558 research outputs found
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Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for course work and student theses in the MPhil in Machine Learning, Speech and Language Technology at the University of Cambridge, (2) research as most of the research work of the Cambridge MT group is based on SGNMT, and (3) technology transfer as we show how SGNMT is helping to transfer research findings from the laboratory to the industry, eg. into a product of SDL plc
Product assurance technology for custom LSI/VLSI electronics
The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification
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SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
This paper introduces SGNMT, our experimental platform for machine translation research. SGNMT provides a generic interface to neural and symbolic scoring modules (predictors) with left-to-right semantic such as translation models like NMT, language models, translation lattices, n-best lists or other kinds of scores and constraints. Predictors can be combined with other predictors to form complex decoding tasks. SGNMT implements a number of search strategies for traversing the space spanned by the predictors which are appropriate for different predictor constellations. Adding new predictors or decoding strategies is particularly easy, making it a very efficient tool for prototyping new research ideas. SGNMT is actively being used by students in the MPhil program in Machine Learning, Speech and Language Technology at the University of Cambridge for course work and theses, as well as for most of the research work in our group.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1)
Syntactically Guided Neural Machine Translation
We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full n-gram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.Engineering and Physical Sciences Research Council (Grant ID: EP/L027623/1
Cryo-EM structures and binding of mouse and human ACE2 to SARS-CoV-2 variants of concern indicate that mutations enabling immune escape could expand host range.
Investigation of potential hosts of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is crucial to understanding future risks of spillover and spillback. SARS-CoV-2 has been reported to be transmitted from humans to various animals after requiring relatively few mutations. There is significant interest in describing how the virus interacts with mice as they are well adapted to human environments, are used widely as infection models and can be infected. Structural and binding data of the mouse ACE2 receptor with the Spike protein of newly identified SARS-CoV-2 variants are needed to better understand the impact of immune system evading mutations present in variants of concern (VOC). Previous studies have developed mouse-adapted variants and identified residues critical for binding to heterologous ACE2 receptors. Here we report the cryo-EM structures of mouse ACE2 bound to trimeric Spike ectodomains of four different VOC: Beta, Omicron BA.1, Omicron BA.2.12.1 and Omicron BA.4/5. These variants represent the oldest to the newest variants known to bind the mouse ACE2 receptor. Our high-resolution structural data complemented with bio-layer interferometry (BLI) binding assays reveal a requirement for a combination of mutations in the Spike protein that enable binding to the mouse ACE2 receptor
Limits on Dark Matter Effective Field Theory Parameters with CRESST-II
CRESST is a direct dark matter search experiment, aiming for an observation
of nuclear recoils induced by the interaction of dark matter particles with
cryogenic scintillating calcium tungstate crystals. Instead of confining
ourselves to standard spin-independent and spin-dependent searches, we
re-analyze data from CRESST-II using a more general effective field theory
(EFT) framework. On many of the EFT coupling constants, improved exclusion
limits in the low-mass region (< 3-4 GeV) are presented.Comment: 7 pages, 9 figure
Results on MeV-scale dark matter from a gram-scale cryogenic calorimeter operated above ground
Models for light dark matter particles with masses below 1 GeV/c are a
natural and well-motivated alternative to so-far unobserved weakly interacting
massive particles. Gram-scale cryogenic calorimeters provide the required
detector performance to detect these particles and extend the direct dark
matter search program of CRESST. A prototype 0.5 g sapphire detector developed
for the -cleus experiment has achieved an energy threshold of
eV, which is one order of magnitude lower than previous
results and independent of the type of particle interaction. The result
presented here is obtained in a setup above ground without significant
shielding against ambient and cosmogenic radiation. Although operated in a
high-background environment, the detector probes a new range of light-mass dark
matter particles previously not accessible by direct searches. We report the
first limit on the spin-independent dark matter particle-nucleon cross section
for masses between 140 MeV/c and 500 MeV/c.Comment: 6 pages, 6 figures, v3: ancillary files added, v4: high energy
spectrum (0.6-12keV) added to ancillary file
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