5,162 research outputs found
Practical Target-Based Synchronization Strategies for Immutable Time-Series Data Tables
As the Internet of Things and industrial monitoring of utilities grow, efficiently synchronizing immutable time-series data streams between databases becomes a pressing issue. Extracting data from critical production databases demands careful consideration of the stress imposed on the machines, so synchronization strategies are required to minimize the transfer of duplicate data and the load imposed on remote sources.
Literature on the synchronization problem is generalized to arbitrary tables and does not consider the characteristics of time-series data streams, so research was required to investigate methods to quickly synchronize source and target time-series data tables. This thesis examines immutable time-series scenarios and synchronization strategies to answer the following question: given several scenarios, which target-based immutable time-series synchronization strategies best optimize run-time, bandwidth, and accuracy?
The strategies explored in this research are implemented into the Meerschaum system, a project intended to leverage these time-series concepts for production deployments. As a practical demonstration, these strategies are used to continuously cache Clemson University’s utilities data
Learning Mixtures of Gaussians in High Dimensions
Efficiently learning mixture of Gaussians is a fundamental problem in
statistics and learning theory. Given samples coming from a random one out of k
Gaussian distributions in Rn, the learning problem asks to estimate the means
and the covariance matrices of these Gaussians. This learning problem arises in
many areas ranging from the natural sciences to the social sciences, and has
also found many machine learning applications. Unfortunately, learning mixture
of Gaussians is an information theoretically hard problem: in order to learn
the parameters up to a reasonable accuracy, the number of samples required is
exponential in the number of Gaussian components in the worst case. In this
work, we show that provided we are in high enough dimensions, the class of
Gaussian mixtures is learnable in its most general form under a smoothed
analysis framework, where the parameters are randomly perturbed from an
adversarial starting point. In particular, given samples from a mixture of
Gaussians with randomly perturbed parameters, when n > {\Omega}(k^2), we give
an algorithm that learns the parameters with polynomial running time and using
polynomial number of samples. The central algorithmic ideas consist of new ways
to decompose the moment tensor of the Gaussian mixture by exploiting its
structural properties. The symmetries of this tensor are derived from the
combinatorial structure of higher order moments of Gaussian distributions
(sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop
new tools for bounding smallest singular values of structured random matrices,
which could be useful in other smoothed analysis settings
Intrinsic subspace convergence in TDD MIMO communication
In numerical linear algebra, students encounter early
the iterative power method, which finds eigenvectors of a matrix
from an arbitrary starting point through repeated normalization
and multiplications by the matrix itself. In practice, more sophisticated
methods are used nowadays, threatening to make the power
method a historical and pedagogic footnote. However, in the context
of communication over a time-division duplex (TDD) multipleinput
multiple-output (MIMO) channel, the power method takes a
special position. It can be viewed as an intrinsic part of the uplink
and downlink communication switching, enabling estimation
of the eigenmodes of the channel without extra overhead. Generalizing
the method to vector subspaces, communication in the
subspaces with the best receive and transmit signal-to-noise ratio
(SNR) is made possible. In exploring this intrinsic subspace convergence
(ISC), we show that several published and new schemes can
be cast into a common framework where all members benefit from
the ISC.Peer Reviewe
Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition
A human-computer interface is developed to provide services of computer assisted machine translation (CAT) and computer assisted transcription of handwritten text images (CATTI). The back-end machine translation (MT) and handwritten text recognition (HTR) systems are provided by the Pattern Recognition and Human Language Technology (PRHLT) research group. The idea is to provide users with easy to use tools to convert interactive translation and transcription feasible tasks. The assisted service is provided by remote servers with CAT or CATTI capabilities. The interface supplies the user with tools for efficient local edition: deletion, insertion and substitution.Ocampo Sepúlveda, JC. (2009). Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition. http://hdl.handle.net/10251/14318Archivo delegad
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