Nach der US-Steuerreform 2018 : Unternehmensbesteuerung in Deutschland im Steuerwettbewerb

Die mit Beginn des Jahres 2018 in Kraft getretene US-Steuerreform erhöht die Attraktivität der USA für Investitionen und verschärft weiter den internationalen Steuerwettbewerb. Deswegen ist eine Senkung der Gewinnsteuern deutscher Unternehmen geboten. Zur Wahrung von Finanzierungsneutralität und Rechtsformneutralität ist die Abgeltungsteuer zu einer Dualen Einkommensteuer weiterzuentwickeln

An improved Green's function algorithm applied to quantum transport in carbon nanotubes

The renormalization-decimation algorithm (RDA) of L\'opez Sancho et al. is used in quantum transport theory to calculate bulk and surface Green's functions. We derive an improved version of the RDA for the case of very long quasi one-dimensional unit cells (in transport direction). This covers not only long unit cells but also supercell-like calculations for structures with disorder or defects. In such large systems, short-range interactions lead to sparse real-space Hamiltonian matrices. We show how this and a corresponding subdivision of the unit cell in combination with the decimation technique can be used to reduce the calculation time. Within the resulting algorithm, separate RDA calculations of much smaller effective Hamiltonian matrices must be done for each Green's function, which enables the treatment of systems too large for the common RDA. Finally, we discuss the performance properties of our improved algorithm as well as some exemplary results for chiral carbon nanotubes

ОНОМАСИОЛОГИЧЕСКИЙ ПОДХОД К ИЗУЧЕНИЮ СЕМАНТИКИ ПРОИЗВОДНОГО СЛОВА

Изучение спектра семантических связей и синтаксических валентностей производящего наряду с “сочетаемостными особенностями морфем”, “ограничениями, которые кладутся в комбинаторику морфем их семантическими свойствами выявляет логику и тенденции деривационных репрезентаций актов номинации в синхронном срезе языка. За всем этим закономерно следует перспектива регулирования и управления словообразовательными процессами (т. е. наполнение пропозициональной структуры типизированной лексикой), обсуждаемая сегодня в исследованиях лингвисто

Spatio-spectral characteristics of parametric down-conversion in waveguide arrays

High dimensional quantum states are of fundamental interest for quantum information processing. They give access to large Hilbert spaces and, in turn, enable the encoding of quantum information on multiple modes. One method to create such quantum states is parametric down-conversion (PDC) in waveguide arrays (WGAs) which allows for the creation of highly entangled photon-pairs in controlled, easily accessible spatial modes, with unique spectral properties. In this paper we examine both theoretically and experimentally the PDC process in a lithium niobate WGA. We measure the spatial and spectral properties of the emitted photon-pairs, revealing strong correlations between spectral and spatial degrees of freedom of the created photons. Our measurements show that, in contrast to prior theoretical approaches, spectrally dependent coupling effects have to be taken into account in the theory of PDC in WGAs. To interpret the results, we developed a theoretical model specifically taking into account spectrally dependent coupling effects, which further enables us to explore the capabilities and limitations for engineering the spatial correlations of the generated quantum states.Comment: 26 pages, 11 figure

Calculation of tunnel-couplings in open gate-defined disordered quantum dot systems

Quantum computation based on semiconductor electron-spin qubits requires high control of tunnel-couplings, both across quantum dots and between the quantum dot and the reservoir. The tunnel-coupling to the reservoir sets the qubit detection and initialization bandwidth for energy-resolved spin-to-charge conversion and is essential to tune single-electron transistors commonly used as charge detectors. Potential disorder and the increasing complexity of the two-dimensional gate-defined quantum computing devices sets high demands on the gate design and the voltage tuning of the tunnel barriers. We present a Green's formalism approach for the calculation of tunnel-couplings between a quantum dot and a reservoir. Our method takes into account in full detail the two-dimensional electrostatic potential of the quantum dot, the tunnel barrier and reservoir. A Markov approximation is only employed far away from the tunnel barrier region where the density of states is sufficiently large. We calculate the tunnel-coupling including potential disorder effects, which become increasingly important for large-scale silicon-based spin-qubit devices. Studying the tunnel-couplings of a single-electron transistor in Si/SiGe as a showcase, we find that charged defects are the dominant source of disorder leading to variations in the tunnel-coupling of four orders of magnitude.Comment: 10 pages, 4 figure

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco- regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints