1,283 research outputs found
Spin-flip phonon-mediated charge relaxation in double quantum dots
We theoretically study the triplet to singlet relaxation rate
in a lateral gate-defined double quantum dot tuned to the regime of Pauli spin
blockade. We present a detailed derivation of the effective phonon density of
states for this specific charge transition, keeping track of the contribution
from piezoelectric as well as deformation potential electron-phonon coupling.
We further investigate two different spin-mixing mechanisms which can couple
the triplet and singlet states: a magnetic field gradient over the double dot
(relevant at low external magnetic field) and spin-orbit interaction (relevant
at high field), and we also indicate how the two processes could interfere at
intermediate magnetic field. Finally, we show how to combine all results and
evaluate the relaxation rate for realistic system parameters.Comment: 9 pages, 4 figure
Non-Gaussian fluctuations of mesoscopic persistent currents
The persistent current in an ensemble of normal-metal rings shows Gaussian
distributed sample-to-sample fluctuations with non-Gaussian corrections, which
are precursors of the transition into the Anderson localized regime. We here
report a calculation of the leading non-Gaussian correction to the current
autocorrelation function, which is of third order in the current. Although the
third-order correlation function is small, inversely proportional to the
dimensionless conductance of the ring, the mere fact that it is nonzero is
remarkable, since it is an odd moment of the current distribution.Comment: 4+ pages, 2 figure
Quantum Tunneling Detection of Two-photon and Two-electron Processes
We analyze the operation of a quantum tunneling detector coupled to a
coherent conductor. We demonstrate that in a certain energy range the output of
the detector is determined by two-photon processes, two-electron processes and
the interference of the two. We show how the individual contributions of these
processes can be resolved in experiments.Comment: 4 pages, 4 figure
Nuclear spin pumping and electron spin susceptibilities
In this work we present a new formalism to evaluate the nuclear spin dynamics
driven by hyperfine interaction with non-equilibrium electron spins. To
describe the dynamics up to second order in the hyperfine coupling, it suffices
to evaluate the susceptibility and fluctuations of the electron spin. Our
approach does not rely on a separation of electronic energy scales or the
specific choice of electronic basis states, thereby overcoming practical
problems which may arise in certain limits when using a more traditional
formalism based on rate equations.Comment: 9 pages, 2 figure
Pauli Spin Blockade in the Presence of Strong Spin-Orbit Coupling
We study electron transport in a double quantum dot in the Pauli spin
blockade regime, in the presence of strong spin-orbit coupling. The effect of
spin-orbit coupling is incorporated into a modified interdot tunnel coupling.
We elucidate the role of the external magnetic field, the nuclear fields in the
dots, and spin relaxation. We find qualitative agreement with experimental
observations, and we propose a way to extend the range of magnetic fields in
which blockade can be observed.Comment: 4 pages, 3 figure
Identifying network communities with a high resolution
Community structure is an important property of complex networks. An
automatic discovery of such structure is a fundamental task in many
disciplines, including sociology, biology, engineering, and computer science.
Recently, several community discovery algorithms have been proposed based on
the optimization of a quantity called modularity (Q). However, the problem of
modularity optimization is NP-hard, and the existing approaches often suffer
from prohibitively long running time or poor quality. Furthermore, it has been
recently pointed out that algorithms based on optimizing Q will have a
resolution limit, i.e., communities below a certain scale may not be detected.
In this research, we first propose an efficient heuristic algorithm, Qcut,
which combines spectral graph partitioning and local search to optimize Q.
Using both synthetic and real networks, we show that Qcut can find higher
modularities and is more scalable than the existing algorithms. Furthermore,
using Qcut as an essential component, we propose a recursive algorithm, HQcut,
to solve the resolution limit problem. We show that HQcut can successfully
detect communities at a much finer scale and with a higher accuracy than the
existing algorithms. Finally, we apply Qcut and HQcut to study a
protein-protein interaction network, and show that the combination of the two
algorithms can reveal interesting biological results that may be otherwise
undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at
http://cic.cs.wustl.edu/qcut/supplemental.pd
Social encounter networks : collective properties and disease transmission
A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of −2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions
Module identification in bipartite and directed networks
Modularity is one of the most prominent properties of real-world complex
networks. Here, we address the issue of module identification in two important
classes of networks: bipartite networks and directed unipartite networks. Nodes
in bipartite networks are divided into two non-overlapping sets, and the links
must have one end node from each set. Directed unipartite networks only have
one type of nodes, but links have an origin and an end. We show that directed
unipartite networks can be conviniently represented as bipartite networks for
module identification purposes. We report a novel approach especially suited
for module detection in bipartite networks, and define a set of random networks
that enable us to validate the new approach
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
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