3,068 research outputs found
Optimal mobility-aware admission control in content delivery networks
This paper addresses the problem of mobility management in Content Delivery Networks (CDN). We introduce a CDN architecture where admission control is performed at mobility aware access routers. We formulate a Markov Modulated Poisson Decision Process for access control that captures the bursty nature of data and packetized traffic together with the heterogeneity of multimedia services. The optimization of performance parameters, like the blocking probabilities and the overall utilization, is conducted and the structural properties of the optimal solutions are also studied. Heuristics are proposed to encompass the computational difficulties of the optimal solution when several classes of multimedia traffic are considered
Homogeneous Transitions during Inflation: a Description in Quantum Cosmology
The usual description of inflationary fluctuations uses the framework of quantum field theory (QFT) in curved spacetime, in which quantum fluctuations are superimposed on a classical background spacetime. Even for large fluctuations, such as those envisioned during a regime of eternal inflation, this framework is frequently used. In the present work we go one step beyond this description by quantising both the scalar field and the scale factor of the universe. Employing the Lorentzian path integral formulation of semi-classical gravity we restrict to a simplified minisuperspace setting by considering homogeneous transitions. This approach allows us to determine the dominant geometry and inflaton evolution contributing to such amplitudes. We find that for precisely specified initial scale factor and inflaton values (and uncertain momenta), two distinct saddle point geometries contribute to the amplitude, leading to interference effects. However, when the momenta of both scale factor and inflaton are specified with sufficient certainty, only a single saddle point is relevant and QFT in curved spacetime is applicable. In particular we find that for inflaton transitions up the potential, meaningful results are only obtained when the initial uncertainty in the inflaton value is large enough, allowing the dominant evolution to be a complexified slow-roll solution \emph{down} from a comparatively unlikely position higher up in the potential
1.4 GHz polarimetric observations of the two fields imaged by the DASI experiment
We present results of polarization observations at 1.4 GHz of the two fields
imaged by the DASI experiment (, and , ,
respectively). Data were taken with the Australia Telescope Compact Array with
3.4 arcmin resolution and mJy beam sensitivity. The emission
is dominated by point sources and we do not find evidence for diffuse
synchrotron radiation even after source subtraction. This allows to estimate an
upper limit of the diffuse polarized emission. The extrapolation to 30 GHz
suggests that the synchrotron radiation is lower than the polarized signal
measured by the DASI experiment by at least 2 orders of magnitude. This further
supports the conclusions drawn by the DASI team itself about the negligible
Galactic foreground contamination in their data set, improving by a factor
the upper limit estimated by Leitch et al. (2005).
The dominant point source emission allows us to estimate the contamination of
the CMB by extragalactic foregrounds. We computed the power spectrum of their
contribution and its extrapolation to 30 GHz provides a framework where the CMB
signal should dominate. However, our results do not match the conclusions of
the DASI team about the negligibility of point source contamination, suggesting
to take into account a source subtraction from the DASI data.Comment: 7 pages, six figures, submitted to MNRA
2MASS J18082002-5104378: The brightest (V=11.9) ultra metal-poor star
Context. The most primitive metal-poor stars are important for studying the
conditions of the early galaxy and are also relevant to big bang
nucleosynthesis. Aims. Our objective is to find the brightest (V<14) most
metal-poor stars. Methods. Candidates were selected using a new method, which
is based on the mismatch between spectral types derived from colors and
observed spectral types. They were observed first at low resolution with EFOSC2
at the NTT/ESO to obtain an initial set of stellar parameters. The most
promising candidate, 2MASS J18082002-5104378 (V=11.9), was observed at high
resolution (R=50 000) with UVES at the VLT/ESO, and a standard abundance
analysis was performed. Results. We found that 2MASS J18082002-5104378 is an
ultra metal-poor star with stellar parameters Teff = 5440 K, log g = 3.0 dex,
vt = 1.5 km/s, [Fe/H] = -4.1 dex. The star has [C/Fe]<+0.9 in a 1D analysis, or
[C/Fe]<=+0.5 if 3D effects are considered; its abundance pattern is typical of
normal (non-CEMP) ultra metal-poor stars. Interestingly, the star has a binary
companion. Conclusions. 2MASS J1808-5104 is the brightest (V=11.9) metal-poor
star of its category, and it could be studied further with even higher S/N
spectroscopy to determine additional chemical abundances, thus providing
important constraints to the early chemical evolution of our Galaxy.Comment: A&A Letter
Agent-based modeling of interdependent critical infrastructures
Critical interdependent infrastructures are complex systems, that if damaged or disrupted can seriously compromise the welfare of our society. This research, part of the CRESCO project, deal with the problem of interdependent critical infrastructures analysis, proposing an agent-based modelling and simulation solution. The approach we put forward, named Federated-ABMS, relies on discrete agent-based modelling and simulation and federated simulation. Federated-ABMS provides a formalism to model compound complex systems, composed of interacting systems, as federation of interacting agents and sector specific simulation models. This paper describes the formal model as well it outlines the steps that characterise the Federated-ABMS methodology, here applied to a target system, composed of a communication network and of a power grid. Moreover we conclude the paper with a thorough discussion of implementation issues
An electrical model for the Korteweg-de Vries equation
In this paper we describe an electrical network, whose current evolution does agree with a Korteweg\u2013de Vries equation. Our aim is to prepare pupils to understand the analytical aspects of nonlinear and dispersive phenomena, which very often are neglected in high-school and graduate textbooks. Some historical remarks introduce the topic and a bibliography is provided. \ua9 1984, American Association of Physics Teachers
Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity
Multilayer networks describe well many real interconnected communication and
transportation systems, ranging from computer networks to multimodal mobility
infrastructures. Here, we introduce a model in which the nodes have a limited
capacity of storing and processing the agents moving over a multilayer network,
and their congestions trigger temporary faults which, in turn, dynamically
affect the routing of agents seeking for uncongested paths. The study of the
network performance under different layer velocities and node maximum
capacities, reveals the existence of delicate trade-offs between the number of
served agents and their time to travel to destination. We provide analytical
estimates of the optimal buffer size at which the travel time is minimum and of
its dependence on the velocity and number of links at the different layers.
Phenomena reminiscent of the Slower Is Faster (SIF) effect and of the Braess'
paradox are observed in our dynamical multilayer set-up.Comment: 5 pages, 3 figure
Forecast B-modes detection at large scales in presence of noise and foregrounds
We investigate the detectability of the primordial CMB polarization B-mode
power spectrum on large scales in the presence of instrumental noise and
realistic foreground contamination. We have worked out a method to estimate the
errors on component separation and to propagate them up to the power spectrum
estimation. The performances of our method are illustrated by applying it to
the instrumental specifications of the Planck satellite and to the proposed
configuration for the next generation CMB polarization experiment COrE. We
demonstrate that a proper component separation step is required in order
achieve the detection of B-modes on large scales and that the final sensitivity
to B-modes of a given experiment is determined by a delicate balance between
noise level and residual foregrounds, which depend on the set of frequencies
exploited in the CMB reconstruction, on the signal-to-noise of each frequency
map, and on our ability to correctly model the spectral behavior of the
foreground components. We have produced a flexible software tool that allows
the comparison of performances on B-mode detection of different instrumental
specifications (choice of frequencies, noise level at each frequency, etc.) as
well as of different proposed approaches to component separation.Comment: 7 pages, 2 tables, 1 figure, accepted by MNRA
A regularized procedure to generate a deep learning model for topology optimization of electromagnetic devices
The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL allows achieving the topo-logically optimized design of electromagnetic devices using desktop class computers and reasonable computation times. An unparametrized bitmap representation of the geometries to be optimized, which is a highly desirable feature needed to discover completely new solutions, is perfectly managed by DL models. On the other hand, optimization algorithms do not easily cope with high dimensional input data, particularly because it is difficult to enforce the searched solutions as feasible and make them belong to expected manifolds. In this work, we propose the use of a variational autoencoder as a data regularization/augmentation tool in the context of topology optimization. The optimization was carried out using a gradient descent algorithm, and the DL neural network was used as a surrogate model to accelerate the resolution of single trial cases in the due course of optimization. The varia-tional autoencoder and the surrogate model were simultaneously trained in a multi-model custom training loop that minimizes total loss—which is the combination of the two models’ losses. In this paper, using the TEAM 25 problem (a benchmark problem for the assessment of electromagnetic numerical field analysis) as a test bench, we will provide a comparison between the computational times and design quality for a “classical” approach and the DL-based approach. Preliminary results show that the variational autoencoder manages regularizing the resolution process and transforms a constrained optimization into an unconstrained one, improving both the quality of the final solution and the performance of the resolution process
- …