Complex-k modes of plasmonic chain waveguides

Nanoparticle chain waveguide based on negative-epsilon material is investigated through a generic 3D finite-element Bloch-mode solver which derives complex propagation constant ($k$). Our study starts from waveguides made of non-dispersive material, which not only singles out "waveguide dispersion" but also motivates search of new materials to achieve guidance at unconventional wavelengths. Performances of gold or silver chain waveguides are then evaluated; a concise comparison of these two types of chain waveguides has been previously missing. Beyond these singly-plasmonic chain waveguides, we examine a hetero-plasmonic chain system with interlacing gold and silver particles, inspired by a recent proposal; the claimed enhanced energy transfer between gold particles appears to be a one-sided view of its hybridized waveguiding behavior --- energy transfer between silver particles worsens. Enabled by the versatile numerical method, we also discuss effects of inter-particle spacing, background medium, and presence of a substrate. Our extensive analyses show that the general route for reducing propagation loss of e.g. a gold chain waveguide is to lower chain-mode frequency with a proper geometry (e.g. smaller particle spacing) and background material setting (e.g. high-permittivity background or even foreign nanoparticles). In addition, the possibility of building mid-infrared chain waveguides using doped silicon is commented based on numerical simulation.Comment: 26 pages, many figures, now including "Supplementary Data". Accepted, Journal of Physics Communicatio

Counting permutations by alternating descents

We find the exponential generating function for permutations with all valleys even and all peaks odd, and use it to determine the asymptotics for its coefficients, answering a question posed by Liviu Nicolaescu. The generating function can be expressed as the reciprocal of a sum involving Euler numbers. We give two proofs of the formula. The first uses a system of differential equations. The second proof derives the generating function directly from general permutation enumeration techniques, using noncommutative symmetric functions. The generating function is an "alternating" analogue of David and Barton's generating function for permutations with no increasing runs of length 3 or more. Our general results give further alternating analogues of permutation enumeration formulas, including results of Chebikin and Remmel

Combined large-N_c and heavy-quark operator analysis for the chiral Lagrangian with charmed baryons

The chiral $SU(3)$ Lagrangian with charmed baryons of spin $J^P=1/2^+$ and $J^P=3/2^+$ is analyzed. We consider all counter terms that are relevant at next-to-next-to-next-to-leading order (N$^3$LO) in a chiral extrapolation of the charmed baryon masses. At N$^2$LO we find 16 low-energy parameters. There are 3 mass parameters for the anti-triplet and the two sextet baryons, 6 parameters describing the meson-baryon vertices and 7 symmetry breaking parameters. The heavy-quark spin symmetry predicts four sum rules for the meson-baryon vertices and degenerate masses for the two baryon sextet fields. Here a large-$N_c$ operator analysis at NLO suggests the relevance of one further spin-symmetry breaking parameter. Going from N$^2$LO to N$^3$LO adds 17 chiral symmetry breaking parameters and 24 symmetry preserving parameters. For the leading symmetry conserving two-body counter terms involving two baryon fields and two Goldstone boson fields we find 36 terms. While the heavy-quark spin symmetry leads to $36-16=20$ sum rules, an expansion in $1/N_c$ at next-to-leading order (NLO) generates $36-7= 29$ parameter relations. A combined expansion leaves 3 unknown parameters only. For the symmetry breaking counter terms we find 17 terms, for which there are $17-9=8$ sum rules from the heavy-quark spin symmetry and $17-5=12$ sum rules from a $1/N_c$ expansion at NLO.Comment: 34 pages - one table - corrections applie

Growth mechanism of nanostructured superparamagnetic rods obtained by electrostatic co-assembly

We report on the growth of nanostructured rods fabricated by electrostatic co-assembly between iron oxide nanoparticles and polymers. The nanoparticles put under scrutiny, {\gamma}-Fe2O3 or maghemite, have diameter of 6.7 nm and 8.3 nm and narrow polydispersity. The co-assembly is driven by i) the electrostatic interactions between the polymers and the particles, and by ii) the presence of an externally applied magnetic field. The rods are characterized by large anisotropy factors, with diameter 200 nm and length comprised between 1 and 100 {\mu}m. In the present work, we provide for the first time the morphology diagram for the rods as a function of ionic strength and concentration. We show the existence of a critical nanoparticle concentration and of a critical ionic strength beyond which the rods do not form. In the intermediate regimes, only tortuous and branched aggregates are detected. At higher concentrations and lower ionic strengths, linear and stiff rods with superparamagnetic properties are produced. Based on these data, a mechanism for the rod formation is proposed. The mechanism proceeds in two steps : the formation and growth of spherical clusters of particles, and the alignment of the clusters induced by the magnetic dipolar interactions. As far as the kinetics of these processes is concerned, the clusters growth and their alignment occur concomitantly, leading to a continuous accretion of particles or small clusters, and a welding of the rodlike structure.Comment: 15 pages, 10 figures, one tabl

Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is not fully exploited. In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem. A novel unsupervised learning method and two inter-sample CNN loss functions are presented. Distinct from previous work, UBR builds a coordinate system for the human body and outputs a continuous score for each axial slice, representing the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled CT volumes that are abundant, thus no extra annotation effort is needed. UBR is simple, fast, and accurate. Quantitative and qualitative experiments validate its effectiveness. In addition, we show two applications of UBR in network initialization and anomaly detection.Comment: Oral presentation in ISBI1