4,025 research outputs found
Flavor constraints in a Bosonic Technicolor model
Flavor constraints in a bosonic Technicolor model are considered. We
illustrate different sources for their origin, and emphasize in particular the
role played by the vector states present in the Technicolor model. This feature
is the essential difference in comparison to an analogous model with two
fundamental Higgs scalar doublets.Comment: 16 pages, 13 figure
Content-Aware User Clustering and Caching in Wireless Small Cell Networks
In this paper, the problem of content-aware user clustering and content
caching in wireless small cell networks is studied. In particular, a service
delay minimization problem is formulated, aiming at optimally caching contents
at the small cell base stations (SCBSs). To solve the optimization problem, we
decouple it into two interrelated subproblems. First, a clustering algorithm is
proposed grouping users with similar content popularity to associate similar
users to the same SCBS, when possible. Second, a reinforcement learning
algorithm is proposed to enable each SCBS to learn the popularity distribution
of contents requested by its group of users and optimize its caching strategy
accordingly. Simulation results show that by correlating the different
popularity patterns of different users, the proposed scheme is able to minimize
the service delay by 42% and 27%, while achieving a higher offloading gain of
up to 280% and 90%, respectively, compared to random caching and unclustered
learning schemes.Comment: In the IEEE 11th International Symposium on Wireless Communication
Systems (ISWCS) 201
Theory of remote entanglement via quantum-limited phase-preserving amplification
We show that a quantum-limited phase-preserving amplifier can act as a
which-path information eraser when followed by heterodyne detection. This 'beam
splitter with gain' implements a continuous joint measurement on the signal
sources. As an application, we propose heralded concurrent remote entanglement
generation between two qubits coupled dispersively to separate cavities.
Dissimilar qubit-cavity pairs can be made indistinguishable by simple
engineering of the cavity driving fields providing further experimental
flexibility and the prospect for scalability. Additionally, we find an analytic
solution for the stochastic master equation, a quantum filter, yielding a
thorough physical understanding of the nonlinear measurement process leading to
an entangled state of the qubits. We determine the concurrence of the entangled
states and analyze its dependence on losses and measurement inefficiencies.Comment: Main text (11 pages, 5 figures), updated to the published versio
Optimization in random field Ising models by quantum annealing
We investigate the properties of quantum annealing applied to the random
field Ising model in one, two and three dimensions. The decay rate of the
residual energy, defined as the energy excess from the ground state, is find to
be with in the range ,
depending on the strength of the random field. Systems with ``large clusters''
are harder to optimize as measured by . Our numerical results suggest
that in the ordered phase whereas in the paramagnetic phase the
annealing procedure can be tuned so that .Comment: 7 pages (2 columns), 9 figures, published with minor changes, one
reference updated after the publicatio
State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing
Determining the magnitude and location of neural sources within the brain
that are responsible for generating magnetoencephalography (MEG) signals
measured on the surface of the head is a challenging problem in functional
neuroimaging. The number of potential sources within the brain exceeds by an
order of magnitude the number of recording sites. As a consequence, the
estimates for the magnitude and location of the neural sources will be
ill-conditioned because of the underdetermined nature of the problem. One
well-known technique designed to address this imbalance is the minimum norm
estimator (MNE). This approach imposes an regularization constraint that
serves to stabilize and condition the source parameter estimates. However,
these classes of regularizer are static in time and do not consider the
temporal constraints inherent to the biophysics of the MEG experiment. In this
paper we propose a dynamic state-space model that accounts for both spatial and
temporal correlations within and across candidate intracortical sources. In our
model, the observation model is derived from the steady-state solution to
Maxwell's equations while the latent model representing neural dynamics is
given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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