3,519 research outputs found
Search for Fourth Generation Quarks at CMS
We summarise the analyses that search for fourth generation quarks at the
Central Muon Solenoid (CMS) experiment. Such particles provide a natural
extension to the Standard Model (SM) and are still consistent with precision
electroweak measurements. Our searches are not limited to fourth generation
chiral quarks and are relevant to many Beyond the Standard Model theories. No
excess over the expected SM background is observed in any of these analyses and
limits are set on the masses of the and quarks at 95%
confidence level at 361 GeV/ and 450 GeV/, respectively.Comment: 10 pages, 6 figures, Proceedings of the DPF-2011 Conference,
Providence, RI, August 8-13, 201
Optically controlled orbital angular momentum generation in a polaritonic quantum fluid
Applications of the orbital angular momentum (OAM) of light range from the
next generation of optical communication systems to optical imaging and optical
manipulation of particles. Here we propose a micron-sized semiconductor source
which emits light with pre-defined OAM components. This source is based on a
polaritonic quantum fluid. We show how in this system modulational
instabilities can be controlled and harnessed for the spontaneous formation of
OAM components not present in the pump laser source. Once created, the OAM
states exhibit exotic flow patterns in the quantum fluid, characterized by
generation-annihilation pairs. These can only occur in open systems, not in
equilibrium condensates, in contrast to well-established vortex-antivortex
pairs
Directional optical switching and transistor functionality using optical parametric oscillation in a spinor polariton fluid
Over the past decade, spontaneously emerging patterns in the density of
polaritons in semiconductor microcavities were found to be a promising
candidate for all-optical switching. But recent approaches were mostly
restricted to scalar fields, did not benefit from the polariton's unique
spin-dependent properties, and utilized switching based on hexagon far-field
patterns with 60{\deg} beam switching (i.e. in the far field the beam
propagation direction is switched by 60{\deg}). Since hexagon far-field
patterns are challenging, we present here an approach for a linearly polarized
spinor field, that allows for a transistor-like (e.g., crucial for
cascadability) orthogonal beam switching, i.e. in the far field the beam is
switched by 90{\deg}. We show that switching specifications such as
amplification and speed can be adjusted using only optical means
High-performance FPGA-based accelerator for Bayesian neural networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space
Sampling Distributions of Random Electromagnetic Fields in Mesoscopic or Dynamical Systems
We derive the sampling probability density function (pdf) of an ideal
localized random electromagnetic field, its amplitude and intensity in an
electromagnetic environment that is quasi-statically time-varying statistically
homogeneous or static statistically inhomogeneous. The results allow for the
estimation of field statistics and confidence intervals when a single spatial
or temporal stochastic process produces randomization of the field. Results for
both coherent and incoherent detection techniques are derived, for Cartesian,
planar and full-vectorial fields. We show that the functional form of the
sampling pdf depends on whether the random variable is dimensioned (e.g., the
sampled electric field proper) or is expressed in dimensionless standardized or
normalized form (e.g., the sampled electric field divided by its sampled
standard deviation). For dimensioned quantities, the electric field, its
amplitude and intensity exhibit different types of
Bessel sampling pdfs, which differ significantly from the asymptotic
Gauss normal and ensemble pdfs when is relatively
small. By contrast, for the corresponding standardized quantities, Student ,
Fisher-Snedecor and root- sampling pdfs are obtained that exhibit
heavier tails than comparable Bessel pdfs. Statistical uncertainties
obtained from classical small-sample theory for dimensionless quantities are
shown to be overestimated compared to dimensioned quantities. Differences in
the sampling pdfs arising from de-normalization versus de-standardization are
obtained.Comment: 12 pages, 15 figures, accepted for publication in Phys. Rev. E, minor
typos correcte
Kaluza-Klein towers for real vector fields in flat space
We consider a free real vector field propagating in a five dimensional flat
space with its fifth dimension compactified either on a strip or on a circle
and perform a Kalaza Klein reduction which breaks SO(4,1) invariance while
reserving SO(3,1) invariance. Taking into account the Lorenz gauge condition,
we obtain from the most general hermiticity conditions for the relevant
operators all the allowed boundary conditions which have to be imposed on the
fields in the extra-dimension. The physical Kaluza-Klein mass towers, which
result in a four-dimensional brane, are determined in the different distinct
allowed cases. They depend on the bulk mass, on the parameters of the boundary
conditions and on the extra parameter present in the Lagrangian. In general,
they involve vector states together with accompanying scalar states.Comment: 28 pages, 4 independent table
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