Public Reason and Teaching Science in a Multicultural World: a Comment on Cobern and Loving: "An Essay for Educators ' in the Light of John Rawls' Political Philosophy

This is a comment on the article "An Essay for Educators: Epistemological Realism Really is Common Sense” written by Cobern and Loving in Science & Education. The skillful analysis of the two authors concerning the problematic role of scientism in school science is fully appreciated, as is their diagnosis that it is scientism not universal scientific realism which is the cause of epistemological imperialism. But how should science teachers deal with scientism in the concrete every day situation of the science classroom and in contact with classes and students? John Rawls' concept of public reason offers three "cardinal strategies” to achieve this aim: proviso, declaration and conjecture. The theoretical framework is provided, the three strategies are described and their relevance is fleshed out in a concrete exampl

RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition

We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.Comment: accepted as demo paper on ACL 201

Language Modeling with Deep Transformers

We explore deep autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifically for language modeling. We show that well configured Transformer models outperform our baseline models based on the shallow stack of LSTM recurrent neural network layers. We carry out experiments on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level and 10K byte-pair encoding subword-level language modeling. We apply our word-level models to conventional hybrid speech recognition by lattice rescoring, and the subword-level models to attention based encoder-decoder models by shallow fusion. Second, we show that deep Transformer language models do not require positional encoding. The positional encoding is an essential augmentation for the self-attention mechanism which is invariant to sequence ordering. However, in autoregressive setup, as is the case for language modeling, the amount of information increases along the position dimension, which is a positional signal by its own. The analysis of attention weights shows that deep autoregressive self-attention models can automatically make use of such positional information. We find that removing the positional encoding even slightly improves the performance of these models.Comment: To appear in the proceedings of INTERSPEECH 201

Improved training of end-to-end attention models for speech recognition

Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks. In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets of LibriSpeech. We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance. In some experiments, we also use an auxiliary CTC loss function to help the convergence. In addition, we train long short-term memory (LSTM) language models on subword units. By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model.Comment: submitted to Interspeech 201

A mirror of society: a discourse analytic study of 15- to 16-year-old Swiss students' talk about environment and environmental protection

Environment and environmental protection are on the forefront of political concerns globally. But how are the media and political discourses concerning these issues mirrored in the public more generally and in the discourses of school science students more specifically? In this study, we analyze the discourse mobilized in whole-class conversations of and interviews with 15- to 16-year-old Swiss junior high school students. We identify two core interpretive repertoires (each unfolding into two second-order repertoires) that turn out to be the building blocks of environmental discourse, which is characteristic not only of these students but also of Swiss society more generally. The analysis of our students' discourse demonstrates how their use of interpretive repertoires locks them in belief talk that they have no control over ecological issues, which can put them in the danger of falling prey to ecological passivity. As a consequence of our findings we suggest that teachers should be endorsed to interpret their teaching of environmental issues in terms of the enriching and enlarging of their students' interpretive repertoire

RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation

We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder design for the LibriSpeech task. Detailed descriptions of the system development, including model design, pretraining schemes, training schedules, and optimization approaches are provided for both system architectures. Both hybrid DNN/HMM and attention-based systems employ bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we employ both LSTM and Transformer based architectures. All our systems are built using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems. Our single hybrid system even outperforms previous results obtained from combining eight single systems. Our comparison shows that on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the attention-based system by 15% relative on the clean and 40% relative on the other test sets in terms of word error rate. Moreover, experiments on a reduced 100h-subset of the LibriSpeech training corpus even show a more pronounced margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201

Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept

With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a number of modeling architectures like encoder-decoder attention models, transducer models and segmental models (direct HMM). While transducer models stay with a frame-level model definition, segmental models are defined on the level of label segments directly. While (soft-)attention-based models avoid explicit alignment, transducer and segmental approach internally do model alignment, either by segment hypotheses or, more implicitly, by emitting so-called blank symbols. In this work, we prove that the widely used class of RNN-Transducer models and segmental models (direct HMM) are equivalent and therefore show equal modeling power. It is shown that blank probabilities translate into segment length probabilities and vice versa. In addition, we provide initial experiments investigating decoding and beam-pruning, comparing time-synchronous and label-/segment-synchronous search strategies and their properties using the same underlying model.Comment: accepted at Interspeech202

Klinisches Assessment Basiswissen für Pflegefachpersonen und Hebammen : Arbeitsheft Abdomen

Die Studierenden können gezielt eine symptomfokussierte Anamnese und die körperliche Untersuchung durchführen, anschliessend die gesammelten Daten zusammenfassen / analysieren und das weitere Vorgehen planen, gemäss SOAP-Schema (Subjective-Objective-Analyse-Plan) können gezielt und systematisch eine Anamnese zum Abdomen erheben, inklusive Grunddaten, Hauptbeschwerden, symptomfokussierter Anamnese anhand der Leitsymptome Abdomen, erweiterter Anamnese Abdomen, medizinischer Vorgeschichte, Familienanamnese, Sozialanamnese führen eine systematische körperliche Untersuchung des Abdomens in folgender Reihenfolge durch und setzen Untersuchungshilfsmittel ein: Allgemeinzustand (AZ), Vitalzeichen (VZ), wichtige systemrelevante Parameter, Inspektion, Auskultation, Perkussion, Palpation führen zusätzliche Untersuchungen durch, wie zum Beispiel Appendizitis-Zeichen erkennen die physiologischen Befunde und / oder deren Abweichungen interpretieren diese und stellen eine Arbeitshypothese auf beurteilen die Dringlichkeit und planen weitere Interventionen rapportieren die Befunde gemäss dem Rapportraster Identifikation – Situation – Background – Assessment – Recommendation (ISBAR) in Fachsprache an das interprofessionelle Team (Arzt / Ärztin – Pflegefachpersonen / Hebammen) und dokumentieren die Ergebnisse des klinischen Assessments in Fachsprache