Novel applications of BEM based Poisson level set approach

Accurate and efficient computation of the distance function d for a given domain is important for many areas of numerical modeling. Partial differential (e.g. HamiltonJacobi type) equation based distance function algorithms have desirable computational efficiency and accuracy. In this study, as an alternative, a Poisson equation based level set (distance function) is considered and solved using the meshless boundary element method (BEM). The application of this for shape topology analysis, including the medial axis for domain decomposition, geometric de-featuring and other aspects of numerical modeling is assessed. © 2011 Elsevier Ltd. All rights reserved.

No evidence for an item limit in change detection.

Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models"). Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models"). Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color) in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

Elemental estimators for the Generalized Extreme Value tail

In a companion paper (McRobie(2013) arxiv:1304.3918), a simple set of `elemental' estimators was presented for the Generalized Pareto tail parameter. Each elemental estimator: involves only three log-spacings; is absolutely unbiased for all values of the tail parameter; is location- and scale-invariant; and is valid for all sample sizes $N$, even as small as $N= 3$. It was suggested that linear combinations of such elementals could then be used to construct efficient unbiased estimators. In this paper, the analogous mathematical approach is taken to the Generalised Extreme Value (GEV) distribution. The resulting elemental estimators, although not absolutely unbiased, are found to have very small bias, and may thus provide a useful basis for the construction of efficient estimators.

Independence is elusive: set size effects on encoding precision in visual search.

Looking for a target in a visual scene becomes more difficult as the number of stimuli increases. In a signal detection theory view, this is due to the cumulative effect of noise in the encoding of the distractors, and potentially on top of that, to an increase of the noise (i.e., a decrease of precision) per stimulus with set size, reflecting divided attention. It has long been argued that human visual search behavior can be accounted for by the first factor alone. While such an account seems to be adequate for search tasks in which all distractors have the same, known feature value (i.e., are maximally predictable), we recently found a clear effect of set size on encoding precision when distractors are drawn from a uniform distribution (i.e., when they are maximally unpredictable). Here we interpolate between these two extreme cases to examine which of both conclusions holds more generally as distractor statistics are varied. In one experiment, we vary the level of distractor heterogeneity; in another we dissociate distractor homogeneity from predictability. In all conditions in both experiments, we found a strong decrease of precision with increasing set size, suggesting that precision being independent of set size is the exception rather than the rule.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

A fixed-grid b-spline finite element technique for fluid-structure interaction

We present a fixed-grid finite element technique for fluid-structure interaction problems involving incompressible viscous flows and thin structures. The flow equations are discretised with isoparametric b-spline basis functions defined on a logically Cartesian grid. In addition, the previously proposed subdivision-stabilisation technique is used to ensure inf-sup stability. The beam equations are discretised with b-splines and the shell equations with subdivision basis functions, both leading to a rotation-free formulation. The interface conditions between the fluid and the structure are enforced with the Nitsche technique. The resulting coupled system of equations is solved with a Dirichlet-Robin partitioning scheme, and the fluid equations are solved with a pressure-correction method. Auxiliary techniques employed for improving numerical robustness include the level-set based implicit representation of the structure interface on the fluid grid, a cut-cell integration algorithm based on marching tetrahedra and the conservative data transfer between the fluid and structure discretisations. A number of verification and validation examples, primarily motivated by animal locomotion in air or water, demonstrate the robustness and efficiency of our approach. © 2013 John Wiley & Sons, Ltd.

A new fluorescent quantitative PCR-based in vitro neutralization assay for white spot syndrome virus

A fluorescent quantitative PCR (FQ-PCR) assay utilizing SYBR green I dye is described for quantitation of white spot syndrome virus (WSSV) particles isolated from infected crayfish, Cambarus clarkii. For this assay, a primer set was designed which amplifies, with high efficiency and specificity, a 129 bp target sequence within ORF167 of the WSSV genome. Conveniently, WSSV particles can be added into the FQ-PCR assay with a simple and convenient method to release its DNA. To establish the basis for an in vitro neutralization test, primary cultures of shrimp cells were challenged with WSSV that had been incubated with a polyclonal anti-WSSV serum or with control proteins. The number of WSSV particles released from the cells after these treatments were assayed by FQ-PCR. This test may serve as a method to screen monoclonal antibody pools or recombinant antibody pools for neutralizing activity prior to in vivo animal experiments. (c) 2007 Elsevier B.V. All rights reserved.

Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.

Food web of macroinvertebrate community in a Yangtze shallow lake: trophic basis and pathways

No detailed food web research on macroinvertebrate community of lacustrine ecosystem was reported in China. The present study is the first attempt on the subject in Lake Biandantang, a macrophytic lake in Hubei Province. Food webs of the macroinvertebrate community were compiled bimonthly from March, 2002 to March, 2003. Dietary information was obtained from gut analysis. Linkage strength was quantified by combining estimates of energy flow (secondary production) with data of gut analysis. The macroinvertebrate community of Lake Biandantang was based heavily on detritus. Quantitative food webs showed the total ingestion ranged from 6930 to 36,340 mg dry mass m(-2) bimonthly. The ingestion of macroinvertebrate community was higher in the months with optimum temperature than that in other periods with higher or lower temperature. Through comparison, many patterns in benthic food web of Lake Biandantang are consistent with other detritus-based webs, such as stream webs, but different greatly from those based on autochthonous primary production (e.g. pelagic systems). It suggests that the trophic basis of the web is essential in shaping food web structure.

Modelling of settlement induced building damage

This thesis focuses on the modelling of settlement induced damage to masonry buildings. In densely populated areas, the need for new space is nowadays producing a rapid increment of underground excavations. Due to the construction of new metro lines, tunnelling activity in urban areas is growing. One of the consequences is a greater attention to the risk of damage on existing structures. Thus, the assessment of potential damage of surface buildings has become an essential stage in the excavation projects in urban areas (Chapter 1). The current damage risk assessment procedure is based on strong simplifications, which not always lead to conservative results. Object of this thesis is the development of an improved damage classification system, which takes into account the parameters influencing the structural response to settlement, like the non-linear behaviour of masonry and the soil-structure interaction. The methodology used in this research is based on experimental and numerical modelling. The design and execution of an experimental benchmark test representative of the problem allows to identify the principal factors and mechanisms involved. The numerical simulations enable to generalize the results to a broader range of physical scenarios. The methodological choice is based on a critical review of the currently available procedures for the assessment of settlement-induced building damage (Chapter 2). A new experimental test on a 1/10th masonry façade with a rubber base interface is specifically designed to investigate the effect of soil-structure interaction on the tunnelling-induced damage (Chapter 3). The experimental results are used to validate a 2D semi-coupled finite element model for the simulation of the structural response (Chapter 4). The numerical approach, which includes a continuum cracking model for the masonry and a non-linear interface to simulate the soil-structure interaction, is then used to perform a sensitivity study on the effect of openings, material properties, initial damage, initial conditions, normal and shear behaviour of the base interface and applied settlement profile (Chapter 5). The results assess quantitatively the major role played by the normal stiffness of the soil-structure interaction and by the material parameters defining the quasi-brittle masonry behaviour. The limitation of the 2D modelling approach in simulating the progressive 3D displacement field induced by the excavation and the consequent torsional response of the building are overcome by the development of a 3D coupled model of building, foundation, soil and tunnel (Chapter 6). Following the same method applied to the 2D semi-coupled approach, the 3D model is validated through comparison with the monitoring data of a literature case study. The model is then used to carry out a series of parametric analyses on geometrical factors: the aspect ratio of horizontal building dimensions with respect to the tunnel axis direction, the presence of adjacent structures and the position and alignment of the building with respect to the excavation (Chapter 7). The results show the governing effect of the 3D building response, proving the relevance of 3D modelling. Finally, the results from the 2D and 3D parametric analyses are used to set the framework of an overall damage model which correlates the analysed structural features with the risk for the building of being damaged by a certain settlement (Chapter 8). This research therefore provides an increased experimental and numerical understanding of the building response to excavation-induced settlements, and sets the basis for an operational tool for the risk assessment of structural damage (Chapter 9).

Molecular basis of transferrin polymorphism in goldfish (Carassius auratus)

Transferrin (TF) polymorphism was investigated in a color variety of goldfish (Carassius auratus), and its molecular basis analyzed. Three TF variants (A(1), A(2) and B-1) were identified from an inbred strain of the goldfish, of which A(1) and B-1 displayed a large electrophoretic difference on both native and SDS-PAGE gels. The TF cDNAs corresponding to variants A(1) and B-1 were cloned and sequenced from A(1)A(1), A(1)B(1) and B1B1 individuals, and their deduced amino acid sequences were analyzed. Substantial amino acid variation occurred between variants A(1) and B-1, with significant differences in peptide length, theoretical molecular weight (Mw) and isoelectric point (pI). No potential glycosylation sites were observed in the two amino acid sequences, which excluded the possibility that carbohydrate difference might cause electrophoretic variation among the TF variants. Further analysis suggested that the distinct electrophoretic mobility of the two variants A(1) and B-1 by SDS-PAGE resulted from their Mw difference, while the difference by the native PAGE could be explained by their pI variation. Furthermore, genomic DNA fragments containing the transferrin alleles were amplified and subjected to RFLP analysis in A(1)A(1), A(1)B(1) and B1B1 individuals. The data revealed characteristic banding patterns for each TF genotype, and demonstrated that the TF alleles A(1) and B-1 could be used as a co-dominant marker system. The initial work relating to the goldfish TF variants will benefit the understanding of the evolutionary and functional significance of TF polymorphism in fish.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.