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- Wiedner, M.C., et al.
(author)
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Heterodyn receiver for the Origins Space Telescope concept 2
- 2018
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In: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 0277-786X .- 1996-756X. ; 10698
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Conference paper (peer-reviewed)abstract
- The Origins Space Telescope (OST) is a NASA study for a large satellite mission to be submitted to the 2020 Decadal Review. The proposed satellite has a fleet of instruments including the HEterodyne Receivers for OST (HERO). HERO is designed around the quest to follow the trail of water from the ISM to disks around protostars and planets. HERO will perform high-spectral resolution measurements with 2x9 pixel focal plane arrays at any frequency between 468GHz to 2,700GHz (617 to 111 μm). HERO builds on the successful Herschel/HIFI heritage, as well as recent technological innovations, allowing it to surpass any prior heterodyne instrument in terms of sensitivity and spectral coverage.
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- West, Jay B., et al.
(author)
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Comparison and evaluation of retrospective intermodality image registration techniques
- 1997
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In: SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering. ; , s. 332-347
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Conference paper (peer-reviewed)abstract
- All retrospective image registration methods have attached to them some intrinsic estimate of registration error. However, this estimate of accuracy may not always be a good indicator of the distance between actual and estimated positions of targets within the cranial cavity. This paper describes a project whose principal goal is to use a prospective method based on fiducial markers as a ’gold standard’ to perform an objective, blinded evaluation of the accuracy of several retrospective image-to-image registration techniques. Image volumes of three modalities – CT, MR, and PET – were taken of patients undergoing neurosurgery at Vanderbilt University Medical Center. These volumes had all traces of the fiducial markers removed, and were provided to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/or from PET to MR, and communicated their transformations to Vanderbilt where the accuracy of each registration was evaluated. In this evaluation the accuracy is measured at multiple ’regions of interest,’ i.e. areas in the brain which would commonly be areas of neurological interest. A region is defined in the MR image and its centroid C is determined. Then the prospective registration is used to obtain the corresponding point C’ in CT or PET. To this point the retrospective registration is then applied, producing C’ in MR. Statistics are gathered on the target registration error (TRE), which is the disparity between the original point C and its corresponding point C’. A second goal of the project is to evaluate the importance of correcting geometrical distortion in MR images, by comparing the retrospective TRE in the rectified images, i.e., those which have had the distortion correction applied, with that of the same images before rectification. This paper presents preliminary results of this study along with a brief description of each registration technique and an estimate of both preparation and execution time needed to perform the registration.
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- Wiedner, M.C., et al.
(author)
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The origins space telescope and the heterodyne receiver for origins (HERO)
- 2019
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In: ISSTT 2019 - 30th International Symposium on Space Terahertz Technology, Proceedings Book. ; , s. 204-207
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Conference paper (peer-reviewed)abstract
- The Origins Space Telescope is one of four large mission concept studies carried out by NASA for the 2020 Decadal survey. Origins is a far-infrared telescope designed to understand the evolution of galaxies and black holes, to follow the trail of water from protostars to habitable planets and to search for biosignatures in the atmospheres of exoplanets. The Heterodyne Receiver for Origins (HERO) is the high spectral resolution receiver. It is the first heterodyne array receiver designed to fly on a satellite and an example for possible future focal plane arrays for space. HERO has focal plane arrays with nine pixels in two polarization. HERO covers a large frequency range between 486 and 2700 GHz in only 4 frequency bands, requiring local oscillators with fractional bandwidth of 45%. HERO uses the best superconducting mixers with noise temperatures between 1 and 3 hf/k and an intermediate bandwidth of 6 to 8 GHz. HERO can carry out dual polarization and dual-frequency observations. The major challenges for the HERO design are the low cooling power and the low electrical power available on a spacecraft, which impact the choice of the cryogenic amplifiers and backends. SiGe cryogenic amplifiers with a consumption of less than 0.5 mW, as well as CMOS spectrometers with a power consumption below 2W are the baseline for HERO. The development plan includes broadband (45%) multiplier-amplifier chains, low noise mixers (1-3 hf/k), low-power consuming (< 05.mW) cryogenic amplifiers and low-power consuming spectrometer backends (< 2W).
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- Adelani, David, et al.
(author)
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A Few Thousand Translations Go A Long Way! Leveraging Pre-trained Models for African News Translation
- 2022
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In: NAACL 2022. - Stroudsburg : Association for Computational Linguistics. - 9781955917711 ; , s. 3053-3070
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Conference paper (peer-reviewed)abstract
- Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages are not well represented on the web and therefore excluded from the large-scale crawls used to create datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a new African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both to additional languages and to additional domains is to fine-tune large pre-trained models on small quantities of high-quality translation data.
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