Einfluss der Bearbeitungsintensität beim Umbruch von Leguminosen-Grasgemengen auf die N-Mineralisierung zur Folge-frucht Winterweizen

Einleitung Leguminosen-Grasgemenge sind als N-Quelle in Fruchtfolgen des Organischen Landbaus von zentraler Bedeutung. Nach meist mehrjährigem Anbau wird der von den Leguminosen assimilierte Stickstoff mit dem Umbruch der Narbe durch die Mineralisation der organischen Substanz wieder freigesetzt. In der Praxis steht für eine Steuerung der Mineralisierung ausschließlich das Instrument der Bodenbearbei-tung zur Verfügung. Ziel dieser Untersuchungen war es, die Beeinflussbarkeit der N Mineralisierung durch eine Variation des Narbenumbruchs zu quantifizieren, um so eine Datengrundlage für die Modellierung der N-Dynamik in Abhängigkeit von der Bodenbearbeitung zu schaffen

Generative Sliced MMD Flows with Riesz Kernels

Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \|x-y\|^r$, $r \in (0,2)$ have exceptional properties which allow for their efficient computation. First, the MMD of Riesz kernels coincides with the MMD of their sliced version. As a consequence, the computation of gradients of MMDs can be performed in the one-dimensional setting. Here, for $r=1$, a simple sorting algorithm can be applied to reduce the complexity from $O(MN+N^2)$ to $O((M+N)\log(M+N))$ for two empirical measures with $M$ and $N$ support points. For the implementations we approximate the gradient of the sliced MMD by using only a finite number $P$ of slices. We show that the resulting error has complexity $O(\sqrt{d/P})$, where $d$ is the data dimension. These results enable us to train generative models by approximating MMD gradient flows by neural networks even for large scale applications. We demonstrate the efficiency of our model by image generation on MNIST, FashionMNIST and CIFAR10

Learning-based approaches for reconstructions with inexact operators in nanoCT applications

Imaging problems such as the one in nanoCT require the solution of an inverse problem, where it is often taken for granted that the forward operator, i.e., the underlying physical model, is properly known. In the present work we address the problem where the forward model is inexact due to stochastic or deterministic deviations during the measurement process. We particularly investigate the performance of non-learned iterative reconstruction methods dealing with inexactness and learned reconstruction schemes, which are based on U-Nets and conditional invertible neural networks. The latter also provide the opportunity for uncertainty quantification. A synthetic large data set in line with a typical nanoCT setting is provided and extensive numerical experiments are conducted evaluating the proposed methods

MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. The code and model weights will be published here: [tba]Comment: Accepted for Miccai 2023 and selected for an ora

On the Doppler effect for light from orbiting sources in Kerr-type metrics

A formula is derived for the combined motional and gravitational Doppler effect in general stationary axisymmetric metrics for a photon emitted parallel or antiparallel to the assumed circular orbital motion of its source. The same formula is derived from eikonal approximation and Killing vector approaches to elucidate connections between observational astronomy and modern Relativity. The formula yields expected results in the limits of a moving or stationary source in the exterior Kerr and Schwarzschild metrics and a moving source in flat space.Comment: Accepted for publication in in Monthly Notices of the Royal Astronomical Society Main Journal 1.23.15. This version has substantially shortened and clarified derivations and added content regarding applicability of the derivation

Motion of charged particles around a rotating black hole in a magnetic field

We study the effects of an external magnetic field, which is assumed to be uniform at infinity, on the marginally stable circular motion of charged particles in the equatorial plane of a rotating black hole. We show that the magnetic field has its greatest effect in enlarging the region of stability towards the event horizon of the black hole. Using the Hamilton-Jacobi formalism we obtain the basic equations governing the marginal stability of the circular orbits and their associated energies and angular momenta. As instructive examples, we review the case of the marginal stability of the circular orbits in the Kerr metric, as well as around a Schwarzschild black hole in a magnetic field. For large enough values of the magnetic field around a maximally rotating black hole we find the limiting analytical solutions to the equations governing the radii of marginal stability. We also show that the presence of a strong magnetic field provides the possibility of relativistic motions in both direct and retrograde innermost stable circular orbits around a Kerr black hole.Comment: 25 pages, 2 figure

Black-Hole Spin Dependence in the Light Curves of Tidal Disruption Events

A star orbiting a supermassive black hole can be tidally disrupted if the black hole's gravitational tidal field exceeds the star's self gravity at pericenter. Some of this stellar tidal debris can become gravitationally bound to the black hole, leading to a bright electromagnetic flare with bolometric luminosity proportional to the rate at which material falls back to pericenter. In the Newtonian limit, this flare will have a light curve that scales as t^-5/3 if the tidal debris has a flat distribution in binding energy. We investigate the time dependence of the black-hole mass accretion rate when tidal disruption occurs close enough the black hole that relativistic effects are significant. We find that for orbits with pericenters comparable to the radius of the marginally bound circular orbit, relativistic effects can double the peak accretion rate and halve the time it takes to reach this peak accretion rate. The accretion rate depends on both the magnitude of the black-hole spin and its orientation with respect to the stellar orbit; for orbits with a given pericenter radius in Boyer-Lindquist coordinates, a maximal black-hole spin anti-aligned with the orbital angular momentum leads to the largest peak accretion rate.Comment: 16 pages, 15 figures, 1 table, PRD published versio

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV

Spintronics: Fundamentals and applications

Spintronics, or spin electronics, involves the study of active control and manipulation of spin degrees of freedom in solid-state systems. This article reviews the current status of this subject, including both recent advances and well-established results. The primary focus is on the basic physical principles underlying the generation of carrier spin polarization, spin dynamics, and spin-polarized transport in semiconductors and metals. Spin transport differs from charge transport in that spin is a nonconserved quantity in solids due to spin-orbit and hyperfine coupling. The authors discuss in detail spin decoherence mechanisms in metals and semiconductors. Various theories of spin injection and spin-polarized transport are applied to hybrid structures relevant to spin-based devices and fundamental studies of materials properties. Experimental work is reviewed with the emphasis on projected applications, in which external electric and magnetic fields and illumination by light will be used to control spin and charge dynamics to create new functionalities not feasible or ineffective with conventional electronics.Comment: invited review, 36 figures, 900+ references; minor stylistic changes from the published versio

Gravitating discs around black holes

Fluid discs and tori around black holes are discussed within different approaches and with the emphasis on the role of disc gravity. First reviewed are the prospects of investigating the gravitational field of a black hole--disc system by analytical solutions of stationary, axially symmetric Einstein's equations. Then, more detailed considerations are focused to middle and outer parts of extended disc-like configurations where relativistic effects are small and the Newtonian description is adequate. Within general relativity, only a static case has been analysed in detail. Results are often very inspiring, however, simplifying assumptions must be imposed: ad hoc profiles of the disc density are commonly assumed and the effects of frame-dragging and completely lacking. Astrophysical discs (e.g. accretion discs in active galactic nuclei) typically extend far beyond the relativistic domain and are fairly diluted. However, self-gravity is still essential for their structure and evolution, as well as for their radiation emission and the impact on the environment around. For example, a nuclear star cluster in a galactic centre may bear various imprints of mutual star--disc interactions, which can be recognised in observational properties, such as the relation between the central mass and stellar velocity dispersion.Comment: Accepted for publication in CQG; high-resolution figures will be available from http://www.iop.org/EJ/journal/CQ