3,535 research outputs found
Event generator for the single- and double-photon emission associated with neutrino pair-production
A new event generator is proposed for two processes e+e- -> nu nu-bar gamma
and e+e- -> nu nu-bar gamma gamma where nu includes all the neutrino species.
The exact matrix elements of single- and double-photon emission, generated by
the GRACE system, are convoluted with the QED parton shower(QEDPS) to deal with
the initial state radiations(ISR). It is pointed out that a careful treatment
is required to avoid the double counting of the radiative photons between the
matrix elements and the ISR part. A detailed comparison of grc-nu-nu-gamma with
the O(alpha) calculations and other similar Monte Carlo generators is discussed
on the total cross section and on various distributions. It is also examined
how the possible effects of the anomalous triple-gauge-boson couplings can be
observed.Comment: 30 pages, 12 figure
Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks
In this paper, we examine the fundamental trade-off between radiated power
and achieved throughput in wireless multi-carrier, multiple-input and
multiple-output (MIMO) systems that vary with time in an unpredictable fashion
(e.g. due to changes in the wireless medium or the users' QoS requirements).
Contrary to the static/stationary channel regime, there is no optimal power
allocation profile to target (either static or in the mean), so the system's
users must adapt to changes in the environment "on the fly", without being able
to predict the system's evolution ahead of time. In this dynamic context, we
formulate the users' power/throughput trade-off as an online optimization
problem and we provide a matrix exponential learning algorithm that leads to no
regret - i.e. the proposed transmit policy is asymptotically optimal in
hindsight, irrespective of how the system evolves over time. Furthermore, we
also examine the robustness of the proposed algorithm under imperfect channel
state information (CSI) and we show that it retains its regret minimization
properties under very mild conditions on the measurement noise statistics. As a
result, users are able to track the evolution of their individually optimum
transmit profiles remarkably well, even under rapidly changing network
conditions and high uncertainty. Our theoretical analysis is validated by
extensive numerical simulations corresponding to a realistic network deployment
and providing further insights in the practical implementation aspects of the
proposed algorithm.Comment: 25 pages, 4 figure
Quantitative information flow, with a view
We put forward a general model intended for assessment of system security against passive eavesdroppers, both quantitatively ( how much information is leaked) and qualitatively ( what properties are leaked). To this purpose, we extend information hiding systems ( ihs ), a model where the secret-observable relation is represented as a noisy channel, with views : basically, partitions of the state-space. Given a view W and n independent observations of the system, one is interested in the probability that a Bayesian adversary wrongly predicts the class of W the underlying secret belongs to. We offer results that allow one to easily characterise the behaviour of this error probability as a function of the number of observations, in terms of the channel matrices defining the ihs and the view W . In particular, we provide expressions for the limit value as n → ∞, show by tight bounds that convergence is exponential, and also characterise the rate of convergence to predefined error thresholds. We then show a few instances of statistical attacks that can be assessed by a direct application of our model: attacks against modular exponentiation that exploit timing leaks, against anonymity in mix-nets and against privacy in sparse datasets
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