Multimode radiation from an unflanged, semi-infinite circular duct with uniform flow.

Multimode sound radiation from an unflanged, semi-infinite, rigid-walled circular duct with uniform subsonic mean flow everywhere is investigated theoretically. The multimode directivity depends on the amplitude and directivity function of each individual cut-on mode. The amplitude of each mode is expressed as a function of cut-on ratio for a uniform distribution of incoherent monopoles, a uniform distribution of incoherent axial dipoles, and for equal power per mode. The directivity function of each mode is obtained by applying a Lorentz transformation to the zero-flow directivity function, which is given by a Wiener-Hopf solution. This exact numerical result is compared to an analytic solution, valid in the high-frequency limit, for multimode directivity with uniform flow. The high-frequency asymptotic solution is derived assuming total transmission of power at the open end of the duct, and gives the multimode directivity function with flow in the forward arc for a general family of mode amplitude distribution functions. At high frequencies the agreement between the exact and asymptotic solutions is shown to be excellent.

The infinite HMM for unsupervised PoS tagging

We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing the number of hidden states in unsupervised Markov models for PoS tagging. We experiment with two non-parametric priors, the Dirichlet and Pitman-Yor processes, on the Wall Street Journal dataset using a parallelized implementation of an iHMM inference algorithm. We evaluate the results with a variety of clustering evaluation metrics and achieve equivalent or better performances than previously reported. Building on this promising result we evaluate the output of the unsupervised PoS tagger as a direct replacement for the output of a fully supervised PoS tagger for the task of shallow parsing and compare the two evaluations. © 2009 ACL and AFNLP.

Branching toughens fibrous networks.

Fibrous collagenous networks are not only stiff but also tough, due to their complex microstructures. This stiff yet tough behavior is desirable for both medical and military applications but it is difficult to reproduce in engineering materials. While the nonlinear hyperelastic behavior of fibrous networks has been extensively studied, the understanding of toughness is still incomplete. Here, we identify a microstructure mimicking the branched bundles of a natural type I collagen network, in which partially cross-linked long fibers give rise to novel combinations of stiffness and toughness. Finite element analysis shows that the stiffness of fully cross-linked fibrous networks is amplified by increasing the fibril length and cross-link density. However, a trade-off of such stiff networks is reduced toughness. By having partially cross-linked networks with long fibrils, the networks have comparable stiffness and improved toughness as compared to the fully cross-linked networks. Further, the partially cross-linked networks avoid the formation of kinks, which cause fibril rupture during deformation. As a result, the branching allows the networks to have stiff yet tough behavior.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Local stability results for the collective behaviors of infinite populations of pulse-coupled oscillators

In this paper, we investigate the behavior of pulse-coupled integrate-and-fire oscillators. Because the stability analysis of finite populations is intricate, we investigate stability results in the approximation of infinite populations. In addition to recovering known stability results of finite populations, we also obtain new stability results for infinite populations. In particular, under a weak coupling assumption, we solve for the continuum model a conjecture still prevailing in the finite dimensional case. © 2011 IEEE.

Comparison among MCNP-based depletion codes applied to burnup calculations of pebble-bed HTR lattices

The double-heterogeneity characterising pebble-bed high temperature reactors (HTRs) makes Monte Carlo based calculation tools the most suitable for detailed core analyses. These codes can be successfully used to predict the isotopic evolution during irradiation of the fuel of this kind of cores. At the moment, there are many computational systems based on MCNP that are available for performing depletion calculation. All these systems use MCNP to supply problem dependent fluxes and/or microscopic cross sections to the depletion module. This latter then calculates the isotopic evolution of the fuel resolving Bateman's equations. In this paper, a comparative analysis of three different MCNP-based depletion codes is performed: Montburns2.0, MCNPX2.6.0 and BGCore. Monteburns code can be considered as the reference code for HTR calculations, since it has been already verified during HTR-N and HTR-N1 EU project. All calculations have been performed on a reference model representing an infinite lattice of thorium-plutonium fuelled pebbles. The evolution of k-inf as a function of burnup has been compared, as well as the inventory of the important actinides. The k-inf comparison among the codes shows a good agreement during the entire burnup history with the maximum difference lower than 1%. The actinide inventory prediction agrees well. However significant discrepancy in Am and Cm concentrations calculated by MCNPX as compared to those of Monteburns and BGCore has been observed. This is mainly due to different Am-241 (n,γ) branching ratio utilized by the codes. The important advantage of BGCore is its significantly lower execution time required to perform considered depletion calculations. While providing reasonably accurate results BGCore runs depletion problem about two times faster than Monteburns and two to five times faster than MCNPX. © 2009 Elsevier B.V. All rights reserved.

Effect of95 241Am(n, γ) reaction branching ratio on fuel cycle and reactor design characteristics

The growing interest in innovative reactors and advanced fuel cycle designs requires more accurate prediction of various transuranic actinide concentrations during irradiation or following discharge because of their effect on reactivity or spent-fuel emissions, such as gamma and neutron activity and decay heat. In this respect, many of the important actinides originate from the 241Am(n,γ) reaction, which leads to either the ground or the metastable state of 242Am. The branching ratio for this reaction depends on the incident neutron energy and has very large uncertainty in the current evaluated nuclear data files. This study examines the effect of accounting for the energy dependence of the 241Am(n,γ) reaction branching ratio calculated from different evaluated data files for different reactor and fuel types on the reactivity and concentrations of some important actinides. The results of the study confirm that the uncertainty in knowing the 241Am(n,γ) reaction branching ratio has a negligible effect on the characteristics of conventional light water reactor fuel. However, in advanced reactors with large loadings of actinides in general, and 241Am in particular, the branching ratio data calculated from the different data files may lead to significant differences in the prediction of the fuel criticality and isotopic composition. Moreover, it was found that neutron energy spectrum weighting of the branching ratio in each analyzed case is particularly important and may result in up to a factor of 2 difference in the branching ratio value. Currently, most of the neutronic codes have a single branching ratio value in their data libraries, which is sometimes difficult or impossible to update in accordance with the neutron spectrum shape for the analyzed system.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

A reversible infinite HMM using normalised random measures

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

Wave dispersion and power flow analysis of an infinite aerofoil-shaped beam

Classic flutter analysis models an aerofoil as a two degree-of-freedom rigid body supported by linear and torsional springs, which represent the bending and torsional stiffness of the aerofoil section. In this classic flutter model, no energy transfer or dissipation can occur in the span-wise direction of the aerofoil section. However, as the aspect ratio of an aerofoil section increases, this span-wise energy transfer - in the form of travelling waves - becomes important to the overall system dynamics. This paper extends the classic flutter model to include travelling waves in the span-wise direction. Namely, wave dispersion and power flow analysis of an infinite, aerofoil-shaped beam, subject to bending, torsion, tension and a constant wind excitation, is used to investigate the overall system stability. Examples of potential applications for these high aspect ratio aerofoil sections include high-altitude balloon tethers, towed cables, offshore risers and mooring lines.