SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201

Optimal Pairs Trading Rules

This thesis derives an optimal trading rule for a pair of historically correlated stocks. When one stock\u27s price increases and the other one\u27s decreases, a trade of the pair is triggered. The idea is to short the winner and to long the loser with the hope that the prices of the two assets will converge again. In this thesis the spread of the two stocks is governed by a mean-reverting model. The objective is to trade the pair in such a way as to maximize an overall return. The same slippage cost is imposed on every trade. Furthermore, a local-time process to the spread is introduced in order to avoid infinitely large gains. We use the associated Hamilton-Jacobi-Bellman equations to characterize the value functions which are solved by using the smooth-fit method. It is shown that the solution of the optimal pairs trading problem can be obtained by solving a set of nonlinear equations. Additionally, a set of sufficient conditions is provided in form of a verification theorem. The thesis concludes with a numerical example

Risk Allocation in Capital Markets: Portfolio Insurance, Tactical Asset Allocation and Collar Strategies

The theory of risk exchange is applied on the allocation of financial risk in capital markets. It is shown how the shape of individual payoff functions depends on risk tolerance and cautiousness. For the special case where the Neumann-Morgenstern utility functions of all individual investors belong to the HARA class and have non decreasing risk tolerance it is proved that generalized versions of "portfolio insurance”, "tactical asset allocation” and "collars” are the only strategies occurring in price equilibriu

A waveguide atom beamsplitter for laser-cooled neutral atoms

A laser-cooled neutral-atom beam from a low-velocity intense source is split into two beams while guided by a magnetic-field potential. We generate our multimode-beamsplitter potential with two current-carrying wires on a glass substrate combined with an external transverse bias field. The atoms bend around several curves over a $10$-cm distance. A maximum integrated flux of $1.5\cdot10^{5} \mathrm{atoms/s}$ is achieved with a current density of $5\cdot10^{4} \mathrm{Ampere/cm^{2}}$ in the 100-$\mathrm{\mu m}$ diameter wires. The initial beam can be split into two beams with a 50/50 splitting ratio

Semi-supervised identification of rarely appearing persons in video by correcting weak labels

Some recent approaches for character identification in movies and TV broadcasts are realized in a semi-supervised manner by assigning transcripts and/or subtitles to the speakers. However, the labels obtained in this way achieve only an accuracy of 80% - 90% and the number of training examples for the different actors is unevenly distributed. In this paper, we propose a novel approach for person identification in video by correcting and extending the training data with reliable predictions to reduce the number of annotation errors. Furthermore, the intra-class diversity of rarely speaking characters is enhanced. To address the imbalance of training data per person, we suggest two complementary prediction scores. These scores are also used to recognize whether or not a face track belongs to a (supporting) character whose identity does not appear in the transcript etc. Experimental results demonstrate the feasibility of the proposed approach, outperforming the current state of the art

Das Pilotprojekt „Kugelschuss auf der Weide“ in der Schweiz

Das System der sogenannten Weideschlachtung/Kugelschuss auf der Weide ist bis dato einmalig in der Schweiz. In Deutschland wird die stressvermeidende Weideschlachtung schon auf mehreren Betrieben durchgeführt. Die Schlachtrinder werden nicht mehr dem Stress bedeutenden Verlust der vertrauten Umgebung ausgesetzt und können ohne Lebendtiertransport und den damit verbundenen Stressfaktoren auf dem heimischen Betrieb betäubt und getötet werden. Nach dem Entbluten wird der Schlachtkörper dann in einem eigens dafür angefertigten PKW-Anhänger, der sämtliche hygienetechnischen Anforderungen erfüllt, in ein nahegelegenes Schlachtlokal verbracht und dort fachgerecht und im konventionellen Sinne weiterverarbeitet. Damit das System der Weideschlachtung einwandfrei funktioniert, die zu schlachtenden Tiere korrekt betäubt und anschließend getötet werden und sämtliche Punkte hinsichtlich Tierschutz, Arbeitssicherheit und Hygiene eingehalten werden können, gilt es Richtlinien einzuhalten. Diese, sowie die einzelnen Schritte einer einwandfreien Weideschlachtung werden in diesem Artikel dargestellt

High-resolution GPS tracking reveals sex differences in migratory behaviour and stopover habitat use in the lesser black-backed gull Larus fuscus

Sex-, size-or age-dependent variation in migration strategies in birds is generally expected to reflect differences in competitive abilities. Theoretical and empirical studies thereby focus on differences in wintering areas, by which individuals may benefit from avoiding food competition during winter or ensuring an early return and access to prime nesting sites in spring. Here, we use GPS tracking to assess sex-and size-related variation in the spatial behaviour of adult Lesser Black-backed Gulls (Larus fuscus) throughout their annual cycle. We did not find sex-or size-dependent differences in wintering area or the timing of spring migration. Instead, sexual differences occurred prior to, and during, autumn migration, when females strongly focussed on agricultural areas. Females exhibited a more protracted autumn migration strategy, hence spent more time on stopover sites and arrived 15 days later at their wintering areas, than males. This shift in habitat use and protracted autumn migration coincided with the timing of moult, which overlaps with chick rearing and migration. Our results suggest that this overlap between energy-demanding activities may lead females to perform a more prolonged autumn migration, which results in spatiotemporal differences in foraging habitat use between the sexes

Classification of Visualization Types and Perspectives in Patents

Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and dataset will be made publicly available.Comment: Accepted in International Conference on Theory and Practice of Digital Libraries (TPDL) 2023 (They have the copyright to publish camera-ready version of this work

Auslegung und Optimierung der Antriebswellen eines Off-road-Konzeptfahrzeuges

nicht vorhande