An Introduction to HDF (1997)
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The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
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Source: http://hdfeos.org/workshops/ws01/presentations/linkwind/linkwind.ppt
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Source: http://localhost/workshops/ws01/presentations/GSFCDAAC/GSFCDAAC.ppt
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Source: http://hdfeos.org/workshops/ws01/presentations/linkwind/linkwind.ppt
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Source: http://localhost/workshops/ws01/presentations/GSFCDAAC/GSFCDAAC.ppt
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2. The KEA image file format implements the GDAL raster data model in HDF5, allowing large raster datasets and attribute tables to be stored together with compression. It was created in 2012 to address limitations of other formats.
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2. The KEA image file format implements the GDAL raster data model in HDF5, allowing large raster datasets and attribute tables to be stored together with compression. It was created in 2012 to address limitations of other formats.
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This document discusses using MATLAB for working with big data and scientific data formats. It provides an overview of MATLAB's capabilities for scientific data, including interfaces for HDF5 and NetCDF formats. It also describes how MATLAB can be used to access, analyze, and visualize big data from sources like Hadoop, databases, and RESTful web services. As a demonstration, it shows how MATLAB can access HDF5 data stored on an HDF Server through RESTful web requests and analyze the data using in-memory data types and functions.
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The document describes HDF Server (h5serv), which exposes HDF5 files via a RESTful API. H5serv allows full read/write access and supports HDF5 features like compression and hyperslab selection. It uses the Tornado framework to implement a stateless, cacheable API accessed through HTTP requests in JSON. This provides a web interface for HDF5 data while maintaining HDF5 functionality. Future plans include client libraries, authentication/authorization, and improving performance for large repositories.
Metadata Requirements for EOSDIS Data Providers
HDF AND HDF-EOS WORKSHOP II (1998)
Source: http://hdfeos.org/workshops/ws02/presentations/khalsa/khalsa.ppt
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HDF AND HDF-EOS WORKSHOP II (1998)
Source: http://hdfeos.org/workshops/ws02/presentations/eddins/eddins.ppt
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The document discusses tools for visualizing and manipulating HDF and HDF-EOS files. It categorizes tools as utilities, which perform specific data functions from the command line, or applications, which allow for significant data processing. Tools are further broken down into ESDIS project tools like DIAL and EOSView, other government tools from agencies like NASA and JPL, and commercial tools from companies. A survey of tool developers found most are working on HDF-EOS support but are unsure of release timelines, and more work is needed in areas like Java interfaces, metadata handling, and mapping/overlay capabilities.
Desktop Support for HDF and HDF-EOS
HDF AND HDF-EOS WORKSHOP II (1998)
Source: http://hdfeos.org/workshops/ws02/presentations/meyer/meyer.ppt
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This document discusses the development of HDF Explorer software for visualizing oceanographic model output data. It describes how the developers switched from using proprietary binary formats to the HDF data format for portability and access to graphical tools. It also discusses the HDF Explorer software itself, which was created to provide a graphical interface for visualizing the specific features of their ocean model, including grids, density fields, and velocity fields. The document outlines future plans to utilize HDF-EOS and add additional visualization capabilities like 3D and contours to the HDF Explorer software.
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The HDF Group: Community models and outreach
The HDF Group aims to build community around its data platform through engagement goals like improving metadata, developing sharing tools, and hosting workshops. It seeks to bring together disciplines by addressing needs, maintaining websites and forums, publishing a blog, and coordinating networks among NASA missions, data centers, research facilities, and models to facilitate cross-disciplinary data sharing, use, and understanding.
Indexing HDF5: A Survey
The document discusses indexing methods for HDF5 files to enable efficient searching and access of data based on data values. It describes several existing implementations of HDF5 indexing, including PyTables, FastQuery/FastBit, Alacrity, and prototypes from The HDF Group. It also outlines current and future work to develop indexing and querying capabilities within HDF5 to allow complex multi-dimensional searches across metadata and datasets.
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This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
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- VFD SWMR improves on legacy SWMR by implementing single-writer multiple-reader capabilities
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This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.HDF5 and Ecosystem: What Is New?
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
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- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
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HDF5 Roadmap 2019-2020
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
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Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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Three primary specifications will be discussed:
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Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
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Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
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📕 Detailed agenda:
Variables and Datatypes
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Arguments
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💻 Extra training through UiPath Academy:
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What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
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