According to Cognitive Market Research, the global In-Memory Computing market size will be USD 16.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 17.2% from 2024 to 2031. Market Dynamics of In-Memory Computing Market

Key Drivers for In-Memory Computing Market

Demand for Real-time Analytics and Decision-making - In-Memory Computing enables real-time analytics and decision-making by processing data instantaneously without the delays inherent in disk-based systems. This capability supports businesses in gaining immediate insights into market trends, customer behaviors, and operational performance, facilitating agile decision-making and responsiveness to dynamic market conditions. Industries such as retail, healthcare, and manufacturing leverage IMC to monitor inventory in real-time, personalize customer experiences at the moment, optimize supply chain operations, and detect anomalies promptly. The ability to perform complex analytics on live data streams enhances competitive advantage by enabling businesses to capitalize on opportunities quickly and mitigate risks proactively.
The demand for scalability and handling big data is anticipated to drive the In-Memory Computing market's expansion in the years ahead.

Key Restraints for In-Memory Computing Market

The substantial upfront costs for in-memory computing infrastructure can hinder the In-Memory Computing industry growth.
The market also faces significant difficulties related to limited scalability.

Introduction of the In-Memory Computing Market

The In-Memory Computing market is at the forefront of revolutionizing data processing and analytics by leveraging high-speed, volatile memory to store and retrieve data rapidly. This technology enables real-time processing of large datasets, accelerating business insights and decision-making across various industries such as finance, healthcare, retail, and telecommunications. In-memory computing systems, like SAP HANA and Oracle TimesTen, offer significant advantages over traditional disk-based databases, including faster query performance, reduced latency, and enhanced scalability for handling massive volumes of data. These systems support complex analytics, predictive modeling, and real-time applications that require instant access to up-to-date information. Despite its benefits, the market faces challenges such as high initial investment costs, integration complexities with existing IT infrastructures, and the need for skilled personnel to manage and optimize in-memory computing environments. However, as organizations increasingly prioritize speed and agility in data processing, the In-Memory Computing market continues to expand, driving innovation and transforming digital landscapes globally.

According to Cognitive Market Research, the global In-Memory Analytics market size is USD 5.80 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 22.10% from 2024 to 2031.

Market Dynamics of In-Memory Analytics Market

Key Drivers for the In-Memory Analytics Market



  Digital Transformation Using Real-Time Data Analytics to Increase the Demand Globally - Digital transformation leveraging real-time data analytics fuels the growth of the market by enabling organizations to harness the power of data for immediate insights and actionable decisions. By processing vast amounts of data in memory, businesses gain agility, responsiveness, and the ability to adapt quickly to changing market dynamics. Real-time analytics empower enterprises to optimize operations, personalize customer experiences, and uncover new revenue opportunities. As businesses increasingly prioritize digital innovation to stay competitive, the demand for in-memory analytics solutions continues to surge.


  Rise in Volume of Data- The rise in volume of data drives the market by necessitating faster processing speeds and real-time insights, prompting organizations to adopt solutions that can efficiently handle large datasets without the latency associated with traditional disk-based storage systems.




Key Restraints for In-Memory Analytics Market



  Lack of Awareness Across Industries- The lack of awareness across industries regarding the benefits and capabilities of in-memory analytics restricts market growth by impeding adoption among potential users who could significantly benefit from its real-time data processing capabilities.


  High initial Investment- Another limiting factor for the market is the complexity and cost associated with implementing and maintaining in-memory analytics solutions.

Introduction of the In-Memory Analytics Market

The In-Memory Analytics Market is a rapidly evolving sector within the broader data analytics industry, characterized by its ability to process large volumes of data in real time by storing it in main memory rather than traditional disk-based storage systems. This approach enables organizations to perform complex analytics, such as predictive modeling and data mining, with exceptional speed and efficiency. In-memory analytics solutions offer businesses the agility to make faster and more informed decisions, uncover valuable insights, and attain a competitive edge in today's data-driven landscape. Key drivers of market growth include the exponential growth of data, increasing demand for real-time analytics capabilities, and advancements in-memory technologies. Additionally, proliferation of the cloud computing and the Internet of Things (IoT) further fuel the adoption of in-memory analytics solutions across various industries, including finance, healthcare, retail, and manufacturing.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The size of the High Bandwidth Memory Market was valued at USD 1.6 Billion in 2023 and is projected to reach USD 8.11 Billion by 2032, with an expected CAGR of 26.10% during the forecast period. The high bandwidth memory market is rapidly growing due to the increasing demand for high-performance computing, artificial intelligence, machine learning, gaming, and data centres. HBM is a memory type that has much higher data transfer rates compared to traditional memory types like DDR (Double Data Rate), making it ideal for applications that require large amounts of data to be processed quickly, such as graphics processing, scientific simulations, and cloud computing. The rapidly increasing demand for graphics cards is one aspect that really drives the market, as modern GPUs require high-speed memory to render complex images and carry intensive computations. More specifically, the growth in AI and machine learning algorithms further drives HBM demand, as these applications need sizeable memory bandwidth to process large datasets. Recent developments include: April 2023: A 12-layer HBM3 was developed, according to SK Hynix, and samples were made available to customers like AMD. The latest model, which is packaged in the same size as its predecessor but has an industry-leading 24 GB memory capacity, demonstrates the company's technological dominance in the market., January 2022: The acquisition of Xilinx was announced by Advanced Micro Devices Inc. In the first year, the business anticipates that the acquisition will increase non-GAAP margins, non-GAAP EPS, and free cash flow generation. Furthermore, AMD asserts that by acquiring Xilinx, it will be able to establish the most advanced and adaptable computing organization in the market by combining a highly complementary set of goods, clients, and markets with unique intellectual property and top-tier talent.. Notable trends are: Increasing demand for smart lighting systems is driving the market growth.

This data collection contains data from the first of four studies conducted on the associated ESRC grant (data from the other studies will be made available as separate datasets in ReShare). The purpose of this study was to investigate the extent to which primary memory development constrains the development of working memory in children, and whether primary memory capacity mediates the relationship between working memory and academic attainment. To that end, a sample of 101 children aged between 5 and 8 years were given three novel experimental measures of primary memory capacity that were designed to estimate the number of items in a child's immediate memory that they could spontaneously recalled in correct serial order. More traditional experimental measures of short-term and working-memory capacity were also administered, as were standardised tests of reading [Sentence Completion Forms of the NFER-Nelson (1998) Group Reading Test II Form A (6–14)] and mathematics [NFER-Nelson (1994) Mathematics 6–14]. These data underpin a paper linked here via Related Resources. The data are also available via the University of Bristol data repository (see Related Resources section).The aim of this project is to build on previous psychological research with both children and adults to provide the most comprehensive model to date of the factors involved in the development of working memory performance in children. In doing so, the project will investigate the extent to which these factors are separable or inter-related. Also the project will assess how these factors contribute to mediating the strong relationships commonly observed between working memory and academic attainment. The research has four specific objectives: To determine whether age-related changes in short-term memory capacity are related to working memory development. To determine how age-related changes in processing speed are related to working memory development. To determine whether age-related changes in long-term memory utilisation are related to working memory development. To determine which of the above factors mediate the relationship between working memory performance and educational attainment. These objectives will be met in a set of empirical studies, using both existing and novel experimental measures. These measures will be related to academic attainment and measures of classroom behaviour. Each study will involve large samples of children in two age groups (around 5 and around 9 years of age). This study used an empirical, experimental data collection method. All tasks, apart from the standardised measures of reading and mathematics were programmed using Runtime Revolution software and presented on Macintosh Powerbook and MacBook computers. A total of 348 words were used in the memory tasks, which were single syllable concrete nouns, with age of acquisition of under 6.2 years. Each word was paired with a colour cartoon image. No words were repeated within or between tasks in a single testing session. All audio material was presented through the internal laptop speakers using male voices. Participants were assessed individually in a school setting. Each child completed three individual testing sessions lasting approximately 30 minutes each. In each of the first two sessions, children completed two memory tasks, and in the final session they were tested on one memory task; these tasks were presented to all children in the order in which they are introduced in the attached 'methodology' file. In addition to the memory measures, all children were tested on the Sentence Completion Forms of the NFER-Nelson (1998) Group Reading Test II Form A (6-14) and the age appropriate test from the NFER-Nelson (1994) Mathematics 6-14 series in separate sessions. The sample consisted of 50 Year 1 pupils (23 males, mean age 6 years 4 months, range 5 years 10 months to 6 years 10 months) and 51 Year 3 pupils (27 males, mean age 8 years 5 months, range 7 years 10 months to 8 years 11 months). All participants completed the experimental memory tasks, with the exception of one individual in Year 1 who was absent for the session in which the split span task was presented. Further absences at the time when the reading and mathematics assessments were given meant that a full data set that also included these measures’ data was only available for 92 children (43 in Year 1, 49 in Year 3).

The purpose of this study was to investigate the extent to which various speeded-related processes constrain the development of working memory in children, and whether these processes mediate the relationship between working memory and academic attainment. To that end, a sample of 112 children aged between 5 and 8 years were given four tasks measuring speed-related aspects of working memory performance, specifically basic speed of processing, articulation speed, forgetting rates, and memory scanning speed. Experimental measures of short-term memory ('simple span') and working memory ('complex span') were also give, as were standardised tests of reading [Sentence Completion Forms of the NFER-Nelson (1998) Group Reading Test II Form A (6–14)] and mathematics [NFER-Nelson (1994) Mathematics 6–14]. This data collection contains data from the second of four studies conducted on the associated ESRC grant (see Related Resources). The aim of this project is to build on previous psychological research with both children and adults to provide the most comprehensive model to date of the factors involved in the development of working memory performance in children. In doing so, the project will investigate the extent to which these factors are separable or inter-related. Also the project will assess how these factors contribute to mediating the strong relationships commonly observed between working memory and academic attainment. The research has four specific objectives: To determine whether age-related changes in short-term memory capacity are related to working memory development. To determine how age-related changes in processing speed are related to working memory development. To determine whether age-related changes in long-term memory utilisation are related to working memory development. To determine which of the above factors mediate the relationship between working memory performance and educational attainment. These objectives will be met in a set of empirical studies, using both existing and novel experimental measures. These measures will be related to academic attainment and measures of classroom behaviour. Each study will involve large samples of children in two age groups (around 5 and around 9 years of age). This study used an empirical, experimental data collection method. All tasks, apart from the standardised measures of reading and mathematics were programmed using Runtime Revolution software and presented on MacBook Pro laptop computers. A total of 180 words were used in processing elements of the tasks, which were familiar concrete nouns, with age of acquisition of under 6.2 years. No words were repeated within or between tasks in a single testing session. All audio material was presented through the internal laptop speakers using pre-recorded male voices. Where a processing task was used in any of the tasks (including decision making in the memory scanning task), reaction times (RTs) were recorded when the child selected their response key (z or /) on the computer keyboard. ‘z’ corresponded to correct, and ‘/’ to incorrect in every task. Participants were assessed individually in three testing sessions lasting approximately 30 minutes each. In sessions 1 and 3, children completed 4 tasks, and in the middle session they were tested on 3 tasks. In session 1, they completed the first verbal and visual baseline processing assessment, digit span, and memory scanning, in that order. In session 2, they completed verbal-visual complex span, articulation speed, and verbal forgetting, in that order. In session 3, they completed the second verbal and visual baseline processing assessment, verbal-verbal complex span, and visual forgetting, in that order (see attached 'methodology' file for details on these tasks). In addition to the memory measures given in these experimental testing sessions, all children were tested on the Sentence Completion Forms of the NFER-Nelson (1998) Group Reading Test II Form A (6-14) and the age appropriate test from the NFER-Nelson (1994) Mathematics 6-14 series in separate sessions. The full sample consisted of 112 children (51 males, mean age 7 years 1 month, range 5 years 8 months to 8 years 8 months, 64 in School Year 1 and 48 in School Year 3). Eight participants were absent during phases of the data collection and so failed to complete the experimental memory tasks. Equipment failure resulted in a further 9 participants’ data being lost for the articulation speed task. Eleven participants were absent at the time when the reading and mathematics assessments were given. As a result, a full data set that include all measures is available for 87 children (50 in Year 1, 37 in Year 3).

The global DDR5 market size is projected to reach USD 21.3 billion by 2033, exhibiting a CAGR of 26.4% during the forecast period. This growth can be attributed to the rising demand for high-performance computing, cloud computing, and artificial intelligence (AI). DDR5 offers several advantages over its predecessor, DDR4, including higher bandwidth, lower power consumption, and improved data integrity. As a result, DDR5 is becoming the preferred memory choice for next-generation computing devices. Key drivers of the DDR5 market include the increasing adoption of cloud computing and AI. Cloud computing is driving the demand for high-performance memory solutions, as cloud servers require large amounts of memory to process data efficiently. AI applications also require high-performance memory, as they often involve large datasets and complex algorithms. In addition, the growing popularity of mobile devices is driving the demand for low-power memory solutions, which DDR5 can provide. Overall, the DDR5 market is expected to experience significant growth over the next decade, driven by the increasing demand for high-performance memory solutions across various industries.

What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Snapshot img

Enterprise Server Market Size 2024-2028

The enterprise server market size is forecast to increase by USD 31,852.7 billion at a CAGR of 7.2% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for computing capacity and workload management in data center infrastructure. Rack optimized servers and rack servers are becoming increasingly popular as businesses seek to maximize space utilization in their data centers. The rise of cloud service providers and the adoption of cloud computing policies have led to an increased need for data centers storage and data center services. Moreover, the emergence of artificial intelligence (AI) and machine learning (ML) applications, as well as the deployment of 5G edge infrastructure, are driving the need for high-performance servers. Supermicro and other leading server manufacturers are responding to these trends by developing servers that offer superior processing power and energy efficiency. In addition, the growing popularity of flash-based storage devices and the increasing consolidation activity in the data center industry are also contributing to market growth. In summary, the market is experiencing strong growth due to the increasing demand for computing capacity, the emergence of new technologies like AI and ML, and the consolidation of data center infrastructure.

What will be the Size of the Market During the Forecast Period?

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The market is a significant segment of the computer hardware industry, focusing on providing high-performance computing solutions for businesses. These servers cater to the demands of big data, business intelligence applications, and high-performance computing needs. Enterprise servers play a crucial role in enhancing network performance and desktop performance for businesses. They offer substantial memory capacity, ensuring the swift processing of large data sets. In today's digital transformation era, these servers are indispensable for handling complex workloads and supporting advanced technologies like cloud computing, artificial intelligence (AI), and the Internet of Things (IoT). Network services, such as Transmission Control Protocol (TCP) and Internet Protocol (IP), are essential components of enterprise servers.



Furthermore, network services, such as Transmission Control Protocol (TCP) and Internet Protocol (IP), are essential components of enterprise servers. They enable consolidated connections and multicast capabilities, ensuring seamless communication between various systems and applications. Hyperscale data centers are the backbone of modern IT infrastructure, and enterprise servers are a vital component of these facilities. These data centers house cloud service providers and support the growing demands for cloud servers and storage capacity. Security is a top priority for businesses, and enterprise servers offer advanced security features. They provide strong operating systems and server classes, including mid-range and volume servers, to cater to various business requirements.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Class Type

  Mid-range
  High-end
  Volume


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Class Type Insights

The mid-range segment is estimated to witness significant growth during the forecast period.

The mid-range segment of the market caters to businesses seeking a balance between cost-effectiveness and computing capacity. These servers are suitable for moderate-sized organizations and specific departments within larger enterprises that require more processing power than volume servers. Mid-range servers offer versatility with support for multiple operating systems, including Linux, Windows, and UNIX, ensuring flexibility in deployment. Rack-optimized servers, a popular configuration, are designed to maximize data center infrastructure efficiency by minimizing rack space and power consumption.

Furthermore, cloud service providers and data center services also leverage mid-range servers for their 5G Edge, Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) applications. Mid-range servers come in various configurations, enabling organizations to select solutions tailored to their unique needs.

Get a glance at the market report of share of various segments Request Free Sample

The mid-range segment was valued at USD 28.416 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to

This dataset tells fascinating stories drawn from treasures of the National Archives collection - http://naa.gov.au/visit-us/exhibitions/memory-of-a-nation/interactive.aspx The dataset includes a …Show full descriptionThis dataset tells fascinating stories drawn from treasures of the National Archives collection - http://naa.gov.au/visit-us/exhibitions/memory-of-a-nation/interactive.aspx The dataset includes a digitised version of every original item that has been put on display in the Archives’ permanent exhibition Memory of a Nation, from its launch in 2007 through to the present day. This includes an original musical score of Waltzing Matilda, a petition for Aboriginal land rights from the Larrakia people of Darwin, and Charles Kingsford Smith’s 1921 application for a pilot’s licence. The dataset covers around 500 record items (eg. a file, object or photograph in the NAA collection). Examples of content - http://naa.gov.au/visit-us/exhibitions/memory-of-a-nation/index.aspx Record Item data fields include • Theme • Subtheme • Title • Keyword tags (names, places, government activities), • Short description • Long description • More info • Year (of record) • Series number (note: a series is a group of records that has resulted from the same filing process), • Control symbol and barcode (record item reference numbers) • Collection (i.e. the government agency or person that created the series) • Format (for example, photograph, letter, bound volume, plan, film, etc.) • File name for scanned images • Number of images • Notes • Digitised and Folio / page number

Light sheet microscopy is a powerful technique for high-speed 3D imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are memory and performance-optimized. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution, and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel ra..., The light sheet, 2-photon, and phase images were collected with homemade light sheet, 2-photon, and oblique illumination "phase" microscopes. The widefield and confocal images were collected with Andor BC43 Benchtop Confocal Microscope (Oxford Instruments). The dataset has been processed with PetaKit5D (https://github.com/abcucberkeley/PetaKit5D)., , # Data for "Image processing tools for petabyte-scale light sheet microscopy data (Part 2/2)"

The image data is organized for the figures in the paper "Image processing tools for petabyte-scale light sheet microscopy data" (https://doi.org/10.1101/2023.12.31.573734):

Description of the data and file structure

Schema of data archive

20220131_Korra_ExM_VNC_2ndtry.zip
├── 20220131_Korra_ExM_VNC_2ndtry
│  ├── Data
│  │  ├── ImageList_from_encoder.csv
│  │  ├── Scan_Iter_0000_000x_00*y_00*z_0000t_JSONsettings.json
│  │  ├── Scan_Iter_0000_000x_00*y_00*z_0000t_Settings.txt
│  │  ├── Scan_Iter_0000_000x_00*y_00*z_0000t_TargetPositions.csv
│  │  ├── Scan_Iter_0000_CamA_ch0_CAM1_stack0000_488nm_0000000msec_00*msecAbs_000x_00*y_00*z_0000t_part0001.tif
│  │  ├── Scan_Iter_0000_CamA_ch0_CAM1_stack0000_488nm_0000000msec_00*msecAbs_000x_00*y_00*z_0000t_part0002.tif
│  │  ├── Scan_Iter_0000_CamA_ch0_CAM1_stack0000_488nm_00...

Speed in MR/m and Peak memory (in GB per process) for querying all databases and dataset KAL_D in FinisTerrae II.

The size of the 3D XPoint Technology Market was valued at USD 2.3 Billion in 2023 and is projected to reach USD 5.15 Billion by 2032, with an expected CAGR of 12.21% during the forecast period. 3D XPoint Technology is a Novel Non-Volatile Memory Technology Introduced by Intel and Micron. It offers better velocity than traditional memory solutions when it comes to writing data into memory and retrieving it. In comparison with NAND flash, 3D XPoint stores data in the three-dimensional structure of a grid of memory cells, which generally enhances their performance by allowing faster access, and reducing latency. It's bridging technology that closes the gap between DRAM and NAND flash, which means bringing a memory module-like speed without missing NAND-type non-volatility. These features are in-line comprise High-Speed Low-Latency Highly Endurant suitable for many rapid applications demanding swift access like cloud storage, Artificial intelligence, and also High-Performance computing edge Computing etc. The key driver for this market is increased demand for data processing speed across sectors such as gaming, data centers, and real-time analytics. One key driver is enterprise environments where there is a demand for better storage solutions with an emphasis on both speed and integrity of data. The benefits that 3D XPoint can offer include less power consumption, better durability, and the handling of large data sets. The ability of the technology to offer faster and much more reliable access to data has made it a leader in transforming memory and storage architectures. Recent developments include: May 2021, Intel unveiled the Optane H20, a revolutionary 3D XPoint-based SSD optimized for thin and light notebooks., July 2022, Micron announced the development of 3D XPoint-based memory modules with storage capacities of up to 2 TB., March 2023, Samsung has announced the creation of a new 3D XPoint-based memory chip with higher read and write rates than existing options.. Notable trends are: Rising demand of 3D imagery to boost market growth.

RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established.

The datasets follow the RLDS format to represent steps and episodes.

DeepMind Lab dataset has several levels from the challenging, partially observable Deepmind Lab suite. DeepMind Lab dataset is collected by training distributed R2D2 by Kapturowski et al., 2018 agents from scratch on individual tasks. We recorded the experience across all actors during entire training runs a few times for every task. The details of the dataset generation process is described in Gulcehre et al., 2021.

We release datasets for five different DeepMind Lab levels: seekavoid_arena_01, explore_rewards_few, explore_rewards_many, rooms_watermaze, rooms_select_nonmatching_object. We also release the snapshot datasets for seekavoid_arena_01 level that we generated the datasets from a trained R2D2 snapshot with different levels of epsilons for the epsilon-greedy algorithm when evaluating the agent in the environment.

DeepMind Lab dataset is fairly large-scale. We recommend you to try it if you are interested in large-scale offline RL models with memory.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('rlu_dmlab_rooms_select_nonmatching_object', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

A fully-labeled C++03 program dataset provides a unique resource to evaluate model checkers in respect to language coverage. To tackle modern aspects of the C++ language, a large-scale benchmark dataset includes more than 1,500 C++03-compliant programs, which cover different aspects of the language, including exception handling, templates, inheritance, polymorphism, the standard template library, and object-oriented design.

People can think about the same event in very specific/detailed terms or in very general/global terms. Differences in how one thinks about an event have been recently discussed in terms of different 'levels of construal.' Theorists use the term 'low level construal' to describe the first example (ie, focusing on the details) and 'high level construal' to describe the second example (ie, focusing on the 'big picture'). Construing an event at either a high or a low level can influence how it is stored in memory, and how it is later recalled. Moreover, construing one event at a high level may make it more likely that other, subsequent events will also be construed at a high level (and likewise for events construed at a low level). This project examines two aspects of how people encountered during an event are remembered: face recognition, and memory for behaviour. Based on previous research, it is expected that high-level construal will facilitate face recognition and memory for the meaning of behaviours (eg, traits and goals) whilst low-level construal will impair face recognition and improve memory for detailed characteristics of behaviour. Other aspects of event memory and mediating and moderating factors will also be explored.

pone.0285212.t004 - A distributed computing model for big data anonymization in the networks

Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and the need to incorporate multi-scale features arise. The resolution of input images can be reduced, however, with significant loss of critical information. Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present ‘UltraMNIST dataset’, a simple yet representative benchmark dataset for this task. UltraMNIST has been designed using the popular MNIST digits with additional levels of complexity added to replicate well the challenges of real-world problems. We present two variants of the problem: ‘UltraMNIST classification’ and ‘Budget-aware UltraMNIST classification’. The standard UltraMNIST classification benchmark is intended to facilitate the development of novel CNN training methods that make the effective use of the best available GPU resources. The budget-aware variant is intended to promote development of methods that work under constrained GPU memory. For the development of competitive solutions, we present several baseline models for the standard benchmark and its budget-aware variant. We study the effect of reducing resolution on the performance and present results for baseline models involving pretrained backbones from among the popular state-of-the-art models. Finally, with the presented benchmark dataset and the baselines, we hope to pave the ground for a new generation of CNN methods suitable for handling large images in an efficient and resource-light manner. UltraMNIST dataset comprises very large-scale images, each of 4000x4000 pixels with 3-5 digits per image. Each of these digits has been extracted from the original MNIST dataset. Your task is to predict the sum of the digits per image, and this number can be anything from 0 to 27.

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. It is not as frequently observed as other meteorologic conditions, but often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. We applied an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes to estimate daily vapor pressure for 83 FLUXNET sites in the United States and Canada. This data package includes all raw data of the 83 FLUXNET sites, input data for model training/validation/test, trained models and results, and python codes for the manuscript "Improving the Estimation of the Atmospheric Water Vapor Pressure Using an Interpretable Long Short-term Memory Network". Specifically, it consists of five parts. - First, "1_Daymet_data_83sites.zip" includes raw data downloaded from Daymet for the 83 sites used in the paper according to their longitude and latitude, in which vapor pressure is used. It also includes a pre-processed CSV data file combining all data from the 83 sites which is specifically used for the paper. - Second, "2_Fluxnet2015_data_83sites.zip" includes raw half hourly data of the 83 sites downloaded from FLUXNET2015 data portal, pre-processed daily data of the 83 sites, a CSV file including combined pre-processed daily data of the 83 sites, and a CSV file including the information (site ID, site name, latitude, longitude, data available period) of the 83 sites. - Third, "3_MODIS_LAI_data_83sites_raw.zip" includes raw leaf area index (LAI) data downloaded from the AppEEARs data portal. - Fourth, "4_Scripts.zip" includes all scripts related to model training and post-processing of a trained model, and a jupyter notebook showing an example for model post-processing. Two typo errors in files titled "run2get_args.py" and "postprocess.py" were corrected on March 27, 2024 to avoid confusions. - Finally, "Trained_models_and_results.zip" includes three folders and three files with suffix ".npy", and each folder corresponds to one file with suffix ".npy" with the same title. Each of the three folders include all trained models associated with one iLSTM model configuration (35 models for each configuration, details are described in the paper). Each file with suffix ".npy" includes the post-processed results of the corresponding 35 models under one iLSTM model configuration.