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- Abbasi, Rasha, et al.
(författare)
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IceCube search for neutrinos from GRB 221009A
- 2023
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Ingår i: Proceedings of 38th International Cosmic Ray Conference (ICRC 2023). - : Sissa Medialab Srl.
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Konferensbidrag (refereegranskat)abstract
- GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremelyhigh flux of high and very-high-energy photons provide a unique opportunity to probe the predictedneutrino counterpart to the electromagnetic emission. We have used a variety of methods to searchfor neutrinos in coincidence with the GRB over several time windows during the precursor, promptand afterglow phases of the GRB. MeV scale neutrinos are studied using photo-multiplier ratescalers which are normally used to search for galactic core-collapse supernovae neutrinos. GeVneutrinos are searched starting with DeepCore triggers. These events don’t have directionallocalization, but instead can indicate an excess in the rate of events. 10 GeV - 1 TeV and >TeVneutrinos are searched using traditional neutrino point source methods which take into accountthe direction and time of events with DeepCore and the entire IceCube detector respectively. The>TeV results include both a fast-response analysis conducted by IceCube in real-time with timewindows of T0 − 1 to T0 + 2 hours and T0 ± 1 day around the time of GRB 221009A, as well asan offline analysis with 3 new time windows up to a time window of T0 − 1 to T0 + 14 days, thelongest time period we consider. The combination of observations by IceCube covers 9 ordersof magnitude in neutrino energy, from MeV to PeV, placing upper limits across the range forpredicted neutrino emission.
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| 2. |
- Johansson, Erika, 1969, et al.
(författare)
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Reinventing Sustainable Construction: Exploring the Paradigm Shift Needed to Reconcile Environmental and Sustainability Objectives
- 2010
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Ingår i: Paper presented at the SB10mad: International Sustainable Building Conference, Madrid, April 28-30, 2010. ; , s. 14-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Nowadays it is a well‐known fact that the conventional architectural and urban development model has difficulty in being adapted to the new requirements that society and industry demands, particularly with regard to cultural aspects and sustainability. Regardless there is a constant progress in such technologyand research, the historical inertia of construction procedures and techniques have had the consequence that the building process is un‐synchronised with current requirements. A recent contribution in this regard is the “House Master School (HMS)” education and career model that integrates the concepts of Sustainable Building (SB), Sustainable Conservation (SC) and Education for Sustainable Development (ESD). Thus, the HMS model integrates a unitary set of transdisciplinary and holistic key concepts involved in sustainable development, planning and design, involving architecture; engineering; conservation; energy; natural and social sciences; economy; innovation; management; quality; logistics; ICT; arts and crafts, that may be locally or globally applied. It is generally applicable, and may be transferred to other countries, regions and/or fields. This paper introduces the HMS model and analyzes the pros and cons of adapting this model in Spain. It analyzes the possible implications of its introduction in the production‐system andbuilding. Potential impact and effects of this change in a Spanish context are discussed, and the role of such implications on decision‐making.
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| 5. |
- Oldenhof, Martijn, et al.
(författare)
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Industry-Scale Orchestrated Federated Learning for Drug Discovery
- 2023
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Ingår i: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. ; 37, s. 15576-15584
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Konferensbidrag (refereegranskat)abstract
- To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.
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