3,771 research outputs found
Sparse Vector Distributions and Recovery from Compressed Sensing
It is well known that the performance of sparse vector recovery algorithms
from compressive measurements can depend on the distribution underlying the
non-zero elements of a sparse vector. However, the extent of these effects has
yet to be explored, and formally presented. In this paper, I empirically
investigate this dependence for seven distributions and fifteen recovery
algorithms. The two morals of this work are: 1) any judgement of the recovery
performance of one algorithm over that of another must be prefaced by the
conditions for which this is observed to be true, including sparse vector
distributions, and the criterion for exact recovery; and 2) a recovery
algorithm must be selected carefully based on what distribution one expects to
underlie the sensed sparse signal.Comment: Originally submitted to IEEE Signal Processing Letters in March 2011,
but rejected June 2011. Revised, expanded, and submitted July 2011 to EURASIP
Journal special issue on sparse signal processin
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
Recommended from our members
Liophis ceii
Number of Pages: 2Integrative BiologyGeological Science
Recommended from our members
Liophis jaegeri
Number of Pages: 4Integrative BiologyGeological Science
Superpartners at LHC and Future Colliders: Predictions from Constrained Compactified M-Theory
We study a realistic top-down M-theory compactification with low-scale
effective Supersymmetry, consistent with phenomenological constraints. A
combination of top-down and generic phenomenological constraints fix the
spectrum. The gluino mass is predicted to be about 1.5 TeV. Three and only
three superpartner channels, , and
(where are Wino-like), are expected
to be observable at LHC-14. We also investigate the prospects of finding heavy
squarks and Higgsinos at future colliders. Gluino-stop-top,
gluino-sbottom-bottom associated production and first generation squark
associated production should be observable at a 100 TeV collider, along with
direct production of heavy Higgsinos. Within this framework the discovery of a
single sparticle is sufficient to determine uniquely the SUSY spectrum,
yielding a number of concrete testable predictions for LHC-14 and future
colliders, and determination of and thereby other fundamental
quantities.Comment: 19 pages, 4 figure
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