Industrial and Interdisciplinary Workshops (past)

Please note the change of venue!

Suppose there is a system where certain objects move through a network. The objects are detected only when they pass through a sparse set of points in the network. For example, the objects could be vehicles moving along a road network, and observed by a radar or other sensor as they pass through (or originate or terminate at) certain key points in the network, but which cannot be observed continuously and tracked as they travel from one point to another. Alternatively they could be data packets in a computer network. The detections only record the time at which an object passes by, and contain no information about identity that would trivially allow the movement of an individual object from one point to another to be deduced. It is desired to determine the statistics of the movement of the objects through the network. I.e. if an object passes through point A at a certain time it is desired to determine the probability density that the same object will pass through a point B at a certain later time.

The system might perhaps be represented by a graph, with a node at each point where detections are made. The detections at each node can be represented by a signal as a function of time, where the signal is a superposition of delta functions (one per detection). The statistics of the movement of objects between nodes must be deduced from the correlations between the signals at each node. The problem is complicated by the possibility that a given object might move between two nodes along several alternative routes (perhaps via other nodes or perhaps not), or might travel along the same route but with several alternative speeds.

What prior knowledge about the network, or constraints on the signals, are needed to make this problem solvable? Is it necessary to know the connections between the nodes or the pdfs for the transition time between nodes a priori, or can this be deduced? What conditions are needed on the information content of the signals? (I.e. if detections are very sparse on the time scale for passage through the network then the transition probabilities can be built up by considering each cascade of detections independently, while if detections are dense then it will presumably be necessary to assume that objects do not move through the network independently, but instead tend to form convoys that are apparent as a pattern of detections that persist for some distance on average). What limits are there on the noise in the signal or amount of unwanted signal, i.e. false detections, or objects which randomly fail to be detected at a particular node, or objects which are detected at one node but which do not pass through any other nodes? Is any special action needed to enforce causality, i.e. positive time delays for transitions between nodes?

We wish to discuss the role of Modelling in Health Care. While risk factor prevalences vary and change with time it is difficult to anticipate the change in disease incidence that will result without accurately modelling the epidemiology. When detailed study of the prevalence of obesity, tobacco and salt intake, for example, are studied clear patterns emerge that can be extrapolated into the future. These can give rise to estimated probability distributions of these risk factors across age, sex, ethnicity, social class groups etc into the future. Micro simulation of individuals from defined populations (eg England 2012) can then estimate disease incidence, prevalence, death, costs and quality of life. Thus future health and other needs can be estimated, and interventions on these risk factors can be simulated for their population effect. Health policy can be better determined by a realistic characterisation of public health. The Foresight microsimulation modelling of the National Heart Forum (UK Health Forum) will be described. We will emphasise some of the mathematical and statistical issues associated with so doing.

The University of Oxford’s modelling4all software is a wonderful tool to simulate early medieval populations and their cemeteries in order to evaluate the influence of palaeodemographic variables, such as mortality, fertility, catastrophic events and disease on settlement dispersal. In my DPhil project I will study archaeological sites in Anglo-Saxon England and the German south-west in a comparative approach. The two regions have interesting similarities in their early medieval settlement pattern and include some of the first sites where both cemeteries and settlements were completely excavated.

An important discovery in bioarchaeology is that an excavated cemetery is not a straightforward representation of the living population. Preservation issues and the limitations of age and sex estimation methods using skeletal material must be considered. But also the statistical procedures to calculate the palaeodemographic characteristics of archaeological populations are procrustean. Agent-based models can help archaeologists to virtually bridge the chasm between the excavated dead populations and their living counterparts in which we are really interested in.

This approach leads very far away from the archaeologist’s methods and ways of thinking and the major challenge therefore is to balance innovative ideas with practicability and tangibility.

Some of the problems for the workshop are:

1.) Finding the best fitting virtual living populations for the excavated cemeteries

2.) Sensitivity analyses of palaeodemographic variables

3.) General methodologies to evaluate the outcome of agent based models

4.) Present data in a way that is both statistically correct and up to date & clear for archaeologists like me

5.) Explore how to include analytical procedures in the model to present the archaeological community with a user-friendly and not necessarily overwhelming toolkit

PLEASE NOTE EARLY START TIME TO AVOID CLASH WITH OCCAM GROUP MEETING

The human influenza A virus causes three to five million cases of severe illness and about 250 000 to 500 000 deaths each year. The 1918 Spanish Flu may have killed more than 40 million people. Yet, the underlying cause of the seasonality of the human influenza virus, its preferential transmission in winter in temperate climates, remains controversial. One of the major forms of the human influenza virus is a sphere made up of lipids selectively derived from the host cell along with specialized viral proteins. I have employed molecular dynamics simulations to study the biophysical properties of a single transmissible unit--an approximately spherical influenza A virion in water (i.e., to mimic the water droplets present in normal transmission of the virus). The surface area per lipid can't be calculated as a ratio of the surface area of the sphere to the number of lipids present as there are many different species of lipid for which different surface area values should be calculated. The 'mosaic' of lipid surface areas may be regarded quantitatively as a Voronoi diagram, but construction of a true spherical Voronoi tessellation is more challenging than the well-established methods for planar Voronoi diagrams. I describe my attempt to implement an approach to the spherical Voronoi problem (based on: Hyeon-Suk Na, Chung-Nim Lee, Otfried Cheong. Computational Geometry 23 (2002) 183–194) and the challenges that remain in the implementation of this algorithm.

Links between:

• storm tracks, sediment movement and an icy environment

• fluvial flash flooding to coastal erosion in the UK

Did you know that the recent Japanese, Chilean and Samoan tsunami all led to strong currents from resonance at the opposite end of the ocean?

Journey around the world, from the north Atlantic to the south Pacific, on a quest to explore and explain the maths of nature.

The issue of resource management arises with any sensor which is capable either of sensing only a part of its total field of view at any one time, or which is capable of having a number of operating modes, or both.

A very simple example is a camera with a telephoto lens.  The photographer has to decide what he is going to photograph, and whether to zoom in to get high resolution on a part of the scene, or zoom out to see more of the scene.  Very similar issues apply, of course, to electro-optical sensors (visible light or infra-red 'TV' cameras) and to radars.

The subject has, perhaps, been most extensively studied in relation to multi mode/multi function radars, where approaches such as neural networks, genetic algorithms and auction mechanisms have been proposed as well as more deterministic mangement schemes, but the methods which have actually been implemented have been much more primitive.

The use of multiple, disparate, sensors on multiple mobile, especially airborne, platforms adds further degrees of freedom to the problem - an extension is of growing interest.

The presentation will briefly review the problem for both the single-sensor and the multi-platform cases, and some of the approaches which have been proposed, and will highlight the remaining current problems.