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Migration und Theologie: historische Reflektionen, theologische Grundelemente und hermeneutische Perspektiven aus der alt- und neutestamentlichen Wissenschaft
No full textIm Kontext wissenschaftlich reflektierter Theologie sind Migration sowie die zugehörigen Themenfelder Flucht und Vertreibung im gesamten Fächerkanon zu einem breit diskutierten und hochaktuellen Gegenstand wissenschaftlicher Forschung geworden. Ein Desiderat ist allerdings die konsequente Reflexion des Themas in der Theologie des Alten wie auch des Neuen Testaments. Die unterschiedlichen theologischen Prägungen der Bücher und Sammlungen der Bibel lassen das Thema Migration in je anderen, aber zentralen Akzentuierungen zum Vorschein kommen. Der innovative Band schließt diese Lücke, indem die Einzelbeiträge thematisch breit aufgestellt die Geltungsansprüche alt- und neutestamentlicher Migrationsthematik im Okular ihres komplexen Verhältnisses von Historie, Theologie und literarischer Genese der Traditionskomplexe betrachten und hermeneutisch reflektieren
Introduction
No full textIs the Hebrew Bible purely a product of Jerusalem or were there various social groups who each played a role in its development during the Second Temple period? This is the guiding question of the present volume which fills a crucial gap in recent research by combining current literary-historical, redactional and text-historical analysis of the Hebrew Bible with the latest results pertaining to the pluriform social and religious shape of early Judaism. For the first time, the volume addresses the phenomenon of religious plurality by bringing together archaeological, (religious-)historical, and literary-critical approaches. The volume comprises thirteen articles by internationally renowned scholars and covers the panorama of currently known social groups of Yahwistic character and the impact of this phenomenon on the making of the Hebrew – from the Persian period down to the time of Qumran
Characterization of the decline in auditory nerve phase locking at high frequencies
No full textThe frequency dependence of phase locking in the auditory nerve influences various auditory coding mechanisms. The decline of phase locking with increasing frequency is commonly described by a low-pass filter. This study compares fitted low-pass filter parameters with the actual rate of phase locking decline. The decline is similar across studies and only 40 dB per decade, corresponding to the asymptotic decline of a second order filter
Die Gerichtsmetapher im Wandel: neue Straftheorien und das Jüngste Gericht
No full textThis paper presents changing conceptions of the court scene as reflected in recent
theories of punishment. These mainly concern the inclusion of emotions, the breaking of the
fixation on the offender and the role of the public. Theology as well as criminal law are working
to dismantle a horror image of the court. How the emergence of new images of horror can
be prevented is the subject of the article with regard to the consequences for a theology of the
Last Judgement
Model-based automation of TSN configuration for industrial distributed systems
No full textThe paradigm shift to more unified network topologie for industrial distributed systems is fueled by the uptake of the Time Sensitive Networking (TSN) standard, which enables the separation of time critical traffic following the Time-Division Multiple Access (TDMA) scheme. However, the configuration of such systems is currently cumbersome and potentially error prone. To provide support at design time when modelling distributed real-time applications that communicate using TSN, this work proposes a general concept for automated configuration and deployment. Our proof-of-concept implementation is based on the IEC 61499 modelling standard for industrial automation systems and is validated in ten representative scenarios, including heterogeneous setups using several hardware platforms. We show that our method upholds the expected synchronisation and timing behaviour for TSN, and provides a significant reduction in configuration steps while functioning correctly in various setups without adjustment
Direct evolutionary optimization of variational autoencoders with binary latents
No full textMany types of data are generated at least partly by discrete causes. Deep generative models such as variational autoencoders (VAEs) with binary latents consequently became of interest. Because of discrete latents, standard VAE training is not possible, and the goal of previous approaches has therefore been to amend (i.e, typically anneal) discrete priors to allow for a training analogously to conventional VAEs. Here, we divert more strongly from conventional VAE optimization: We ask if the discrete nature of the latents can be fully maintained by applying a direct, discrete optimization for the encoding model. In doing so, we sidestep standard VAE mechanisms such as sampling approximation, reparameterization and amortization. Direct optimization of VAEs is enabled by a combination of evolutionary algorithms and truncated posteriors as variational distributions. Such a combination has recently been suggested, and we here for the first time investigate how it can be applied to a deep model. Concretely, we (A) tie the variational method into gradient ascent for network weights, and (B) show how the decoder is used for the optimization of variational parameters. Using image data, we observed the approach to result in much sparser codes compared to conventionally trained binary VAEs. Considering the for sparse codes prototypical application to image patches, we observed very competitive performance in tasks such as ‘zero-shot’ denoising and inpainting. The dense codes emerging from conventional VAE optimization, on the other hand, seem preferable on other data, e.g., collections of images of whole single objects (CIFAR etc.), but less preferable for image patches. More generally, the realization of a very different type of optimization for binary VAEs allows for investigating advantages and disadvantages of the training method itself. And we here observed a strong influence of the method on the learned encoding with significant impact on VAE performance for different tasks
Generic unsupervised optimization for a latent variable model with exponential family observables
Get PDFLatent variable models (LVMs) represent observed variables by parameterized functions of latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic PCA or probabilistic sparse coding which both assume a weighted linear summation of the latents to determine the mean of a Gaussian distribution for the observables. In many cases, however, observables do not follow a Gaussian distribution. For unsupervised learning, LVMs which assume specific non-Gaussian observables (e.g., Bernoulli or Poisson) have therefore been considered. Already for specific choices of distributions, parameter optimization is challenging and only a few previous contributions considered LVMs with more generally defined observable distributions. In this contribution, we do consider LVMs that are defined for a range of different distributions, i.e., observables can follow any (regular) distribution of the exponential family. Furthermore, the novel class of LVMs presented here is defined for binary latents, and it uses maximization in place of summation to link the latents to observables. In order to derive an optimization procedure, we follow an expectation maximization approach for maximum likelihood parameter estimation. We then show, as our main result, that a set of very concise parameter update equations can be derived which feature the same functional form for all exponential family distributions. The derived generic optimization can consequently be applied (without further derivations) to different types of metric data (Gaussian and non-Gaussian) as well as to different types of discrete data. Moreover, the derived optimization equations can be combined with a recently suggested variational acceleration which is likewise generically applicable to the LVMs considered here. Thus, the combination maintains generic and direct applicability of the derived optimization procedure, but, crucially, enables efficient scalability. We numerically verify our analytical results using different observable distributions, and, furthermore, discuss some potential applications such as learning of variance structure, noise type estimation and denoising