Оценка экологической опасности отходов горнодобывающих предприятий республики Хакасия с применением метода биотестирования

Представлены результаты геохимического анализа проб отходов горнодобывающих предприятий Республики Хакасия и их биотестирования. При биотестировании в эксперименте с использованием тест-объекта Drosophila melanogaster оценивались: соотношение полов, морфозы, высота подъема куколок, средняя длина тела и крыла по отношению к концентрации пробы в среде. Сделаны выводы о воздействии отходов на живые объекты, выделены химические элементы, оказывающие токсическое действие

Towards Real-World Streaming Speech Translation for Code-Switched Speech

Code-switching (CS), i.e. mixing different languages in a single sentence, is a common phenomenon in communication and can be challenging in many Natural Language Processing (NLP) settings. Previous studies on CS speech have shown promising results for end-to-end speech translation (ST), but have been limited to offline scenarios and to translation to one of the languages present in the source (\textit{monolingual transcription}). In this paper, we focus on two essential yet unexplored areas for real-world CS speech translation: streaming settings, and translation to a third language (i.e., a language not included in the source). To this end, we extend the Fisher and Miami test and validation datasets to include new targets in Spanish and German. Using this data, we train a model for both offline and streaming ST and we establish baseline results for the two settings mentioned earlier

Hierarchical Neural Networks Feature Extraction for LVCSR system

This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR systems for Meetings data (RT05 evaluation data) and for Arabic Broadcast News (BNAT05 evaluation data). The hierarchical NNs outperforms the single NN features consistently on different type of data and tasks and provides significant improvements w.r.t. respective baselines systems. Best result is obtained when different time resolutions are used at different level of the hierarchy

E-Learning-Strategie an der Universität Duisburg-Essen - mehr als ein Artefakt?

In den vergangenen 15 Jahren gab es in Duisburg-Essen zahlreiche Einzelinitiativen zum E-Learning, die bottom-up entstanden sind. Bedarfe der Qualitätsentwicklung und Flexibilisierung der Studienstrukturen angesichts größerer und diversifizierter Studierendenkohorten haben zur Entwicklung einer top-down-gesteuerten, hochschulweiten E-Learning-Strategie geführt, die finanziell, technisch und didaktisch unterfüttert wird. Die Ziele und ersten Schritte der Implementation sowie erste dabei gemachte Erfahrungen werden vorgestellt. Dem Anspruch nach soll die Strategie perspektivisch auch die Tiefenstruktur der Hochschulorganisation mit ihren Prinzipien und Werten erreichen - und mehr als ein Artefakt sein. 27.04.2015 | Julia Liebscher, Anke Petschenka, Holger Gollan, Sandrina Heinrich, Isabell van Ackeren & Christian Ganseuer (Duisburg-Essen

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration