Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userESCAPE -- addressing Open Science challenges
163
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
ESCAPE (European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures) is an EU H2020 project that addresses the Open Science challenges shared by the astrophysics and and accelerator-based physics and nuclear physics ESFRI projects and landmarks. This project is embedded in the context of the European Open Science Cloud (EOSC) and involves activities to develop a prototype Data Lake and Science Platform, as well as support of an Open Source Software Repository, connection of the Virtual Observatory framework to EOSC, and engaging the public in citizen science. In this poster paper we provide a brief overview of the project and the results presented at ADASS.
rate research
Read More
Cloud computing is a new computing paradigm which allows sharing of resources on remote server such as hardware, network, storage using internet and provides the way through which application, computing power, computing infrastructure can be delivered to the user as a service. Cloud computing unique attribute promise cost effective Information Technology Solution (IT Solution) to the user. All computing needs are provided by the Cloud Service Provider (CSP) and they can be increased or decreased dynamically as required by the user. As data and Application are located at the server and may be beyond geographical boundary, this leads a number of concern from the user prospective. The objective of this paper is to explore the key issues of cloud computing which is delaying its adoption.
In this paper, several works are proposed to address practical challenges for deploying RNN Transducer (RNN-T) based speech recognition system. These challenges are adapting a well-trained RNN-T model to a new domain without collecting the audio data, obtaining time stamps and confidence scores at word level. The first challenge is solved with a splicing data method which concatenates the speech segments extracted from the source domain data. To get the time stamp, a phone prediction branch is added to the RNN-T model by sharing the encoder for the purpose of force alignment. Finally, we obtain word-level confidence scores by utilizing several types of features calculated during decoding and from confusion network. Evaluated with Microsoft production data, the splicing data adaptation method improves the baseline and adaptation with the text to speech method by 58.03% and 15.25% relative word error rate reduction, respectively. The proposed time stamping method can get less than 50ms word timing difference from the ground truth alignment on average while maintaining the recognition accuracy of the RNN-T model. We also obtain high confidence annotation performance with limited computation cost.
The International Virtual Observatory Alliance (IVOA) has developed and built, in the last two decades, an ecosystem of distributed resources, interoperable and based upon open shared technological standards. In doing so the IVOA has anticipated, putting into practice for the astrophysical domain, the ideas of FAIR-ness of data and service resources and the Open-ness of sharing scientific results, leveraging on the underlying open standards required to fill the above. In Europe, efforts in supporting and developing the ecosystem proposed by the IVOA specifications has been provided by a continuous set of EU funded projects up to current H2020 ESCAPE ESFRI cluster. In the meantime, in the last years, Europe has realised the importance of promoting the Open Science approach for the research communities and started the European Open Science Cloud (EOSC) project to create a distributed environment for research data, services and communities. In this framework the European VO community, had to face the move from the interoperability scenario in the astrophysics domain into a larger audience perspective that includes a cross-domain FAIR approach. Within the ESCAPE project the CEVO Work Package (Connecting ESFRI to EOSC through the VO) has one task to deal with this integration challenge: a challenge where an existing, mature, distributed e-infrastructure has to be matched to a forming, more general architecture. CEVO started its works in the first months of 2019 and has already worked on the integration of the VO Registry into the EOSC e-infrastructure. This contribution reports on the first year and a half of integration activities, that involve applications, services and resources being aware of the VO scenario and compatible with the EOSC architecture.
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps.
In classical analyses of $gamma$-ray data from IACTs, such as H.E.S.S., aperture photometry, or photon counting, is applied in a (typically circular) region of interest (RoI) encompassing the source. A key element in the analysis is to estimate the amount of background in the RoI due to residual cosmic ray-induced air showers in the data. Various standard background estimation techniques have been developed in the last decades, most of them rely on a measurement of the background from source-free regions within the observed field of view. However, in particular in the Galactic plane, source analysis and background estimation are hampered by the large number of, sometimes overlapping, $gamma$-ray sources and large-scale diffuse $gamma$-ray emission. For complicated fields of view, a three-dimensional (3D) likelihood analysis shows the potential to be superior to classical analysis. In this analysis technique, a spectromorphological model, consisting of one or multiple source components and a background component, is fitted to the data, resulting in a complete spectral and spatial description of the field of view. For the application to IACT data, the major challenge of such an approach is the construction of a robust background model. In this work, we apply the 3D likelihood analysis to various test data recently made public by H.E.S.S., using the open analysis frameworks ctools and Gammapy. First, we show that, when using these tools in a classical analysis approach and comparing to the proprietary H.E.S.S. analysis framework, virtually identical high-level analysis results are obtained. We then describe the construction of a generic background model from data of H.E.S.S. observations, and demonstrate that a 3D likelihood analysis using this background model yields high-level analysis results that are highly compatible with those obtained from the classical analyses. (abridged)
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
