Gemeinsame Arbeitsphasen produktiv nutzen – verschiedene Methoden kooperativen Lernens im Mathematikunterricht der Grundschule - Bruns, Julia; Gasteiger, H. (mathematik lehren)

Early Childhood Educators’ Issues and Perspectives in Mathematics Education - Cooke, Audrey; Bruns, Julia (Contemporary Research and Perspectives on Early Childhood Mathematics Education)
This chapter presents an overview of the contributions in TSG 1: Early Childhood Mathematics Education at ICME-13 which focused on issues from the perspective of early childhood educators. A basic assumption of this chapter is that the opportunities for young children to develop mathematical understandings and skills are influenced by several conditions at a macro, meso and micro level. First, curricula provide a framework for early mathematics teaching and learning with varying expectations (by teachers) about what can occur in the pre-school environment—informal learning (such as through play), content to learn and activities to experience. Second, early childhood educators’ mathematical knowledge, pedagogical knowledge, understandings, beliefs, and perceptions influence how they enact these expectations. These competencies can be developed and supported by professional learning. Third, educational programs, resources and activities used in the pre-school environment impact on the mathematical opportunities children engage in at the micro level.

Das Deutsche Zentrum für Lehrerbildung Mathematik – DZLM - Barzel, Bärbel; Biehler, Rolf; Blömeke, Sigrid; Brandtner, Regine; Bruns, Julia; Dohrmann, Christian; Kortenkamp, Ulrich; Lange, Thomas; Leuders, Timo; Rösken-Winter, Bettina; Scherer, Petra; Selter, Christoph (Mathematikfortbildungen professionalisieren: Konzepte, Beispiele und Erfahrungen des Deutschen Zentrums für Lehrerbildung Mathematik)
Im vorangegangenen Kapitel wurde die Vorgeschichte beschrieben, wie es durch Initiative der Deutsche Telekom Stiftung zur Gründung des DZLM gekommen ist. In diesem zweiten Kapitel werden die Tätigkeitsfelder und Konzepte des DZLM in seiner ersten Förderphase vom 01.10.2011 bis 30.09.2016 beschrieben.

EmMa - Fortbildung für elementarpädagogische Fachpersonen zur frühen mathematischen Bildung - Bruns, Julia; Eichen, Lars (Mathematikfortbildungen professionalisieren. Konzepte, Beispiele und Erfahrungen des Deutschen Zentrums für Lehrerbildung Mathematik)

Methoden empirischer Bildungsforschung im Kontext Frühkindliche Bildung und Entwicklung

Near-Optimal Node Selection Procedure for Aging Monitor Placement - Sadeghi-Kohan, Somayeh; Vafaei, Arash; Navabi, Zainalabedin (2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS))
Transistor and interconnect wearout is accelerated with transistor scaling resulting in timing variations and consequently reliability challenges in digital circuits. With the emergence of new issues like Electro-migration these problems are getting more crucial. Age monitoring methods can be used to predict and deal with the aging problem. Selecting appropriate locations for placement of aging monitors is an important issue. In this work we propose a procedure for selection of appropriate internal nodes that expose smaller overheads to the circuit, using correlation between nodes and the shareability amongst them. To select internal nodes, we first prune some nodes based on some attributes and thus provide a near-optimal solution that can effectively get a number of internal nodes and consider the effects of electro-migration as well. We have applied our proposed scheme to several processors and ITC benchmarks and have looked at its effectiveness for these circuits.

Coherent ePIC Receiver for 64 GBaud QPSK in 0.25μm Photonic BiCMOS Technology - Gudyriev, Sergiy; Kress, Christian; Zwickel, Heiner; Kemal, Juned N.; Lischke, Stefan; Zimmermann, Lars; Koos, Christian; Scheytt, Christoph (IEEE/OSA Journal of Lightwave Technology)
In this paper, we present a monolithically integrated coherent receiver with on-chip grating couplers, 90° hybrid, photodiodes and transimpedance amplifiers. A transimpedance gain of 7.7 kΩ was achieved by the amplifiers. An opto-electrical 3 dB bandwidth of 34 GHz for in-phase and quadrature channel was measured. A real-time data transmission of 64 GBd-QPSK (128 Gb/s) for a single polarization was performed.

Local intersections of Lagrangian manifolds correspond to catastrophe theory - Offen, Christian (arXiv:1811.10165)
Two smooth map germs are right-equivalent if and only if they generate two Lagrangian submanifolds in a cotangent bundle which have the same contact with the zero-section. In this paper we provide a reverse direction to this classical result of Golubitsky and Guillemin. Two Lagrangian submanifolds of a symplectic manifold have the same contact with a third Lagrangian submanifold if and only if the intersection problems correspond to stably right equivalent map germs. We, therefore, obtain a correspondence between local Lagrangian intersection problems and catastrophe theory while the classical version only captures tangential intersections. The correspondence is defined independently of any Lagrangian fibration of the ambient symplectic manifold, in contrast to other classical results. Moreover, we provide an extension of the correspondence to families of local Lagrangian intersection problems. This gives rise to a framework which allows a natural transportation of the notions of catastrophe theory such as stability, unfolding and (uni-)versality to the geometric setting such that we obtain a classification of families of local Lagrangian intersection problems. An application is the classification of Lagrangian boundary value problems for symplectic maps.

Hamiltonian boundary value problems, conformal symplectic symmetries, and conjugate loci - McLachlan, Robert I; Offen, Christian (New Zealand Journal of Mathematics)
In this paper we continue our study of bifurcations of solutions of boundary-value problems for symplectic maps arising as Hamiltonian diffeomorphisms. These have been shown to be connected to catastrophe theory via generating functions and ordinary and reversal phase space symmetries have been considered. Here we present a convenient, coordinate free framework to analyse separated Lagrangian boundary value problems which include classical Dirichlet, Neumann and Robin boundary value problems. The framework is then used to prove the existence of obstructions arising from conformal symplectic symmetries on the bifurcation behaviour of solutions to Hamiltonian boundary value problems. Under non-degeneracy conditions, a group action by conformal symplectic symmetries has the effect that the flow map cannot degenerate in a direction which is tangential to the action. This imposes restrictions on which singularities can occur in boundary value problems. Our results generalise classical results about conjugate loci on Riemannian manifolds to a large class of Hamiltonian boundary value problems with, for example, scaling symmetries.
Keywords: Hamiltonian boundary value problems; singularities; conformal symplectic geometry; catastrophe theory; conjugate loci

OpenCL-based FPGA Design to Accelerate the Nodal Discontinuous Galerkin Method for Unstructured Meshes - Kenter, Tobias; Mahale, Gopinath; Alhaddad, Samer; Grynko, Yevgen; Schmitt, Christian; Afzal, Ayesha; Hannig, Frank; Förstner, Jens; Plessl, Christian (Proc. Int. Symp. on Field-Programmable Custom Computing Machines (FCCM))
The exploration of FPGAs as accelerators for scientific simulations has so far mostly been focused on small kernels of methods working on regular data structures, for example in the form of stencil computations for finite difference methods. In computational sciences, often more advanced methods are employed that promise better stability, convergence, locality and scaling. Unstructured meshes are shown to be more effective and more accurate, compared to regular grids, in representing computation domains of various shapes. Using unstructured meshes, the discontinuous Galerkin method preserves the ability to perform explicit local update operations for simulations in the time domain. In this work, we investigate FPGAs as target platform for an implementation of the nodal discontinuous Galerkin method to find time-domain solutions of Maxwell's equations in an unstructured mesh. When maximizing data reuse and fitting constant coefficients into suitably partitioned on-chip memory, high computational intensity allows us to implement and feed wide data paths with hundreds of floating point operators. By decoupling off-chip memory accesses from the computations, high memory bandwidth can be sustained, even for the irregular access pattern required by parts of the application. Using the Intel/Altera OpenCL SDK for FPGAs, we present different implementation variants for different polynomial orders of the method. In different phases of the algorithm, either computational or bandwidth limits of the Arria 10 platform are almost reached, thus outperforming a highly multithreaded CPU implementation by around 2x.
Keywords: tet_topic_hpc

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices - Lass, Michael; Mohr, Stephan; Wiebeler, Hendrik; Kühne, Thomas; Plessl, Christian (Proc. Platform for Advanced Scientific Computing (PASC) Conference)
We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications. Following the idea of Approximate Computing, we allow imprecision in the final result in order to utilize the sparsity of the input matrix and to allow massively parallel execution. For an n x n matrix, the proposed algorithm allows to distribute the calculations over n nodes with only little communication overhead. The result matrix exhibits the same sparsity pattern as the input matrix, allowing for efficient reuse of allocated data structures. We evaluate the algorithm with respect to the error that it introduces into calculated results, as well as its performance and scalability. We demonstrate that the error is relatively limited for well-conditioned matrices and that results are still valuable for error-resilient applications like preconditioning even for ill-conditioned matrices. We discuss the execution time and scaling of the algorithm on a theoretical level and present a distributed implementation of the algorithm using MPI and OpenMP. We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 665x compared to single-threaded execution.
Keywords: approximate computing; linear algebra; matrix inversion; matrix p-th roots; numeric algorithm; parallel computing

Automated Code Acceleration Targeting Heterogeneous OpenCL Devices - Riebler, Heinrich; Vaz, Gavin Francis; Kenter, Tobias; Plessl, Christian (Proc. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP))
Keywords: htrop

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants - Habernal, Ivan; Wachsmuth, Henning; Gurevych, Iryna; Stein, Benno (Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers))

Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation - Habernal, Ivan; Wachsmuth, Henning; Gurevych, Iryna; Stein, Benno (Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers))

What is the Essence of a Claim? Cross-Domain Claim Identification - Daxenberger, Johannes; Eger, Steffen; Habernal, Ivan; Stab, Christian; Gurevych, Iryna (Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing)

Front-End Processing for the CHiME-5 Dinner Party Scenario - Boeddeker, Christoph; Heitkaemper, Jens; Schmalenstroeer, Joerg; Drude, Lukas; Heymann, Jahn; Haeb-Umbach, Reinhold (Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India)
This contribution presents a speech enhancement system for the CHiME-5 Dinner Party Scenario. The front-end employs multi-channel linear time-variant filtering and achieves its gains without the use of a neural network. We present an adaptation of blind source separation techniques to the CHiME-5 database which we call Guided Source Separation (GSS). Using the baseline acoustic and language model, the combination of Weighted Prediction Error based dereverberation, guided source separation, and beamforming reduces the WER by 10:54% (relative) for the single array track and by 21:12% (relative) on the multiple array track.

MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks - Afifi, Haitham; Schmalenstroeer, Joerg; Ullmann, Joerg; Haeb-Umbach, Reinhold; Karl, Holger (Speech Communication; 13th ITG-Symposium)
Signal processing in WASNs is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented.

Discrimination of Stationary from Moving Targets with Recurrent Neural Networks in Automotive Radar - Grimm, Christopher; Breddermann, Tobias; Farhoud, Ridha; Fei, Tai; Warsitz, Ernst; Haeb-Umbach, Reinhold (International Conference on Microwaves for Intelligent Mobility (ICMIM) 2018)
In this paper, we present a neural network based classification algorithm for the discrimination of moving from stationary targets in the sight of an automotive radar sensor. Compared to existing algorithms, the proposed algorithm can take into account multiple local radar targets instead of performing classification inference on each target individually resulting in superior discrimination accuracy, especially suitable for non rigid objects, like pedestrians, which in general have a wide velocity spread when multiple targets are detected.

Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery - Glarner, Thomas; Hanebrink, Patrick; Ebbers, Janek; Haeb-Umbach, Reinhold (INTERSPEECH 2018, Hyderabad, India)
The invention of the Variational Autoencoder enables the application of Neural Networks to a wide range of tasks in unsupervised learning, including the field of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational Autoencoder (HMMVAE) allows a joint training of a neural network based feature extractor and a structured prior for the latent space given by a Hidden Markov Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM based systems on the AUD task. However, the HMMVAE cannot autonomously infer the number of acoustic units and thus relies on the GMM-HMM system for initialization. This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder (BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework with a Dirichlet Process Prior for the distribution of the acoustic units, and diagonal or full-covariance Gaussians as emission distributions. Experiments on TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable number of acoustic units, can be initialized without supervision by a GMM-HMM system, achieves computationally efficient stochastic variational inference by using natural gradient descent, and, additionally, improves the AUD performance over the HMMVAE.

Organizational views of NIST cryptographic standards and testing and validation programs - Haney, Julie; Theofanos, Mary; Acar, Yasemin; Prettyman, Sandra Spickard