organization

UOXF

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country: United Kingdom
1,475 Projects, page 1 of 295
  • Funder: EC Project Code: 274903
    Partners: UOXF
  • Open Access mandate for Publications
    Funder: EC Project Code: 851990
    Overall Budget: 1,499,440 EURFunder Contribution: 1,499,440 EUR
    Partners: UOXF

    This project aims to synthesize expertise and insights from the fields of ancient Indian and modern Western linguistics, to enable deeper understanding and innovation in linguistic theory. An extensive and highly sophisticated linguistic tradition flourished in ancient India between c. 500 BC and 1700 AD. Panini’s grammar the Astadhyayi is often recognized by generative linguists as the earliest generative grammar ever developed, more than 2000 years before Chomsky. Yet beyond this recognition, modern Western linguistics has very little knowledge of the millennia of linguistic insights and analyses developed in India. In the context of the academic enterprise - building on the achievements of our predecessors to advance human knowledge and understanding - this ignorance is a hindrance to the progress of linguistic science. The aims of this project are: 1. To systematically explore and analyse the neglected riches of ancient Indian linguistic thought; 2. To uncover lost linguistic insights and analyses; 3. To build on these insights to create innovative approaches to contemporary issues in modern Western linguistics. The project will focus on ancient Indian contributions to linguistic thought in three broad areas: morphosyntax and formal language systems, semantics/pragmatics and the philosophy of language, and phonetics/phonology. In all three fields ancient Indian analyses provide new perspectives which challenge standard assumptions of modern Western linguistics. This project will bring together expertise in modern linguistics and the ancient Indian linguistic tradition, enabling innovative interactions between traditions. This project is challenging, but the potential rewards for modern linguistics are significant. This project aims to be paradigm changing, redefining modern linguistics as a field which can and does draw and build on three thousand years of academic insights, rather than drawing merely on two hundred years of linguistic work in the West.

  • Open Access mandate for Publications
    Funder: EC Project Code: 741112
    Overall Budget: 2,494,120 EURFunder Contribution: 2,494,120 EUR
    Partners: UOXF

    The aim of this project is to develop a new synergy between climate and computer science to increase the accuracy and hence reliability of comprehensive weather and climate models. The scientific basis for this project lies in the PI’s pioneering research on stochastic sub-grid parametrisations for climate models. These parametrisations provide estimates of irreducible uncertainty in weather and climate models, and will be used to determine where numerical precision for model variables can be reduced without degradation. By identifying those bits that carry negligible information – typically in high-wavenumber components of the dynamical core and within parametrisation and Earth-System modules – computational resources can be reinvested into areas (resolution, process representation, ensemble size) where they are sorely needed. This project will determine scale-dependent estimates of information content as rigorously as possible based on a variety of new tools, which include information-theoretic diagnostics and emulators of imprecision, and in a variety of models, from idealised to comprehensive. The project will contribute significantly to the development of next-generation weather and climate models and is well timed for the advent of exascale supercomputing where energy efficiency is paramount and where movement of bits, being the single biggest determinant of power consumption, must be minimised. The ideas will be tested on emerging hardware capable of exploiting the benefits of mixed-precision arithmetic. A testable scientific hypothesis is presented: a proposed increase in forecast reliability arising from an increase in the forecast model’s vertical resolution, the cost being paid for by a reduction in precision of small-scale variables. This project can be expected to provide new scientific understanding of how different scales interact in the nonlinear climate system, for example in maintaining persistent atmospheric flow regimes.

  • Open Access mandate for Publications
    Funder: EC Project Code: 838058
    Overall Budget: 149,919 EURFunder Contribution: 149,919 EUR
    Partners: UOXF

    Automatic Speech Recognition (ASR) is considered to represent the most natural man-machine interface across the spectrum of technological space. Current commercial ASR systems rely on a ‘rich’ representation of an acoustic signal for words and their variants, resulting in major challenges in the deployment of ASR systems in areas where it could have substantial social impact. Our central goal is to translate research results from the ERC funded project MORPHON into a novel ASR system to remove such barriers. We have previously demonstrated that the use of a universal set of phonological features delivers an isolated word recognition system (FlexSR) with enhanced phoneme recognition accuracy. It is more robust under conditions of non-standard speech, dialect variation and can be easily adapted to new languages. These aspects are problematic for current ASR systems which rely on the probabilistic sequencing of whole words in their language model (LM) based on large written text corpora for training. Obtaining sufficient training data for a new LM is prohibitively expensive. Instead, MorSR will incorporate linguistic information about word-structure to reject improbable words. This reduces the search space and increases the probability of identifying correct words. A major outcome will be an innovative LM based on linguistic principles. Unlike existing approaches, it is based on speech data to capture crucial regularities that are lost in text corpora. Combined with FlexSR's key strengths in identifying subtle phonological contrasts, MorSR will not only enable improved predictions of word sequences in running speech, but also dramatically reduce the requirement for training data when adapting the system to a new language. MorSR's strengths include: (a) prediction of fine-grained possibilities of word sequences based on grammatical principles; (b) requiring considerably less training data; (c) easily adaptable to new languages; and (d) will be fast, secure and accurate.