5,836 research outputs found
A general framework for online audio source separation
We consider the problem of online audio source separation. Existing
algorithms adopt either a sliding block approach or a stochastic gradient
approach, which is faster but less accurate. Also, they rely either on spatial
cues or on spectral cues and cannot separate certain mixtures. In this paper,
we design a general online audio source separation framework that combines both
approaches and both types of cues. The model parameters are estimated in the
Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation
(GEM) algorithm with multiplicative updates. The separation performance is
evaluated as a function of the block size and the step size and compared to
that of an offline algorithm.Comment: International conference on Latente Variable Analysis and Signal
Separation (2012
Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model
In this paper, we exploit the discrete Coxian distribution and propose a novel form of stochastic model, termed as the Coxian hidden semi-Makov model (Cox-HSMM), and apply it to the task of recognising activities of daily living (ADLs) in a smart house environment. The use of the Coxian has several advantages over traditional parameterization (e.g. multinomial or continuous distributions) including the low number of free parameters needed, its computational efficiency, and the existing of closed-form solution. To further enrich the model in real-world applications, we also address the problem of handling missing observation for the proposed Cox-HSMM. In the domain of ADLs, we emphasize the importance of the duration information and model it via the Cox-HSMM. Our experimental results have shown the superiority of the Cox-HSMM in all cases when compared with the standard HMM. Our results have further shown that outstanding recognition accuracy can be achieved with relatively low number of phases required in the Coxian, thus making the Cox-HSMM particularly suitable in recognizing ADLs whose movement trajectories are typically very long in nature.<br /
RA2: predicting simulation execution time for cloud-based design space explorations
Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds
Technical Report: Using Static Analysis to Compute Benefit of Tolerating Consistency
Synchronization is the Achilles heel of concurrent programs. Synchronization
requirement is often used to ensure that the execution of the concurrent
program can be serialized. Without synchronization requirement, a program
suffers from consistency violations. Recently, it was shown that if programs
are designed to tolerate such consistency violation faults (\cvf{s}) then one
can obtain substantial performance gain. Previous efforts to analyze the effect
of \cvf-tolerance are limited to run-time analysis of the program to determine
if tolerating \cvf{s} can improve the performance. Such run-time analysis is
very expensive and provides limited insight.
In this work, we consider the question, `Can static analysis of the program
predict the benefit of \cvf-tolerance?' We find that the answer to this
question is affirmative. Specifically, we use static analysis to evaluate the
cost of a \cvf and demonstrate that it can be used to predict the benefit of
\cvf-tolerance. We also find that when faced with a large state space, partial
analysis of the state space (via sampling) also provides the required
information to predict the benefit of \cvf-tolerance. Furthermore, we observe
that the \cvf-cost distribution is exponential in nature, i.e., the probability
that a \cvf has a cost of is , where and are constants,
i.e., most \cvf{s} cause no/low perturbation whereas a small number of \cvf{s}
cause a large perturbation. This opens up new aveneus to evaluate the benefit
of \cvf-tolerance
Dust masses of disks around 8 Brown Dwarfs and Very Low-Mass Stars in Upper Sco OB1 and Ophiuchus
We present the results of ALMA band 7 observations of dust and CO gas in the
disks around 7 objects with spectral types ranging between M5.5 and M7.5 in
Upper Scorpius OB1, and one M3 star in Ophiuchus. We detect unresolved
continuum emission in all but one source, and the CO J=3-2 line in two
sources. We constrain the dust and gas content of these systems using a grid of
models calculated with the radiative transfer code MCFOST, and find disk dust
masses between 0.1 and 1 M, suggesting that the stellar mass / disk
mass correlation can be extrapolated for brown dwarfs with masses as low as
0.05 M. The one disk in Upper Sco in which we detect CO emission, 2MASS
J15555600, is also the disk with warmest inner disk as traced by its H - [4.5]
photometric color. Using our radiative transfer grid, we extend the correlation
between stellar luminosity and mass-averaged disk dust temperature originally
derived for stellar mass objects to the brown dwarf regime to , applicable to spectral types
of M5 and later. This is slightly shallower than the relation for earlier
spectral type objects and yields warmer low-mass disks. The two prescriptions
cross at 0.27 L, corresponding to masses between 0.1 and 0.2 M
depending on age.Comment: 9 pages,6 figures, accepted to ApJ on 26/01/201
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