Modeling Warp in Corrugated Cardboard Based on Homogenization Techniques for In-Process Measurement Applications

A model for describing warp—characterized as a systematic, large-scale deviation from the intended flat shape—in corrugated board based on Kirchhoff plate theory is proposed. It is based on established homogenization techniques and only a minimum of model assumptions. This yields general results applicable to any kind of corrugated cardboard. Since the model is intended to be used with industrial data, basic material properties which are usually not measured in practice are summarized to a few parameters. Those parameters can easily be fitted to the measurement data, allowing the user to systematically identify ways to reduce warp in a given situation in practice. In particular, the model can be used both as a filter to separate the warp from other surface effects such as washboarding, and to interpolate between discrete sample points scattered across the surface of a corrugated board sheet. Applying the model only requires height measurements of the corrugated board at several known (not necessarily exactly predetermined) locations across the corrugated board and acts as an interpolation or regression method between those points. These data can be acquired during production in a cost-efficient way and do not require any destructive testing of the board. The principle of an algorithm for fitting measured data to the model is presented and illustrated with examples taken from ongoing measurements. Additionally, the case of warp-free board is analyzed in more detail to deduce additional theoretical conditions necessary to reach this state

Widefield fluorescence microscopy with extended resolution

Widefield fluorescence microscopy is seeing dramatic improvements in resolution, reaching today 100nm in all three dimensions. This gain in resolution is achieved by dispensing with uniform Köhler illumination. Instead, non-uniform excitation light patterns with sinusoidal intensity variations in one, two, or three dimensions are applied combined with powerful image reconstruction techniques. Taking advantage of non-linear fluorophore response to the excitation field, the resolution can be further improved down to several 10nm. In this review article, we describe the image formation in the microscope and computational reconstruction of the high-resolution dataset when exciting the specimen with a harmonic light pattern conveniently generated by interfering laser beams forming standing waves. We will also discuss extensions to total internal reflection microscopy, non-linear microscopy, and three-dimensional imagin

Measuring student teachers development of metacognition and self-regulated learning in professional dialogue

Teachers are often considered to be the most important agents in reforming education and in bringing about change in practices (Lieberman & Mace, 2008). Since the publication of ‘Teaching Scotland’s Future’ (Donaldson, 2010), the teacher education sector in Scotland has gone through significant changes. One of the key changes is to allow teachers to develop as reflective practitioners. Reflection on one’s own perceptions, experiences and practices is at the heart of all activities that teachers do. Reflective practice enables learning by thinking back and articulating the acquisition of knowledge and strategies. For teachers at the pre-service stage, this can be particularly powerful and even transformative (Kramarski & Kohen, 2016). More recently, researchers have argued that the development of reflective skills can be enhanced by combining reflection with professional dialogue (Simoncini et al., 2014), enabling teachers to ‘maintain an awareness of their learning and be attuned both to evidence of changes to content and pedagogic knowledge as well as to the impact on professional and personal identity that can be revealed through the conversations themselves’ (Lofthouse & Hall, 2014, p. 759). This paper addresses this important aspect of teacher education. We aim to investigate how student teachers on a Scottish teacher education programme learn by reflecting on their professional dialogue experiences. We are particularly interested in the self-regulated learning (SRL) and metacognitive processes in their reflection

Energy of sections of the Deligne–Hitchin twistor space

We study a natural functional on the space of holomorphic sections of the Deligne–Hitchin moduli space of a compact Riemann surface, generalizing the energy of equivariant harmonic maps corresponding to twistor lines. We show that the energy is the residue of the pull-back along the section of a natural meromorphic connection on the hyperholomorphic line bundle recently constructed by Hitchin. As a byproduct, we show the existence of a hyper-Kähler potentials for new components of real holomorphic sections of twistor spaces of hyper-Kähler manifolds with rotating S1-action. Additionally, we prove that for a certain class of real holomorphic sections of the Deligne–Hitchin moduli space, the energy functional is basically given by the Willmore energy of corresponding equivariant conformal map to the 3-sphere. As an application we use the functional to distinguish new components of real holomorphic sections of the Deligne–Hitchin moduli space from the space of twistor lines. © 2020, The Author(s)

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