A visual repository of test data for use in comparative studies of algorithms for numerical linear algebra, featuring nearly 500 sparse matrices from a variety of applications, as well as matrix generation tools and services.

GENERAL NOTES : The Territorial Population and Employment Data Matrix (TPEDM) is compiled by the Planning Department and updated regularly to tie in with the updated population projections of the Population Census. It provides estimated data on territorial distributions of population and employment in the future years for use as reference materials by government departments and stakeholders involved in the planning of developments and services. The 2021-based TPEDM was completed in 2024 by adopting the Census and Statistics Department's projections of territorial population released in August 2023 as the control totals. It includes the population and employment estimates for the base year (i.e. 2021) and the projections for two future years (i.e. 2026 and 2031). The population and employment data is presented in 18 District Council Districts and 5 Sub-regions as shown on Map 1 and Map 2. TPEDM was compiled using different assumptions regarding future territorial population, employment structure, economic growth as well as the planned and latest known development proposals of relevant Government departments, quasi-government bodies and the private sector. The assumptions on future development may be subject to change after the compilation. SPECIAL NOTES : Population Coverage The population of 2021-based TPEDM refers to land-based Hong Kong Resident Population (HKRP). HKRP comprises “Usual Residents” and “Mobile Residents”, and foreign domestic helpers are included. (a) “Usual Residents” refer to two categories of people: (i) Hong Kong Permanent Residents who have stayed in Hong Kong for at least 3 months during the 6 months before or for at least 3 months during the 6 months after the reference time-point, regardless of whether they are in Hong Kong or not at the reference time-point; and (ii) Hong Kong Non-permanent Residents who are in Hong Kong at the reference time-point. (b) For those Hong Kong Permanent Residents who are not “Usual Residents”, they are classified as “Mobile Residents” if they have stayed in Hong Kong for at least 1 month but less than 3 months during the 6 months before or for at least 1 month but less than 3 months during the 6 months after the reference time-point, regardless of whether they are in Hong Kong or not at the reference time-point. Geographical distribution of population is generally made on the basis of place of residence. Accordingly, population of a particular area is made up of persons who live there. Employment Coverage Employment refers to the total number of full or part time jobs with establishments in Hong Kong. Positions taken up by working proprietors, self-employed, temporary employees as well as foreign domestic helpers are included. Jobs with establishments outside Hong Kong are excluded. Employment is measured by the number of jobs held. Thus, a person holding more than one job will be counted separately. On the other hand, job vacancies are excluded. Geographical distribution of employment is generally made on the basis of place of jobs. Accordingly, employment of a particular area is made up of number of jobs there. Geographical Demarcation Systems This version of 2021-based TPEDM presents estimates and projections of population and employment distribution at two levels of a geographical demarcation system, namely District Council District (DCD) and Sub-region. District Council District (DCD)[1] At the DCD level, the data are grouped and presented in respect of 18 DCDs boundaries (see Map 1) to facilitate population and employment data extraction for land use and public facilities planning in District Council level. [1] The Loop Area is currently not included in a DCD, its figures are included in Yuen Long District.

Summary: Database of confusion matrices retrieved from scientific literature. Suitable for research on the creation and explotation of the confusion matrix that remain as interestint topics, such as new tools, sampling design, indices derived from the matrix, proposals in testing statistical hypotheses and so on.Format: SQLite

A recently discovered universal rank-based matrix method to extract trends from noisy time series is described in Ierley and Kostinski (2019) but the formula for the output matrix elements, implemented there as an open-access supplement MATLAB computer code, is O(N^4), with N the matrix dimension. This can become prohibitively large for time series with hundreds of sample points or more. Based on recurrence relations, here we derive a much faster O(N^2) algorithm and provide code implementations in MATLAB and in open-source JULIA. In some cases one has the output matrix and needs to solve an inverse problem to obtain the input matrix. A fast algorithm and code for this companion problem, also based on the recurrence relations, are given. Finally, in the narrower, but common, domains of (i) trend detection and (ii) parameter estimation of a linear trend, users require, not the individual matrix elements, but simply their accumulated mean value. For this latter case we provide a yet faster O(N) heuristic approximation that relies on a series of rank one matrices. These algorithms are illustrated on a time series of high energy cosmic rays with N > 4 x 10^4 .

The Product Service Codes (PSC) and North American Industrial Classification Systems (NAICS) are the two methods that the Federal government classifies contracts. They are used as a mechanism to identify scope of the products and services and business segment covered under the award. This data can be used as a mechanism to understand the scope of GSA programs. This can be used as means to identify best fit. While a GSA contract can offer great opportunities for many businesses, the process of applying for that contract will take a significant amount of time and resources. Understanding best GSA contract for your products and services is a preliminary step to take prior to responding to a GSA solicitation.

Summary: Database of confusion matrices retrieved from scientific literature. Suitable for research on the creation and explotation of the confusion matrix that remain as interestint topics, such as new tools, sampling design, indices derived from the matrix, proposals in testing statistical hypotheses and so on.Format: Microsoft Access

Data Matrix Validator Market Outlook

The global Data Matrix Validator market size is expected to reach $3.8 billion by 2032, up from $1.5 billion in 2023, with a compound annual growth rate (CAGR) of 10.5%. The robust growth in this market is driven by an increasing need for accurate product tracking and inventory management across various industries, coupled with advancements in data matrix technology.

One of the primary growth factors of the Data Matrix Validator market is the escalating demand for traceability in supply chains. Companies across sectors such as healthcare, manufacturing, and retail are increasingly adopting data matrix technology to ensure the integrity and authenticity of their products. This capability is crucial in industries where product recalls or counterfeiting pose significant risks. Enhanced traceability solutions provided by data matrix validators help companies comply with stringent regulatory requirements, further driving their adoption. The integration of these technologies into automated systems and IoT devices is also streamlining operations, reducing human error, and enhancing overall efficiency.

Another significant driver for the market is technological advancements in data matrix validation systems. Innovations such as high-speed scanning, improved accuracy, and real-time data processing are making data matrix validators more reliable and effective. These enhancements are particularly beneficial in high-volume industries like retail and logistics, where speed and precision are paramount. Additionally, the development of cloud-based validation solutions offers greater flexibility and scalability, allowing businesses of all sizes to implement advanced tracking systems without significant upfront investment. The shift towards Industry 4.0 and smart manufacturing is further fueling the demand for sophisticated data matrix validation solutions.

The third major growth factor is the increasing adoption of these systems in emerging markets. Regions such as Asia Pacific and Latin America are witnessing rapid industrialization and urbanization, leading to a surge in demand for advanced inventory management solutions. Governments in these regions are also implementing policies to enhance product safety and traceability, which is boosting the market for data matrix validators. Moreover, the rising e-commerce sector in these regions is creating additional opportunities for market growth as businesses seek efficient ways to manage and track a growing volume of shipments.

Regionally, North America and Europe continue to dominate the Data Matrix Validator market due to the presence of a well-established industrial base and stringent regulatory frameworks. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid adoption of advanced technologies in countries like China, India, and Japan is a significant factor driving market expansion. Furthermore, increasing investments in manufacturing and logistics infrastructure, along with growing awareness about the benefits of data matrix validation, are contributing to the market's regional growth. Latin America and the Middle East & Africa are also expected to grow steadily, supported by rising industrial activities and improving economic conditions.

Component Analysis

The Data Matrix Validator market is segmented into three main components: Software, Hardware, and Services. Each of these components plays a critical role in the overall functionality and effectiveness of data matrix validation systems. The software component includes the programs and applications used to scan, decode, and verify data matrix codes. The hardware component comprises the physical devices such as scanners and sensors required to capture the data. Services include installation, maintenance, and technical support provided to ensure the systems operate efficiently.

Starting with the software component, this segment is anticipated to experience substantial growth over the forecast period. The rise in demand for customized software solutions that can integrate seamlessly with existing ERP and inventory management systems is a driving factor. Additionally, advancements in artificial intelligence and machine learning are enhancing the capabilities of data matrix validation software, making them more intelligent and capable of handling complex tasks. Cloud-based software solutions are also gaining traction, offering businesses the advantage of remote access and real-time data analytics.

In the hardware segment, the market

The size and share of the market is categorized based on Type (Wireless, Wired) and Application (Mechanical Engineering, Automotive Industry, Aerospace, Oil And Gas, Chemical Industry, Medical Technology, Electrical Industry) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This Deposition contains the data and code underlying the paper "The Phase Transition of Matrix Recovery from Gaussian Measurements Matches the Minimax MSE of Matrix Denoising" By David Donoho , Matan Gavish, and Andrea Montanari, In press, PNAS 2013.

Life history traits are used to predict asymptotic odds of extinction from dynamic conditions. Less is known about how life history traits interact with stochasticity and population structure of finite populations to predict near-term odds of extinction. Through empirically parameterized matrix population models, we study the impact of life history (reproduction, pace), stochasticity (environmental, demographic), and population history (existing, novel) on the transient population dynamics of finite populations of plant species. Among fast and slow pace and either uniform or increasing reproductive intensity or short or long reproductive lifespan, slow, semelparous species are at the greatest risk of extinction. Long reproductive lifespans buffer existing populations from extinction while the odds of extinction of novel populations decreases when reproductive effort is uniformly spread across the reproductive lifespan. Our study highlights the importance of population structure, pace, a..., We gathered empirically derived stage-based population models from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step (Iles et al. 2016), and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species., , # Data from: Pace and parity predict short-term persistence of small plant populations

Access these datasets on Dryad https://doi.org/10.5061/dryad.2547d7wzv

Empirically derived stage-based population models were collected from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step, and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species.

Life history traits were estimated from the matrix population models using the R package Rage (Jones et al. 2022).

Plant matrix population models were used to simulate asymptotic growth, demographic and environmental stochasticity and test the impact of initial population size, population structure, stochasticity, and life history on the odds of extinction. The impa...

Hadamard mathematical matrices for weighing.

Description of the corpora

The set of these datasets are made to analyze ifnormation credibility in general (rumor and disinformation for English and French documents), and occuring on the social web. Target databases about rumor, hoax and disinformation helped to collect obviously misinformation. Some topic (with keywords) helps us to made corpora from the micrroblogging platform Twitter, great provider of rumors and disinformation.

1 corpus describes Texts from the web database about rumors and disinformation. 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French). 4 corpora from Social Media Twitter randomly built (2 in English, 2 in French). 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French).

Size of different corpora :

Social Web Rumorous corpus: 1,612

French Hollande Rumorous corpus (Twitter): 371 French Lemon Rumorous corpus (Twitter): 270 English Pin Rumorous corpus (Twitter): 679 English Swine Rumorous corpus (Twitter): 1024

French 1st Random corpus (Twitter): 1000 French 2st Random corpus (Twitter): 1000 English 3st Random corpus (Twitter): 1000 English 4st Random corpus (Twitter): 1000

French Rihanna Event corpus (Twitter): 543 English Rihanna Event corpus (Twitter): 1000 French Euro2016 Event corpus (Twitter): 1000 English Euro2016 Event corpus (Twitter): 1000

A matrix links tweets with most 50 frequent words

Text data :

_id : message id body text : string text data

Matrix data :

52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 11,102 lines (each line is a message)

Hidalgo corpus: lines range 1:75 Lemon corpus : lines range 76:467 Pin rumor : lines range 468:656 swine : lines range 657:1311

random messages : lines range 1312:11103

Sample contains : French Pin Rumorous corpus (Twitter): 679 Matrix data :

52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 189 lines (each line is a message)

Matrices_SpatialSpan.csv includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, & Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.).

robustCor.R is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned.

Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set.

The cataloging of nuclei and building agglomerations of historical matrix della Liguria is aimed at the enhancement of the environmental country heritage, the promotion of tourism-cultural and socio-cultural analysis, historical-environmental of the existing heritage. The objective is to create an information system of the buildings and agglomerations of historical matrix from existing bibliographic sources, studies, territorial plans and information levels. — Coverage: Entire Regional Territory — Origin: Georeferencing on Regional Technical Charter

Some features of the phase diagram of the two-dimensional ANNNI model have long been debated. The extended structural correlations and relaxation times associated with its Kosterlitz-Thouless (KT) phase indeed result in analytical and numerical treatments making contradictory predictions. Here, we introduce a numerical transfer matrix approach that bypasses these problems, and thus clears up various ambiguities. In particular, we confirm the transition temperatures and the order of the transition to the floating incommensurate phase. Our approach motivates considering transfer matrices for solving long-standing problems in related models.

Waters co-produced during petroleum extraction are normally considered wastes but are also possible resources, especially in water-stressed regions. Produced waters can be chemically complex. High salinity, naturally occurring radioactive materials, and organic substances derived from the producing formation can complicate treatment processes. Rapid screening methods to characterize produced waters could be important in determining effective and efficient treatment strategies, as the composition of these produced waters can vary dramatically. In this study, excitation-emission matrix spectroscopy (EEMs) was used to assess the types of fluorescent dissolved organic matter (fDOM) present in produced waters from six unconventional petroleum plays in the United States. EEMs is a fast analysis that requires little to no sample preparation and can be done on-site of oil and gas operations, making it an ideal field screening tool. Eighteen produced water samples were analyzed using EEMs, absorption spectroscopy, and non-purgeable dissolved organic carbon analysis.

Data matrix V2 and list of literature references for: An ecological trait matrix of Neotropical freshwater fishes (Albert at al., Nature Scientific Data) (2024-08-26)

The evaluation of epidemiological evidence was carried out in project ALTERNATIVE by a systematic review of the literature. The project defined the literature search strategy and build a data matrix from the collected evidence. The matrix consists of all the variables collected from each selected evidence and is presented in separate tables. This file presents the original data matrix.

Runtime and memory usage of matrix self-cross-products recorded for matrices with 40,000 elements and different dimensions. Native R functions %*% and crossprod, numpy in Python, and two user-defined functions in R and Python were compared.