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- Gonoskov, Arkady, 1984, et al.
(författare)
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Extended particle-in-cell schemes for physics in ultrastrong laser fields : Review and developments
- 2015
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Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics. - 1539-3755 .- 1550-2376. ; 92:2
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Tidskriftsartikel (refereegranskat)abstract
- We review common extensions of particle-in-cell (PIC) schemes which account for strong field phenomena in laser-plasma interactions. After describing the physical processes of interest and their numerical implementation, we provide solutions for several associated methodological and algorithmic problems. We propose a modified event generator that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step. Based on this, we also develop an adaptive event generator that subdivides the time step for locally resolving QED events, allowing for efficient simulation of cascades. Further, we present a unified technical interface for including the processes of interest in different PIC implementations. Two PIC codes which support this interface, PICADOR and ELMIS, are also briefly reviewed.
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- Pivodic, Aldina, 1978, et al.
(författare)
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Development and validation of a new clinical decision support tool to optimize screening for retinopathy of prematurity
- 2022
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Ingår i: British Journal of Ophthalmology. - : BMJ Publishing Group Ltd. - 0007-1161 .- 1468-2079. ; 106:11, s. 1573-1580
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND/AIMS: Prematurely born infants undergo costly, stressful eye examinations to uncover the small fraction with retinopathy of prematurity (ROP) that needs treatment to prevent blindness. The aim was to develop a prediction tool (DIGIROP-Screen) with 100% sensitivity and high specificity to safely reduce screening of those infants not needing treatment. DIGIROP-Screen was compared with four other ROP models based on longitudinal weights.METHODS: Data, including infants born at 24-30 weeks of gestational age (GA), for DIGIROP-Screen development (DevGroup, N=6991) originate from the Swedish National Registry for ROP. Three international cohorts comprised the external validation groups (ValGroups, N=1241). Multivariable logistic regressions, over postnatal ages (PNAs) 6-14 weeks, were validated. Predictors were birth characteristics, status and age at first diagnosed ROP and essential interactions.RESULTS: ROP treatment was required in 287 (4.1%)/6991 infants in DevGroup and 49 (3.9%)/1241 in ValGroups. To allow 100% sensitivity in DevGroup, specificity at birth was 53.1% and cumulatively 60.5% at PNA 8 weeks. Applying the same cut-offs in ValGroups, specificities were similar (46.3% and 53.5%). One infant with severe malformations in ValGroups was incorrectly classified as not needing screening. For all other infants, at PNA 6-14 weeks, sensitivity was 100%. In other published models, sensitivity ranged from 88.5% to 100% and specificity ranged from 9.6% to 45.2%.CONCLUSIONS: DIGIROP-Screen, a clinical decision support tool using readily available birth and ROP screening data for infants born GA 24-30 weeks, in the European and North American populations tested can safely identify infants not needing ROP screening. DIGIROP-Screen had equal or higher sensitivity and specificity compared with other models. DIGIROP-Screen should be tested in any new cohort for validation and if not validated it can be modified using the same statistical approaches applied to a specific clinical setting.
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- Adalbjornsson, S. I., et al.
(författare)
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Estimating Periodicities in Symbolic Sequences Using Sparse Modeling
- 2015
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Ingår i: Ieee Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 63:8, s. 2142-2150
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a method for estimating statistical periodicities in symbolic sequences. Different from other common approaches used for the estimation of periodicities of sequences of arbitrary, finite, symbol sets, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation in a sparse modeling framework to represent the periodic behavior of the sequence. The resulting criterion includes a restriction on the cardinality of the solution; two approximate solutions are suggested-one greedy and one using an iterative convex relaxation strategy to ease the cardinality restriction. The performance of the proposed methods are illustrated using both simulated and real DNA data, showing a notable performance gain as compared to other common estimators.
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- Gonoskov, Arkady, 1984, et al.
(författare)
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Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics
- 2019
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can “read” features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.
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