| 1. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 2. |
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| 3. |
- Ademuyiwa, Adesoji O., et al.
(författare)
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Determinants of morbidity and mortality following emergency abdominal surgery in children in low-income and middle-income countries
- 2016
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Ingår i: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Child health is a key priority on the global health agenda, yet the provision of essential and emergency surgery in children is patchy in resource-poor regions. This study was aimed to determine the mortality risk for emergency abdominal paediatric surgery in low-income countries globally.Methods: Multicentre, international, prospective, cohort study. Self-selected surgical units performing emergency abdominal surgery submitted prespecified data for consecutive children aged <16 years during a 2-week period between July and December 2014. The United Nation's Human Development Index (HDI) was used to stratify countries. The main outcome measure was 30-day postoperative mortality, analysed by multilevel logistic regression.Results: This study included 1409 patients from 253 centres in 43 countries; 282 children were under 2 years of age. Among them, 265 (18.8%) were from low-HDI, 450 (31.9%) from middle-HDI and 694 (49.3%) from high-HDI countries. The most common operations performed were appendectomy, small bowel resection, pyloromyotomy and correction of intussusception. After adjustment for patient and hospital risk factors, child mortality at 30 days was significantly higher in low-HDI (adjusted OR 7.14 (95% CI 2.52 to 20.23), p<0.001) and middle-HDI (4.42 (1.44 to 13.56), p=0.009) countries compared with high-HDI countries, translating to 40 excess deaths per 1000 procedures performed.Conclusions: Adjusted mortality in children following emergency abdominal surgery may be as high as 7 times greater in low-HDI and middle-HDI countries compared with high-HDI countries. Effective provision of emergency essential surgery should be a key priority for global child health agendas.
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| 4. |
- Koubarakis, M., et al.
(författare)
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From copernicus big data to extreme earth analytics
- 2019
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Ingår i: Advances in Database Technology - EDBT. - : OpenProceedings. - 9783893180813 ; , s. 690-693
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Konferensbidrag (refereegranskat)abstract
- Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.
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| 5. |
- Seif, M. R., et al.
(författare)
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Polynomial approximation for fast generation of associated Legendre functions
- 2018
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Ingår i: Acta Geodaetica et Geophysica. - : Springer. - 2213-5812 .- 2213-5820. ; 53:2, s. 275-293
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Tidskriftsartikel (refereegranskat)abstract
- Today high-speed computers have simplified many computational problems, but fast techniques and algorithms are still relevant. In this study, the Hermitian polynomial approximation is used for fast evaluation of the associated Legendre functions (ALFs). It has lots of applications in geodesy and geophysics. This method approximates the ALFs instead of computing them by recursive formulae and generate them several times faster. The approximated ALFs by the Newtonian polynomials are compared with Hermitian ones and their differences are discussed. Here, this approach is applied for computing a global geoid model point-wise from EGM08 to degree and order 2160 and in propagating the orbit of a low Earth orbiting satellite. Our numerical results show that the CPU-time decreases at least two times for orbit propagation, and five times for geoid computation comparing to the case where recursive formulae for generation of ALFs are used. The approximation error in the orbit computation is at a sub-millimeter level over two weeks and that the computed geoid 0.01 mm, with a maximum of 1 mm
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| 6. |
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| 7. |
- Meldrum, M., et al.
(författare)
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Arcon : Continuous and deep data stream analytics
- 2019
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. - 9781450376600
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Konferensbidrag (refereegranskat)abstract
- Contemporary end-to-end data pipelines need to combine many diverse workloads such as machine learning, relational operations, stream dataflows, tensor transformations, and graphs. For each of these workload types, there exists several frontends (e.g., SQL, Beam, Keras) based on different programming languages as well as different runtimes (e.g., Spark, Flink, Tensorflow) that optimize for a particular frontend and possibly a hardware architecture (e.g., GPUs). The resulting pipelines suffer in terms of complexity and performance due to excessive type conversions, materialization of intermediate results, and lack of cross-framework optimizations. Arcon aims to provide a unified approach to declare and execute tasks across frontend-boundaries as well as enabling their seamless integration with event-driven services at scale. In this demonstration, we present Arcon and through a series of use-case scenarios demonstrate that its execution model is powerful enough to cover existing as well as upcoming real-time computations for analytics and application-specific needs.
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| 8. |
- Rahimian, Fatemeh, et al.
(författare)
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A distributed algorithm for large-scale graph partitioning
- 2015
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - ACM : Association for Computing Machinery. - 1556-4665 .- 1556-4703. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- Balanced graph partitioning is an NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems, including the optimal storage of large sets of graph-structured data over several hosts. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable because they typically involve frequent global operations over the entire graph. In this article, we propose a fully distributed algorithm called Ja-be-Ja that uses local search and simulated annealing techniques for two types of graph partitioning: edge-cut partitioning and vertex-cut partitioning. The algorithm is massively parallel: There is no central coordination, each vertex is processed independently, and only the direct neighbors of a vertex and a small subset of random vertices in the graph need to be known locally. Strict synchronization is not required. These features allow Ja-be-Ja to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We show that the minimal edge-cut value empirically achieved by Ja-be-Ja is comparable to state-of-the-art centralized algorithms such as Metis. In particular, on large social networks, Ja-be-Ja outperforms Metis. We also show that Ja-be-Ja computes very low vertex-cuts, which are proved significantly more effective than edge-cuts for processing most real-world graphs.
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