| 1. |
- Andersson, Kristina, et al.
(author)
-
Smarta urbana trafikzoner : en del av den flexibla staden där fordon rör sig på människors villkor - resultatrapport steg 2
- 2022
-
Reports (other academic/artistic)abstract
- Projektet Smarta urbana trafikzoner har genomfört tre demonstrationer av smarta zoner som kan bli ett kraftfullt verktyg för att bidra till tystare, säkrare och hälsosammare urbana miljöer. Den smarta zonen bygger på digitala verktyg såsom uppkopplade sensorer och geofencing. Ett geofence är ett digitalt definierat område där fordonet styrs av digitalt uppsatta regler, vilket möjliggör kontroll av hur ett fordon körs inom zonen, utan mänsklig inblandning.
|
|
| 2. |
- Bjerkne Wenneberg, Sandra, et al.
(author)
-
Association between inflammatory response and outcome after subarachnoid haemorrhage.
- 2021
-
In: Acta neurologica Scandinavica. - : Hindawi Limited. - 1600-0404 .- 0001-6314. ; 143:2, s. 195-205
-
Journal article (peer-reviewed)abstract
- Recent reports suggest an association between the inflammatory response after aneurysmal subarachnoid haemorrhage (aSAH) and patients' outcome. The primary aim of this study was to identify a potential association between the inflammatory response after aSAH and 1-year outcome. The secondary aim was to investigate whether the inflammatory response after aSAH could predict the development of delayed cerebral ischaemia (DCI).This prospective observational pilot study included patients with an aSAH admitted to Sahlgrenska University Hospital, Gothenburg, Sweden, between May 2015 and October 2016. The patients were stratified according to the extended Glasgow Outcome Scale (GOSE) as having an unfavourable (score: 1-4) or favourable outcome (score: 5-8). Furthermore, patients were stratified depending on development of DCI or not. Patient data and blood samples were collected and analysed at admission and after 10days.Elevated serum concentrations of inflammatory markers such as tumour necrosis factor-α and interleukin (IL)-6, IL-1Ra, C-reactive protein and intercellular adhesion molecule-1 were detected in patients with unfavourable outcome. When adjustments for Glasgow coma scale were made, only IL-1Ra remained significantly associated with poor outcome (p=0.012). The inflammatory response after aSAH was not predictive of the development of DCI.Elevated serum concentrations of inflammatory markers were associated with poor neurological outcome 1-year after aSAH. However, inflammatory markers are affected by many clinical events, and when adjustments were made, only IL-1Ra remained significantly associated with poor outcome. The robustness of these results needs to be tested in a larger trial.
|
|
| 3. |
- Chien, Wei Der, et al.
(author)
-
NoaSci : A Numerical Object Array Library for I/O of Scientific Applications on Object Storage
- 2022
-
In: <em>2022</em> 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Conference paper (peer-reviewed)abstract
- The strong consistency and stateful workflow are seen as the major factors for limiting parallel I/O performance because of the need for locking and state management. While the POSIX-based I/O model dominates modern HPC storage infrastructure, emerging object storage technology can potentially improve I/O performance by eliminating these bottlenecks.Despite a wide deployment on the cloud, its adoption in HPCremains low. We argue one reason is the lack of a suitable programming interface for parallel I/O in scientific applications. In this work, we introduce NoaSci, a Numerical Object Arraylibrary for scientific applications. NoaSci supports different data formats (e.g. HDF5, binary), and focuses on supporting node-local burst buffers and object stores. We demonstrate for the first time how scientific applications can perform parallel I/Oon Seagate’s Motr object store through NoaSci. We evaluate NoaSci’s preliminary performance using the iPIC3D spaceweather application and position against existing I/O methods.
|
|
| 4. |
- Dykes, Tim, et al.
(author)
-
Mamba: Portable Array-based Abstractions for Heterogeneous High-Performance Systems
- 2021
-
In: 2021 International Workshop on Performance, Portability and Productivity in HPC (P3HPC). - : Institute of Electrical and Electronics Engineers (IEEE).
-
Conference paper (peer-reviewed)abstract
- High performance computing architectures have become increasingly heterogeneous in recent times. This growing architectural variety presents a multi-faceted portability problem affecting applications, libraries, programming models, languages, compilers, run-times, and system software. Approaches for performance portability typically focus heavily on efficient usage of parallel compute architectures and less on data locality abstractions and complex memory systems, with minimal support afforded to effective memory management in traditional HPC languages such as C and Fortran. We present Mamba, a library to facilitate usage of heterogeneous memory systems by high performance application/library developers through high level array-based abstractions for memory management supported by a low-level generic memory API. We detail the library design and implementation, demonstrating generic memory allocation, data layout specification, array tiling and heterogeneous transport. We evaluate performance in the context of a typical matrix transposition, DNA sequencing benchmark, and an application use case for high-order spectral element based incompressible flow.
|
|
| 5. |
|
|
| 6. |
- Pallon, Jon, et al.
(author)
-
Presence of microorganisms in children with pharyngotonsillitis and healthy controls: a prospective study in primary healthcare
- 2021
-
In: Infection. - : Springer Science and Business Media LLC. - 0300-8126 .- 1439-0973. ; 49:4, s. 715-724
-
Journal article (peer-reviewed)abstract
- Purpose Most studies on paediatric pharyngotonsillitis focus on group A streptococci. This study, however, analyses a broad spectrum of bacteria and viruses related to paediatric pharyngotonsillitis and evaluates their associated clinical symptoms and courses. Methods This observational prospective study in primary healthcare includes 77 children aged < 15 with a sore throat and 34 asymptomatic children, all of whom were sampled from the tonsils with an E-swab(R) for analysis with culture and PCR for 14 bacteria and 15 viruses. Patients were evaluated clinically, and their symptoms recorded in diaries for 10 days. Participants were followed up for 3 months by reviewing medical records. Results A pathogen was detected in 86% of patients and in 71% of controls (P = 0.06). Bacteria were found in 69% of patients and 59% of controls (P = 0.3), and viruses in 36% and 26%, respectively (P = 0.3). Group A streptococci was the most common finding, with a prevalence of 49% and 32%, respectively (P = 0.1). Clinical signs were not useful for distinguishing pathogens. None of the controls and 16% of the patients reconsulted for a sore throat within 3 months. Conclusion Bacteria were more common than viruses in both study groups. The high rate of pathogens in asymptomatic children interferes with diagnoses based on aetiology.
|
|
| 7. |
- Podobas, Artur, et al.
(author)
-
StreamBrain : An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
- 2021
-
In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM).
-
Conference paper (peer-reviewed)abstract
- The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other - less-known - machine learning algorithms with a mature and solid theoretical foundation whose performance remains unexplored. One such example is the brain-like Bayesian Confidence Propagation Neural Network (BCPNN). In this paper, we introduce StreamBrain - a framework that allows neural networks based on BCPNN to be practically deployed in High-Performance Computing systems. StreamBrain is a domain-specific language (DSL), similar in concept to existing machine learning (ML) frameworks, and supports backends for CPUs, GPUs, and even FPGAs. We empirically demonstrate that StreamBrain can train the well-known ML benchmark dataset MNIST within seconds, and we are the first to demonstrate BCPNN on STL-10 size networks. We also show how StreamBrain can be used to train with custom floating-point formats and illustrate the impact of using different bfloat variations on BCPNN using FPGAs.
|
|
| 8. |
- Svedin, Martin, et al.
(author)
-
Benchmarking the Nvidia GPU Lineage : From Early K80 to Modern A100 with Asynchronous Memory Transfers
- 2021
-
In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM).
-
Conference paper (peer-reviewed)abstract
- For many, Graphics Processing Units (GPUs) provides a source of reliable computing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs, the Ampere 100 (A100), claiming significant performance improvements over previous generations, particularly for AI-workloads, as well as introducing new architectural features such as asynchronous data movement. But how well does the A100 perform on non-AI benchmarks, and can we expect the A100 to deliver the application improvements we have grown used to with previous GPU generations? In this paper, we benchmark the A100 GPU and compare it to four previous generations of GPUs, with a particular focus on empirically quantifying our derived performance expectations. We find that the A100 delivers less performance increase than previous generations for the well-known Rodinia benchmark suite; we show that some of these performance anomalies can be remedied through clever use of the new data-movement features, which we microbenchmark and demonstrate where (and more importantly, how) they should be used.
|
|
| 9. |
- Svedin, Martin, et al.
(author)
-
Higgs Boson Classification : Brain-inspired BCPNN Learning with StreamBrain
- 2021
-
In: 2021 IEEE International Conference On Cluster Computing (CLUSTER 2021). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 705-710
-
Conference paper (peer-reviewed)abstract
- One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning (ML) techniques that are inspired by brain models. Such methods use alternative learning rules potentially more efficiently than established learning rules. In this work, we focus on the potential of brain-inspired ML for exploiting High-Performance Computing (HPC) resources to solve ML problems: we discuss the BCPNN and an HPC implementation, called StreamBrain, its computational cost, suitability to HPC systems. As an example, we use StreamBrain to analyze the Higgs Boson dataset from High Energy Physics and discriminate between background and signal classes in collisions of high-energy particle colliders. Overall, we reach up to 69.15% accuracy and 76.4% Area Under the Curve (AUC) performance.
|
|
| 10. |
- Tordön, Rikard, Doktorand, 1969-
(author)
-
Health, Experienced Support and School Performance among Children in Out-of-home care
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Children in out-of-home care (OHC) have higher risks for developing poorer health and school achievement, being subjected to more abuse experiences, as well as negative long-term outcomes related to occupational performance, socioeconomic status, addiction, and criminality. Research related to OHC children is fragmented and the effects of interventions are under-studied.This thesis aimed to explore health, abuse, support, and preconditions for school among children in OHC and to assess changes after an intervention targeting foster children’s school performance.Paper I compared OHC pupils in last year high school to non-OHC peers in a national survey with 5 839 pupils. The study showed that risks of abuse and poor mental health are evident for adolescents in out-of-home care. Also, results indicated a lower disclosure rate of sexual abuse, particularly to police or social services.Paper II compared OHC pupils to peers in birth parent care by analyzing responses in four consecutive year surveys in a regional sample comprising 23 798 pupils in 8th-year compulsory and 2nd-year high school. Responses from the 311 pupils in OHC showed poorer outcomes than did birth-parent care peers in perceived satisfaction with social life and relations, trust to other persons in different relations, abuse experiences online, and sense of security in the school and at home. These results also applied when compared to a subset of pupils living with a single birth parent.Paper III analyzed prospective test and questionnaire data of intelligence, adaptive behavior, mathematics, literacy skills, and psychosocial wellbeing from 856 children in foster care. Results revealed poorer preconditions for school performance of between 0.5 and 1.0 standard deviations below age-standardized norms. The analysis also provided results regarding different intelligence domains, where working memory showed the lowest scores while perceptual functioning were close to norms. Boys generally scored poorer than girls except in mathematics.Paper IV explored the effects of a school-based intervention, Skolfam, on a subset of Paper III cohort (n= 475). Results showed improved skills in higher-order cognitive executive functions such as reading comprehension, sentence chains, mathematics, and intelligence. For less complex cognitive functions, affective functioning or psychosocial symptoms, no improvements were seen, except for reduced hyperactivity.Conclusion: The studies confirm that children in OHC have poorer mental health, are less satisfied with social life, have more adverse experiences both online and in real life and have poorer preconditions for school performance than do non-OHC peers. Importantly, Skolfam intervention can partially enhance preconditions for school performance. Further studies on longitudinal risk, with a design to identify specific protective factors, development of school-related competencies and ways to support OHC children in school are needed.
|
|
