Flexible and accurate inference and learning for deep generative models

We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but unlike adverserial methods) our approach learns an explicit inference or "recognition" model to approximate the posterior distribution over the latent variables. Unlike in these earlier methods, the posterior representation is not limited to a narrow tractable parameterised form (nor is it represented by samples). To train the generative and recognition models we develop an extended wake-sleep algorithm inspired by the original Helmholtz Machine. This makes it possible to learn hierarchical latent models with both discrete and continuous variables, where an accurate posterior representation is essential. We demonstrate that the new algorithm outperforms current state-of-the-art methods on synthetic, natural image patch and the MNIST data sets

Towards a precession driven dynamo experiment

The most ambitious project within the DREsden Sodium facility for DYNamo and thermohydraulic studies (DRESDYN) at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) is the set-up of a precession-driven dynamo experiment. After discussing the scientific background and some results of water pre-experiments and numerical predictions, we focus on the numerous structural and design problems of the machine. We also outline the progress of the building's construction, and the status of some other experiments that are planned in the framework of DRESDYN.Comment: 9 pages, 6 figures, submitted to Magnetohydrodynamic

Sentient Networks

In this paper we consider the question whether a distributed network of sensors and data processors can form "perceptions" based on the sensory data. Because sensory data can have exponentially many explanations, the use of a central data processor to analyze the outputs from a large ensemble of sensors will in general introduce unacceptable latencies for responding to dangerous situations. A better idea is to use a distributed "Helmholtz machine" architecture in which the collective state of the network as a whole provides an explanation for the sensory data.Comment: PostScript, 14 page

A basic Helmholtz Kernel Information Profile for machine-actionable FAIR Digital Objects

To reach the declared goal of the Helmholtz Metadata Collaboration Platform, making the depth and breadth of research data produced by Helmholtz Centres findable, accessible, interoperable, and reusable (FAIR) for the whole science community, the concept of FAIR Digital Objects (FAIR DOs) has been chosen as top-level commonality across all research fields and their existing and future infrastructures. Over the last years, not only by the Helmholtz Metadata Collaboration Platform, but on an international level, the roads towards realizing FAIR DOs has been paved more and more by concretizing concepts and implementing base services required for realizing FAIR DOs, e.g., different instances of Data Type Registries for accessing, creating, and managing Data Types required by FAIR DOs and technical components to support the creation and management of FAIR DOs: The Typed PID Maker providing machine actionable interfaces for creating, validating, and managing PIDs with machine-actionable metadata stored in their PID record, or the FAIR DO testbed, currently evolving into the FAIR DO Lab, serving as reference implementation for setting up a FAIR DO ecosystem. However, introducing FAIR DOs is not only about providing technical services, but also requires the definition and agreement on interfaces, policies, and processes. A first step in this direction was made in the context of HMC by agreeing on a Helmholtz Kernel Information Profile. In the concept of FAIR DOs, PID Kernel Information is key to machine actionability of digital content. Strongly relying on Data Types and stored in the PID record directly at the PID resolution service, PID Kernel Information is allowed to be used by machines for fast decision making. In this session, we will shortly present the Helmholtz Kernel Information Profile and a first demonstrator allowing the semi-automatic creation of FAIR DOs for arbitrary DOIs accessible via the well-known Zenodo repository

Von Kempelen et al. : remarks on the history of articulatory-acoustic modelling

The contribution of von Kempelen’s “Mechanism of Speech” to the ‘phonetic sciences‘ will be analyzed with respect to his theoretical reasoning on speech and speech production on the one hand and on the other in connection with his practical insights during his struggle in constructing a speaking machine. Whereas in his theoretical considerations von Kempelen’s view is focussed on the natural functioning of the speech organs – cf. his membraneous glottis model – in constructing his speaking machine he clearly orientates himself towards the auditory result – cf. the bag pipe model for the sound generator used for the speaking machine instead. Concerning vowel production his theoretical description remains questionable, but his practical insight that vowels and speech sounds in general are only perceived correctly in connection with their surrounding sounds – i.e. the discovery of coarticulation – is clearly a milestone in the development of the phonetic sciences: He therefore dispenses with the Kratzenstein tubes, although they might have been based on more thorough acoustic modelling. Finally, von Kempelen’s model of speech production will be discussed in relation to the discussion of the acoustic nature of vowels afterwards [Willis and Wheatstone as well as von Helmholtz and Hermann in the 19th century and Stumpf, Chiba & Kajiyama as well as Fant and Ungeheuer in the 20th century]

Form and Data - from linear Calculus to cybernetic Computation and Interaction

Digital architecture developed in the 1960s and, supported by CAAD the 1990s, has created the path towards an architecture produced by computer and architect in a mutual relationship. The evolution of architecture since the 1970s led to the beginning of the first digital turn in the 1990s, and subsequently to the emergence of new typologies of buildings, architects and design tools; atom-based, bit-based (virtual) [1], and cyber-physical as a combination of both. The paper provides an insight into historical foundations of CAAD insofar as it engages with complexity in mechanics, geometry, and space between the 1600s and 1950s. I will address a selection of principles discovered, and mechanisms invented before computer-aided-architectural-design; those include the typewriter, the Cartesian grid and a pre-cyber-physical system by Hermann von Helmholtz. The paper concludes with a summary and an outlook to the future of CAAD challenged by the variety of correlations of disparate data sets