Venture capital: New ways of financing technology innovation

human development, technology

Mapping the Conditions for Hydrodynamic Instability on Steady State Accretion Models of Protoplanetary Disks

Hydrodynamical instabilities in disks around young stars depend on the thermodynamic stratification of the disk and on the local rate of thermal relaxation. Here, we map the spatial extent of unstable regions for the Vertical Shear Instability (VSI), the Convective OverStability (COS), and the amplification of vortices via the Subcritical Baroclinic Instability (SBI). We use steady state accretion disk models, including stellar irradiation, accretion heating and radiative transfer. We determine the local radial and vertical stratification and thermal relaxation rate in the disk, in dependence of the stellar mass, disk mass and mass accretion rate. We find that passive regions of disks - i.e. the midplane temperature dominated by irradiation - are COS unstable about one pressure scale height above the midplane and VSI unstable at radii $> 10 \, \text{au}$. Vortex amplification via SBI should operate in most parts of active and passive disks. For active parts of disks (midplane temperature determined by accretion power) COS can become active down to the midplane. Same is true for the VSI because of the vertically adiabatic stratification of an internally heated disk. If hydro instabilities or other non-ideal MHD processes are able to create $\alpha$-stresses ($> 10^{-5}$) and released accretion energy leads to internal heating of the disk, hydrodynamical instabilities are likely to operate in significant parts of the planet forming zones in disks around young stars, driving gas accretion and flow structure formation. Thus hydro-instabilities are viable candidates to explain the rings and vortices observed with ALMA and VLT.Comment: 24 pages, 13 figures, Accepted for publication in Ap

Regulation and competition in German banking: an assessment

In Germany a public discussion on the "power of banks" has been going on for decades now with power having at least two meanings. On the one hand it is the power of banks to control public corporations through direct shareholdings or the exercise of proxy votes - this is the power of banks in corporate control. On the other hand it is market power - due to imperfect competition in markets for financial services - that banks exercise vis-à-vis their loan and deposit customers. In the past, bank regulation has often been blamed to undermine competition and the working of market forces in the financial industry for the sake of soundness and stability of financial services firms. This chapter tries to shed some light on the historical development and current state of bank regulation in Germany. In so doing it tries to embed the analysis of bank regulation into a more general industrial organisation framework. For every regulated industry, competition and regulation are deeply interrelated as most regulatory institutions - even if they do not explicitly address the competitiveness of the market - either affect market structure or conduct. This paper tries to uncover some of the specific relationships between monetary policy, government interference and bank regulation on the one hand and bank market structure and economic performance on the other. In so doing we hope to point to several areas for fruitful research in the future. While our focus is on Germany, some of the questions that we raise and some of our insights might also be applicable to banking systems elsewhere. Revised version forthcoming in "The German Financial System", edited by Jan P. Krahnen and Reinhard H. Schmidt, Oxford University Press

The Gerasimov-Drell-Hearn sum rule and the infinite-momentum limit

We study the current-algebra approach to the Gerasimov-Drell-Hearn sum rule, paying particular attention to the infinite-momentum limit. Employing the order-alpha^2 Weinberg-Salam model of weak interactions as a testing ground, we find that the legitimacy of the infinite-momentum limit is intimately connected with the validity of the naive equal-times algebra of electric charge densities. Our results considerably reduce the reliability of a recently proposed modification of the Gerasimov-Drell-Hearn sum rule, originating from an anomalous charge-density algebra.Comment: 12 pages; 6 figures; LaTeX; submitted to Z.Phys.

Neuromorphic Learning towards Nano Second Precision

Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival times of sound signals are shifted between both ears. In order to deter- mine these interaural time differences, the phase difference of the signals is measured. We implemented this biologically inspired network on a neuromorphic hardware system and demonstrate spike-timing dependent plasticity on an analog, highly accelerated hardware substrate. Our neuromorphic implementation enables the resolution of time differences of less than 50 ns. On-chip Hebbian learning mechanisms select inputs from a pool of neurons which code for the same sound frequency. Hence, noise caused by different synaptic delays across these inputs is reduced. Furthermore, learning compensates for variations on neuronal and synaptic parameters caused by device mismatch intrinsic to the neuromorphic substrate.Comment: 7 pages, 7 figures, presented at IJCNN 2013 in Dallas, TX, USA. IJCNN 2013. Corrected version with updated STDP curves IJCNN 201

How do patients with end-stage ankle arthritis decide between two surgical treatments?:A qualitative study

To examine how patients decide between ankle fusion and ankle replacement in end-stage ankle arthritis

Towards Research Object Crates 1.2, with ro-crate-java

Research Object Crate (RO-Crate) is an open, community driven data package specification to describe all kinds of file-based data, as well as entities outside the package. In order to do so, it uses the widespread JSON-format, representing Linked Data (JSON-LD), allowing to link to external information. This makes the format flexible and machine-readable. These packages are being referred to as (RO-)crates. Similar to other formats, RO-Crates is based on files and folders and has a single metadata file to describe the whole package. Therefore, such packages are easy to share between different computer systems and software. In order to create such crates, the RO-Crate community developed libraries written in different programming languages like Python, Ruby, JavaScript, and Java. With Describo, there is also a graphical user interface available. We developed the ro-crate-java library, which allows creating, modifying and validating crates using the Java Programming Language. The focus of development was the ease of use: We aimed to make it intuitive and easy to create valid crates, without knowing the specification too well. Our implementation can be used for integration into repositories or other services or tools. The library was introduced in the HMC conference 2022 poster session. This follow-up poster will give a preview on a draft feature which is available in the RO-Crate 1.2-DRAFT specification and has been requested a lot: the ability to specify the conformance with multiple profiles within one crate. Profiles are “a set of conventions, types and properties that one minimally can require and expect to be present in that subset of RO-Crates” (RO-Spec 1.1). They may be used to validate the crate against institutional constraints or to guarantee required information for further processing or visualization. The new specification includes the possibility to create crates with multiple profiles being specified. As this is an often requested feature, this is now a supported feature since ro-crate-java v1.1.0. The library now makes a difference between stable and unstable features and will update the specification version accordingly. This research has been supported by the Helmholtz Metadata Collaboration (HMC) Platform, the German National Research Data Infrastructure (NFDI) and the German Research Foundation (DFG)

Numerische Methoden für marine biogeochemische Modelle

Marine ecosystem models are an indispensable component in the forecast of climate change. CO2 is the major anthropogenic greenhouse gas which substantially determines global warming. As an essential component of the global carbon cycle, the marine ecosystem absorbs atmospheric CO2 and, hence, slows down global warming. More specifically, the marine ecosystem stores the CO2 over a long time period, for example by fixing it through biogeochemical conversion processes. Marine ecosystem models facilitate the simulation of the marine ecosystem and, thus, the research of different processes in this ecosystem and a forecast of the evolution of the marine ecosystem. Owing to a high computational effort, the simulation of marine ecosystem models is limited by the available computing power, even on high-performance computers. To reduce the computational effort for the computation of a steady annual cycle for a marine ecosystem model, this thesis comprises the investigation of the reduction of the computational effort by using larger time steps and by predicting the steady annual cycle by means of an artificial neural network. To apply the time step always as large as possible without relying on any manual selection, two methods based on the automatic time step adjustment during the simulation are presented. The prediction of an artificial neural network served as an initial concentration for an additional simulation because the accuracy of the prediction was insufficient. These approaches, in particular, lowered the computational effort with a tolerable loss of accuracy. By the use of the surrogate-based optimization, the approaches to reduce the computational effort were applied for a parameter identification which optimizes the model parameters to adapt the marine ecosystem model output to observational data. This optimization yielded parameters close to the target ones and lowered the computational effort clearly.Marine Ökosystemmodelle sind ein unverzichtbarer Bestandteil zur Vorhersage des Klimawandels. Die globale Erwärmung wird im Wesentlichen durch Emissionen des bedeutendsten anthropogenen Treibhausgases Kohlenstoffdioxid (CO2) bestimmt. Als eine zentrale Komponente des globalen Kohlenstoffkreislaufs absorbiert das marine Ökosystem atmosphärisches CO2 und verlangsamt so die globale Erwärmung. Marine Ökosystemmodelle ermöglichen die Simulation und somit die Erforschung verschiedener Prozesse innerhalb des marinen Ökosystems sowie eine Vorhersage der zu erwartenden Entwicklung. Allerdings erfordert eine solche Simulation einen immensen Rechenaufwand und unterliegt selbst auf Hochleistungsrechnern durch die begrenzte Rechenleistung erheblichen Einschränkungen. Für die Berechnung einer jährlich periodischen Lösung des marinen Ökosystemmodells zeigt diese Arbeit Wege zur Reduktion des Rechenaufwands durch die Verwendung größerer Zeitschritte und durch die Vorhersage eines neuronalen Netzes auf. Es werden zwei Methoden vorgestellt, die auf der automatischen Anpassung des Zeitschritts während der Simulation basieren, um ohne manuelle Wahl immer den größtmöglichen Zeitschritt zu verwenden. Die Vorhersage der periodischen Lösung mit Hilfe eines neuronalen Netzes diente als Anfangskonzentration für eine zusätzliche Simulation, da die Genauigkeit der Vorhersage unzureichend war. Beide Ansätze verringerten den Rechenaufwand bei einem tolerierbaren Genauigkeitsverlust. Die Konzepte zur Reduktion des Rechenaufwands wurden für eine Parameteroptimierung mit der surrogat-basierten Optimierung verwendet, die die Modellparameter zur Anpassung des marinen Ökosystemmodells an Beobachtungsdaten optimiert. Diese Optimierung lieferte nahezu die anvisierten Parameter und verringerte den Rechenaufwand