Dark matter sensitivity of multi-ton liquid xenon detectors

We study the sensitivity of multi ton-scale time projection chambers using a liquid xenon target, e.g., the proposed DARWIN instrument, to spin-independent and spin-dependent WIMP-nucleon scattering interactions. Taking into account realistic backgrounds from the detector itself as well as from neutrinos, we examine the impact of exposure, energy threshold, background rejection efficiency and energy resolution on the dark matter sensitivity. With an exposure of 200 t x y and assuming detector parameters which have been already demonstrated experimentally, spin-independent cross sections as low as $2.5 \times 10^{-49}$ cm$^2$ can be probed for WIMP masses around 40 GeV/$c^2$. Additional improvements in terms of background rejection and exposure will further increase the sensitivity, while the ultimate WIMP science reach will be limited by neutrinos scattering coherently off the xenon nuclei.Comment: 21 pages, 8 Figures; matches version accepted by JCA

The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges - predicting the level of trustworthiness - no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).Comment: accepted versio

Experimental Investigation of the Spread of Airborne CFU in a Research-OR under Different Air Flow Regimes using Tracer Particles

Aim of this experimental study is to compare different types of ventilation in operating rooms (OR) regarding the highest possible patient protection against airborne germs based on particle counting. Tracer particles with the size of the airborne colony-forming units (CFU) occurring in OR shall be generated to derive representative statements about the removal of germs. In addition, they origin from aerosol generators mounted on heated person simulators to obtain a realistic dispersion of the contamination. It can be shown that the aerosol generators designed produce particles in the relevant size classes of the airborne germs emitted by OR personnel.publishedVersio

A physiologically-adapted gold standard for arousal during stress

Emotion is an inherently subjective psychophysiological human-state and to produce an agreed-upon representation (gold standard) for continuous emotion requires a time-consuming and costly training procedure of multiple human annotators. There is strong evidence in the literature that physiological signals are sufficient objective markers for states of emotion, particularly arousal. In this contribution, we utilise a dataset which includes continuous emotion and physiological signals - Heartbeats per Minute (BPM), Electrodermal Activity (EDA), and Respiration-rate - captured during a stress inducing scenario (Trier Social Stress Test). We utilise a Long Short-Term Memory, Recurrent Neural Network to explore the benefit of fusing these physiological signals with arousal as the target, learning from various audio, video, and textual based features. We utilise the state-of-the-art MuSe-Toolbox to consider both annotation delay and inter-rater agreement weighting when fusing the target signals. An improvement in Concordance Correlation Coefficient (CCC) is seen across features sets when fusing EDA with arousal, compared to the arousal only gold standard results. Additionally, BERT-based textual features' results improved for arousal plus all physiological signals, obtaining up to .3344 CCC compared to .2118 CCC for arousal only. Multimodal fusion also improves overall CCC with audio plus video features obtaining up to .6157 CCC to recognize arousal plus EDA and BPM

Ultrafast photocurrents at the surface of the three-dimensional topological insulator Bi2Se3\mathrm{Bi}_2\mathrm{Se}_3

Topological insulators constitute a new and fascinating class of matter with insulating bulk yet metallic surfaces that host highly mobile charge carriers with spin-momentum locking. Remarkably, the direction and magnitude of surface currents can be controlled with tailored light beams, but the underlying mechanisms are not yet well understood. To directly resolve the "birth" of such photocurrents we need to boost the time resolution to the scale of elementary scattering events ($\sim$ 10 fs). Here, we excite and measure photocurrents in the three-dimensional model topological insulator $\mathrm{Bi}_2\mathrm{Se}_3$ with a time resolution as short as 20 fs by sampling the concomitantly emitted broadband THz electromagnetic field from 1 to 40 THz. Remarkably, the ultrafast surface current response is dominated by a charge transfer along the Se-Bi bonds. In contrast, photon-helicity-dependent photocurrents are found to have orders of magnitude smaller magnitude than expected from generation scenarios based on asymmetric depopulation of the Dirac cone. Our findings are also of direct relevance for optoelectronic devices based on topological-insulator surface currents

Embracing and exploiting annotator emotional subjectivity: an affective rater ensemble model

Automated recognition of continuous emotions in audio-visual data is a growing area of study that aids in understanding human-machine interaction. Training such systems presupposes human annotation of the data. The annotation process, however, is laborious and expensive given that several human ratings are required for every data sample to compensate for the subjectivity of emotion perception. As a consequence, labelled data for emotion recognition are rare and the existing corpora are limited when compared to other state-of-the-art deep learning datasets. In this study, we explore different ways in which existing emotion annotations can be utilised more effectively to exploit available labelled information to the fullest. To reach this objective, we exploit individual raters’ opinions by employing an ensemble of rater-specific models, one for each annotator, by that reducing the loss of information which is a byproduct of annotation aggregation; we find that individual models can indeed infer subjective opinions. Furthermore, we explore the fusion of such ensemble predictions using different fusion techniques. Our ensemble model with only two annotators outperforms the regular Arousal baseline on the test set of the MuSe-CaR corpus. While no considerable improvements on valence could be obtained, using all annotators increases the prediction performance of arousal by up to. 07 Concordance Correlation Coefficient absolute improvement on test - solely trained on rate-specific models and fused by an attention-enhanced Long-short Term Memory-Recurrent Neural Network

Design and Implementation of a Distributed Ledger Technology Platform to Support Customs Processes within Supply Chains

In international trade, customs clearance fulfills complex and country-specific tasks in the execution of supply chain processes. Importers and exporters have to integrate customs authorities into the information flow, as customs authorities require information, e.g., on the bill of lading and the commercial invoice apart from the customs declaration. In addition, involved sub-service providers increase the problem of information asymmetry and the required coordination effort. Practice and research consider Distributed Ledger Technology (DLT) as a potential solution since this technology maintains a mutually agreed and secure database of value-creation partners. However, research has hardly investigated the design of such DLT systems. Therefore, we present a requirements catalogue, a concept, and a prototype of a DLT platform to address the outlined problem of information asymmetry, especially with a focus on customs processes

Aerosol emission of adolescents voices during speaking, singing and shouting

Since the outbreak of the COVID-19 pandemic, singing activities for children and young people have been strictly regulated with far-reaching consequences for music education in schools and ensemble and choir singing in some places. This is also due to the fact, that there has been no reliable data available on aerosol emissions from adolescents speaking, singing, and shouting. By utilizing a laser particle counter in cleanroom conditions we show, that adolescents emit fewer aerosol particles during singing than what has been known so far for adults. In our data, the emission rates ranged from 16 P/s to 267 P/s for speaking, 141 P/s to 1240 P/s for singing, and 683 P/s to 4332 P/s for shouting. The data advocate an adaptation of existing risk management strategies and rules of conduct for groups of singing adolescents, like gatherings in an educational context, e.g. singing lessons or choir rehearsals

The MuSe 2021 Multimodal Sentiment Analysis Challenge: sentiment, emotion, physiological-emotion, and stress

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities. The purpose of MuSe 2021 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), the sentiment analysis community (symbol-based), and the health informatics community. We present four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus on continuous emotion (valence and arousal) prediction; MuSe-Sent, in which participants recognise five classes each for valence and arousal; and MuSe-Physio, in which the novel aspect of 'physiological-emotion' is to be predicted. For this year's challenge, we utilise the MuSe-CaR dataset focusing on user-generated reviews and introduce the Ulm-TSST dataset, which displays people in stressful depositions. This paper also provides detail on the state-of-the-art feature sets extracted from these datasets for utilisation by our baseline model, a Long Short-Term Memory-Recurrent Neural Network. For each sub-challenge, a competitive baseline for participants is set; namely, on test, we report a Concordance Correlation Coefficient (CCC) of .4616 CCC for MuSe-Wilder; .5088 CCC for MuSe-Stress, and .4908 CCC for MuSe-Physio. For MuSe-Sent an F1 score of 32.82% is obtained