row sparse (Sparse Model B)

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

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.

We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sam...

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).

Multiarray EMG data set for classifier evaluation

The artifact consists of the necessary data to reproduce the results reported in the SAT-20 Paper titled "On the Sparsity of XORs in Approximate Model Counting".

In particular, the artifact consists of the binaries, the log files generated by our computing cluster, and scripts to generate tables and the plots used in the paper.

Rural and Urban Definitions Grid showing settlement classification Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year].

EMG finger press data for classification.

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

The dataset used in this paper for elastic source imaging with very sparse data, both in space and time.

Basic properties of the two datasets. and respectively represent the number of users, items, ratings and social activities. and denotes the data sparsity of information and social networks respectively.

These data supports our response to the mmvec publication

Rural and Urban Definitions Grid showing settlement classification

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.

KDDCup15.rar and XuetangX.rar are the metadata used in the paper "A Survival Analysis based Volatility and Sparsity ModelingNetwork for Student Dropout Prediction". Both of them have be drawn from the largest MOOC platform in China, XuetangX (see https://www.xuetangx.com/). If any interested parties want to fetch the original dataset, they may follow the URLs bellow:

KDDCup 2015 dataset is available at https://www.biendata.xyz/competition/kddcup2015/data/.

XuetangX dataset is available at http://moocdata.cn/data/user-activity.

Usage of high-level intermediate representations promises the generation of fast code from a high-level description, improving the productivity of developers while achieving the performance traditionally only reached with low-level programming approaches.

High-level IRs come in two flavors: 1) domain-specific IRs designed to express only for a specific application area; or 2) generic high-level IRs that can be used to generate high-performance code across many domains. Developing generic IRs is more challenging but offers the advantage of reusing a common compiler infrastructure various applications.

In this paper, we extend a generic high-level IR to enable efficient computation with sparse data structures. Crucially, we encode sparse representation using reusable dense building blocks already present in the high-level IR. We use a form of dependent types to model sparse matrices in CSR format by expressing the relationship between multiple dense arrays explicitly separately...

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.

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.

The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods.

This comparison is based on 490 sparse matrices from the SuiteSparse Matrix Collection (https://sparse.tamu.edu) and 6 matrix reordering algorithms. The dataset provided here supplies the permutations and reordered matrices in Matrix Market file format for 3 matrices and 6 reorderings.