Deep Neural Baselines for Computational Paralinguistics

Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-end deep learning approach to detect and classify patterns reflecting sleepiness in the human voice. Our approach is based solely on a moderately complex deep neural network architecture. It may be applied directly on the audio data without requiring any specific feature engineering, thus remaining transferable to other audio classification tasks. Nevertheless, our approach performs similar to state-of-the-art machine learning models.Comment: 5 pages, 3 figures; This paper was accepted at INTERSPEECH 2019, Graz, 15-19th September 2019. DOI will be added after publishment of the accepted pape

Meson retardation in deuteron electrodisintegration

The effect of meson retardation in $NN$-interaction and exchange currents on deuteron electrodisintegration is studied in a coupled channel approach including $NN$-, $N \Delta$- and $\pi d$-channels. It is shown that the influence of retardation depends on the energy regime: Whereas below $\pi$-threshold calculations with static and retarded operators yield almost identical results, they differ significantly in the $\Delta$-region. Especially, the longitudinal and the longitudinal-transverse interference structure functions are strongly affected.Comment: 6 pages, 6 figure

CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing

The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data. Even though this prevents overfitting to the training environment(s), it hinders policy optimization. Crafting a suitable observation, only containing crucial information, has been shown to be a challenging task itself. To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. By providing only relevant information, overfitting to a specific training layout is precluded and generalization to unseen environments is improved. We formulate three CROPs that can be applied to fully observable observation- and action-spaces and provide methodical foundation. We empirically show the improvements of CROP in a distributionally shifted safety gridworld. We furthermore provide benchmark comparisons to full observability and data-augmentation in two different-sized procedurally generated mazes.Comment: 9 pages, 5 figures, accepted for publication at IJCAI 202

Real-frequency quantum field theory applied to the single-impurity Anderson model

A major challenge in the field of correlated electrons is the computation of dynamical correlation functions. For comparisons with experiment, one is interested in their real-frequency dependence. This is difficult to compute, as imaginary-frequency data from the Matsubara formalism require analytic continuation, a numerically ill-posed problem. Here, we apply quantum field theory to the single-impurity Anderson model (AM), using the Keldysh instead of the Matsubara formalism with direct access to the self-energy and dynamical susceptibilities on the real-frequency axis. We present results from the functional renormalization group (fRG) at one-loop level and from solving the self-consistent parquet equations in the parquet approximation. In contrast to previous Keldysh fRG works, we employ a parametrization of the four-point vertex which captures its full dependence on three real-frequency arguments. We compare our results to benchmark data obtained with the numerical renormalization group and to second-order perturbation theory. We find that capturing the full frequency dependence of the four-point vertex significantly improves the fRG results compared to previous implementations, and that solving the parquet equations yields the best agreement with the NRG benchmark data, but is only feasible up to moderate interaction strengths. Our methodical advances pave the way for treating more complicated models in the future.Comment: 25 pages, 20 figure

Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability

Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research due to a strong focus on state-based centralized training for decentralized execution (CTDE) and benchmarks that lack sufficient stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this paper, we propose Attention-based Embeddings of Recurrence In multi-Agent Learning (AERIAL) to approximate value functions under stochastic partial observability. AERIAL replaces the true state with a learned representation of multi-agent recurrence, considering more accurate information about decentralized agent decisions than state-based CTDE. We then introduce MessySMAC, a modified version of SMAC with stochastic observations and higher variance in initial states, to provide a more general and configurable benchmark regarding stochastic partial observability. We evaluate AERIAL in Dec-Tiger as well as in a variety of SMAC and MessySMAC maps, and compare the results with state-based CTDE. Furthermore, we evaluate the robustness of AERIAL and state-based CTDE against various stochasticity configurations in MessySMAC.Comment: Accepted at ICML 202

Elastic electron deuteron scattering with consistent meson exchange and relativistic contributions of leading order

The influence of relativistic contributions to elastic electron deuteron scattering is studied systematically at low and intermediate momentum transfers ($Q^2\leq 30$ fm$^{-2}$). In a $(p/M)$-expansion, all leading order relativistic $\pi$-exchange contributions consistent with the Bonn OBEPQ models are included. In addition, static heavy meson exchange currents including boost terms and lowest order $\rho\pi\gamma$-currents are considered. Sizeable effects from the various relativistic two-body contributions, mainly from $\pi$-exchange, have been found in form factors, structure functions and the tensor polarization $T_{20}$. Furthermore, static properties, viz. magnetic dipole and charge quadrupole moments and the mean square charge radius are evaluated.Comment: 15 pages Latex including 5 figures, final version accepted for publication in Phys.Rev.C Details of changes: (i) The notation of the curves in Figs. 1 and 2 have been clarified with respect to left and right panels. (ii) In Figs. 3 and 4 an experimental point for T_20 has been added and a corresponding reference [48] (iii) At the end of the text we have added a paragraph concerning the quality of the Bonn OBEPQ potential

The scenario coevolution paradigm: adaptive quality assurance for adaptive systems

Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018)