row sparse (Sparse Model B)

Sparse Basic Linear Algebra Subprograms (BLAS), comprise of computational kernels for the calculation sparse vectors and matrices operations.

Data used for the paper SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

It contains over 1GB of high-quality motion capture data recorded with an Xsens Awinda system while using a variety of VR applications in Meta Quest devices.

Visit the paper website!

If you find our data useful, please cite our paper:

@article{10.1145/3625264, author = {Ponton, Jose Luis and Yun, Haoran and Aristidou, Andreas and Andujar, Carlos and Pelechano, Nuria}, title = {SparsePoser: Real-Time Full-Body Motion Reconstruction from Sparse Data}, year = {2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {0730-0301}, url = {https://doi.org/10.1145/3625264}, doi = {10.1145/3625264}, journal = {ACM Trans. Graph.}, month = {oct}}

Landsat satellite imagery is used to derive woody vegetation extent products that discriminate between forest, sparse woody and non-woody land cover across a time series from 1988 to 2018. A forest is defined as woody vegetation with a minimum 20 per cent canopy cover, potentially reaching 2 metres high and a minimum area of 0.2 hectares. Sparse woody is defined as woody vegetation with a canopy cover between 5-19 per cent.

The three-class classification (forest, sparse woody and non-woody) supersedes the two class classification (forest and non-forest) from 2016. The new classification is produced using the same approach in terms of time series processing (conditional probability networks) as the two-class method, to detect woody vegetation cover. The three-class algorithm better encompasses the different types of woody vegetation across the Australian landscape.

This dataset is a raster format GeoTIFF representing the percentage of density in each pixel of sparse vegetation. It includes any geographic areas were the cover of natural vegetation is between 2% and 10%, including permanently or regularly flooded areas. Sparse vegetation dataset is part of the Global Land Cover-SHARE (GLC-SHARE) database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.

This code is part of the Ph.D. thesis of Isabelle M. van Schilt, Delft University of Technology. It calculates supply chain visibility for a given supply chain network with sparse data.

The first capture of the area North of the Floreat Surf Life Saving Club, these sand dunes were captured by UAV imagery on 17th Aug 2021 for the Cambridge Coastcare beach dune modelling and monitoring project. It was created as part of an initiative to innovatively monitor coastal dune erosion and visualize these changes over time for future management and mitigation. This data includes Orthomosaic, DSM, DTM, Elevation Contours, 3D Mesh, 3D Point Cloud and LiDAR constructed from over 500 images captured from UAV (drone) and processed in Pix4D. All datasets can be freely accessed through DataWA. Link to Animated video fly-through of this 3D data model Link to the Sketchfab visualisation of the 3D textured mesh The dataset is a Sparse 3D Point Cloud (i.e. a 3D set of points): the X,Y,Z position and colour information is stored for each point of the point cloud. This dataset is of the area North of Floreat SLSC (2021 Flight-2 project area).

In this paper we propose an innovative learning algorithm - a variation of One-class ? Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class ? SVMs while reducing both training time and test time by several factors.

The p-values of the pairwise one-tailed Wilcoxon rank sum tests on the test set of the AR face database with sunglasses and scarf occlusions (sub-image size 21×32).

Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. This paper posits that these methods can be extremely useful for understanding large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using parse regression or classifi?cation; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. We validate our approach using a corpus of Aviation Safety Reporting System (ASRS) reports and demonstrate that the methods can reveal causal and contributing factors in runway incursions. Furthermore, we show that the methods automatically discover four main tasks that pilots perform during flight, which can aid in further understanding the causal and contributing factors to runway incursions and other drivers for aviation safety incidents. Citation: L. El Ghaoui, G. C. Li, V. Duong, V. Pham, A. N. Srivastava, and K. Bhaduri, “Sparse Machine Learning Methods for Understanding Large Text Corpora,” Proceedings of the Conference on Intelligent Data Understanding, 2011.

Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP-VAR model which determines which parameters truly vary when a break is detected. By doing so, the number of new parameters to be estimated at each regime is drastically reduced and the break dynamics becomes easier to be interpreted. The Sparse CP-VAR model disentangles the dynamics of the mean parameters and the covariance matrix. The former uses CP dynamics with shrinkage prior distributions, while the latter is driven by an infinite hidden Markov framework. An extensive simulation study is carried out to compare our approach with existing ones. We provide applications to financial and macroeconomic systems. It turns out that many off-diagonal VAR parameters are zero for the entire sample period and that most break activity is in the covariance matrix. We show that this has important consequences for portfolio optimization, in particular when future instabilities are included in the predictive densities. Forecasting-wise, the Sparse CP-VAR model compares favorably to several time-varying parameter models in terms of density and point forecast metrics.

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal’s movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions, and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse global positioning system (GPS) data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 hours from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-hour data. We then subsampled the 2-hour data to one location every 12 hours (a proxy for sparse data) and fitted BBMMs to the 12-hour data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1,200 m2; elk: 600–1,600 m2) successfully delineated migration corridors similar to the 2-hour baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1,000 m2 (elk) decreased the inflation of the 12-hour corridors relative to the 2-hour corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation planning across the globe.

The dataset in this repository complements the publication: Adrian Grille Guerra, Andrea Sciacchitano, Fulvio Scarano; Iterative modal reconstruction for sparse particle tracking data. Physics of Fluids 1 July 2024; 36 (7): 075107. https://doi.org/10.1063/5.0209527. The dataset contains the electronic supplementary material also available in the online version of the journal (three videos), a digital version of the figures of the publication in Matlab figure format, the full dataset discussed in the publication and also a sample code of the proposed methodology.

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.

Landsat satellite imagery is used to derive woody vegetation extent products that discriminate between forest, sparse woody and non-woody land cover across a time series from 1988 to 2022. A forest is defined as woody vegetation with a minimum 20 per cent canopy cover, at least 2 metres high and a minimum area of 0.2 hectares. Note that this product is not filtered to the 0.2ha criteria for forest to allow for flexibility in different use cases. Filtering to remove areas less than 0.2ha is undertaken in downstream processing for the purposes of Australia's National Inventory Reports. Sparse woody is defined as woody vegetation with a canopy cover between 5-19 per cent.

The three-class classification (forest, sparse woody and non-woody) supersedes the two-class classification (forest and non-forest) from 2016. The new classification is produced using the same approach in terms of time series processing (conditional probability networks) as the two-class method, to detect woody vegetation cover. The three-class algorithm better encompasses the different types of woody vegetation across the Australian landscape.

Unlike previous versions of the National Forest and Sparse Woody Vegetation data releases where 35 tiles have been released concurrently as part of the product, only the 25 southern tiles were supplied in the initial v7.0 release in June 2023. The 10 northern tiles have been released in July 2024 as v7.1 as a supplement to the initial product release to complete the standard 35 tiles. Please see the National Forest and Sparse Woody Vegetation data metadata pdf (Version 7.1 - 2022 release) for more information.

The presented data set, inspired by the SophiaBeads Dataset Project for X-ray Computed Tomography, is collected for studies involving sparsity-regularised reconstruction. The aim is to provide tomographic data for various samples where the sparsity in the image varies.

This dataset is made available as part of the publication

"SparseBeads Data: Benchmarking Sparsity-Regularized Computed Tomography", Jakob S Jørgensen et al, 2017. Meas. Sci. Technol. 28 124005.

Direct link: https://doi.org/10.1088/1361-6501/aa8c29.

This manuscript is published as part of Special Feature on Advanced X-ray Tomography (open access). We refer the users to this publication for an extensive detail in the experimental planning and data acquisition.

Each zipped data folder includes

• The meta data for data acquisition and geometry parameters of the scan (.xtekct and .ctprofile.xml).

• A sinogram of the central slice (CentreSlice > Sinograms > .tif) along with meta data for the 2D slice (.xtek2dct and .ct2dprofile.xml),

• List of projection angles (.ang)

• and a 2D FDK reconstruction using the CTPro reconstruction suite (RECON2D > .vol) with volume visualisation parameters (.vgi), added as a reference.

We also include an extra script for those that wish to use the SophiaBeads Dataset Project Codes, which essentially replaces the main script provided, sophiaBeads.m (visit https://zenodo.org/record/16539). Please note that sparseBeads.m script will have to be placed in the same folder as the project codes. The latest version of this script can be found here: https://github.com/jakobsj/SparseBeads_code

For more information, please contact

• jakj [at] dtu.dk
• jakob.jorgensen [at] manchester.ac.uk

Codes, syntetic and empirical data for "Bayesian estimation of information-theoretic metrics for sparsely sampled distributions"

Abstract:

Estimating the Shannon entropy of a discrete distribution from which we have only observed a small sample is challenging. Estimating other information-theoretic metrics, such as the Kullback-Leibler divergence between two sparsely sampled discrete distributions, is even harder. Here, we propose a fast, semi-analytical estimator for sparsely sampled distributions. Its derivation is grounded in probabilistic considerations and uses a hierarchical Bayesian approach to extract as much information as possible from the few observations available. Our approach provides estimates of the Shannon entropy with precision at least comparable to the benchmarks we consider, and most often higher; it does so across diverse distributions with very different properties. Our method can also be used to obtain accurate estimates of other information-theoretic metrics, including the notoriously challenging Kullback-Leibler divergence. Here, again, our approach has less bias, overall, than the benchmark estimators we consider.

simulated agents can be created to emulate the behavior of the lost person. Within this study

MATLAB code + demo to reproduce results for "Sparse Principal Component Analysis with Preserved Sparsity". This code calculates the principal loading vectors for any given high-dimensional data matrix. The advantage of this method over existing sparse-PCA methods is that it can produce principal loading vectors with the same sparsity pattern for any number of principal components. Please see Readme.md for more information.

Training procedure of the paper