2,057 research outputs found
Applying Metric Regularity to Compute a Condition Measure of a Smoothing Algorithm for Matrix Games
We develop an approach of variational analysis and generalized
differentiation to conditioning issues for two-person zero-sum matrix games.
Our major results establish precise relationships between a certain condition
measure of the smoothing first-order algorithm proposed by Gilpin et al.
[Proceedings of the 23rd AAAI Conference (2008) pp. 75-82] and the exact bound
of metric regularity for an associated set-valued mapping. In this way we
compute the aforementioned condition measure in terms of the initial matrix
game data
Some preconditioners for systems of linear inequalities
We show that a combination of two simple preprocessing steps would generally
improve the conditioning of a homogeneous system of linear inequalities. Our
approach is based on a comparison among three different but related notions of
conditioning for linear inequalities
Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
This paper presents a novel approach for indoor acoustic source localization
using microphone arrays and based on a Convolutional Neural Network (CNN). The
proposed solution is, to the best of our knowledge, the first published work in
which the CNN is designed to directly estimate the three dimensional position
of an acoustic source, using the raw audio signal as the input information
avoiding the use of hand crafted audio features. Given the limited amount of
available localization data, we propose in this paper a training strategy based
on two steps. We first train our network using semi-synthetic data, generated
from close talk speech recordings, and where we simulate the time delays and
distortion suffered in the signal that propagates from the source to the array
of microphones. We then fine tune this network using a small amount of real
data. Our experimental results show that this strategy is able to produce
networks that significantly improve existing localization methods based on
\textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN
method exhibits better resistance against varying gender of the speaker and
different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table
Avoiding core's DUE & SDC via acoustic wave detectors and tailored error containment and recovery
The trend of downsizing transistors and operating voltage scaling has made the processor chip more sensitive against radiation phenomena making soft errors an important challenge. New reliability techniques for handling soft errors in the logic and memories that allow meeting the desired failures-in-time (FIT) target are key to keep harnessing the benefits of Moore's law. The failure to scale the soft error rate caused by particle strikes, may soon limit the total number of cores that one may have running at the same time. This paper proposes a light-weight and scalable architecture to eliminate silent data corruption errors (SDC) and detected unrecoverable errors (DUE) of a core. The architecture uses acoustic wave detectors for error detection. We propose to recover by confining the errors in the cache hierarchy, allowing us to deal with the relatively long detection latencies. Our results show that the proposed mechanism protects the whole core (logic, latches and memory arrays) incurring performance overhead as low as 0.60%. © 2014 IEEE.Peer ReviewedPostprint (author's final draft
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