351 research outputs found
Electronic transport in metallic carbon nanotubes with mixed defects within the strong localization regime
We study the electron transport in metallic carbon nanotubes (CNTs) with
realistic defects of different types. We focus on large CNTs with many defects
in the mesoscopic range. In a recent paper we demonstrated that the electronic
transport in those defective CNTs is in the regime of strong localization. We
verify by quantum transport simulations that the localization length of CNTs
with defects of mixed types can be related to the localization lengths of CNTs
with identical defects by taking the weighted harmonic average. Secondly, we
show how to use this result to estimate the conductance of arbitrary defective
CNTs, avoiding time consuming transport calculations
Improved recursive Green's function formalism for quasi one-dimensional systems with realistic defects
We derive an improved version of the recursive Green's function formalism
(RGF), which is a standard tool in the quantum transport theory. We consider
the case of disordered quasi one-dimensional materials where the disorder is
applied in form of randomly distributed realistic defects, leading to partly
periodic Hamiltonian matrices. The algorithm accelerates the common RGF in the
recursive decimation scheme, using the iteration steps of the renormalization
decimation algorithm. This leads to a smaller effective system, which is
treated using the common forward iteration scheme. The computational complexity
scales linearly with the number of defects, instead of linearly with the total
system length for the conventional approach. We show that the scaling of the
calculation time of the Green's function depends on the defect density of a
random test system. Furthermore, we discuss the calculation time and the memory
requirement of the whole transport formalism applied to defective carbon
nanotubes
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
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