Nuclear power plants work by releasing huge amounts of energy when they cause atoms to break apart. This is fission. The sun and other stars combine atoms, making their mass smaller and releasing enormous amounts of heat and energy.
Microsoft Tests a Renewable Energy-Powered Data Center at the Bottom of the O...Abaram Network Solutions
Microsoft estimates that more than half of the world’s population lives within about 120 miles of the coast. Therefore by placing data centers near coastal cities, data would have a shorter distance to travel to reach its destination.
This statement of completion certifies that Beethoven Adelson Plaisir successfully finished the online, non-credit course "Data Mining with Weka" provided by the University of Waikato on February 28, 2017, which covered machine learning algorithms, representing learned models, filtering data, classification methods, data visualization, and training, testing and evaluation. However, this statement does not represent or confer credit towards a University of Waikato qualification or verify the person's identity.
A lecture to the National University of Ireland, Galway honours year and masters students in oceanography (14th November 2016) on the basics of marine data management.
Using Erddap as a building block in Ireland's Integrated Digital OceanAdam Leadbetter
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Where Linked Data meets Big Data: Applying standard data models to environmen...Adam Leadbetter
This document discusses applying standard data models to environmental data streams from ocean observations. It presents examples of encoding oceanographic observation data using semantic web standards like the W3C Observation and Measurement ontology. These approaches aim to integrate live sensor data with linked open data to support interoperability across scientific domains.
Various industrial challenges in full scale data handling situations in shipping are considered in this study. These large scale data handling approaches are often categorized as "Big Data" challenges; therefore various solutions to overcome such situations are identified. The proposed approach consists of a marine engine centered data flow path with various data handling layers to address the same challenges. These layers are categorized as: sensor fault detection, data classification, data compression, data transmission and receiver, data expansion, integrity verification, and data regression. The functionalities of each data handling layer with respect to ship performance and navigation information of a selected vessel are discussed and additional challenges that are encountered during this process are also summarized. Hence, these results can be used to develop data analytics that are related to energy efficiency and system reliability applications of shipping.
Linked Ocean Data - Exploring connections between marine datasets in a Big Da...Adam Leadbetter
Adam Leadbetter works for the Marine Institute in Ireland and is interested in data management, oceanography, and long-distance running. The document provides his contact information and describes his interests using RDF triples. It also includes several links to resources about ocean data, sensors, observations, and semantic web standards for observational data.
Nuclear power plants work by releasing huge amounts of energy when they cause atoms to break apart. This is fission. The sun and other stars combine atoms, making their mass smaller and releasing enormous amounts of heat and energy.
Microsoft Tests a Renewable Energy-Powered Data Center at the Bottom of the O...Abaram Network Solutions
Microsoft estimates that more than half of the world’s population lives within about 120 miles of the coast. Therefore by placing data centers near coastal cities, data would have a shorter distance to travel to reach its destination.
This statement of completion certifies that Beethoven Adelson Plaisir successfully finished the online, non-credit course "Data Mining with Weka" provided by the University of Waikato on February 28, 2017, which covered machine learning algorithms, representing learned models, filtering data, classification methods, data visualization, and training, testing and evaluation. However, this statement does not represent or confer credit towards a University of Waikato qualification or verify the person's identity.
A lecture to the National University of Ireland, Galway honours year and masters students in oceanography (14th November 2016) on the basics of marine data management.
Using Erddap as a building block in Ireland's Integrated Digital OceanAdam Leadbetter
The document discusses using Erddap as part of Ireland's Integrated Digital Ocean platform. Erddap is used to aggregate data from various sources and provide it to users through standardized APIs and web interfaces. This allows diverse data and applications to interoperate through common access points and data flows, minimizing the distances between different technologies and systems. The Marine Institute of Ireland has implemented this approach to integrate ocean observation data and provide open access through their Digital Ocean portal.
Where Linked Data meets Big Data: Applying standard data models to environmen...Adam Leadbetter
This document discusses applying standard data models to environmental data streams from ocean observations. It presents examples of encoding oceanographic observation data using semantic web standards like the W3C Observation and Measurement ontology. These approaches aim to integrate live sensor data with linked open data to support interoperability across scientific domains.
Various industrial challenges in full scale data handling situations in shipping are considered in this study. These large scale data handling approaches are often categorized as "Big Data" challenges; therefore various solutions to overcome such situations are identified. The proposed approach consists of a marine engine centered data flow path with various data handling layers to address the same challenges. These layers are categorized as: sensor fault detection, data classification, data compression, data transmission and receiver, data expansion, integrity verification, and data regression. The functionalities of each data handling layer with respect to ship performance and navigation information of a selected vessel are discussed and additional challenges that are encountered during this process are also summarized. Hence, these results can be used to develop data analytics that are related to energy efficiency and system reliability applications of shipping.
Linked Ocean Data - Exploring connections between marine datasets in a Big Da...Adam Leadbetter
Adam Leadbetter works for the Marine Institute in Ireland and is interested in data management, oceanography, and long-distance running. The document provides his contact information and describes his interests using RDF triples. It also includes several links to resources about ocean data, sensors, observations, and semantic web standards for observational data.
The document discusses the virtualization landscape in the financial industry. It addresses how virtualization relates to cloud computing, the challenges of regulations/standards, sustaining resiliency during flip/flops between production and DR sites, integrating legacy systems, and storage issues. The document also outlines where the industry currently stands with cloud services and active/active setups, and concludes by stating that financial institutions are embracing new technologies but fully embracing public cloud remains uncertain due to regulatory requirements.
Where did my layer come from? The semantics of data releaseAdam Leadbetter
This document discusses the semantics of spatial data release and provenance metadata. It introduces Adam Leadbetter from the Marine Institute and provides several relevant links on topics like linked data, the PROV ontology, and information on data publication and citation. Several citations and the author's contact details are also included.
Nexergy CEO Darius Salgo's presentation from the All Energy conference, Oct 2017. In the presentation he outlines the shift from a one-way to two-way distributed energy future, and the value of new tools like local energy trading in better managing the grid.
How to Build Consistent and Scalable Workspaces for Data Science TeamsElaine K. Lee
This document discusses how to build consistent and scalable workspaces for data science teams. It recommends identifying system requirements, stabilizing dependencies, increasing test coverage, and using continuous integration to ensure resources are available. It also suggests creating a pool of worker machines and asynchronous task queue to scale workloads. This allows tasks to run in isolated, identical environments and provides flexible use of cloud computing resources. Benefits include guaranteed task environments, extensibility, and a reusable command line interface. Examples of use cases provided are quality assurance testing and parallelizable data and model tasks.
The session I conducted at the Pre-Bootcamp series of AI-Driven Sri Lanka.
The following topics were covered:
• Growth engineering / Hacking
• Dave McClure’s Pirate Metrics / Growth funnel
• Growth Framework
• Data architecture
• Azure monitor logs
• A/B testing
• How to get into data science
Presentation talk: https://youtu.be/sxQxOlK5aGI
This document summarizes a presentation about Myria, a relational algorithmics-as-a-service platform developed by researchers at the University of Washington. Myria allows users to write queries and algorithms over large datasets using declarative languages like Datalog and SQL, and executes them efficiently in a parallel manner. It aims to make data analysis scalable and accessible for researchers across many domains by removing the need to handle low-level data management and integration tasks. The presentation provides an overview of the Myria architecture and compiler framework, and gives examples of how it has been used for projects in oceanography, astronomy, biology and medical informatics.
SERENE 2014 School: Measurement-Driven Resilience Design of Cloud-Based Cyber...SERENEWorkshop
SERENE 2014 School on Engineering Resilient Cyber Physical Systems
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This document discusses the roles that cloud computing and virtualization can play in reproducible research. It notes that virtualization allows for capturing the full computational environment of an experiment. The cloud builds on this by providing scalable resources and services for storage, computation and managing virtual machines. Challenges include costs, handling large datasets, and cultural adoption issues. Databases in the cloud may help support exploratory analysis of large datasets. Overall, the cloud shows promise for improving reproducibility by enabling sharing of full experimental environments and resources for computationally intensive analysis.
Developing Sakai 3 style tools in Sakai 2.xAuSakai
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This document appears to be a student's project file on developing a School Management System. It includes sections like preface, certificate, acknowledgement, introduction, objectives, source code, and output. The project aims to create an automated system to enhance management of a school. It allows maintaining student and staff records, tracking attendance, and facilitating communication between stakeholders. The system is developed using Python and SQL for the backend database. It offers features like admission, updating details, generating transfer certificates for students and hiring, updating, deleting employee records.
W-JAX Keynote - Big Data and Corporate Evolutionjstogdill
A look at corporate evolution from the industrial revolution to the information age - with a focus on how Big Data will make an impact.
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The thorough integration of information technology and resources into scientific workflows has nurtured a new paradigm of data-intensive science. However, far too much research activity still takes place in silos, to the detriment of open scientific inquiry and advancement. Data-intensive science would be facilitated by more universal adoption of good data management practices ensuring the ongoing viability and usability of all legitimate research outputs, including data, and the encouragement of data publication and sharing for reuse. The centerpiece of such data sharing is the digital repository, acting as the foundation for external value-added services supporting and promoting effective data acquisition, publication, discovery, and dissemination. Since a general-purpose curation repository will not be able to offer the same level of specialized user experience provided by disciplinary tools and portals, a layered model built on a stable repository core is an appropriate division of labor, taking best advantage of the relative strengths of the concerned systems.
The Merritt repository, operated by the University of California Curation Center (UC3) at the California Digital Library (CDL), functions as a curation core for several data sharing initiatives, including the eScholarship open access publishing platform, the DataONE network, and the Open Context archaeological portal. This presentation with highlight two recent examples of external integration for purposes of research data sharing: DataShare, an open portal for biomedical data at UC, San Francisco; and Research Hub, an Alfresco-based content management system at UC, Berkeley. They both significantly extend Merritt’s coverage of the full research data lifecycle and workflows, both upstream, with augmented capabilities for data description, packaging, and deposit; and downstream, with enhanced domain-specific discovery. These efforts showcase the catalyzing effect that coupled integration of curation repositories and well-known public disciplinary search environments can have on research data sharing and scientific advancement.
Webinar: How Microsoft is changing the game with Windows AzureCommon Sense
The Windows Azure Common Sense Webinar! Solution Specialist with Microsoft Nate Shea-han will present “How Microsoft is changing the game with Windows Azure“.
Learn the difference between Azure (PAAS) and Infrastructure As A Service and standing up virtual machines; how the datacenter evolution is driving down the cost of enterprise computing and about the modular datacenter and containers.
Nate’s focus area is on cloud offerings focalized on the Azure platform. He has a strong systems management and security background and has applied knowledge in this space to how companies can successfully and securely leverage the cloud as organization look to migrate workloads and application to the cloud. Nate currently resides in Houston, Tx and works with customers in Texas, Oklahoma, Arkansas and Louisiana.
The webinar is intended for:CIOs, CTOs, IT Managers, IT Developers, Lead Developers
Presentation on Infrastructure as a Service (IaaS) and Software as a Service (SaaS): Projects in Tennessee Higher Education that are being done to reduce the overall cost to students improving ROI / TCO of existing systems and support.
This document discusses chaos engineering and patterns for architecting distributed systems to fail gracefully. It introduces concepts like chaos monkey which intentionally introduces failures into systems to test resilience. Fallback patterns are discussed to handle failures through sacrificing accuracy or latency. The document advocates embracing a culture of chaos engineering to proactively test systems rather than only fixing failures reactively.
Presentation at the International Industry-Academia Workshop on Cloud Reliability and Resilience. 7-8 November 2016, Berlin, Germany.
Organized by EIT Digital and Huawei GRC, Germany.
Twitter: @CloudRR2016
Failures happen. Building resilient cloud infrastructure requires an end-to-end automated approach to failure remediation. This approach must go beyond the current DevOps model of monitoring the system and getting engineers alerted when a failure condition occurs.
Recently, event driven automation and workflows re-emerged as a way to automate troubleshooting, remediation, and a variety of Day-2 operations. Facebook famously uses FBAR to "save 16,000 engineer-hours, a day, in ops". Similar approaches had been reported by other hyper-scale cloud providers. Open-source auto-remediation platforms like StackStorm are replacing legacy Runbook automation products, and have been successfully used to automate applications, networks, security, and cloud infrastructure.
In this presentation we give a brief history of workflow automation, overview the common architecture ingredients of a typical event driven automation framework, compare and contrast alternative approaches to day-2 automation, and, most importantly, share real-world use cases and examples of applying event driven automation in operations.
This document discusses using Schema.org to describe marine data and link ocean data on the web. It provides background on linked data and Schema.org. It describes work done by various organizations to apply Schema.org to describe datasets, organizations, projects, and other marine data. This includes developing schemas and cataloging various types of marine data. Future work is discussed, such as supporting tabular data and linking to other vocabularies for different data types.
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The document discusses developing Sakai 3 style tools in Sakai 2.x. It provides an overview of the Mandatory Subject Information project which aims to integrate subject outlines into Sakai using AJAX technology for improved usability and consistency. Examples are given of how AJAX can improve the development workflow and a sample outline management tool is demonstrated, including the JSON response structure and client-side processing.
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Recently, event driven automation and workflows re-emerged as a way to automate troubleshooting, remediation, and a variety of Day-2 operations. Facebook famously uses FBAR to "save 16,000 engineer-hours, a day, in ops". Similar approaches had been reported by other hyper-scale cloud providers. Open-source auto-remediation platforms like StackStorm are replacing legacy Runbook automation products, and have been successfully used to automate applications, networks, security, and cloud infrastructure.
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Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
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The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
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We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
The cost of acquiring information by natural selection
Practical solutions to implementing "Born Connected" data systems
1. Practical solutions to implementing
"Born Connected" data systems
Adam Leadbetter, Marine Institute
(adam.leadbetter@marine.ie)
Justin Buck, British Oceanographic Data Centre
Paul Stacey, Institute of Technology Blanchardstown
11. An example URI in SenseOcean:
http://linked.systems.ac.uk/System/
AanderaaOxygenOptode4531/XX34213/
ClassName for the
System
SerialNumber
Class for System or
SensingDevice
Host
A Unique identifier for the concept or data
A Unique reference for the concept
Computer readable
Looks familiar
24. Adam Leadbetter, Marine Institute, Ireland
adam.leadbetter@marine.ie
@AdamLeadbetter
https://github.com/IrishMarineInstitute/
sensor-observation-service
https://github.com/peterataylor/om-json
Editor's Notes
Acknowledge:
Janet Fredericks @ WHOI
Damian Smyth & Rob Fuller @ MI
Alexandra Kokinakki @ BODC
Born Digital -> Born Semantic -> Born Connected
Why?
Traditionally – ocean data has been structured, and particularly, linked post fact
Why?
Traditionally – ocean data has been structured, and particularly, linked post fact
Gliders, Argo floats, ROVs, seafloor observatories break the sustainability of that model
Why?
Traditionally – ocean data has been structured, and particularly, linked post fact
Gliders, Argo floats, ROVs, seafloor observatories break the sustainability of that model
Shepherd’s metaphor
Do you have the time to go to Dagobah and take the training with the “Big Data” age
Lesley Wyborn “Data needs to be ‘Born Connected’ to enable Transdisciplinary Science” – and beginning at conceived connected!
So…
Extending the “Born Semantic” to ultra-constrained observation environments
Achieving “Born Semantic” data in an ultra-constrained environment presents more difficulties. Communications may be intermittent, very low bandwidth, data-logger must be highly power efficient etc..
Extending the “Born Semantic” to ultra-resource constrained environments
There has been a recently flurry of development activity around Internet of Things (IoT) technologies. This has lead to a drive for IoT enabling technologies that presents opportunities to further realise the concept of Born Semantic data, pushing the semantic annotation closer to the data capture point.
These technologies are all about “squeezing the bits” reducing storage, processing and communication overhead.
Low-power, highly efficient operating systems such as TinyOS and Contiki (among others) provide “powerful enough” capabilities to leverage semantic annotation efforts.
Fernandez et al. have recently addressed compression of RDF, with the Header-Dictionary-Triples approach that compresses tuple elements into a dictionary, followed by a compressed representation of triples of dictionary keys. This approach is only applicable to large data sets, which is not an option in a constrained environment. Wiselib TupleStore and RDF provider, provides a suitable solution here as it is a light weight flexible data storage solution.
The Constrained Application Protocol (CoAP) is a specialised web transfer protocol for use with constrained embedded systems and networks. CoAP is designed to easily interface with HTTP for integration with the Web with very low overhead, and simplicity for constrained environments. Although HTTP is the defacto standard for RESTful architectures CoAP.
CoAP specifies a minimal subset of REST requests (GET, POST, PUT, and DELETE) it also relies on UDP as a transport protocol while providing reliability with a simple built-in retransmission mechanism and so the communications overhead is small compared to HTTP.
Ocean Data Interoperability Platform
52N plus others
Different encodings for SOS results
RESTful URLs for SOS access
EGU 2014 – prototypes in RDFa (CTD);
SensorML 1.0 (Qartod 2 OGC – now re-funded as X-DOMES;
Direct embedding concept ids in file headers (Lake Ellswort Drilling Project) or SWE XML definitions (Q2O).
Funding from EU SenseOCEAN, BRIDGES, OpenGovIntelligence
Funding from SEAI
Onto SenseOCEAN – slides from BODC
First step is a sensor / instrument register
Built on Fuseki – with custom Java API
Live in next few months
SSN = has some issues with alignment later with O&M, which will be introduced in the next slide– Simon Cox will go into details…
Ideally associated with something like an ORCiD not just the person’s name
We have created the models. But we are still gathering metadata from the manufacturers. So will be able to publish some example sensor descriptions soon enough (couple of months).
Single machine
Distributed processing
One-to-one communication
Publish-subscribe pattern
No fault tolerance
Replication, auto-recovery
Fixed schema, encoding
Schema management, evolvable encoding
Simon Cox & Peter Taylor presentation at OGC TC in September 2015
Work ongoing in Ocean Acidification community to use the proposed O&M JSON schema
Here is a snapshot from a SOS call to the Galway Bay Cable Observatory
Adding a JSON-LD context to the output allows us to generate a triple-ified model of the SOS output…