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Production and purification of molecular ²²⁵Ac at CERN-ISOLDE
Full text linkThe radioactive nuclide ²²⁵Ac is one of the few promising candidates for cancer treatment by targeted-α-therapy, but worldwide production of ²²⁵Ac faces significant limitations. In this work, the Isotope Separation On-Line method was used to produce actinium by irradiating targets made of uranium carbide and thorium carbide with 1.4-GeV protons. Actinium fluoride molecules were formed, ionized through electron impact, then extracted and mass-separated as a beam of molecular ions. The composition of the mass-selected ion beam was verified using time-of-flight mass spectrometry, α- and γ-ray decay spectrometry. Extracted quantities of ²²⁵Ac¹⁹F₂⁺ particles per μC of incident protons were 3.9(3)×10⁷ from a uranium carbide target and 4.3(4)×10⁷ for a thorium carbide target. Using a magnetic mass separator, the long-lived contamination ²²⁷Ac is suppressed to <5.47×10⁻⁷ (95% confidence interval) with respect to ²²⁵Ac by activity. Measured rates scale to collections of 108 kBqμA⁻¹h⁻¹ of directly produced ²²⁵Ac¹⁹F₂⁺
Quantitative comparison of a mobile, tablet-based eye-tracker and two stationary, video-based eye-trackers
Full text linkThe analysis of eye movements is a noninvasive, reliable and fast method to detect and quantify brain (dys)function. Here, we investigated the performance of two novel eye-trackers—the Thomas Oculus Motus-research mobile (TOM-rm) and the TOM-research stationary (TOM-rs)—and compared them with the performance of a well-established video-based eye-tracker, i.e., the EyeLink 1000 Plus (EL). The TOM-rm is a fully integrated, tablet-based mobile device that presents visual stimuli and records head-unrestrained eye movements at 30 Hz without additional infrared illumination. The TOM-rs is a stationary, video-based eye-tracker that records eye movements at either high spatial or high temporal resolution. We compared the performance of all three eye-trackers in two different behavioral tasks: pro- and anti-saccade and free viewing. We collected data from 30 human subjects while running all three eye-tracking devices in parallel. Parameters requiring a high spatial or temporal resolution (e.g., saccade latency or gain), as derived from the data, differed significantly between the EL and the TOM-rm in both tasks. Differences between results derived from the TOM-rs and the EL were most likely due to experimental conditions, which could not be optimized for both systems simultaneously. We conclude that the TOM-rm can be used for measuring basic eye-movement parameters, such as the error rate in a typical pro- and anti-saccade task, or the number and position of fixations in a visual foraging task, reliably at comparably low spatial and temporal resolution. The TOM-rs, on the other hand, can provide high-resolution oculomotor data at least on a par with an established reference system
The stochastic primitive equations with transport noise and turbulent pressure
Full text linkIn this paper we consider the stochastic primitive equation for geophysical flows subject to transport noise and turbulent pressure. Admitting very rough noise terms, the global existence and uniqueness of solutions to this stochastic partial differential equation are proven using stochastic maximal L²-regularity, the theory of critical spaces for stochastic evolution equations, and global a priori bounds. Compared to other results in this direction, we do not need any smallness assumption on the transport noise which acts directly on the velocity field and we also allow rougher noise terms. The adaptation to Stratonovich type noise and, more generally, to variable viscosity and/or conductivity are discussed as well
Procrustean Statistical Inference of Deformations
Full text linkA two step method has been devised for the statistical inference of deformation changes. In the first step of this method and based on Procrustes analysis of deformation tensors, the significance of the change in a time or space series of deformation tensors is statistically analyzed. In the second step significant change(s) in deformations are localized. In other words, they are assigned to certain parameters of deformation tensor. This is done using the Global Model Test. Because of the key role of Procrustes analysis in the proposed method for the inference of deformation changes, it has been given the name of Procrustean Statistical Inference of Deformations. The method has been implemented to synthetic and real deformations. The 3D-deformation tensors of a regional GPS network in the Kenai Peninsula, for analyzing the spatial variation of deformation tensors or the change of deformation within the study area, and a local GPS network in France, for analyzing the temporal variation of deformation tensors or the change of deformation in time at every point of the network in the study area have been used for illustrating the practical application of the proposed method
Crystal structure of di[potassium(diaza-18-crown-6)]dicaesium nonastannide( 4–) diethylenediamine, [K(C₁₂H₂₆N₂O₄)]2Cs₂[Sn₉] · 2C₂N₂H₈, a polyanion ² ͚ [KCs₂Sn₉]⁻ with low-dimensional arrangement of [Sn₉] clusters
No full textC₂₈H₆₈Cs₂K₂N₈O₈Sn₉, monoclinic, P121/c1 (No. 14), a = 13.897(3) Å, b = 16.091(3) Å, c = 26.432(5) Å, β = 91.03(3)°, V = 5909.7 ų, Z = 4, Rgt(F) = 0.050, wRref(F²) = 0.127, T = 153 K
Stochastic multilevel methods for deep learning
No full textAs the use cases for neural networks become increasingly complex, modern neural networks must also grow deeper and more intricate to keep up. However, with increased depth and complexity, common training methods like stochastic gradient descent methods (SGD) become more computationally expensive.
Multilevel methods, traditionally used to solve differential equations through hierarchies of discretizations, offer the potential to reduce computational effort.
These methods operate across multiple levels of complexity. On lower levels, a coarse approximation of the solution can be obtained quickly and at low computational cost. These coarse approximations are then refined on higher levels to achieve a more accurate solution. By alternating between levels, multilevel methods can accelerate convergence while maintaining precision.
This thesis introduces a multilevel stochastic gradient descent algorithm (MLSGD) that combines both concepts aiming to accelerate neural network training through multilevel techniques. The core contribution of this thesis is the development and analysis of MLSGD. As in traditional multilevel methods, prolongation and restriction operators enable transitions between levels. To ensure first-order coherence, a gradient correction is added to the objective function as well as additional conditions including step size regularization and an angle condition.
We analyze the convergence properties of the method under the assumption of fixed step sizes. Additionally, we investigate the influence of stochastic directions in the gradient correction as a replacement for full gradients as well as the effect of variance reduction in both cases.
Finally, we evaluate the practical performance of the method as well as the effect of stochastic gradient correction and variance reduction. To this end, MLSGD is applied to the image classification dataset CIFAR-10. We explore two different approaches to constructing a hierarchy, based either on network depth or image resolution. For both approaches, we construct suitable prolongation and restriction operators
Implementation of quantum and classical discrete fractional Fourier transforms
Full text linkFourier transforms, integer and fractional, are ubiquitous mathematical tools in basic and applied science. Certainly, since the ordinary Fourier transform is merely a particular case of a continuous set of fractional Fourier domains, every property and application of the ordinary Fourier transform becomes a special case of the fractional Fourier transform. Despite the great practical importance of the discrete Fourier transform, implementation of fractional orders of the corresponding discrete operation has been elusive. Here we report classical and quantum optical realizations of the discrete fractional Fourier transform. In the context of classical optics, we implement discrete fractional Fourier transforms of exemplary wave functions and experimentally demonstrate the shift theorem. Moreover, we apply this approach in the quantum realm to Fourier transform separable and path-entangled biphoton wave functions. The proposed approach is versatile and could find applications in various fields where Fourier transforms are essential tools
Einflussfaktoren auf die Bondemissionsprämie im Bereich der alternativen Energien: Eine Note
Full text linkDie folgende Analyse präsentiert aktuelle empirische Evidenz, die es erlaubt, eine bessere Vorstellung der von den potentiellen Investoren geforderten Risikoprämie zum Emissionszeitpunkt zu erhalten. Untersucht werden zwischen 2008 und 2012 in Deutschland emittierte Anleihen im Bereich der alternativen Energien. Neben dem schon hinreichend bekannten Einfluss der Risikoklassifizierung durch ein Rating zeigen sich weitere bedeutende Variablen. Mit zunehmenden Emissionsvolumina und höherer Stückelung geht ebenfalls der Zinsaufschlag zurück. Offensichtlich konnten besonders große an institutionelle Investoren gerichtete Branchenvertreter in der Vergangenheit von günstigen Anleiheemissionen profitieren. Ebenfalls für eine niedrigere Emissionsprämie sorgen eine positive Konjunkturlage und ein hohes risikoloses Zinsniveau. Dies macht deutlich, dass das derzeitig in Deutschland herrschende Niedrigzinsniveau gepaart mit mäßigem Wachstum die kostengünstige Aufnahme von Fremdkapital über den Kapitalmarkt deutlich erschwert. Eine Durchsetzung der Maßnahmen der durch BMU und BMWi angekündigten „Strompreisbremse" würde die Situation für Unternehmen noch verschärfen
The Alternative Three-Factor Model: Evidence from the German Stock Market
Full text linkThis article applies the alternative three-factor model introduced by Chen / Novy-Marx / Zhang (2010) to the German stock market for the sample period of 2004 through 2015. We construct two new factors INV („investment") and ROA („return on assets") for companies listed on the highest segment of the Frankfurt Stock Exchange, and examine whether they can explain various stock market anomalies using linear time series regressions. Our results reveal that the theoretical assumptions of the model are valid for the German stock market. Firms with higher investments generally exhibit lower returns, while more profitable firms exhibit higher returns. However, we find that the alternative three-factor model does not explain capital market anomalies in the German market better than the factors of the traditional Fama / French (1993) three-factor model
The Importance of Metal–Support Interactions for CO₂ Hydrogenation: An Operando Near Ambient Pressure X-ray Photoelectron Spectroscopy Study on Gold-Loaded In₂O₃ and CeO₂ Catalysts
Full text linkMetal–support interactions, which are essential for the design of supported metal catalysts, used, e.g., for CO₂ activation, are still only partially understood. In this study of gold-loaded In₂O₃ and CeO₂ catalysts during CO₂ hydrogenation using near-ambient pressure X-ray photoelectron spectroscopy, supported by near edge X-ray absorption fine structure, we demonstrate that the role of the noble metal strongly depends upon the choice of the support material. Temperature-dependent analyses of X-ray photoelectron spectra under reaction conditions reveal that gold is reduced on CeO₂, enabling direct H₂ activation, but oxidized on In₂O₃, leading to decreased activity of Au/In₂O₃ compared to bare In₂O₃. At elevated temperatures, the catalytic activity of the In₂O₃ catalysts strongly increases as a result of facilitated CO₂ and (In₂O₃-based) H₂ activation, while the catalytic activity of Au/CeO₂ is limited by reoxidation by CO₂. Our results underline the importance of operando studies for understanding metal–support interactions to enable a rational support selection in the future