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UNIVERSITY OF CAMBRIDGE

UNIVERSITY OF CAMBRIDGE

6,444 Projects, page 1 of 1,289
  • Funder: UK Research and Innovation Project Code: 2884650

    As part of the collaborative research project 'Re-evaluating on Punch Marks on Early Italian Paintings', Kyoko's research project aims to explore application of imaging techniques including 3D imaging for punch marks on gilded background of early Italian paintings. The use of different methods of imaging and technical analysis combined with art historical research promises to explore historical interactions between Italian schools in the 13-14th centuries, with a special focus on Florentine and Sienese schools. A comprehensive study of punch marks published by Erling S. Skaug in 1994 clearly showed a potential of the study of punch marks as a new approach to art historical research on early Italian panel paintings. This proposal would develop this approach, and enrich the 2D photographic and diagrammatic information collected by Skaug. As there is sparce mention of punching tools or shapes of punch marks used by artists in historical texts, the study of punch marks is crucial to advance our understanding of how the artists of the two schools could have exchanged materials, tools and techniques. In addition, 3D imaging techniques have great potential to provide more detailed information about punch marks which could make it possible to pinpoint punching tools or marks used by specific artists or studios. My research scope will also include evaluating advantages and limitations of the imaging techniques applied for the paintings.

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  • Funder: UK Research and Innovation Project Code: 2928874

    TBC

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  • Funder: UK Research and Innovation Project Code: 2908503

    The Acland family archives would be the chief source for my proposed project, which is a collaborative doctoral award between Cambridge University and the National Trust. The key theme of my proposed project is understanding the Acland family's 'mission' in the long nineteenth century, that is the motivations and beliefs which underpinned their actions and enterprises as a family and as individuals. I plan to explore this idea through the Aclands' connections to empire, particularly their antislavery beliefs. My project will contribute most to three broad fields of historiography; histories of elite families; the Victorian antislavery movement; and spatial histories of the British Empire. I propose an approach that foregrounds the Acland family house, Killerton, as the site on which the family's global connections converged and where their different individual missions coalesced. Could the various campaigns undertaken by different family members, as dictated by social roles and hierarchies, all form part of one overarching mission? Furthermore, the focus on the home would retrieve from the margins voices and groups often excluded from or with limited capacity in public life: notably women, children, and household staff. My project would join the 'global turn' in history whilst maintaining the specificity of local contexts. It would also contribute to an important body of work that seeks to reunderstand the antislavery movement without lionizing it.

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  • Funder: UK Research and Innovation Project Code: 2908492

    Causal models are widely used in a variety of fields to identify underlying causes and mechanisms of discrimination. Causal methodologies offer structured methods for evaluating claims of injustice by revealing causal links between protected attributes and negative outcomes. The construction of these models, however, hinges on making substantive assumptions about how to conceptualise and measure discrimination, and relatedly how to operationalise protected categories like gender or race - decisions which are deeply influenced by background political and philosophical assumptions about the structure of our social world. My project seeks to unite work in political and legal philosophy with work on causal inference methodologies to probe the theoretical grounding of methodological decisions regarding the modelling of injustice, and to develop some practical considerations.

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  • Funder: UK Research and Innovation Project Code: 2925314

    Simulation tools are only as useful as the accuracy of the data and results that they produce. Particularly in robotics, when using machine learning techniques, there exists a real-world simulation gap. This means that data or learning schema produced in simulation, won't necessarily account for real-world nuances. In terra-mechanics (and therefore machine-soil interaction), this problem is only exacerbated by almost random terrain compounds (soils are a culmination of all the materials laden within it), and the terrains complex self-interactions. Even miniscule changes in mixture percentages (not to mention other environmental effects such as humidity) could have a large effect on the outcome of any particular attempt to manipulate the earth. However, within the field of terra-mechanics there is a lack of rigorous datasets that are able to provide well needed foundations to help guide the simulation tools. Utilising our industry standard 6-axis robotic manipulator and a custom-built earth-box, we will produce high quality datasets describing precise force measurements and the resulting terrain deformation. These tests will be repeated for numerous different shapes/tools/form factors. For example, most quadrupedal robots have simple ball feet. With this shape as the end effector, following a fully-fleshed out experimental process which considers all possible movements for a wide range of forces. During/following the gathering of these datasets, the project plans to build an autonomous zen-garden robot system (ZenBot) that is able to interact with the material and draw provided patterns. This would be a closed-loop system, where the current garden is compared against the desired garden. This is a challenge that encompasses not only the prior-mentioned problem but also requires skills ranging from terrain mapping, to control algorithm design. The data gathered would have many potential use cases. One of particular interest to me is utilising the knowledge of the terrain to inform locomotion policies for quadrupedal robotics. Knowing the environment could be used to understand how particular movements not only deform the terrain, but propel the robot in a given direction. This could enable a new wave of intelligent robotics, that not only understands where to move, but how to move most efficiently.

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