| 1. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Abend, Sven, et al.
(författare)
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Terrestrial very-long-baseline atom interferometry : Workshop summary
- 2024
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Ingår i: AVS Quantum Science. - : American Institute of Physics (AIP). - 2639-0213. ; 6:2
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Forskningsöversikt (refereegranskat)abstract
- This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more kilometer--scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions.
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| 7. |
- Davis, P. E. D., et al.
(författare)
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Suppressed basal melting in the eastern Thwaites Glacier grounding zone
- 2023
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 614:7948
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Tidskriftsartikel (refereegranskat)abstract
- Thwaites Glacier is one of the fastest-changing ice-ocean systems in Antarctica(1-3). Much of the ice sheet within the catchment of Thwaites Glacier is grounded below sea level on bedrock that deepens inland(4), making it susceptible to rapid and irreversible ice loss that could raise the global sea level by more than half a metre(2,3,5). The rate and extent of ice loss, and whether it proceeds irreversibly, are set by the ocean conditions and basal melting within the grounding-zone region where Thwaites Glacier first goes afloat(3,6), both of which are largely unknown. Here we show-using observations from a hot-water-drilled access hole-that the grounding zone of Thwaites Eastern Ice Shelf (TEIS) is characterized by a warm and highly stable water column with temperatures substantially higher than the in situ freezing point. Despite these warm conditions, low current speeds and strong density stratification in the ice-ocean boundary layer actively restrict the vertical mixing of heat towards the ice base(7,8), resulting in strongly suppressed basal melting. Our results demonstrate that the canonical model of ice-shelf basal melting used to generate sea-level projections cannot reproduce observed melt rates beneath this critically important glacier, and that rapid and possibly unstable grounding-line retreat may be associated with relatively modest basal melt rates.
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| 9. |
- Ormenisan, Alexandru-Adrian, et al.
(författare)
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Time travel and provenance for machine learning pipelines
- 2020
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Ingår i: OpML 2020 - 2020 USENIX Conference on Operational Machine Learning. - : USENIX Association.
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Konferensbidrag (refereegranskat)abstract
- Machine learning pipelines have become the defacto paradigm for productionizing machine learning applications as they clearly abstract the processing steps involved in transforming raw data into engineered features that are then used to train models. In this paper, we use a bottom-up method for capturing provenance information regarding the processing steps and artifacts produced in ML pipelines. Our approach is based on replacing traditional intrusive hooks in application code (to capture ML pipeline events) with standardized change-data-capture support in the systems involved in ML pipelines: the distributed file system, feature store, resource manager, and applications themselves. In particular, we leverage data versioning and time-travel capabilities in our feature store to show how provenance can enable model reproducibility and debugging.
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