| 1. |
- Aamodt, K., et al.
(författare)
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Alignment of the ALICE Inner Tracking System with cosmic-ray tracks
- 2010
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 5
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Konferensbidrag (refereegranskat)abstract
- ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 mu m in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10(5) charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.
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| 4. |
- Chapnick, J. V., et al.
(författare)
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Techniques for multimodality image registration
- 1993
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Ingår i: Bioengineering, Proceedings of the Northeast Conference. - 0780309251 ; , s. 221-222
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Konferensbidrag (refereegranskat)abstract
- The authors describe the development of techniques used for cross-modality correlation of medical images. To accomplish this goal, software routines were developed which automate and standardize the comparison of images within and between three-dimensional tomographic imaging modalities. Data from phantoms and clinical studies reflect the success of this technique.
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| 5. |
- de Lemos, Rogerio, et al.
(författare)
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Software Engineering for Self-Adaptive Systems : A Second Research Roadmap
- 2013
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Ingår i: Software Engineering for Self-Adaptive Systems II. - Berlin, Heidelberg : Springer. - 9783642358128 ; , s. 1-32
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.
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| 9. |
- Robishaw, T., et al.
(författare)
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Measuring magnetic fields near and far with the SKA via the zeeman effect
- 2014
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Ingår i: Proceedings of Science. - 1824-8039. ; 9-13-June-2014
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Konferensbidrag (refereegranskat)abstract
- The measurement of Zeeman splitting in spectral lines-both in emission and absorption-can provide direct estimates of the magnetic field strength and direction in atomic and molecular clouds, both in our own MilkyWay and in external galaxies. This method will probe the magnetic field in the warm and cold neutral components of the interstellar medium, providing a complement to the extensive SKA Faraday studies planning to probe the field in the ionized components.
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| 10. |
- Souza, Artur, et al.
(författare)
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Bayesian Optimization with a Prior for the Optimum
- 2021
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Ingår i: Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030865221 ; 12977 LNAI, s. 265-296
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Konferensbidrag (refereegranskat)abstract
- While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to waste function evaluations on bad design choices (e.g., machine learning hyperparameters) that the expert already knows to work poorly. To address this issue, we introduce Bayesian Optimization with a Prior for the Optimum (BOPrO). BOPrO allows users to inject their knowledge into the optimization process in the form of priors about which parts of the input space will yield the best performance, rather than BO’s standard priors over functions, which are much less intuitive for users. BOPrO then combines these priors with BO’s standard probabilistic model to form a pseudo-posterior used to select which points to evaluate next. We show that BOPrO is around 6.67 × faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application. We also show that BOPrO converges faster even if the priors for the optimum are not entirely accurate and that it robustly recovers from misleading priors.
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