Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00069
D. V. Kuppevelt, Frank W. Takes, E. Heemskerk
n/a
N/A
{"title":"Understanding Evolving Communities in Transnational Board Interlock Networks","authors":"D. V. Kuppevelt, Frank W. Takes, E. Heemskerk","doi":"10.1109/eScience.2018.00069","DOIUrl":"https://doi.org/10.1109/eScience.2018.00069","url":null,"abstract":"n/a","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"18 1","pages":"312-313"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85894928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00135
Javier Conejero, Cristian Ramon-Cortes, K. Serradell, Rosa M. Badia
Task-based programming is becoming a tool of large interest for boosting High-Performance Computing (HPC) and Big Data applications. In particular, COMP Superscalar (COMPSs), is showing to be an effective task-based programming model for distributed computing of Big Data applications within HPC environments. Applications like NMMB-MONARCH, which is a dust forecast application composed by a set of steps (being some of them binaries with or without MPI), are perfect candidates for PyCOMPSs, the Python binding of COMPSs. This paper describes the success story of the adaptation of the NMMB-MONARCH online multi-scale atmospheric dust model to PyCOMPSs in order to exploit its inherent parallelism with the minimal developer effort. The paper also includes an evaluation of this implementation in the Nord3 supercomputer, a scalability analysis and an in-depth behaviour study. The main results presented in this paper are: (1) PyCOMPSs is able to extract the parallelism from the NMMB-MONARCH application; (2) it is able to improve the dust forecasting in terms of performance when compared with previous versions, and (3) PyCOMPSs is able to interact and share the resources with MPI applications when included in the workflow as tasks. Finally, we present the keys for exporting the knowledge of this experience to other applications in order to benefit from using PyCOMPSs.
{"title":"Boosting Atmospheric Dust Forecast with PyCOMPSs","authors":"Javier Conejero, Cristian Ramon-Cortes, K. Serradell, Rosa M. Badia","doi":"10.1109/eScience.2018.00135","DOIUrl":"https://doi.org/10.1109/eScience.2018.00135","url":null,"abstract":"Task-based programming is becoming a tool of large interest for boosting High-Performance Computing (HPC) and Big Data applications. In particular, COMP Superscalar (COMPSs), is showing to be an effective task-based programming model for distributed computing of Big Data applications within HPC environments. Applications like NMMB-MONARCH, which is a dust forecast application composed by a set of steps (being some of them binaries with or without MPI), are perfect candidates for PyCOMPSs, the Python binding of COMPSs. This paper describes the success story of the adaptation of the NMMB-MONARCH online multi-scale atmospheric dust model to PyCOMPSs in order to exploit its inherent parallelism with the minimal developer effort. The paper also includes an evaluation of this implementation in the Nord3 supercomputer, a scalability analysis and an in-depth behaviour study. The main results presented in this paper are: (1) PyCOMPSs is able to extract the parallelism from the NMMB-MONARCH application; (2) it is able to improve the dust forecasting in terms of performance when compared with previous versions, and (3) PyCOMPSs is able to interact and share the resources with MPI applications when included in the workflow as tasks. Finally, we present the keys for exporting the knowledge of this experience to other applications in order to benefit from using PyCOMPSs.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"29 1","pages":"464-474"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89638784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00129
M. Kraak, Andreas Weber, J. V. Lottum, Y. Engelhardt
This abstract sketches the basic design of a prototype that enables the proper display, exploration, and analysis of historical shipping data in an adaptable WebVR environment. In the environment users will be able to create visually networked ‘eventscapes’ which allow to identify spatio-temporal patterns in digitized maritime heritage and similar datasets.
{"title":"Toward VR Eventscapes for Spatio-Temporal Access to Digital Maritime Heritage","authors":"M. Kraak, Andreas Weber, J. V. Lottum, Y. Engelhardt","doi":"10.1109/eScience.2018.00129","DOIUrl":"https://doi.org/10.1109/eScience.2018.00129","url":null,"abstract":"This abstract sketches the basic design of a prototype that enables the proper display, exploration, and analysis of historical shipping data in an adaptable WebVR environment. In the environment users will be able to create visually networked ‘eventscapes’ which allow to identify spatio-temporal patterns in digitized maritime heritage and similar datasets.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"50 1","pages":"413-414"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75803717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00026
Lasse Wollatz, Mark Scott, Steven J. Johnston, P. Lackie, S. Cox
Microfocus X-ray computed tomography (µCT) and 3D microscopy scanning create scientific data in the form images. These images are each several tens of gigabytes in size. E-Scientists in medicine require a user-friendly way of storing the data and related metadata and accessing it. Existing management systems allow computer scientists to create automatic image workflows through the use of application programming interfaces (APIs) but do not offer an easy alternative for users less familiar with programming. We present a new approach to the management and curation of biomedical image data and related metadata. Our system, Mata, uses a network file share to give users direct access to their data and also provides access to metadata. Mata also enables a variety of visualization options as required by e-Scientists in medicine.
{"title":"Curation of Image Data for Medical Research","authors":"Lasse Wollatz, Mark Scott, Steven J. Johnston, P. Lackie, S. Cox","doi":"10.1109/eScience.2018.00026","DOIUrl":"https://doi.org/10.1109/eScience.2018.00026","url":null,"abstract":"Microfocus X-ray computed tomography (µCT) and 3D microscopy scanning create scientific data in the form images. These images are each several tens of gigabytes in size. E-Scientists in medicine require a user-friendly way of storing the data and related metadata and accessing it. Existing management systems allow computer scientists to create automatic image workflows through the use of application programming interfaces (APIs) but do not offer an easy alternative for users less familiar with programming. We present a new approach to the management and curation of biomedical image data and related metadata. Our system, Mata, uses a network file share to give users direct access to their data and also provides access to metadata. Mata also enables a variety of visualization options as required by e-Scientists in medicine.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"61 1","pages":"105-113"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84567614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00097
M. E. Astigarraga
The ATLAS Collaboration
ATLAS合作
{"title":"ATLAS Trigger and Data Acquisition Upgrades for the High Luminosity LHC","authors":"M. E. Astigarraga","doi":"10.1109/eScience.2018.00097","DOIUrl":"https://doi.org/10.1109/eScience.2018.00097","url":null,"abstract":"The ATLAS Collaboration","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"34 1","pages":"358-359"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75612508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00112
S. Yatawatta, H. Spreeuw, F. Diblen
We have modified the LBFGS optimizer in PyTorch based on our knowledge in using the LBFGS algorithm in radio interferometric calibration (SAGECal). We give results to show the performance improvement of PyTorch in various machine learning applications due to our improvements.
{"title":"Improving LBFGS Optimizer in PyTorch: Knowledge Transfer from Radio Interferometric Calibration to Machine Learning","authors":"S. Yatawatta, H. Spreeuw, F. Diblen","doi":"10.1109/eScience.2018.00112","DOIUrl":"https://doi.org/10.1109/eScience.2018.00112","url":null,"abstract":"We have modified the LBFGS optimizer in PyTorch based on our knowledge in using the LBFGS algorithm in radio interferometric calibration (SAGECal). We give results to show the performance improvement of PyTorch in various machine learning applications due to our improvements.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"91 1","pages":"386-387"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72826870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00016
A. Struck
Research software is an integral part of scientific investigations. The paper identifies challenges, risks and new opportunities in research software publication and discovery. The diverse code discovery landscape is mapped and agents with their business models identified. Examples for discovery tools and strategies are given to support the classification. Reproducibility of research and reuse of code may improve if software discovery was easier. Researchers conducting a search for existing software in the context of a state-of-the-art report or a software management plan could use this paper as a guideline for their information retrieval strategy.
{"title":"Research Software Discovery: An Overview","authors":"A. Struck","doi":"10.1109/eScience.2018.00016","DOIUrl":"https://doi.org/10.1109/eScience.2018.00016","url":null,"abstract":"Research software is an integral part of scientific investigations. The paper identifies challenges, risks and new opportunities in research software publication and discovery. The diverse code discovery landscape is mapped and agents with their business models identified. Examples for discovery tools and strategies are given to support the classification. Reproducibility of research and reuse of code may improve if software discovery was easier. Researchers conducting a search for existing software in the context of a state-of-the-art report or a software management plan could use this paper as a guideline for their information retrieval strategy.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"45 1","pages":"33-37"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89738074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00048
D. Bari
Low visibility conditions have a strong impact on air and road traffics and their prediction is still a challenge for meteorologists, particularly its spatial coverage. In this study, an estimated visibility product over the north of Morocco, from the operational NWP model AROME outputs using the state-of-the art of Machine-learning regression, has been developed. The performance of the developed model has been assessed, over the continental part only, based on real data collected at 37 synoptic stations over 2 years. Results analysis points out that the developed model for estimating visibility has shown a strong ability to differentiate between visibilities occurring during daytime and nighttime. However, the KDD-developed model have shown low performance of generality across time. The performance evaluation indicates a bias of -9m, a mean absolute error of 1349m with 0.87 correlation and a root mean-square error of 2150m.
{"title":"Visibility Prediction Based on Kilometric NWP Model Outputs Using Machine-Learning Regression","authors":"D. Bari","doi":"10.1109/eScience.2018.00048","DOIUrl":"https://doi.org/10.1109/eScience.2018.00048","url":null,"abstract":"Low visibility conditions have a strong impact on air and road traffics and their prediction is still a challenge for meteorologists, particularly its spatial coverage. In this study, an estimated visibility product over the north of Morocco, from the operational NWP model AROME outputs using the state-of-the art of Machine-learning regression, has been developed. The performance of the developed model has been assessed, over the continental part only, based on real data collected at 37 synoptic stations over 2 years. Results analysis points out that the developed model for estimating visibility has shown a strong ability to differentiate between visibilities occurring during daytime and nighttime. However, the KDD-developed model have shown low performance of generality across time. The performance evaluation indicates a bias of -9m, a mean absolute error of 1349m with 0.87 correlation and a root mean-square error of 2150m.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"52 1","pages":"278-278"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83387136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00056
Lauren Roberts, Peter Michalák, S. Heaps, M. Trenell, D. Wilkinson, P. Watson
There has been a dramatic growth in the number and range of Internet of Things (IoT) sensors that generate healthcare data. These sensors stream high-dimensional time series data that must be analysed in order to provide the insights into medical conditions that can improve patient healthcare. This raises both statistical and computational challenges, including where to deploy the streaming data analytics, given that a typical healthcare IoT system will combine a highly diverse set of components with very varied computational characteristics, e.g. sensors, mobile phones and clouds. Different partitionings of the analytics across these components can dramatically affect key factors such as the battery life of the sensors, and the overall performance. In this work we describe a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth. We illustrate this using our implementation of a statistical model predicting the glucose levels of type II diabetes patients in order to reduce the risk of hyperglycaemia.
{"title":"Automating the Placement of Time Series Models for IoT Healthcare Applications","authors":"Lauren Roberts, Peter Michalák, S. Heaps, M. Trenell, D. Wilkinson, P. Watson","doi":"10.1109/eScience.2018.00056","DOIUrl":"https://doi.org/10.1109/eScience.2018.00056","url":null,"abstract":"There has been a dramatic growth in the number and range of Internet of Things (IoT) sensors that generate healthcare data. These sensors stream high-dimensional time series data that must be analysed in order to provide the insights into medical conditions that can improve patient healthcare. This raises both statistical and computational challenges, including where to deploy the streaming data analytics, given that a typical healthcare IoT system will combine a highly diverse set of components with very varied computational characteristics, e.g. sensors, mobile phones and clouds. Different partitionings of the analytics across these components can dramatically affect key factors such as the battery life of the sensors, and the overall performance. In this work we describe a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth. We illustrate this using our implementation of a statistical model predicting the glucose levels of type II diabetes patients in order to reduce the risk of hyperglycaemia.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"145 1","pages":"290-291"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90712009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00047
S. E. Haupt, J. Cowie, Seth Linden, Tyler C. McCandless, B. Kosović, S. Alessandrini
The National Center for Atmospheric Research (NCAR) has a long history of applying machine learning to weather forecasting challenges. The Dynamic Integrated foreCasting (DICast®) System was one of the first automated weather forecasting engines. It is now in use in quite a few companies with many applications. Some applications being accomplished at NCAR that include DICast and other artificial intelligence technologies include renewable energy, surface transportation, and wildland fire forecasting.
{"title":"Machine Learning for Applied Weather Prediction","authors":"S. E. Haupt, J. Cowie, Seth Linden, Tyler C. McCandless, B. Kosović, S. Alessandrini","doi":"10.1109/eScience.2018.00047","DOIUrl":"https://doi.org/10.1109/eScience.2018.00047","url":null,"abstract":"The National Center for Atmospheric Research (NCAR) has a long history of applying machine learning to weather forecasting challenges. The Dynamic Integrated foreCasting (DICast®) System was one of the first automated weather forecasting engines. It is now in use in quite a few companies with many applications. Some applications being accomplished at NCAR that include DICast and other artificial intelligence technologies include renewable energy, surface transportation, and wildland fire forecasting.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"37 1","pages":"276-277"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88817239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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