Single kernel NIR assessment for QTL identification

Development and evaluation of a single kernel NIR assessment method for improving baley malting quality QTL identification.

Aspects of atomic decompositions and Bergman projections

The concept of an atomic decomposition was introduced by Coifman and Rochberg (1980) for weighted Bergman spaces on the unit disk. By the Riemann mapping theorem, functions in every simply connected domain in the complex plane have an atomic decomposition. However, a decomposition resulting from a conformal mapping of the unit disk tends to be very implicit and often lacks a clear connection to the geometry of the domain that it has been mapped into. The lattice of points, where the atoms of the decomposition are evaluated, usually follows the geometry of the original domain, but after mapping the domain into another this connection is easily lost and the layout of points becomes seemingly random. In the first article we construct an atomic decomposition directly on a weighted Bergman space on a class of regulated, simply connected domains. The construction uses the geometric properties of the regulated domain, but does not explicitly involve any conformal Riemann map from the unit disk. It is known that the Bergman projection is not bounded on the space L-infinity of bounded measurable functions. Taskinen (2004) introduced the locally convex spaces LV-infinity consisting of measurable and HV-infinity of analytic functions on the unit disk with the latter being a closed subspace of the former. They have the property that the Bergman projection is continuous from LV-infinity onto HV-infinity and, in some sense, the space HV-infinity is the smallest possible substitute to the space H-infinity of analytic functions. In the second article we extend the above result to a smoothly bounded strictly pseudoconvex domain. Here the related reproducing kernels are usually not known explicitly, and thus the proof of continuity of the Bergman projection is based on generalised Forelli-Rudin estimates instead of integral representations. The minimality of the space LV-infinity is shown by using peaking functions first constructed by Bell (1981). Taskinen (2003) showed that on the unit disk the space HV-infinity admits an atomic decomposition. This result is generalised in the third article by constructing an atomic decomposition for the space HV-infinity on a smoothly bounded strictly pseudoconvex domain. In this case every function can be presented as a linear combination of atoms such that the coefficient sequence belongs to a suitable Köthe co-echelon space.

Composition operators, Aleksandrov measures and value distribution of analytic maps in the unit disc

A composition operator is a linear operator that precomposes any given function with another function, which is held fixed and called the symbol of the composition operator. This dissertation studies such operators and questions related to their theory in the case when the functions to be composed are analytic in the unit disc of the complex plane. Thus the subject of the dissertation lies at the intersection of analytic function theory and operator theory. The work contains three research articles. The first article is concerned with the value distribution of analytic functions. In the literature there are two different conditions which characterize when a composition operator is compact on the Hardy spaces of the unit disc. One condition is in terms of the classical Nevanlinna counting function, defined inside the disc, and the other condition involves a family of certain measures called the Aleksandrov (or Clark) measures and supported on the boundary of the disc. The article explains the connection between these two approaches from a function-theoretic point of view. It is shown that the Aleksandrov measures can be interpreted as kinds of boundary limits of the Nevanlinna counting function as one approaches the boundary from within the disc. The other two articles investigate the compactness properties of the difference of two composition operators, which is beneficial for understanding the structure of the set of all composition operators. The second article considers this question on the Hardy and related spaces of the disc, and employs Aleksandrov measures as its main tool. The results obtained generalize those existing for the case of a single composition operator. However, there are some peculiarities which do not occur in the theory of a single operator. The third article studies the compactness of the difference operator on the Bloch and Lipschitz spaces, improving and extending results given in the previous literature. Moreover, in this connection one obtains a general result which characterizes the compactness and weak compactness of the difference of two weighted composition operators on certain weighted Hardy-type spaces.

Probiotic Bacillus amyloliquefaciens Strain H57 Improves the Performance of Pregnant and Lactating Ewes Fed a Diet Based on Palm Kernel Meal

Probiotic supplements are single or mixed strain cultures of live microorganisms that benefit the host by improving the properties of the indigenous microflora (Seo et al 2010). In a pilot study at the University of Queensland, Norton et al (2008) found that Bacillus amyloliquefaciens Strain H57 (H57), primarily investigated as an inoculum to make high-quality hay, improved feed intake and nitrogen utilisation over several weeks in pregnant ewes. The purpose of the following study was to further challenge the potential of H57 -to show it survives the steam-pelleting process, and that it improves the performance of ewes fed pellets based on an agro-industrial by-product with a reputation for poor palatability, palm kernel meal (PKM), (McNeill 2013). Thirty-two first-parity White Dorper ewes (day 37 of pregnancy, mean liveweight = 47.3 kg, mean age = 15 months) were inducted into individual pens in the animal house at the University of Queensland, Gatton. They were adjusted onto PKM-based pellets (g/kg drymatter (DM): PKM, 408; sorghum, 430; chick pea hulls, 103; minerals and vitamins; Crude protein, 128; ME: 11.1MJ/kg DM) until day 89 of pregnancy and thereafter fed a predominately pelleted diet incorporating with or without H57 spores (10 9 colony forming units (cfu)/kg pellet, as fed), plus 100g/ewe/day oaten chaff, until day 7 of lactation. From day 7 to 20 of lactation the pelleted component of the diet was steadily reduced to be replaced by a 50:50 mix of lucerne: oaten chaff, fed ad libitum, plus 100g/ewe/day of ground sorghum grain with or without H57 (10 9 cfu/ewe/day). The period of adjustment in pregnancy (day 37-89) extended beyond expectations due to some evidence of mild ruminal acidosis after some initially high intakes that were followed by low intakes. During that time the diet was modified, in an attempt to improve palatability, by the addition of oaten chaff and the removal of an acidifying agent (NH4Cl) that was added initially to reduce the risk of urinary calculi. Eight ewes were removed due to inappetence, leaving 24 ewes to start the trial at day 90 of pregnancy. From day 90 of pregnancy until day 63 of lactation, liveweights of the ewes and their lambs were determined weekly and at parturition. Feed intakes of the ewes were determined weekly. Once lambing began, 1 ewe was removed as it gave birth to twin lambs (whereas the rest gave birth to a single lamb), 4 due to the loss of their lambs (2 to dystocia), and 1 due to copper toxicity. The PKM pellets were suspected to be the cause of the copper toxicity and so were removed in early lactation. Hence, the final statistical analysis using STATISTICA 8 (Repeated measures ANOVA for feed intake, One-way ANOVA for liveweight change and birth weight) was completed on 23 ewes for the pregnancy period (n = 11 fed H57; n = 12 control), and 18 ewes or lambs for the lactation period (n = 8 fed H57; n = 10 control). From day 90 of pregnancy until parturition the H57 supplemented ewes ate 17 more DM (g/day: 1041 vs 889, sed = 42.4, P = 0.04) and gained more liveweight (g/day: 193 vs 24.0, sed = 25.4, P = 0.0002), but produced lambs with a similar birthweight (kg: 4.18 vs 3.99, sed = 0.19, P = 0.54). Over the 63 days of lactation the H57 ewes ate similar amounts of DM but grew slower than the control ewes (g/day: 1.5 vs 97.0, sed = 21.7, P = 0.012). The lambs of the H57 ewes grew faster than those of the control ewes for the first 21 days of lactation (g/day: 356 vs 265, sed = 16.5, P = 0.006). These data support the findings of Norton et al (2008) and Kritas et al (2006) that certain Bacillus spp. supplements can improve the performance of pregnant and lactating ewes. In the current study we particularly highlighted the capacity of H57 to stimulate immature ewes to continue to grow maternal tissue through pregnancy, possibly through an enhanced appetite, which appeared then to stimulate a greater capacity to partition nutrients to their lambs through milk, at least for the first few weeks of lactation, a critical time for optimising lamb survival. To conclude, H57 can survive the steam pelleting process to improve feed intake and maternal liveweight gain in late pregnancy, and performance in early lactation, of first-parity ewes fed a diet based on PKM.

Social processes of health and physical education teachers’ identity formation: Reproducing and changing culture

This paper examines Initial Teacher Education students’ experiences of participation in health and physical education (HPE) subject department offices and the impact on their understandings and identity formation. Pierre Bourdieu’s concepts of habitus, field, and practice along with Wenger’s communities of practice form the theoretical frame used in the paper. Data were collected using surveys and interviews with student‐teachers following their teaching practicum and analysed using coding and constant comparison. Emergent themes revealed students’ participation in masculine‐dominated sports, gendered body constructions, and repertoires of masculine domination. Findings are discussed in relation to their impact on student‐teachers’ learning, identity formation, and marginalizing practices in the department offices. Implications for teacher education and HPE are explored.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

On the growth of the Bergman kernel near an infinite-type point

We study diagonal estimates for the Bergman kernels of certain model domains in C-2 near boundary points that are of infinite type. To do so, we need a mild structural condition on the defining functions of interest that facilitates optimal upper and lower bounds. This is a mild condition; unlike earlier studies of this sort, we are able to make estimates for non-convex pseudoconvex domains as well. Thisn condition quantifies, in some sense, how flat a domain is at an infinite-type boundary point. In this scheme of quantification, the model domains considered below range-roughly speaking-from being mildly infinite-type'' to very flat at the infinite-type points.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

THE BERGMAN KERNEL FUNCTION

In this note, we point out that a large family of n x n matrix valued kernel functions defined on the unit disc D subset of C, which were constructed recently in [9], behave like the familiar Bergman kernel function on ID in several different ways. We show that a number of questions involving the multiplication operator on the corresponding Hilbert space of holomorphic functions on D can be answered using this likeness.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Observation of a power-law memory kernel for fluctuations within a single protein molecule

The fluctuation of the distance between a fluorescein-tyrosine pair within a single protein complex was directly monitored in real time by photoinduced electron transfer and found to be a stationary, time-reversible, and non-Markovian Gaussian process. Within the generalized Langevin equation formalism, we experimentally determine the memory kernel K(t), which is proportional to the autocorrelation function of the random fluctuating force. K(t) is a power-law decay, t(-0.51 +/- 0.07) in a broad range of time scales (10(-3)-10 s). Such a long-time memory effect could have implications for protein functions.

The heat kernel on AdS(3) and its applications

We derive the heat kernel for arbitrary tensor fields on S-3 and (Euclidean) AdS(3) using a group theoretic approach. We use these results to also obtain the heat kernel on certain quotients of these spaces. In particular, we give a simple, explicit expression for the one loop determinant for a field of arbitrary spin s in thermal AdS(3). We apply this to the calculation of the one loop partition function of N = 1 supergravity on AdS(3). We find that the answer factorizes into left- and right-moving super Virasoro characters built on the SL(2, C) invariant vacuum, as argued by Maloney and Witten on general grounds.

Solution concepts in continuous-kernel multicriteria games

This paper considers nonzero-sum multicriteria games with continuous kernels. Solution concepts based on the notions of Pareto optimality, equilibrium, and security are extended to these games. Separate necessary and sufficient conditions and existence results are presented for equilibrium, Pareto-optimal response, and Pareto-optimal security strategies of the players.

Properties of Toeplitz operators on analytic function spaces : from function symbols to distributions

Toeplitz operators are among the most important classes of concrete operators with applications to several branches of pure and applied mathematics. This doctoral thesis deals with Toeplitz operators on analytic Bergman, Bloch and Fock spaces. Usually, a Toeplitz operator is a composition of multiplication by a function and a suitable projection. The present work deals with generalizing the notion to the case where the function is replaced by a distributional symbol. Fredholm theory for Toeplitz operators with matrix-valued symbols is also considered. The subject of this thesis belongs to the areas of complex analysis, functional analysis and operator theory. This work contains five research articles. The articles one, three and four deal with finding suitable distributional classes in Bergman, Fock and Bloch spaces, respectively. In each case the symbol class to be considered turns out to be a certain weighted Sobolev-type space of distributions. The Bergman space setting is the most straightforward. When dealing with Fock spaces, some difficulties arise due to unboundedness of the complex plane and the properties of the Gaussian measure in the definition. In the Bloch-type spaces an additional logarithmic weight must be introduced. Sufficient conditions for boundedness and compactness are derived. The article two contains a portion showing that under additional assumptions, the condition for Bergman spaces is also necessary. The fifth article deals with Fredholm theory for Toeplitz operators having matrix-valued symbols. The essential spectra and index theorems are obtained with the help of Hardy space factorization and the Berezin transform, for instance. The article two also has a part dealing with matrix-valued symbols in a non-reflexive Bergman space, in which case a condition on the oscillation of the symbol (a logarithmic VMO-condition) must be added.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.