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 $\mu$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 $T_{cr}$, in the presence of strong resonant ac driving have been investigated. The SQUID contains two Nb/Al-AlO$_{x}$/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 $W$ and at certain frequencies $\nu$ and $\nu$/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 $I$ 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 $W$ further increases, the primary peak gradually disappears and the resonant peak grows into a single one while shifting further to lower $I$. At certain $W$, a second resonant peak appears, which can locate at very low $I$ depending on the value of $\nu$. 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 $T\ll T_{cr}$, 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 $L^{0}-$convex topology and in particular a characterization for a locally $L^{0}-$convex module to be $L^{0}-$pre$-$barreled. Section 7 gives some basic results on $L^{0}-$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 $L^{\infty}-$type of conditional convex risk measure and every continuous $L^{p}-$type of convex conditional risk measure ($1\leq p<+\infty$) can be extended to an $L^{\infty}_{\cal F}({\cal E})-$type of $\sigma_{\epsilon,\lambda}(L^{\infty}_{\cal F}({\cal E}), L^{1}_{\cal F}({\cal E}))-$lower semicontinuous conditional convex risk measure and an $L^{p}_{\cal F}({\cal E})-$type of ${\cal T}_{\epsilon,\lambda}-$continuous conditional convex risk measure ($1\leq p<+\infty$), 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