Renormalization Group Theory for a Perturbed KdV Equation

We show that renormalization group(RG) theory can be used to give an analytic description of the evolution of a perturbed KdV equation. The equations describing the deformation of its shape as the effect of perturbation are RG equations. The RG approach may be simpler than inverse scattering theory(IST) and another approaches, because it dose not rely on any knowledge of IST and it is very concise and easy to understand. To the best of our knowledge, this is the first time that RG has been used in this way for the perturbed soliton dynamics.Comment: 4 pages, no figure, revte

Annealing schedule from population dynamics

We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a value maximizing expected rewards at each time step. Thereby, the mutation rate is eliminated as a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.

Strain-induced partially flat band, helical snake states, and interface superconductivity in topological crystalline insulators

Topological crystalline insulators in IV-VI compounds host novel topological surface states consisting of multi-valley massless Dirac fermions at low energy. Here we show that strain generically acts as an effective gauge field on these Dirac fermions and creates pseudo-Landau orbitals without breaking time-reversal symmetry. We predict the realization of this phenomenon in IV-VI semiconductor heterostructures, due to a naturally occurring misfit dislocation array at the interface that produces a periodically varying strain field. Remarkably, the zero-energy Landau orbitals form a flat band in the vicinity of the Dirac point, and coexist with a network of snake states at higher energy. We propose that the high density of states of this flat band gives rise to interface superconductivity observed in IV-VI semiconductor multilayers at unusually high temperatures, with non-BCS behavior. Our work demonstrates a new route to altering macroscopic electronic properties to achieve a partially flat band, and paves the way for realizing novel correlated states of matter.Comment: Accepted by Nature Physic

Genioglossal muscle response to CO2 stimulation during NREM sleep

STUDY OBJECTIVES: The objective was to evaluate the responsiveness of upper airway muscles to hypercapnia with and without intrapharyngeal negative pressure during non-rapid eye movement (NREM) sleep and wakefulness. DESIGN: We assessed the genioglossal muscle response to CO2 off and on continuous positive airway pressure (CPAP) (to attenuate negative pressure) during stable NREM sleep and wakefulness in the supine position. SETTING: Laboratory of the Sleep Medicine Division, Brigham and Women's Hospital. PATIENTS OR PARTICIPANTS: Eleven normal healthy subjects. INTERVENTIONS: During wakefulness and NREM sleep, we measured genioglossal electromyography (EMG) on and off CPAP at the normal eupneic level and at levels 5 and 10 mm Hg above the awake eupneic level. MEASUREMENTS AND RESULTS: We observed that CO2 could increase upper-airway muscle activity during NREM sleep and wakefulness in the supine position with and without intrapharyngeal negative pressure. The application of nasal CPAP significantly decreased genioglossal EMG at all 3 levels of PETCO2 during NREM sleep (13.0 +/- 4.9% vs. 4.6 +/- 1.6% of maximal EMG, 14.6 +/- 5.6% vs. 7.1 +/- 2.3% of maximal EMG, and 17.3 +/- 6.3% vs. 10.2 +/- 3.1% of maximal EMG, respectively). However, the absence of negative pressure in the upper airway did not significantly affect the slope of the pharyngeal airway dilator muscle response to hypercapnia during NREM sleep (0.72 +/- 0.30% vs. 0.79 +/- 0.27% of maximal EMG per mm Hg PCO2, respectively, off and on CPAP). CONCLUSIONS: We conclude that both chemoreceptive and negative pressure reflex inputs to this upper airway dilator muscle are still active during stable NREM sleep

Dynamics of Flux Creep in Underdoped Single Crystals of Y_1-xPr_xBa_2Cu_3O_7-d

Transport as well as magnetic relaxation properties of the mixed state were studied on strongly underdoped Y_1-xPr_xBa_2Cu_3O_7-d crystals. We observed two correlated phenomena - a coupling transition and a transition to quantum creep. The distribution of transport current below the coupling transition is highly nonuniform, which facilitates quantum creep. We speculate that in the mixed state below the coupling transition, where dissipation is nonohmic, the current distribution may be unstable with respect to self-channeling resulting in the formation of very thin current-carrying layers.Comment: 11 pages, 9 figures, Submitted to Phys. Rev.

Absence of a Zero Temperature Vortex Solid Phase in Strongly Disordered Superconducting Bi Films

We present low temperature measurements of the resistance in magnetic field of superconducting ultrathin amorphous Bi films with normal state sheet resistances, $R_N$, near the resistance quantum, $R_Q={\hbar\over {e^2}}$. For $R_N<R_Q$, the tails of the resistive transitions show the thermally activated flux flow signature characteristic of defect motion in a vortex solid with a finite correlation length. When $R_N$ exceeds $R_Q$, the tails become non-activated. We conclude that in films where $R_N>R_Q$ there is no vortex solid and, hence, no zero resistance state in magnetic field. We describe how disorder induced quantum and/or mesoscopic fluctuations can eliminate the vortex solid and also discuss implications for the magnetic-field-tuned superconductor-insulator transition.Comment: REVTEX, 4 pages, 3 figure

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor