by unreliable communication channels. It is necessary in these conditions to have a method for accurate, real-time state estimation in the presence of channel noise. This problem is addressed, for the case of polynomial-growth-rate state spaces, through a new type of error-correcting code that is online and computationally efficient. This solution establishes a constructive analog, for some applications in estimation and control, of the Shannon coding theorem
A calculus for bordered Floer homology
We consider a class of manifolds with torus boundary admitting bordered
Heegaard Floer homology of a particularly simple form, namely, the type D
structure may be described graphically by a disjoint union of loops. We develop
a calculus for studying bordered invariants of this form and, in particular,
provide a complete description of slopes giving rise to L-space Dehn fillings
as well as necessary and sufficient conditions for L-spaces resulting from
identifying two such manifolds along their boundaries. As an application, we
show that Seifert fibered spaces with torus boundary fall into this class,
leading to a proof that, among graph manifolds containing a single JSJ torus,
the property of being an L-space is equivalent to non-left-orderability of the
fundamental group and to the non-existence of a coorientable taut foliation.Comment: 79 pages, 14 figures, uses tik
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
We present two graph-based algorithms for multiclass segmentation of
high-dimensional data. The algorithms use a diffuse interface model based on
the Ginzburg-Landau functional, related to total variation compressed sensing
and image processing. A multiclass extension is introduced using the Gibbs
simplex, with the functional's double-well potential modified to handle the
multiclass case. The first algorithm minimizes the functional using a convex
splitting numerical scheme. The second algorithm is a uses a graph adaptation
of the classical numerical Merriman-Bence-Osher (MBO) scheme, which alternates
between diffusion and thresholding. We demonstrate the performance of both
algorithms experimentally on synthetic data, grayscale and color images, and
several benchmark data sets such as MNIST, COIL and WebKB. We also make use of
fast numerical solvers for finding the eigenvectors and eigenvalues of the
graph Laplacian, and take advantage of the sparsity of the matrix. Experiments
indicate that the results are competitive with or better than the current
state-of-the-art multiclass segmentation algorithms.Comment: 14 page
Improving Transportation Construction Project Performance: Development of a Model to Support the Decision-Making Process for Incentive/Disincentive Construction Projects, MTI Report 09-07
This research presents a project time and cost performance simulation model to assist project planners and managers by providing a complete picture during the Incentive/Disincentive (I/D) contracting decision-making process of possible performance outcomes with probabilities based on historical data. This study was performed by collecting transportation construction project data. The collected project data from the Florida Department of Transportation were evaluated using time and cost performance indices and then statistical data analysis was performed to identify important factors that influence construction project time performance. Using Monte Carlo simulation procedures, this study demonstrated a methodology for developing an I/D project time and cost performance prediction model. User-friendly visual interfaces were developed to perform the simulation and report results using Visual Basic Application programming. The developed model was validated using additional cases of transportation construction projects. Based on statistical analysis, this research found that several project factors influence I/D contracting performance. The important factors that had significant impacts on project performance were the effects of contract type, project type, district, project size, project length, maximum incentive amount, and daily I/D amount. In conclusion, the developed model applied to I/D contracting projects will be a useful tool to assist the project planners and managers during the decision-making process and will promote the efficient use of I/D contracting, which will benefit the traveling public by saving their travel time from construction delays. With additional project data, the developed model can be updated easily and the more data used for the model, the better the accuracy of prediction that can be expected
Slowing-down of non-equilibrium concentration fluctuations in confinement
Fluctuations in a fluid are strongly affected by the presence of a
macroscopic gradient making them long-ranged and enhancing their amplitude.
While small-scale fluctuations exhibit diffusive lifetimes, larger-scale
fluctuations live shorter because of gravity, as theoretically and
experimentally well-known. We explore here fluctuations of even larger size,
comparable to the extent of the system in the direction of the gradient, and
find experimental evidence of a dramatic slowing-down in their dynamics. We
recover diffusive behaviour for these strongly-confined fluctuations, but with
a diffusion coefficient that depends on the solutal Rayleigh number. Results
from dynamic shadowgraph experiments are complemented by theoretical
calculations and numerical simulations based on fluctuating hydrodynamics, and
excellent agreement is found. The study of the dynamics of non-equilibrium
fluctuations allows to probe and measure the competition of physical processes
such as diffusion, buoyancy and confinement.Comment: Includes see Supplementary Material. Submitted to PR
The Influence of Network Topology on Sound Propagation in Granular Materials
Granular materials, whose features range from the particle scale to the
force-chain scale to the bulk scale, are usually modeled as either particulate
or continuum materials. In contrast with either of these approaches, network
representations are natural for the simultaneous examination of microscopic,
mesoscopic, and macroscopic features. In this paper, we treat granular
materials as spatially-embedded networks in which the nodes (particles) are
connected by weighted edges obtained from contact forces. We test a variety of
network measures for their utility in helping to describe sound propagation in
granular networks and find that network diagnostics can be used to probe
particle-, curve-, domain-, and system-scale structures in granular media. In
particular, diagnostics of meso-scale network structure are reproducible across
experiments, are correlated with sound propagation in this medium, and can be
used to identify potentially interesting size scales. We also demonstrate that
the sensitivity of network diagnostics depends on the phase of sound
propagation. In the injection phase, the signal propagates systemically, as
indicated by correlations with the network diagnostic of global efficiency. In
the scattering phase, however, the signal is better predicted by meso-scale
community structure, suggesting that the acoustic signal scatters over local
geographic neighborhoods. Collectively, our results demonstrate how the force
network of a granular system is imprinted on transmitted waves.Comment: 19 pages, 9 figures, and 3 table
Multiple alignment of protein sequences with repeats and rearrangements
Multiple sequence alignments are the usual starting point for analyses of protein structure and evolution. For proteins with repeated, shuffled and missing domains, however, traditional multiple sequence alignment algorithms fail to provide an accurate view of homology between related proteins, because they either assume that the input sequences are globally alignable or require locally alignable regions to appear in the same order in all sequences. In this paper, we present ProDA, a novel system for automated detection and alignment of homologous regions in collections of proteins with arbitrary domain architectures. Given an input set of unaligned sequences, ProDA identifies all homologous regions appearing in one or more sequences, and returns a collection of local multiple alignments for these regions. On a subset of the BAliBASE benchmarking suite containing curated alignments of proteins with complicated domain architectures, ProDA performs well in detecting conserved domain boundaries and clustering domain segments, achieving the highest accuracy to date for this task. We conclude that ProDA is a practical tool for automated alignment of protein sequences with repeats and rearrangements in their domain architecture
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