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 $Q$ 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