Audio Caption: Listen and Tell

Increasing amount of research has shed light on machine perception of audio events, most of which concerns detection and classification tasks. However, human-like perception of audio scenes involves not only detecting and classifying audio sounds, but also summarizing the relationship between different audio events. Comparable research such as image caption has been conducted, yet the audio field is still quite barren. This paper introduces a manually-annotated dataset for audio caption. The purpose is to automatically generate natural sentences for audio scene description and to bridge the gap between machine perception of audio and image. The whole dataset is labelled in Mandarin and we also include translated English annotations. A baseline encoder-decoder model is provided for both English and Mandarin. Similar BLEU scores are derived for both languages: our model can generate understandable and data-related captions based on the dataset.Comment: accepted by ICASSP201

A new precision measurement of the {\alpha}-decay half-life of 190Pt

A laboratory measurement of the $\alpha$-decay half-life of $^{190}$Pt has been performed using a low background Frisch grid ionisation chamber. A total amount of 216.60(17) mg of natural platinum has been measured for 75.9 days. The resulting half-life is $(4.97\pm0.16)\times 10^{11}$ years, with a total uncertainty of 3.2%. This number is in good agreement with the half-life obtained using the geological comparison method

Influencing Factors of Clinical Patient Recruitment Systems Design

Clinical patient recruitment (CPR) is a critical function in clinical research. However, there is no holistic design for CPR systems that incorporates functions to support all critical success factors of clinical trial performance. In order to fill this gap, a study based on a literature review and several semi-structured expert interviews was conducted. Existing theory was synthesized with newly found influence factors using categories from CPR theory and factors gathered from literature and experts. The result is a systematization of influence factors of CPR that can be used for derivation of requirements for CPR systems in a subsequent research step or for the purpose of causal modeling

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation

The availability of datasets for analytical solution development is a common bottleneck in data-driven predictive maintenance. Therefore, novel solutions are mostly based on synthetic benchmarking examples, such as NASA’s C-MAPSS datasets, where researchers from various disciplines like artificial intelligence and statistics apply and test their methodical approaches. The majority of studies, however, only evaluate the overall solution against a final prediction score, where we argue that a more fine-grained consideration is required distinguishing between detailed method components to measure their particular impact along the prognostic development process. To address this issue, we first conduct a literature review resulting in more than one hundred studies using the C-MAPSS datasets. Subsequently, we apply a taxonomy approach to receive dimensions and characteristics that decompose complex analytical solutions into more manageable components. The result is a first draft of a systematic benchmarking framework as a more comparable basis for future development and evaluation purposes

Machine learning and deep learning

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.Comment: Published online first in Electronic Market

Enhanced quantum coherence in exchange coupled spins via singlet-triplet transitions

Manipulation of spin states at the single-atom scale underlies spin-based quantum information processing and spintronic devices. Such applications require protection of the spin states against quantum decoherence due to interactions with the environment. While a single spin is easily disrupted, a coupled-spin system can resist decoherence by employing a subspace of states that is immune to magnetic field fluctuations. Here, we engineered the magnetic interactions between the electron spins of two spin-1/2 atoms to create a clock transition and thus enhance their spin coherence. To construct and electrically access the desired spin structures, we use atom manipulation combined with electron spin resonance (ESR) in a scanning tunneling microscope (STM). We show that a two-level system composed of a singlet state and a triplet state is insensitive to local and global magnetic field noise, resulting in much longer spin coherence times compared with individual atoms. Moreover, the spin decoherence resulting from the interaction with tunneling electrons is markedly reduced by a homodyne readout of ESR. These results demonstrate that atomically-precise spin structures can be designed and assembled to yield enhanced quantum coherence