The document discusses nuclear computation approaches and developments. It describes how fast computing developments can guide dangerous experiments like in nuclear reactors, complementing direct measurements. Collaboration across fields is needed, especially the underlying sciences for measurements. The speaker presents on TensorFlow, GPU and distributed computing applications in nuclear physics like reconstructing fusion plasmas. Real-time computation can guide fusion reactor experiments every 20 minutes based on pushed data and inverse problem calculations.
This is a high level document enlisting the various types of tasks completed in quantum technologies under various programs and training sessions. One key task was the completion of three courses at MIT Open Learning Library
DSD-INT 2023 - Delft3D User Days - Welcome - Day 3 - AfternoonDeltares
Presentation by Tim Leijnse (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 15 November 2023, Delft.
Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...Boston Data Engineering
What is Kedro?
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
Kedro 2-minute Intro Video: https://youtu.be/KEdmJ2ADy_M
Kedro Docs: https://kedro.readthedocs.io
Kedro GitHub repo: https://github.com/quantumblacklabs/kedro
Meetup: https://www.meetup.com/f7324858-b804-4ed8-ba45-580c262189f1/events/280986950/
This is a high level document enlisting the various types of tasks completed in quantum technologies under various programs and training sessions. One key task was the completion of three courses at MIT Open Learning Library
DSD-INT 2023 - Delft3D User Days - Welcome - Day 3 - AfternoonDeltares
Presentation by Tim Leijnse (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 15 November 2023, Delft.
Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...Boston Data Engineering
What is Kedro?
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
Kedro 2-minute Intro Video: https://youtu.be/KEdmJ2ADy_M
Kedro Docs: https://kedro.readthedocs.io
Kedro GitHub repo: https://github.com/quantumblacklabs/kedro
Meetup: https://www.meetup.com/f7324858-b804-4ed8-ba45-580c262189f1/events/280986950/
Science Gateways – Leveraging Modeling and Simulations in HPC Infrastructure...Sandra Gesing
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"This presentation outlines CERN's approach for decoding data from IoT devices connected to
a LoRa network using Apache Kafka, Kafka Connect, and Kafka Streams, along with Kaitai Struct,
a declarative binary format parsing language used for payload decoding.
We introduce CERN's IoT LoRa network deployment, our LoRa to Kafka bridge, and the processing platform based on Kaitai Struct specifications.
This platform is designed to automatically generate payload decoders and, if necessary, database schemas.
These are then used within Kafka Streams applications to process, decode, and inject the database schema into the payload data as required.
Our focus is on offering a simple, quick, and accessible method for end-users to easily develop and integrate new IoT streaming applications able to handle any binary data format from heterogeneous sensors (multi type, multi vendor) such as proximity, temperature, structural integrity, etc."
STI 2022 - Generating large-scale network analyses of scientific landscapes i...Michele Pasin
The growth of large, programatically accessible bibliometrics databases presents new opportunities for complex analyses of publication metadata. In addition to providing a wealth of information about authors and institutions, databases such as those provided by Dimensions also provide conceptual information and links to entities such as grants, funders and patents. However, data is not the only challenge in evaluating patterns in scholarly work: These large datasets can be challenging to integrate, particularly for those unfamiliar with the complex schemas necessary for accommodating such heterogeneous information, and those most comfortable with data mining may not be as experienced in data visualisation. Here, we present an open-source Python library that streamlines the process accessing and diagramming subsets of the Dimensions on Google BigQuery database and demonstrate its use on the freely available Dimensions COVID-19 dataset. We are optimistic that this tool will expand access to this valuable information by streamlining what would otherwise be multiple complex technical tasks, enabling more researchers to examine patterns in research focus and collaboration over time.
HICSS-2014-Big Island, Hawaii, United States, 08 January 2014Charith Perera
Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos, MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices, Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS), Kona, Hawaii, USA, January, 2014
Memory Efficient Graph Convolutional Network based Distributed Link Predictionmiyurud
Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph. Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).
Using the concept of fog to implement a unified IoT platform
Dynamic replacing the applications or algorithms
Managing the resources of the IoT devices
Collecting the data to analyze and improve the performance
Imagine if we want to alert or notify someone in by ringing bell remotely through your smartphone. This project was taken consideration for such cause.
Upgrade our custom automated School Bell with emerging concept of IOT (Internet of thing) where the entire process of ringing bell is automated and integrated with internet and can set alarm or ring alarm in case of emergency remotely through a smartphone. Highly encrypted with support of Firebase server.
Institutional Repository Single Sources of TruthLighton Phiri
Academic talk, on Institutional Repository Single Sources of Truth, given to The University of Zambia [1] CSC 5741 postgraduate students [2].
[1] https://www.unza.zm
[2] http://lis.unza.zm/~lightonphiri/teaching/unza/2020/csc5741
Dr Matthew Berryman, IT Architect, presented an overview of his research as part of the SMART Seminar Series on 15 November 2016.
More information: http://smart.uow.edu.au/events/UOW223675.html
A modern approach for wiring the smart world (A.Rossi).pptxFIWARE
Join and fire walk with us to unravel the secrets of how a world made of Digital Twins living in the cloud-to-edge continuum can be implemented with FIWARE components, relying on the NGSI-LD standard, and get insight into real Digital Twin use cases.
Digital Twins enable system integration at multiple levels and are a core FIWARE concept. Participants will be able to see how an NGSI-LD based Digital Twin system can get its capabilities incrementally increased. The status and the roadmap of NGSI-LD in ETSI ISG CIM will be presented, including work on recent additions for operations in distributed deployments, how Asset Administration Shell (AAS) concepts from Industry 4.0 or Linked Data Event Stream functions can be supported.
Participants will discover also how to connect Digital Twins using Linked Open Data compliant with the FIWARE Smart Data Models. It includes a scenario for harvesting data from external sources, making them FIWARE compatible, and publishing them as LOD in Open Data Portals.
With Digital Twins, system integration becomes a breeze, and in this session, you will also learn how different frameworks bring an overlay on top of an NGSI-LD based environment to empower non-IT specialists. Also how recipes for easy deployment and operation of FIWARE systems managing digital twin data end to end can help creation of solutions based on this paradigm.
Deel deep into the secrets of how to unleash the full potential of FIWARE, the standard-based open source framework of reference for development and integration of systems using digital twins!
Science Gateways – Leveraging Modeling and Simulations in HPC Infrastructure...Sandra Gesing
A tutorial on science gateways at the cHiPSet Training School – New Trends in Modeling and Simulation in HPC Systems, 21-23 September 2016 in Bucharest, Romania.
"This presentation outlines CERN's approach for decoding data from IoT devices connected to
a LoRa network using Apache Kafka, Kafka Connect, and Kafka Streams, along with Kaitai Struct,
a declarative binary format parsing language used for payload decoding.
We introduce CERN's IoT LoRa network deployment, our LoRa to Kafka bridge, and the processing platform based on Kaitai Struct specifications.
This platform is designed to automatically generate payload decoders and, if necessary, database schemas.
These are then used within Kafka Streams applications to process, decode, and inject the database schema into the payload data as required.
Our focus is on offering a simple, quick, and accessible method for end-users to easily develop and integrate new IoT streaming applications able to handle any binary data format from heterogeneous sensors (multi type, multi vendor) such as proximity, temperature, structural integrity, etc."
STI 2022 - Generating large-scale network analyses of scientific landscapes i...Michele Pasin
The growth of large, programatically accessible bibliometrics databases presents new opportunities for complex analyses of publication metadata. In addition to providing a wealth of information about authors and institutions, databases such as those provided by Dimensions also provide conceptual information and links to entities such as grants, funders and patents. However, data is not the only challenge in evaluating patterns in scholarly work: These large datasets can be challenging to integrate, particularly for those unfamiliar with the complex schemas necessary for accommodating such heterogeneous information, and those most comfortable with data mining may not be as experienced in data visualisation. Here, we present an open-source Python library that streamlines the process accessing and diagramming subsets of the Dimensions on Google BigQuery database and demonstrate its use on the freely available Dimensions COVID-19 dataset. We are optimistic that this tool will expand access to this valuable information by streamlining what would otherwise be multiple complex technical tasks, enabling more researchers to examine patterns in research focus and collaboration over time.
HICSS-2014-Big Island, Hawaii, United States, 08 January 2014Charith Perera
Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos, MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices, Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS), Kona, Hawaii, USA, January, 2014
Memory Efficient Graph Convolutional Network based Distributed Link Predictionmiyurud
Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph. Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).
Using the concept of fog to implement a unified IoT platform
Dynamic replacing the applications or algorithms
Managing the resources of the IoT devices
Collecting the data to analyze and improve the performance
Imagine if we want to alert or notify someone in by ringing bell remotely through your smartphone. This project was taken consideration for such cause.
Upgrade our custom automated School Bell with emerging concept of IOT (Internet of thing) where the entire process of ringing bell is automated and integrated with internet and can set alarm or ring alarm in case of emergency remotely through a smartphone. Highly encrypted with support of Firebase server.
Institutional Repository Single Sources of TruthLighton Phiri
Academic talk, on Institutional Repository Single Sources of Truth, given to The University of Zambia [1] CSC 5741 postgraduate students [2].
[1] https://www.unza.zm
[2] http://lis.unza.zm/~lightonphiri/teaching/unza/2020/csc5741
Dr Matthew Berryman, IT Architect, presented an overview of his research as part of the SMART Seminar Series on 15 November 2016.
More information: http://smart.uow.edu.au/events/UOW223675.html
A modern approach for wiring the smart world (A.Rossi).pptxFIWARE
Join and fire walk with us to unravel the secrets of how a world made of Digital Twins living in the cloud-to-edge continuum can be implemented with FIWARE components, relying on the NGSI-LD standard, and get insight into real Digital Twin use cases.
Digital Twins enable system integration at multiple levels and are a core FIWARE concept. Participants will be able to see how an NGSI-LD based Digital Twin system can get its capabilities incrementally increased. The status and the roadmap of NGSI-LD in ETSI ISG CIM will be presented, including work on recent additions for operations in distributed deployments, how Asset Administration Shell (AAS) concepts from Industry 4.0 or Linked Data Event Stream functions can be supported.
Participants will discover also how to connect Digital Twins using Linked Open Data compliant with the FIWARE Smart Data Models. It includes a scenario for harvesting data from external sources, making them FIWARE compatible, and publishing them as LOD in Open Data Portals.
With Digital Twins, system integration becomes a breeze, and in this session, you will also learn how different frameworks bring an overlay on top of an NGSI-LD based environment to empower non-IT specialists. Also how recipes for easy deployment and operation of FIWARE systems managing digital twin data end to end can help creation of solutions based on this paradigm.
Deel deep into the secrets of how to unleash the full potential of FIWARE, the standard-based open source framework of reference for development and integration of systems using digital twins!
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Komputasi Nuklir: Pendekatan dan Perkembangannya
1. Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 1
Komputasi Nuklir:
Pendekatan dan Perkembangannya
Sparisoma Viridi
Nuclear Physics and Biophysics Research Division,
Faculty of Mathematics and Natural Sciences,
Institut Teknologi Bandung,
Bandung 40132, Indonesia
v20201212_3
2. Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 2
Kerangka
• Pendahuluan
• TensorFlow
• GPU and Distributed Computing
• Penutup
Self introduction
..
8. Two cases
• One case of the computation in future nuclear
engineering is for a new field of innovative
nuclear engineering under development
Innovative nuclear reactors case
• In another case, the development of the
computer enables calculation that was
impossible to date Accident managements
case
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 8
11. Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 11
A majority of the codes
represent the cylindrical fuel
rod in a so-called one-and-
a-half dimension (1.5D). In
such simulation tools, all
transport processes are
solved in one (radial)
dimension, and the axial
segments are coupled via
balance equations. Hence,
the solution of the transport
processes is usually
programmed in separate
modules for each axial
slice, using couplers to
manage interactions
between models and slices.
15. Why TensorFlow
• TensorFlow is an end-to-end open source
platform for machine learning
• It has a comprehensive, flexible ecosystem of
tools, libraries and community resources that
lets researchers push the state-of-the-art in
ML and developers easily build and deploy ML
powered applications
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 15
url https://www.tensorflow.org/ [20201212].
18. A game with scenarios
• An overseas cyber-attack cases and attacking
scenario on the control facility of the nuclear
power plant
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 18
19. Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 19
GPU and Distributed
Computing
20. Istilah
• GPU computing is the use of a GPU (graphics
processing unit) as a co-processor to accele-rate
CPUs for general-purpose scientific and engineering
computing (boston.co.uk, 2020)
• Distributed computing also refers to the use of
distributed systems to solve computational pro-
blems, which is divided into many tasks, each of
which is solved by one or more computers, which
communicate with each other via message passing
(wikipedia, 2020)
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 20
21. Pemanfaatan
• Astrofisika, Mencari pulsar dengan data sinyal
radio dan gelombang gravitasi, ..
• Mencari model terbaik untuk menjelaskan
alam semesta, ..
• Pelipatan protein, struktur protein ..
• Mengembangkan model prediksi cuaca, ..
• ..
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 21
Wikipedia contributors, “”, Wikipedia, The Free Encyclopedia, 9 Dec 2020 08:45 UTS, url
https://en.wikipedia.org/w/index.php?oldid=993197397 [20201212].
22. Reconstructing Fusion Plasmas
• Ian Langmore reconstructs plasma tempera-
ture, density, and B-field from measurements
in partnership with TAE Technologies (a priva-
te fussion energy company)
• This is a Bayesian inverse problem (not DL)
• TensorFlow libraries:
distribution
tensorflow_probability
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 22
TensorFlow, “Reconstructing Fusion Plasmas (TensorFlow Dev Summit 2018)”, YouTube, 30 Mar 2018, url
https://www.youtube.com/watch?v=Bb1_zlrjo1c [20201212].
26. An inverse problem guided experiment
• Fussion reactor does experiment every 20 '
• The system pushes the data
• TensorFlow pulls the data to the code and do
the calculation
• Visualization is constructed and given back to
the reactor operators as guidance for the next
experiment
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 26
TensorFlow, “At the intersection of TensorFlow & nuclear physics (TensorFlow Meets)”, YouTube, 28 Jun 2018, url
https://www.youtube.com/watch?v=T7G94kEoZTU&t=3m54s [20201212].
28. Ringkasan
• Perkembangan perhitungan komputasi yang
cepat dapat memandu eksperimen yang ber-
bahaya seperti dalam reaktor nuklir, meleng-
kapi pengukuran langsung (analog), semi-lang-
sung (analog2digital)
• Kolaborasi antar berbagai bidang diperlukan,
terutama sains yang mendasari pengukuran-
nya
Webdinar Nuklir 12 Desember 2020, Bandung, Indonesia 28