19,439 research outputs found
First-principles study of high conductance DNA sequencing with carbon nanotube electrodes
Rapid and cost-effective DNA sequencing at the single nucleotide level might
be achieved by measuring a transverse electronic current as single-stranded DNA
is pulled through a nano-sized pore. In order to enhance the electronic
coupling between the nucleotides and the electrodes and hence the current
signals, we employ a pair of single-walled close-ended (6,6) carbon nanotubes
(CNTs) as electrodes. We then investigate the electron transport properties of
nucleotides sandwiched between such electrodes by using first-principles
quantum transport theory. In particular we consider the extreme case where the
separation between the electrodes is the smallest possible that still allows
the DNA translocation. The benzene-like ring at the end cap of the CNT can
strongly couple with the nucleobases and therefore both reduce conformational
fluctuations and significantly improve the conductance. The optimal molecular
configurations, at which the nucleotides strongly couple to the CNTs, and which
yield the largest transmission, are first identified. Then the electronic
structures and the electron transport of these optimal configurations are
analyzed. The typical tunneling currents are of the order of 50 nA for voltages
up to 1 V. At higher bias, where resonant transport through the molecular
states is possible, the current is of the order of several A. Below 1 V
the currents associated to the different nucleotides are consistently
distinguishable, with adenine having the largest current, guanine the
second-largest, cytosine the third and finally thymine the smallest. We further
calculate the transmission coefficient profiles as the nucleotides are dragged
along the DNA translocation path and investigate the effects of configurational
variations. Based on these results we propose a DNA sequencing protocol
combining three possible data analysis strategies.Comment: 12 pages, 17 figures, 3 table
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation
Aircraft engine manufacturers collect large amount of engine related data
during flights. These data are used to detect anomalies in the engines in order
to help companies optimize their maintenance costs. This article introduces and
studies a generic methodology that allows one to build automatic early signs of
anomaly detection in a way that is understandable by human operators who make
the final maintenance decision. The main idea of the method is to generate a
very large number of binary indicators based on parametric anomaly scores
designed by experts, complemented by simple aggregations of those scores. The
best indicators are selected via a classical forward scheme, leading to a much
reduced number of indicators that are tuned to a data set. We illustrate the
interest of the method on simulated data which contain realistic early signs of
anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St.
Petersburg : Russian Federation (2014
Quantum and classical resonant escapes of a strongly-driven Josephson junction
The properties of phase escape in a dc SQUID at 25 mK, which is well below
quantum-to-classical crossover temperature , in the presence of strong
resonant ac driving have been investigated. The SQUID contains two
Nb/Al-AlO/Nb tunnel junctions with Josephson inductance much larger than
the loop inductance so it can be viewed as a single junction having adjustable
critical current. We find that with increasing microwave power and at
certain frequencies and /2, the single primary peak in the
switching current distribution, \textrm{which is the result of macroscopic
quantum tunneling of the phase across the junction}, first shifts toward lower
bias current and then a resonant peak develops. These results are explained
by quantum resonant phase escape involving single and two photons with
microwave-suppressed potential barrier. As further increases, the primary
peak gradually disappears and the resonant peak grows into a single one while
shifting further to lower . At certain , a second resonant peak appears,
which can locate at very low depending on the value of . Analysis
based on the classical equation of motion shows that such resonant peak can
arise from the resonant escape of the phase particle with extremely large
oscillation amplitude resulting from bifurcation of the nonlinear system. Our
experimental result and theoretical analysis demonstrate that at ,
escape of the phase particle could be dominated by classical process, such as
dynamical bifurcation of nonlinear systems under strong ac driving.Comment: 10 pages, 9 figures, 1 tabl
On the existence and uniqueness of solutions to stochastic differential equations driven by G-Brownian motion with integral-Lipschitz coefficients
In this paper, we study the existence and uniqueness of solutions to
stochastic differential equations driven by G-Brownian motion (GSDEs) with
integral-Lipschitz conditions on their coefficients
Evaluating Two-Stream CNN for Video Classification
Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets for video analytics. Among the many practical needs,
classifying videos (or video clips) based on their major semantic categories
(e.g., "skiing") is useful in many applications. In this paper, we conduct an
in-depth study to investigate important implementation options that may affect
the performance of deep nets on video classification. Our evaluations are
conducted on top of a recent two-stream convolutional neural network (CNN)
pipeline, which uses both static frames and motion optical flows, and has
demonstrated competitive performance against the state-of-the-art methods. In
order to gain insights and to arrive at a practical guideline, many important
options are studied, including network architectures, model fusion, learning
parameters and the final prediction methods. Based on the evaluations, very
competitive results are attained on two popular video classification
benchmarks. We hope that the discussions and conclusions from this work can
help researchers in related fields to quickly set up a good basis for further
investigations along this very promising direction.Comment: ACM ICMR'1
1D Exciton Spectroscopy of Semiconductor Nanorods
We have theoretically shown that optical properties of semiconductor nanorods
are controlled by 1D excitons. The theory, which takes into account anisotropy
of spacial and dielectric confinement, describes size dependence of interband
optical transitions, exciton binding energies. We have demonstrated that the
fine structure of the ground exciton state explains the linear polarization of
photoluminescence. Our results are in good agreement with the measurements in
CdSe nanorods
Recent progress in random metric theory and its applications to conditional risk measures
The purpose of this paper is to give a selective survey on recent progress in
random metric theory and its applications to conditional risk measures. This
paper includes eight sections. Section 1 is a longer introduction, which gives
a brief introduction to random metric theory, risk measures and conditional
risk measures. Section 2 gives the central framework in random metric theory,
topological structures, important examples, the notions of a random conjugate
space and the Hahn-Banach theorems for random linear functionals. Section 3
gives several important representation theorems for random conjugate spaces.
Section 4 gives characterizations for a complete random normed module to be
random reflexive. Section 5 gives hyperplane separation theorems currently
available in random locally convex modules. Section 6 gives the theory of
random duality with respect to the locally convex topology and in
particular a characterization for a locally convex module to be
prebarreled. Section 7 gives some basic results on convex
analysis together with some applications to conditional risk measures. Finally,
Section 8 is devoted to extensions of conditional convex risk measures, which
shows that every representable type of conditional convex risk
measure and every continuous type of convex conditional risk measure
() can be extended to an type
of lower semicontinuous conditional convex risk measure and an
type of continuous
conditional convex risk measure (), respectively.Comment: 37 page
Codimension Two Branes in Einstein-Gauss-Bonnet Gravity
Codimension two branes play an interesting role in attacking the cosmological
constant problem. Recently, in order to handle some problems in codimension two
branes in Einstein gravity, Bostock {\it et al.} have proposed using
six-dimensional Einstein-Gauss-Bonnet (EGB) gravity instead of six-dimensional
Einstein gravity. In this paper, we present the solutions of codimension two
branes in six-dimensional EGB gravity. We show that Einstein's equations take a
"factorizable" form for a factorized metric tensor ansatz even in the presence
of the higher-derivative Gauss-Bonnet term. Especially, a new feature of the
solution is that the deficit angle depends on the brane geometry. We discuss
the implication of the solution to the cosmological constant problem. We also
comment on a possible problem of inflation model building on codimension two
branes.Comment: 16 pages, no figures. v2: References added; v3: Reference added,
Sec.4 and 5 combined into one; v4: References added, minor corrections, to
appear in Physical Review
Solving Quantum Ground-State Problems with Nuclear Magnetic Resonance
Quantum ground-state problems are computationally hard problems; for general
many-body Hamiltonians, there is no classical or quantum algorithm known to be
able to solve them efficiently. Nevertheless, if a trial wavefunction
approximating the ground state is available, as often happens for many problems
in physics and chemistry, a quantum computer could employ this trial
wavefunction to project the ground state by means of the phase estimation
algorithm (PEA). We performed an experimental realization of this idea by
implementing a variational-wavefunction approach to solve the ground-state
problem of the Heisenberg spin model with an NMR quantum simulator. Our
iterative phase estimation procedure yields a high accuracy for the
eigenenergies (to the 10^-5 decimal digit). The ground-state fidelity was
distilled to be more than 80%, and the singlet-to-triplet switching near the
critical field is reliably captured. This result shows that quantum simulators
can better leverage classical trial wavefunctions than classical computers.Comment: 11 pages, 13 figure
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