Automatic Behavior Assessment from Uncontrolled Everyday Audio Recordings by Deep Learning

The manual categorization of behavior from sensory observation data to facilitate further analyses is a very expensive process. To overcome the inherent subjectivity of this process, typically, multiple domain experts are involved, resulting in increased efforts for the labeling. In this work, we investigate whether social behavior and environments can automatically be coded based on uncontrolled everyday audio recordings by applying deep learning. Recordings of daily living were obtained from healthy young and older adults at randomly selected times during the day by using a wearable device, resulting in a dataset of uncontrolled everyday audio recordings. For classification, a transfer learning approach based on a publicly available pretrained neural network and subsequent fine-tuning was implemented. The results suggest that certain aspects of social behavior and environments can be automatically classified. The ambient noise of uncontrolled audio recordings, however, poses a hard challenge for automatic behavior assessment, in particular, when coupled with data sparsity

A questionnaire to estimate the needs for research data management

The development of a data management plan requires detailed knowledge about the research data and the corresponding processes. Moreover, detailed knowledge about the entire data life-cycle in the respective research group is necessary when providing support with respect to reproducibility and data management. In order to allow curators to gain this knowledge, we developed a questionnaire, which provides a set of topics including questions to inspire discussions on data management and the data life-cycle. This questionnaire consists of a collection of questions targeting different phases of the data life-cycle. It was developed in order to gain insight about the data management practices in the subproject of the collaborative research centre 1270 ELAINE, but can also be used as guideline for so interviews with individual research groups

Разработка комплексной технологии для процедурной генерации виртуальных пространств с использованием методов машинного обучения и теории автоматизации в Unreal Engine 4

Разработка посвящена созданию приложения, которая производит процедурную генерацию виртуального пространства при помощи методов машинного обучения. Помещение генерируется с учётом пожеланий пользователя, сформулированных в словесной форме, с возможностью последующего редактирования сгенерированного помещения. Генерация будет производиться при помощи различных методов машинного обучения.The development is devoted to creating an application that produces a procedural generation of a virtual space using machine learning techniques. The room is generated based on the user's wishes, formulated in verbal form, with the possibility of later editing the generated room. The generation will be done with the help of various machine learning methods

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture -- the Bounded Information Bottleneck Autoencoder -- for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full GEANT4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.Comment: 17 pages, 12 figure

New Angles on Fast Calorimeter Shower Simulation

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.Comment: 26 pages, 19 figure

Growing blood vessels in space : preparation studies of the SPHEROIDS project using related ground-based studies

Endothelial cells (ECs) grow as single layers on the bottom surface of cell culture flasks under normal (1g) culture conditions. In numerous experiments using simulated microgravity we noticed that the ECs formed three-dimensional, tube-like cell aggregates resembling the intima of small, rudimentary blood vessels. The SPHEROIDS project has now shown that similar processes occur in space. For the first time, we were able to observe scaffold-free growth of human ECs into multicellular spheroids and tubular structures during an experiment in real microgravity. With further investigation of the space samples we hope to understand endothelial 3D growth and to improve the in vitro engineering of biocompatible vessels which could be used in surgery

High-Intensity Interval Training Decreases Resting Urinary Hypoxanthine Concentration in Young Active Men—A Metabolomic Approach

High-intensity interval training (HIIT) is known to improve performance and skeletal muscle energy metabolism. However, whether the body’s adaptation to an exhausting short-term HIIT is reflected in the resting human metabolome has not been examined so far. Therefore, a randomized controlled intervention study was performed to investigate the effect of a ten-day HIIT on the resting urinary metabolome of young active men. Fasting spot urine was collected before (−1 day) and after (+1 day; +4 days) the training intervention and 65 urinary metabolites were identified by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Metabolite concentrations were normalized to urinary creatinine and subjected to univariate statistical analysis. One day after HIIT, no overall change in resting urinary metabolome, except a significant difference with decreasing means in urinary hypoxanthine concentration, was documented in the experimental group. As hypoxanthine is related to purine degradation, lower resting urinary hypoxanthine levels may indicate a training-induced adaptation in purine nucleotide metabolism

Adaptations and transformations of hunter-gatherers in forest environments: New archaeological and anthropological insights

Like any other living being, humans constantly influence their environment, be it intentionally or unintentionally. By extracting natural resources, they shape their environment and also that of plants and other animals. A great difference setting people apart from all other living beings is the ability to construct and develop their own niche intentionally, and the unique tool for this is cultural behaviour. Here, we discuss anthropogenic environmental changes of hunter-gatherers and present new palaeoecological and palynological data. The studies are framed with ethnoarchaeological data from Western Siberia to gain a better understanding of how different triggers lead to coping mechanisms. For archaeological implication, we use two Mesolithic case studies from Germany: One of them focuses on hazelnut economy around ancient Lake Duvensee, and the other broaches the issue of selective roe deer hunt and its consequences at the site of Friesack. We address the archaeological evidence from the perspective of active alteration and its consequences, starting our argumentation from a perspective of niche construction theory. This approach has rarely been applied to early Holocene hunter-gatherers in Northern Europe even though the available data render possible to discuss human–environment interaction from such a perspective. It is demonstrated that archaeological research has tools at hand that enables to detect anthropogenic niche construction. However, the ethnoarchaeological example shows limitations and archaeologically invisible triggers and consequent results of human adaptations. The critical revision of such perspectives based on empirical data provides a better understanding of social and environmental transformations in the early- and mid-Holocene

Натечные карбонатные новообразования набережной реки Туры: особенности строения и процессы формирования

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