82,270 research outputs found
Significance Regression: A Statistical Approach to Biased Linear Regression and Partial Least Squares
This paper first examines the properties of biased regressors that proceed by restricting the search for the optimal regressor to a subspace. These properties suggest features such biased regression methods should incorporate. Motivated by these observations, this work proposes a new formulation for biased regression derived from the principle of statistical significance. This new formulation, significance regression (SR), leads to partial least squares (PLS) under certain model assumptions and to more general methods under various other model kumptions. For models with multiple outputs, SR will be shown to have certain advantages over PLS. Using the new formulation a significance test is advanced for determining the number of directions to be used; for PLS, cross-validation has been the primary method for determining this quantity. The prediction and estimation properties of SR are discussed. A brief numerical example illustrates the relationship between SR and PLS
A Peer-to-Peer Middleware Framework for Resilient Persistent Programming
The persistent programming systems of the 1980s offered a programming model
that integrated computation and long-term storage. In these systems, reliable
applications could be engineered without requiring the programmer to write
translation code to manage the transfer of data to and from non-volatile
storage. More importantly, it simplified the programmer's conceptual model of
an application, and avoided the many coherency problems that result from
multiple cached copies of the same information. Although technically
innovative, persistent languages were not widely adopted, perhaps due in part
to their closed-world model. Each persistent store was located on a single
host, and there were no flexible mechanisms for communication or transfer of
data between separate stores. Here we re-open the work on persistence and
combine it with modern peer-to-peer techniques in order to provide support for
orthogonal persistence in resilient and potentially long-running distributed
applications. Our vision is of an infrastructure within which an application
can be developed and distributed with minimal modification, whereupon the
application becomes resilient to certain failure modes. If a node, or the
connection to it, fails during execution of the application, the objects are
re-instantiated from distributed replicas, without their reference holders
being aware of the failure. Furthermore, we believe that this can be achieved
within a spectrum of application programmer intervention, ranging from minimal
to totally prescriptive, as desired. The same mechanisms encompass an
orthogonally persistent programming model. We outline our approach to
implementing this vision, and describe current progress.Comment: Submitted to EuroSys 200
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
A unified approach to discriminators for searches of gravitational waves from compact binary coalescences
We describe a general mathematical framework for discriminators in
the context of the compact binary coalescence search. We show that with any
is associated a vector bundle over the signal manifold, that is, the
manifold traced out by the signal waveforms in the function space of data
segments. The is then defined as the square of the norm of the
data vector projected onto a finite dimensional subspace (the fibre) of the
Hilbert space of data trains and orthogonal to the signal waveform - any such
fibre leads to a discriminator and the full vector bundle comprising
the subspaces and the base manifold constitute the discriminator. We
show that the discriminators used so far in the CBC searches
correspond to different fiber structures constituting different vector bundles
on the same base manifold, namely, the parameter space. The general formulation
indicates procedures to formulate new s which could be more effective
in discriminating against commonly occurring glitches in the data. It also
shows that no with a reasonable degree of freedom is foolproof. It
could also shed light on understanding why the traditional works so
well. As an example, we propose a family of ambiguity discriminators
that is an alternative to the traditional one. Any such ambiguity
makes use of the filtered output of the template bank, thus adding negligible
cost to the overall search. We test the performance of ambiguity on
simulated data using spinless TaylorF2 waveforms. We show that the ambiguity
essentially gives a clean separation between glitches and signals.
Finally, we investigate the effects of mismatch between signal and templates on
the and also further indicate how the ambiguity can be
generalized to detector networks for coherent observations.Comment: 21 pages, 5 figure, abstract is shortened to comply with the arXiv's
1920 characters limitation, v2: accepted for publication in PR
High-rate codes that are linear in space and time
Multiple-antenna systems that operate at high rates require simple yet effective space-time transmission schemes to handle the large traffic volume in real time. At rates of tens of bits per second per hertz, Vertical Bell Labs Layered Space-Time (V-BLAST), where every antenna transmits its own independent substream of data, has been shown to have good performance and simple encoding and decoding. Yet V-BLAST suffers from its inability to work with fewer receive antennas than transmit antennas-this deficiency is especially important for modern cellular systems, where a base station typically has more antennas than the mobile handsets. Furthermore, because V-BLAST transmits independent data streams on its antennas there is no built-in spatial coding to guard against deep fades from any given transmit antenna. On the other hand, there are many previously proposed space-time codes that have good fading resistance and simple decoding, but these codes generally have poor performance at high data rates or with many antennas. We propose a high-rate coding scheme that can handle any configuration of transmit and receive antennas and that subsumes both V-BLAST and many proposed space-time block codes as special cases. The scheme transmits substreams of data in linear combinations over space and time. The codes are designed to optimize the mutual information between the transmitted and received signals. Because of their linear structure, the codes retain the decoding simplicity of V-BLAST, and because of their information-theoretic optimality, they possess many coding advantages. We give examples of the codes and show that their performance is generally superior to earlier proposed methods over a wide range of rates and signal-to-noise ratios (SNRs)
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