Past Industrial and Interdisciplinary Workshops

Lein Applied Diagnostics has a novel optical measurement technique that is used to measure various parameters in the body for medical applications.

Two particular areas of interest are non-invasive glucose measurement for diabetes care and the diagnosis of diabetes. Both measurements are based on the eye and involve collecting complex data sets and modelling their links to the desired parameter.

If we take non-invasive glucose measurement as an example, we have two data sets – that from the eye and the gold standard blood glucose reading. The goal is to take the eye data and create a model that enables the calculation of the glucose level from just that eye data (and a calibration parameter for the individual). The eye data consists of measurements of apparent corneal thickness, anterior chamber depth, optical axis orientation; all things that are altered by the change in refractive index caused by a change in glucose level. So, they all correlate with changes in glucose as required but there are also noise factors as these parameters also change with alignment to the meter etc. The goal is to get to a model that gives us the information we need but also uses the additional parameter data to discount the noise features and thereby improve the accuracy.

The costs to Vodafone of calls terminating on other networks – especially fixed networks – are largely determined by the termination charges levied by other telecoms operators.  We interconnect to several other telecoms operators, who charge differently; within one interconnect operator, costs vary depending on which of their switching centres we deliver calls to, and what the terminating phone number is.  So, while these termination costs depend partly on factors that we cannot control (such as the number called, the call duration and the time of day), they are also influenced by some factors that we can control.  In particular, we can route calls within our network before handing them over from our network to the other telecoms operator; where this “handover” occurs has an impact on termination cost.  
Vodafone would like to develop a repeatable capability to determine call delivery cost efficiency and identify where network routing changes can be made to improve matters, and determine traffic growth forecasts.

Railway traffic management is the combination of monitoring the progress of trains, forecasting of the likely future progression of trains, and evaluating the impact of intervention options in near real time in order to make traffic adjustments that minimise the combined delay of trains when measured against the planned timetable.

In a time of increasing demand for rail travel, the desire to maximise the usage of the available infrastructure capacity competes with the need for contingency space to allow traffic management when disruption occurs. Optimisation algorithms and decision support tools therefore need to be increasingly sophisticated and traffic management has become a crucial function in meeting the growing expectations of rail travellers for punctuality and quality of service.

Resonate is a technology company specialising in rail and connected transport solutions. We have embarked on a drive to maximise capacity and performance through the use of mathematical, statistical, data-driven and machine learning based methods driving decision support and automated traffic management solutions.

In certain business environments, it is essential to the success of the business that workers stick closely to their plans and are not distracted, diverted or stopped. A warehouse is a great example of this for businesses where customers order goods online and the merchants commit to delivery dates.  In a warehouse, somewhere, a team of workers are scheduled to pick the items which will make up those orders and get them shipped on time.  If the workers do not deliver to plan, then orders will not be shipped on time, reputations will be damaged, customer will be lost and companies will go out of business.

StayLinked builds software which measures what these warehouse workers do and measures the factors which cause them to be distracted, diverted or stopped.  We measure whenever they start or end a task or process (e.g. start an order, pick an item in an order, complete an order). Some of the influencing factors we measure include the way the worker interacts with the device (using keyboard, scanner, gesture), navigates through the application (screens 1-3-4-2 instead of 1-2-3-4), the performance of the battery (dead battery stops work), the performance of the network (connected to access point or not, high or low latency), the device types being used, device form factor, physical location (warehouse 1, warehouse 2), profile of worker, etc.

We are seeking to build a configurable real-time mathematical model which will allow us to take all these factors into account and confidently demonstrate a measure of their impact (positive or negative) on the business process and therefore on the worker’s productivity. We also want to alert operational staff as soon as we can identify that important events have happened.  These alerts can then be quickly acted upon and problems resolved at the earliest possible opportunity.

In this project, we would like to collaborate with the maths faculty to understand the appropriate mathematical techniques and tools to use to build this functionality.  This product is being used right now by our customers so it would also be a great opportunity for a student to quickly see the results of their work in action in a real-world environment.

The National Physical Laboratory is the national measurement institute. Researchers in the Data Science Division analyse various types of data using mathematical, statistical and machine learning based methods. The goal of the workshop is to describe a set of exciting mathematical problems that are of interest to NPL and more generally to the Data Science community. In particular, I will describe the problem of clustering using minimum spanning trees (MST-Clustering), Non-Negative Matrix Factorisation (NMF), adaptive Compressed Sensing (CS) for tomography, and sparse polynomial chaos expansion (PCE) for parametrised PDE’s.

Conventional neutron reflection is a very powerful tool to characterise surfactants, polymers and other materials at the solid/liquid and air/liquid interfaces. Usually the analysis considers molecular layers with coherent addition of reflected waves that give the resultant reflected intensity. In this short workshop talk I will illustrate recent developments in this approach to address a wide variety of challenges of academic and commercial interest. Specifically I will introduce the challenges of using substrates that are thick on the coherence lengthscale of the radiation and the issues that brings in the structural analysis. I also invite the audience to consider if there may be some mathematical analysis that might lead us to exploit this incoherence to optimise our analysis. In particular, facilitating the removal of the 'background substrate contribution' to help us focus on the adsorbed layers of most interest.

Liquid crystals are classical examples of mesophases or materials that are intermediate in character between conventional solids and liquids. There are different classes of liquid crystals and we focus on the simplest and most widely used nematic liquid crystals. Nematic liquid crystals are simply put, anisotropic liquids with distinguished directions and are the working material of choice for the multi-billion dollar liquid crystal display industry. In this workshop, we briefly review the mathematical theories for nematic liquid crystals, the modelling framework and some recent work on modelling experiments on confined liquid crystalline systems conducted by the Aarts Group (Chemistry Oxford) and experiments on nematic microfluidics by Anupam Sengupta (ETH Zurich). This is joint work with Alexander Lewis, Peter Howell, Dirk Aarts, Ian Griffiths, Maria Crespo Moya and Angel Ramos.
We conclude with a brief overview of new experiments on smectic liquid crystals in the Aarts laboratory and questions related to the recycling of liquid crystal displays originating from informal discussions with Votechnik ( a company dealing with automated recycling technologies , http://votechnik.com/).
 

Road travel is taking longer each year in the UK. This has been true for the last four years. Travel times have increased by 4% in the last two years. Applying the principle finding of the Eddington Report 2006, this change over the last two years will cost the UK economy an additional £2bn per year going forward even without further deterioration. Additional travel times are matched by a greater unreliability of travel times.

Knowing demand and road capacity, can we predict travel times?

We will look briefly at previous partial solutions and the abundance of motorway data in the UK. Can we make a breakthrough to achieve real-time predictions?

The projects include melting of the paste, fluid flow, particle segregation, baking, shrinkage and material properties of baked electrode.

As part of a suite of products that provide a drive thorough vehicle tyre inspection system the assessment of wheel alignment would be useful to drivers in maintaining their vehicles and reducing tyre wear.  The current method of assessing wheel alignment involves fitting equipment to the tyre and assessment within a garage environment. 

The challenge is to develop a technique that can be used in the roadway with no equipment fitted to the vehicle.  The WheelRight equipment is already capturing images of tyres from both  front and side views.  Pressure sensors in the roadway also allow a tyre pressure footprint to be created.  Using the existing data to interpret the alignment of the wheels on each axle is a preferred way forward.

Hotels.com is one of the world’s leading accommodation booking websites featuring an inventory of around 300.000 hotels and 100s of millions of users. A crucial part of our business is to act as an agent between these two sides of the market, thus reducing search costs and information asymmetries to enable our visitors to find the right hotel in the most efficient way.

From this point of view selling hotels is one large recommendation challenge: given a set of items and a set of observed choices/ratings, identify a user’s preference profile.  Over the last years this particular problem has been intensively studied by a strongly interdisciplinary field based on ideas from choice theory, linear algebra, statistics, computer science and machine learning. This pluralism is reflected in the broad array of techniques that are used in today’s industry applications, i.e. collaborative filtering, matrix factorization, graph-based algorithms, decision trees or generalized linear models.

The aim of this workshop is twofold.

Firstly we want to give some insight into the statistical modelling techniques and assumptions employed at hotels.com, the practical challenges one has to face when designing a flexible and scalable recommender system and potential gaps between current research and real-world applications.

Secondly we are going to consider some more advanced questions around learning to rank from partial/incomplete feedback (1), dealing with selection-bias correction (2) and how econometrics and behavioral theory (eg Luce, Kahneman /Tversky) can be used to complement existing techniques (3).

Environmental risk assessments for chemicals in the EU rely heavily upon modelled estimates of potential concentrations in soil and water.  A key parameter used by these models is the degradation of the chemical in soil which is derived from a kinetic fitting of laboratory data using standard fitting routines.  Several different types of kinetic can be represented such as: Simple First Order (SFO), Double First Order in Parallel (DFOP), and First Order Multi-Compartment (FOMC). Choice of a particular kinetic and selection of a representative degradation rate can have a huge influence on the outcome of the risk assessment. This selection is made from laboratory data that are subject to experimental error.  It is known that the combination of small errors in time and concentration can in certain cases have an impact upon the goodness of fit and kinetic predicted by fitting software.  Syngenta currently spends in the region of 4m GBP per annum on laboratory studies to support registration of chemicals in the EU and the outcome of the kinetic assessment can adversely affect the potential registerability of chemicals having sales of several million pounds.  We would therefore like to understand the sensitivities involved with kinetic fitting of laboratory studies.  The aim is to provide guidelines for the conduct and fitting of laboratory data so that the correct kinetic and degradation rate of chemicals in environmental risk assessments is used.

Dstl are interested in removing liquid contaminants from capillary features (cracks in surfaces, screw threads etc.). We speculated that liquid decontaminants with low surface tension would have beneficial properties. The colloid literature, and in particular the oil recovery literature, discusss the properties of multiphase systems in terms of “Winsor types”, typically consisting of “brine” (water + electrolyte), “oil” (non-polar, water-insoluble solvent) and surfactant. Winsor I systems are oil-in-water microemulsions and Winsor II systems are water-in-oil microemulsions. Under certain circumstances, the mixture will separate into three phases. The middle (Winsor III) phase is surfactant-rich, and is reported to exhibit ultra-low surface tension. The glycol ethers (“Cellosolve” type solvents) consist of short (3-4) linked ether groups attached to short (3-4 carbon) alkyl chains. Although these materials would not normally be considered to be surfactants, their polar head, non-polar tail properties allow them to form a “surfactantless” Winsor III middle phase. We have found that small changes in temperature, electrolyte concentration or addition of contaminant can cause these novel colloids to phase separate. In our decontamination experiments, we have observed that contaminant-induced phase separation takes the form of droplets of the separating phase. These droplets are highly mobile, exhibiting behaviour that is visually similar to Brownian motion, which induces somewhat turbulent liquid currents in the vicinity of the contaminant. We tentatively attribute this behaviour to the Marangoni effect. We present our work as an interesting physics/ physical chemistry phenomenon that should be suitable for mathematical analysis.

Currently all medical x-ray imaging is performed using point-like sources which produce cone or fan beams. In planar radiology the source is fixed relative to the patient and detector array and therefore only 2D images can be produced. In CT imaging, the source and detector are rotated about the patient and through reconstruction (such as Radon methods), a 3D image can be formed. In Tomosynthesis, a limited range of angles are captured which greatly reduces the complexity and cost of the device and the dose exposure to the patient while largely preserving the clinical utility of the 3D images. Conventional tomosynthesis relies on mechanically moving a source about a fixed trajectory (e.g. an arc) and capturing multiple images along that path. Adaptix is developing a fixed source with an electronically addressable array that allows for a motion-free tomosynthesis system. The Adaptix approach has many advantages including reduced cost, portability, angular information acquired in 2D, and the ability to shape the radiation field (by selectively activating only certain emitters).

The proposed work would examine the effects of patient motion and apply suitable corrections to the image reconstruction (or raw data). Many approaches have been considered in the literature for motion correction, and only some of these may be of use in tomosynthesis. The study will consider which approaches are optimal, and apply them to the present geometry.

A related but perhaps distinct area of investigation is the use of “structured light” techniques to encode the x-rays and extract additional information from the imaging. Most conventional structured light approaches are not suitable for transmissive operation nor for the limited control available in x-rays. Selection of appropriate techniques and algorithms, however, could prove very powerful and yield new ways of performing medical imaging.

Adaptix is a start-up based at the Begbroke Centre for Innovation and Enterprise. Adaptix is transforming planar X-ray – the diagnostic imaging modality most widely used in healthcare worldwide. We are adding low-dose 3D capability – digital tomosynthesis - to planar X-ray while making it more affordable and truly portable so radiology can more easily travel to the patient. This transformation will enhance patient’s access to the world’s most important imaging technologies and likely increases the diagnostic accuracy for many high incidence conditions such as cardiovascular and pulmonary diseases, lung cancer and osteoporosis.