17,809 research outputs found
Multiplexing regulated traffic streams: design and performance
The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sources’ traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system
On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling
A multi-fidelity surrogate model for highly nonlinear multiscale problems is
proposed. It is based on the introduction of two different surrogate models and
an adaptive on-the-fly switching. The two concurrent surrogates are built
incrementally starting from a moderate set of evaluations of the full order
model. Therefore, a reduced order model (ROM) is generated. Using a hybrid
ROM-preconditioned FE solver, additional effective stress-strain data is
simulated while the number of samples is kept to a moderate level by using a
dedicated and physics-guided sampling technique. Machine learning (ML) is
subsequently used to build the second surrogate by means of artificial neural
networks (ANN). Different ANN architectures are explored and the features used
as inputs of the ANN are fine tuned in order to improve the overall quality of
the ML model. Additional ANN surrogates for the stress errors are generated.
Therefore, conservative design guidelines for error surrogates are presented by
adapting the loss functions of the ANN training in pure regression or pure
classification settings. The error surrogates can be used as quality indicators
in order to adaptively select the appropriate -- i.e. efficient yet accurate --
surrogate. Two strategies for the on-the-fly switching are investigated and a
practicable and robust algorithm is proposed that eliminates relevant technical
difficulties attributed to model switching. The provided algorithms and ANN
design guidelines can easily be adopted for different problem settings and,
thereby, they enable generalization of the used machine learning techniques for
a wide range of applications. The resulting hybrid surrogate is employed in
challenging multilevel FE simulations for a three-phase composite with
pseudo-plastic micro-constituents. Numerical examples highlight the performance
of the proposed approach
On Multiscale Methods in Petrov-Galerkin formulation
In this work we investigate the advantages of multiscale methods in
Petrov-Galerkin (PG) formulation in a general framework. The framework is based
on a localized orthogonal decomposition of a high dimensional solution space
into a low dimensional multiscale space with good approximation properties and
a high dimensional remainder space{, which only contains negligible fine scale
information}. The multiscale space can then be used to obtain accurate Galerkin
approximations. As a model problem we consider the Poisson equation. We prove
that a Petrov-Galerkin formulation does not suffer from a significant loss of
accuracy, and still preserve the convergence order of the original multiscale
method. We also prove inf-sup stability of a PG Continuous and a Discontinuous
Galerkin Finite Element multiscale method. Furthermore, we demonstrate that the
Petrov-Galerkin method can decrease the computational complexity significantly,
allowing for more efficient solution algorithms. As another application of the
framework, we show how the Petrov-Galerkin framework can be used to construct a
locally mass conservative solver for two-phase flow simulation that employs the
Buckley-Leverett equation. To achieve this, we couple a PG Discontinuous
Galerkin Finite Element method with an upwind scheme for a hyperbolic
conservation law
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
Freedman [Adv. in Appl. Math. 40 (2008) 180-193; Ann. Appl. Stat. 2 (2008)
176-196] critiqued ordinary least squares regression adjustment of estimated
treatment effects in randomized experiments, using Neyman's model for
randomization inference. Contrary to conventional wisdom, he argued that
adjustment can lead to worsened asymptotic precision, invalid measures of
precision, and small-sample bias. This paper shows that in sufficiently large
samples, those problems are either minor or easily fixed. OLS adjustment cannot
hurt asymptotic precision when a full set of treatment-covariate interactions
is included. Asymptotically valid confidence intervals can be constructed with
the Huber-White sandwich standard error estimator. Checks on the asymptotic
approximations are illustrated with data from Angrist, Lang, and Oreopoulos's
[Am. Econ. J.: Appl. Econ. 1:1 (2009) 136--163] evaluation of strategies to
improve college students' achievement. The strongest reasons to support
Freedman's preference for unadjusted estimates are transparency and the dangers
of specification search.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS583 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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