4,802 research outputs found
X-ray quantum optics with M\"ossbauer nuclei embedded in thin film cavities
A promising platform for the emerging field of x-ray quantum optics are
M\"ossbauer nuclei embedded in thin film cavities probed by near-resonant x-ray
light, as used in a number of recent experiments. Here, we develop a quantum
optical framework for the description of experimentally relevant settings
involving nuclei embedded in x-ray waveguides. We apply our formalism to two
settings of current experimental interest based on the archetype M\"ossbauer
isotope 57Fe. For present experimental conditions, we derive compact analytical
expressions and show that the alignment of medium magnetization as well as
incident and detection polarization enable the engineering advanced quantum
optical level schemes. The model encompasses non-linear and quantum effects
which could become accessible in future experiments.Comment: 13 pages, 6 figure
Collective effects between multiple nuclear ensembles in an x-ray cavity-QED setup
The setting of Moessbauer nuclei embedded in thin-film cavities has
facilitated an aspiring platform for x-ray quantum optics as shown in several
recent experiments. Here, we generalize the theoretical model of this platform
that we developed earlier [Phys. Rev. A 88, 043828 (2013)]. The theory
description is extended to cover multiple nuclear ensembles and multiple modes
in the cavity. While the extensions separately do not lead to qualitatively new
features, their combination gives rise to cooperative effects between the
different nuclear ensembles and distinct spectral signatures in the
observables. A related experiment by Roehlsberger et al. [Nature 482, 199
(2012)] is successfully modeled, the scalings derived with semiclassical
methods are reproduced, and a microscopic understanding of the setting is
obtained with our quantum mechanical description.Comment: 18 pages, 6 figure
Towards Informative Path Planning for Acoustic SLAM
Acoustic scene mapping is a challenging task as microphone arrays can often localize sound sources only in terms of their directions. Spatial diversity can be exploited constructively to infer source-sensor range when using microphone arrays installed on moving platforms, such as robots. As the absolute location of a moving robot is often unknown in practice, Acoustic Simultaneous Localization And Mapping (a-SLAM) is required in order to localize the moving robot’s positions and jointly map the sound sources. Using a novel a-SLAM approach, this paper investigates the impact of the choice of robot paths on source mapping accuracy. Simulation results demonstrate that a-SLAM performance can be improved by informatively planning robot paths
Negative refraction with tunable absorption in an active dense gas of atoms
Applications of negative index materials (NIM) presently are severely limited
by absorption. Next to improvements of metamaterial designs, it has been
suggested that dense gases of atoms could form a NIM with negligible losses. In
such gases, the low absorption is facilitated by quantum interference. Here, we
show that additional gain mechanisms can be used to tune and effectively remove
absorption in a dense gas NIM. In our setup, the atoms are coherently prepared
by control laser fields, and further driven by a weak incoherent pump field to
induce gain. We employ nonlinear optical Bloch equations to analyze the optical
response. Metastable Neon is identified as a suitable experimental candidate at
infrared frequencies to implement a lossless active negative index material.Comment: 10 pages, 9 figure
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks in deep neural networks. This method was evaluated in
within-corpus and various cross-corpus classification experiments that simulate
conditions "in the wild". In comparison to Single-Task Learning (STL) based
state of the art methods, we found that our MTL method proposed improved
performance significantly. Particularly, models using both gender and
naturalness achieved more gains than those using either gender or naturalness
separately. This benefit was also found in the high-level representations of
the feature space, obtained from our method proposed, where discriminative
emotional clusters could be observed.Comment: Published in the proceedings of INTERSPEECH, Stockholm, September,
201
Dynamic formation of Rydberg aggregates at off-resonant excitation
The dynamics of a cloud of ultra-cold two-level atoms is studied at
off-resonant laser driving to a Rydberg state. We find that resonant excitation
channels lead to strongly peaked spatial correlations associated with the
buildup of asymmetric excitation structures. These aggregates can extend over
the entire ensemble volume, but are in general not localized relative to the
system boundaries. The characteristic distances between neighboring excitations
depend on the laser detuning and on the interaction potential. These properties
lead to characteristic features in the spatial excitation density, the Mandel
parameter, and the total number of excitations. As an application an
implementation of the three-atom CSWAP or Fredkin gate with Rydberg atoms is
discussed. The gate not only exploits the Rydberg blockade, but also utilizes
the special features of an asymmetric geometric arrangement of the three atoms.
We show that continuous-wave off-resonant laser driving is sufficient to create
the required spatial arrangement of atoms out of a homogeneous cloud.Comment: 8 pages, 7 figure
Learning spectro-temporal features with 3D CNNs for speech emotion recognition
In this paper, we propose to use deep 3-dimensional convolutional networks
(3D CNNs) in order to address the challenge of modelling spectro-temporal
dynamics for speech emotion recognition (SER). Compared to a hybrid of
Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our
proposed 3D CNNs simultaneously extract short-term and long-term spectral
features with a moderate number of parameters. We evaluated our proposed and
other state-of-the-art methods in a speaker-independent manner using aggregated
corpora that give a large and diverse set of speakers. We found that 1) shallow
temporal and moderately deep spectral kernels of a homogeneous architecture are
optimal for the task; and 2) our 3D CNNs are more effective for
spectro-temporal feature learning compared to other methods. Finally, we
visualised the feature space obtained with our proposed method using
t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct
clusters of emotions.Comment: ACII, 2017, San Antoni
A hybrid model for Rydberg gases including exact two-body correlations
A model for the simulation of ensembles of laser-driven Rydberg-Rydberg
interacting multi-level atoms is discussed. Our hybrid approach combines an
exact two-body treatment of nearby atom pairs with an effective approximate
treatment for spatially separated pairs. We propose an optimized evolution
equation based only on the system steady state, and a time-independent Monte
Carlo technique is used to efficiently determine this steady state. The hybrid
model predicts features in the pair correlation function arising from
multi-atom processes which existing models can only partially reproduce. Our
interpretation of these features shows that higher-order correlations are
relevant already at low densities. Finally, we analyze the performance of our
model in the high-density case.Comment: significantly expanded and revised version (more observables,
high-density regime); 9 pages, 8 figure
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