Our project, DEBIAS, aims to develop a generalisable framework to quantify and adjust existing biases in Digital Footprint Data (DFD) on human mobility. To this end, we will use DFD on human mobility obtained from users of the social media platforms Facebook and X (previously Twitter), and from smartphone applications collected by the company Huq. We will use data from the UK, but our framework will be reproducible and transferable to any DF source and geographical setting. Our framework will rely on aggregate human mobility data capturing flows of people between origins and destinations. Key benefits of using this data structure are that these data are more easily accessible. They help overcome ethical concerns ensuring anonymisation and represent a common format used by data providers to share DFD on mobility. Why mobility? Understanding how humans move is key to supporting appropriate policy responses to address population issues, carbon emission, urban planning, service delivery, public health and disaster management. DFD, such as location data collected from smartphone apps offer a unique opportunity to analyse population movements at high geographic and temporal granularity, with extensive coverage in near real-time. Research leveraging DFD has have a transformative impact expanding existing theories and developing new analytical tools and infrastructure of social and spatial human behaviour across the social sciences. Challenge: Biases in DFD have represented a major methodological barrier to reaping their benefits, contributing to scepticism and deterring wider usage of DFD. Biases mainly exist due to differences in: 1) the access and usage of the digital technologies used to collect the data (e.g. only 70% of the British population uses Facebook); and, 2) the demographic and socioeconomic profiles of users of the technology (e.g. Twitter has a young adult and male-dominated user profile mainly from urban areas). As such, human mobility data derived from DFD offer a partial representation of the overall population, limiting our capacity to draw conclusions about the overall local population. Promise: DEBIAS will deliver a framework to adjust biases in DF-derived mobility data and an open-source software package and training materials to implement it. These outcomes will contribute to delivering the Smart Data Research UK (SDRUK) programme aim of unlocking the power of new forms of data for research and innovation to tackle social challenges by 1) enabling the monitorisation and prediction of patterns of human mobility by facilitating robust real-time analysis based on DFs; 2) augmenting the technical social science research capacity in the use of DFD; 3) expanding existing theories and developing new explanations on spatial human behaviour by supporting research on highly granular space-time mobility patterns; and, 4) supporting the long-term access to robust and ethical DF-derived mobility data. DEBIAS will thus contribute to SDRUK-specific objectives by providing secure data access, safeguarding public trust, and building capability for cutting-edge research. To maximise impact, we will engage with direct beneficiaries of our work: a) researchers and analysts; b) public sector agencies; and, c) commercial stakeholders or third sector organisations engaged in data for public good initiatives and working in mobility, transport and migration. We will establish an advisory board representing expertise from the commercial (Meta), academic (Northeastern U.) and transgovernmental (UN) sectors, to inform the development of our software tool and training materials. Working with Meta and UN-IOM will enable the exploration of opportunities for long-term strategic partnerships for data access and application of our approach to new problems. We will disseminate and increase the awareness of our work via research articles, presentations and workshops targeting different audiences and adopting open science principles.

Magnetic systems and materials are essential to modern society, permitting the interconversion of electrical, mechanical and, increasingly, thermal energies for the automotive, aerospace, energy and biomedical fields, among others. The need for more efficient and sustainable magnetic systems is acute, with both global end users and manufacturers acknowledging that magnetic materials with improved performance and comprised of non-critical elements are crucial for next-generation and future technologies. Improvements in performance can only be achieved through better understanding and control of the magnet structure at multiple scales during manufacture. To this end, this award applies an integrated experimental-computational approach to hone in on the roles of thermal, magnetic and/or strain fields in the development of magneto-functional materials during their construction from atoms to crystals and finally to useful microstructures. This project has the potential to realise new and sustainable materials and processes to support national prosperity, security and environmental imperatives. It develops a bilateral cohort of under-represented minority students who have interest in conducting research at the intersection of manufacturing, energy and environment. Guided by first-principles nanoscale and finite element analysis (FEA) computation, testbed proxies including magnetostrictive and permanent magnet systems for technologically important magnetic materials will be processed using the Northeastern University custom-built lab-scale "MultiDriver" Furnace that can apply a saturating magnetic field and/or uniaxial stress during thermal treatment. In addition to uniform magnetic fields, the MultiDriver Furnace has the unique capability to apply a large yet entirely passive gradient magnetic field, offering the exciting prospect of accelerating elemental diffusion without the need for highly elevated temperatures that can damage microstructures. This proposal integrates a novel processing approach that is based on a fundamental Gibbs energy framework and relies on multi-scale computational insight for realising improved magnetic systems. The theoretical work is done in collaboration with researchers at the University of Warwick, UK. While the proposed techniques can be applied to almost any type of material, they have the greatest effects on magnetic materials as the magnetic response is extraordinarily sensitive to synthesis and processing effects (including degree/scale of crystallinity, chemical homogeneity, defect state and strain). The project generates new knowledge concerning unifying principles to identify the types and magnitudes of, and interactions between, various free energy terms that are important in magneto-responsive systems.

Teams of robots are expected to revolutionise industry and other other parts of society. However, decision making in such so-called multiagent systems (MASs) under uncertainty is computationally very complex. The decentralized partially observable Markov decision process (Dec-POMDP) framework facilitates principled formulation of such decision making problems, but currently there are no scalable solution methods that provide guarantees on task performance. To simplify coordination in MASs, agent organisations assign an abstracted, easier problem to each agent. Typically only the most rigid organisations, which completely decouple the agents, have led to clear computational benefits. However, these come at the expense of task performance: full decoupling means that agents can no longer collaborate to divide the workload. This project will focus on flexible distributed organisations (FDOs) for Dec-POMDPs, which restrict considered interactions to spatially nearby agents without imposing full decoupling. Currently no scalable decision making methods with guarantees on task performance exist for FDOs: the main goal of the project is to develop such methods along with the theory that supports their formalisation. To accomplish this goal, it will investigate the use of deep learning techniques to learn representations of 'influence' in FDOs and use those representations to develop novel planning methods. If successful, this will provide the proof-of-concept that learned influence representations can enable principled decision making in large-scale MASs. This will be the basis for a larger research program investigating such influence representations for different forms of abstraction and will spark applied research that investigates deployment of the developed algorithms in real robotic teams.

Magnetic materials are ubiquitous in modern society, present in advanced devices, sensors and motors of every kind. As the magnetic force loses strength only over very long distances, it allows for communication between components that are physically well-separated. This unique property permits the conversion of electrical to mechanical energy, assists microwave devices in telecommunications, transmission and distribution of electric power, enables data storage systems and facilitates sensing of ambient conditions. Steady effort has been extended since the invention of the magnetic compass (first reported in the Qin Dynasty, 221 BC) to tailor and optimize magnetic materials' performance. Two thousand years later it is clear that breakthrough advances in the performance of magnetic devices will require new materials and novel design principles to control magnetic performance. In this project we will clarify the origins of a significant but poorly-understood phenomenon of extrinsic control of a classically intrinsic parameter - the magnetic transition temperature - in layered systems comprised of magnetic materials with strong electron-lattice coupling. This will be done by a joint transatlantic programme of research between the University of Leeds and STFC Rutherford Appleton Laboratory in the UK, and Northeastern University in the USA. Brookhaven National Laboratory will participate as a project partner. We shall use FeRh, which crystallizes in the CsCl phase, as a model system: this material undergoes a phase transition from antiferromagnetic (AF) to ferromagnetic (F) on warming through a critical temperature that is conveniently located at about 100 degrees Celsius, accompanied by an isotropic lattice expansion. As well as providing a material with the fascinating property that magnetism can be switched on and off at will, deep questions about the underlying mechanism for the transition remain.We have already demonstrated the capability to grow epitaxial thin films of this material in Leeds and the intrinsic transition has been characterized by SQUID, synchrotron x-ray diffraction, x-ray magnetic circular dichroism, and polarised neutron reflectometry by our research partners in the USA and UK. We now seek to use joint NSF-EPSRC support to cement this link, and carry out some novel experiments where we seek to control the AF-F phase transition using extrinsic parameters. In the films we have at present, as is known in the bulk, the position of the phase boundary can be controlled intrinsically by the exact FeRh stoichiometry. A few tantalising results are present in the literature where the transition has been quite markedly affected by other external parameters by building heterostructures incorporating magnetostructural materials. Here we will throw light on the underlying mechanism for the magnetostructural response by exploring such heterostructures and the response of the FeRh to extrinsic strain, magnetostatic and exchange fields, and seek ways in which they might be combined to enhance each other.

The objective of this proposal is to improve imaging techniques which exploit the interaction of waves with matter to reconstruct the physical properties of an object. To date these techniques have been limited by the tradeoff between resolution and imaging depth. While long wavelengths can penetrate deep into a medium, the classical diffraction limit precludes the possibility of observing subwavelength structures. Over the past twenty years, near-field microscopy has demonstrated that the diffraction limit can be broken by bringing a probing sensor within one wavelength distance from the surface of the object to be imaged. Now, the scope of near field microscopy has been extended to the reconstruction of subwavelength structures from measurements performed in the far-field, this approach having a much wider range of applications since often the structure to be imaged is not directly accessible. The key to subwavelength resolution lies in the physical models employed to describe wave scattering. Conventional imaging methods use the Born approximation which does not take into account the distortion of the probing wave as it travels through the medium to be imaged, so neglecting what is known as multiple scattering. On the other hand, multiple scattering is the key mechanism which can encode subwavelength information in the far-field, thus leading to a potentially unlimited resolution. New experimental evidence has shown that a resolution better than a quarter of the wavelength can be achieved for an object more than 70 wavelengths away from the probing sensors. This preliminary work has established the fundamental principle that subwavelength resolution from far-field measurements is possible as long as the Born approximation is abandoned and more accurate models for the wave-matter interaction are adopted. The aim of this proposal is to pursue this new and exciting idea and to focus on more specific applications such as medical diagnostics and geophysical imaging.