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First Kyushu-UNSW Joint Workshop on the Mathematics underpinning Industry and Innovation

18th November 2016 (Friday), 11:00 - 17:30 conf

First Kyushu-UNSW Joint Workshop on the Mathematics underpinning Industry and Innovation

Date and Time: 18th November 2016 (Friday), 11:00 - 17:30
Venue: Red Centre 4082, Kensington Campus, University of New South Wales

John Roberts (School of Mathematics and Statistics, University of New South Wales)
Kenji Kajiwara (Institute of Mathematics for Industry, Kyushu University)

Speakers from Kyushu:

(1) Yasuhide Fukumoto (Institute of Mathematics for Industry, Kyushu University)
Title:  Topological ideas in magnetohydrodynamics (MHD) and its application to spectra and stability of MHD rotating flows
There are a few topological invariants associated with the ideal
magnetohydrodynamics (MHD), namely, dynamics of an electrically
conducting fluid subject to a magnetic field. We expose the
iso-magnetovortical structure lying behind these invariants. A steady
flow of an ideal incompressible MHD is characterized as an extremum of
the total energy (=the Hamiltonian) with respect to perturbations
constrained to an iso-magnetovortical sheet. We exploit this structure
to calculate the spectra and stability of MHD rotating flows.

In galaxies, stars are formed at the center of accretion disks by absorbing
matters. Since the total angular momentum is conserved, the angular
momentum must be transported away while the mass is absorbed to the
center. The magnetorotational instability (MRI) is a desired mechanism
for triggering turbulence necessary to account for outwards transport of
the angular momentum. We develop a Lagrangian approach, combined with
the WKB analysis, to short-wave instability of axisymmetric rotating MHD
flows subjected to azimuthal magnetic field.  An attempt is also made
to incorporate the effect of the viscosity and the electric resistivity.

This work is a collaboration with Rong Zou.

(2) Kei Hirose (Institute of Mathematics for Industry, Kyushu University)
Title: MM algorithm for high-dimensional robust graphical modeling
We introduce a robust sparse Gaussian graphical modeling.  The robust
estimation is realized by the gamma-divergence (Fujisawa and Eguchi,
2008, JMVA).  The parameter estimation procedure is constructed using
the Majorize-Minimization (MM) algorithm, which guarantees that the
objective function monotonically decreases at each iteration.

(3) Kenji Kajiwara (Institute of Mathematics for Industry, Kyushu University)
Title: Integrable Deformations of Plane/Space Curves
It is well-known that classical differential geometry is one of the
sources of integrable systems which dates back to 19th century, as seen
in the classical works of Bäcklund and Darboux on the construction of
transformations of surfaces.  Dynamics of space/plane curves is another
interesting interface between integrable systems and differential
geometry, which was initiated by the pioneering work of Hasimoto
followed by Lamb in 70s. It is an intriguing problem to formulate
discretized theories preserving underlying relationship with integrable

In this talk, we start from some historical remarks and
basic ideas of integrable systems and their discretization.  We next
explain how the hierarchies of integrable systems naturally arise in the
context of deformation theory of plane/space curves. We then present a
discrete model of vortex filaments in 3D fluid, which is a discrete
analogue of the space curve deformation driven by the binormal flow
described by the nonlinear Schrödinger equation.

(4) Takashi Okayasu (Faculty of Agriculture, Kyushu University)
Title: Measurement and Visualization of Agricultural Information by Using ICT Information
Communication Technology (ICT) can be applied to improve agricultural production in
terms of advancing the knowledge and techniques of farming, reducing
production costs, and improving the quality of agricultural produce.
Various ICT systems to support and improve agricultural practices have
been developed, focusing on environmental measurement and control, plant
growth and motion estimation, and farm work recording. The development
of several ICT systems to support small- and medium-scale farms in Japan
using affordable smart devices such as low-price microcomputers and
sensors, and open-source software, and its evaluation results for their
feasibility will be introduced.

(5) Trinh Khanh Duy (Institute of Mathematics for Industry, Kyushu University)
Title: Global asymptotic behaviours of Gaussian beta ensembles
Gaussian beta ensembles, as generalizations of Gaussian
Orthogonal/Unitary/Symplectic Ensembles (GOE, GUE and GSE), were
initially defined in terms of joint distribution of eigenvalues. They
are now realized as eigenvalues of certain random symmetric tridiagonal
matrices, also called Jacobi matrices, with independent entries
distributed according to specific distributions. This talk introduces
recent developments in spectra statistics of the ensembles, with
emphasis on a new interpretation of classical Wigner's semi-circle law.

(6) Yoshihiro Yamanishi (Medical Institute of Bioregulation, Kyushu University)
Title: Statistical machine learning for drug discovery
In this study, we develop a new statistical machine learning method for
drug discovery based on various biomedical big data of genes, proteins,
and diseases. The originality lies in the kernel-based distance learning
algorithm in a framework of supervised network inference. The proposed
method enables us to efficiently find drug candidate compounds for a
wide range of diseases.

Speakers from UNSW:

(1)  Dr Quoc Le Gia (School of Mathematics and Statistics, University of New South Wales)
Title: Bayesian estimations in partial differential equations with random coefficients
Bayesian estimations of solutions to parametric operator equations arise in
numerical uncertainty quantification of operator equations with
distributed uncertain inputs, such as uncertain coefficients, uncertain
domains or uncertain source terms and boundary data.

We propose and analyze deterministic multilevel approximations for Bayesian
estimations of operator equations with uncertain distributed parameters,
subject to additive Gaussian measurement data. The algorithms use a
multilevel approach based on deterministic, higher order quasi-Monte
Carlo quadrature for approximating the high-dimensional expectations,
which arise in the Bayesian estimators, and a Petrov-Galerkin method for
approximating the solution to the underlying partial differential

This is a joint work with Josef Dick (UNSW) and Robert Gantner and Christoph Schwab (ETH)

(2) Takehito Yoshiki (School of Mathematics and Statistics, University of New South Wales)
Title: Quasi Monte Carlo method with higher order convergence
My research area is numerical integration over the high dimensional
domain. In particular, I study about Quasi Monte Carlo integration. 
(Not Quasi) Monte Carlo integration using N random points gives us the
convergence rate of the integration error 1/N^(1/2). On the other hand,
Quasi Monte Carlo integration uses a deterministic point set with
cardinality N.  If we choose a good quadrature point set, we can provide
good convergence rate compared with Monte Carlo integration, say 1/N^C
for some constant C. In this talk, we show how to choose a good point
sets with my previous research results and my future research vision.

(3) Herbert Huppert (School of Mathematics and Statistics, University of New South Wales/DAMTP, University of Cambridge)
Title: Gravity currents: from the desk-top through gigantic umbrella clouds to Mars
The presentation will describe the present understanding of gravity
currents, which occur whenever fluid of one density flows mainly
horizontally into fluid of a different density. The talk will explain
effects associated with currents at both high and low Reynolds, and due
to particle content, as well as outlining the effects of mean flows and
the role of gravity currents in Carbon sequestration. Different
situations will be outlined which require the solution of nonlinear
governing equations, sometimes using similarity methods or numerical
analysis (at least in part). Some results will be compared with
experiments in the laboratory and observations in the field. A desk-top
experiment will be performed that will show how easy it is to transform a
stable current to an unstable one.

(4) Adelle Coster (School of Mathematics and Statistics, University of New South Wales)
Title: Biochemical Network Dynamics: data-driven structure and design
Experimental data usually comes from a variety of sources which often measure
different aspects of a system under various perturbations and using
different techniques. This complicates the process of building a
universal model that encompasses all the known behaviours of the
system.  One approach to identifying the network structure for a model
is to simultaneously optimise the parameters across multiple data sets,
and then iteratively adapting the model. This requires the
deconstruction of experimental information into meaningful and
quantitative variables and importantly the construction of
representations of what the model system would produce under the
different experimental conditions. The model can then be analysed
explored to determine the points at which other influences and
perturbations affect the system. For instance, if a particular
biochemical component is knocked-down or removed, how and where does
this affect the dynamics? This greatly improves the process of selecting
high priority targets for, and the design of, future experiments.  An
example of this approach is presented for the GLUT4 translocation system
in cells. GLUT4, or glucose transporter 4, is the main
insulin-responsive glucose transporter in mammalian fat and muscle
cells. GLUT4 dynamically cycles to and from the cell surface controlling
the level of glucose uptake by the cell. I will discuss the approaches
we have employed to construct data-driven mathematical models of
insulin-controlled glucose transport and the benefits such models
provide in disentangling complex biological behaviour.

(5) Dinh T Tran (School of Mathematics and Statistics, University of New South Wales)
Title: Poisson brackets of mappings obtained from lattice equations
In this talk, I will present Poisson brackets of  mappings obtained from
quad-graph equations. The first class of mappings  is a class of 
4-dimensional maps which are  obtained as lifts of integrable lattice
equations. Their Poisson brackets can be found from two ways: Sklyanin
bracket  and the existence of three-leg forms. The second class of
mappings  is obtained as periodic reductions of lattice equations. Their
Poisson brackets can be derived by using  the existence  of Lagrangians
and the discrete analogue of Ostrogradsky transformation.

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