This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Palm Springs, CA.

Here's a sample Production Bates Range for a Gap Analysis exercise via Python. It's a CSV with one column containing a range of numbers following the convention "D0000001, D0000002, .... D0099999."

This script can be run against a variable/column on a document production index to identify document sequence gaps, which can be helpful to determine missing documents in a set or to diagnose a technical issue during data processing or exchange phases.

More broadly, this code can be updated to apply over any sequential data range (dates, student ID, serial number, item number, etc.), to show any gaps or available digits.

This dataset consists of mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

## Example questions

 Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.
 Answer: 4
 
 Question: Calculate -841880142.544 + 411127.
 Answer: -841469015.544
 
 Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).
 Answer: 54*a - 30

It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:

• algebra (linear equations, polynomial roots, sequences)
• arithmetic (pairwise operations and mixed expressions, surds)
• calculus (differentiation)
• comparison (closest numbers, pairwise comparisons, sorting)
• measurement (conversion, working with time)
• numbers (base conversion, remainders, common divisors and multiples, primality, place value, rounding numbers)
• polynomials (addition, simplification, composition, evaluating, expansion)
• probability (sampling without replacement)

dataset_a.json: 1000 random numbers over the range 0-100dataset_b.json: new numbers from the original 1000 numbers in 1_a.json using the equation y=3x+6results.png: generated by these two datasets

This dataset provides information about the number of properties, residents, and average property values for Range View Road cross streets in Estes Park, CO.

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research.

About this dataset

Context

The dataset tabulates the population of Grass Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Grass Range. The dataset can be utilized to understand the population distribution of Grass Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Grass Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Grass Range.

Key observations

Largest age group (population): Male # 35-39 years (7) | Female # 70-74 years (36). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Grass Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Grass Range is shown in the following column.
• Population (Female): The female population in the Grass Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Grass Range for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Gender. You can refer the same here

This repository contains the Wallhack1.8k dataset for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the CAD models (of 3D-printable parts) of the WiFi systems proposed in [2].

PyTroch Dataloader

A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: https://github.com/StrohmayerJ/wallhack1.8k

Dataset Description

The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).

To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:

LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system

LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system

NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system

NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system

These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.

To extract the 52 L-LTF subcarriers used in [1], the following indices of A are to be selected:

52 L-LTF subcarriers

csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]

Additional 56 HT-LTF subcarriers can be selected via:

56 HT-LTF subcarriers

csi_valid_subcarrier_index += [i for i in range(66, 94)]
csi_valid_subcarrier_index += [i for i in range(95, 123)]

For more details on subcarrier selection, see ESP-IDF (Section Wi-Fi Channel State Information) and esp-csi.

Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the spectrograms/ directory.

The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]

Spectrogram index: [0, ..., n]

Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."

Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.

Dataset Overview:

Table 1: Raw WiFi packet sequences.

Scenario System "no presence" / label 0 "walking" / label 1 "walking + arm-waving" / label 2 Total

LoS BQ b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

LoS PIFA b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

NLoS BQ b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

NLoS PIFA b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

4 20 20 44

Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.

Scenario System

"no presence" / label 0

"walking" / label 1

"walking + arm-waving" / label 2 Total

LoS BQ 149 154 155

LoS PIFA 149 160 152

NLoS BQ 148 150 152

NLoS PIFA 143 147 147

589 611 606 1,806

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].

[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 42-56). Cham: Springer Nature Switzerland, doi: https://doi.org/10.1007/978-3-031-63211-2_4.

[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: https://doi.org/10.1109/ICIP51287.2024.10647666.

BibTeX citations:

@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}@INPROCEEDINGS{10647666, author={Strohmayer, Julian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, year={2024}, volume={}, number={}, pages={3594-3599}, keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32}, doi={10.1109/ICIP51287.2024.10647666}}

About this dataset

Context

The dataset tabulates the data for the Grass Range, MT population pyramid, which represents the Grass Range population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Grass Range, MT, is 17.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Grass Range, MT, is 160.0.
• Total dependency ratio for Grass Range, MT is 177.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Grass Range, MT is 0.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Grass Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Grass Range for the selected age group is shown in the following column.
• Population (Female): The female population in the Grass Range for the selected age group is shown in the following column.
• Total Population: The total population of the Grass Range for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Age. You can refer the same here

Description:

👉 Download the dataset here

Discover the rich and intricate patterns of Persian Handwritten Digits Dataset with our extensive dataset, thoughtfully curated to provide an unparalleled resource for Al and machine learning applications. This comprehensive collection comprises 150,000 high-resolution images, each meticulously generated to represent the full spectrum of Persian digits from 0 to 9. Leveraging advanced Generative Adversarial Networks (GANs), these images capture the subtle nuances of Persian handwriting, offering both diversity and authenticity in each digit's representation.

Download Dataset

Dataset Highlights

Total Images: 150,000 high-quality images, offering a substantial dataset for robust model training and testing.

Class Distribution: 15,000 images per digit class (0-9), ensuring a balanced and representative dataset across all Persian numerals.

Image Resolution: Each image is rendered at a resolution of 28×28 pixels, ideal for a variety of machine learning tasks.

Authenticity: Images are created using state-of-the-art GANs, ensuring that each digit closely mimics real Persian handwriting with realistic variations.

Versatility: This dataset is perfectly suited for a wide range of applications, including but not limited to:

Digit recognition

Generative modeling

Handwriting analysis

OCR (Optical Character Recognition) systems development

Cultural studies in Persian script

Extended Features

Diverse Handwriting Styles: The dataset captures a variety of handwriting styles, from bold and precise to delicate and ornate, reflecting the cultural and individual diversity in Persian script.

Generative Learning Applications: The GAN-generated images serve as an excellent resource for those interested in exploring generative learning models, providing a rich dataset for experiments in generating or augmenting handwritten text.

Pre-Processed & Ready-to-Use: The dataset is pre-processed and ready for immediate use, eliminating the need for additional formatting or resizing, saving valuable time and effort in your Al projects.

Cultural and Linguistic Value: Beyond its technical applications, the dataset holds cultural significance, offering insights into Persian script, which can be valuable for linguists and cultural historians alike.

Applications

This dataset is an essential tool for:

Researchers: Dive into the complexities of Persian script and explore novel approaches in digit recognition or handwriting analysis.

Data Scientists: Enhance your machine learning models with a dataset that offers both quality and quantity, ensuring accurate and reliable outcomes.

Al Developers: Build and refine OCR systems tailored specifically for Persian script, or explore the potential of GANs in generating handwritten text.

Educators and Students: Utilize this dataset in academic settings to teach and learn about handwriting recognition, machine learning, and cultural studies.

Conclusion

Whether you're pioneering new frontiers in digit recognition, developing advanced OCR systems, or simply exploring the intersection of Al and cultural studies, the Persian Handwritten Digits dataset offers a wealth of possibilities. Its balanced, high-quality images and extensive class representation make it a valuable resource for anyone working with Persian script or interested in the broader field of handwritten digit recognition.

This dataset is sourced from Kaggle.

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Bailey, CO.

We assess model performance using six datasets encompassing a broad taxonomic range. The number of species per dataset ranges from 28 to 239 (mean=118, median=94), and range shifts were observed over periods ranging from 20 to 100+ years. Each dataset was derived from previous evaluations of traits as range shift predictors and consists of a list of focal species, associated species-level traits, and a range shift metric.

272,700 two-alternative forced choice responses in a simple numerical task modeled after Tenenbaum (1999, 2000), collected from 606 Amazon Mechanical Turk workers. Subjects were shown sets of numbers length 1 to 4 from the range 1 to 100 (e.g. {12, 16}), and asked what other numbers were likely to belong to that set (e.g. 1, 5, 2, 98). Their generalization patterns reflect both rule-like (e.g. “even numbers,” “powers of two”) and distance-based (e.g. numbers near 50) generalization. This data set is available for further analysis of these simple and intuitive inferences, developing of hands-on modeling instruction, and attempts to understand how probability and rules interact in human cognition.

1) Introduction

The GESDPD is a depth images database containing 22000 frames , that simulates to have been taken with a sensor in an elevated front position, in an indoor environment. It was designed to fulfill the following objectives: • Allow the train and evaluation of people detection algorithms based on depth , or RGB-D data, without the need of manually labeling. • Provide quality synthetic data to the research community in people detection tasks. The people detection task can also be extended to practical applications such as video-surveillance, access control, people flow analysis, behaviour analysis or event capacity management.

2) General contents

GESDPD is composed of 22000 depth synthetic images, that simulates to have been taken with a sensor in an elevated front position, in a rectangular, indoor working environment. These have been generated using the simulation software Blender. The synthetic images show a room with different persons walking in different directions. The camera perspective is not stationary, it moves around the room along the database, which avoids a constant background. Some examples of the different views are shown in the next figures. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2Fdf423fca8672eab818d38a456ad36546%2Fsala_blend.png?generation=1578587944416132&alt=media" alt="">

3) Quantitative details on the database content are provided below.

• Number of frames: 22000

• Number of different people: 4 (3 men and 1 woman)

• Number of labeled people: 20800

• Image resolution: 320 × 240 pixels

For each image, the are provided the depth map and the ground truth including the position of each person in the scene.

To give you an idea on what to expect, the next figure shows some examples of images from the dataset. In this figure, depth values are represented in millimeters, using a colormap. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2F30197eb1dbbfb1e6e5bcbf0d5354b3f2%2Fsample-synthetic-images.png?generation=1578588087445161&alt=media" alt="">

4) ** Geometry details** As it has been said before, the dataset simulates to have been taken with a sensor in an elevated front position, in a rectangular indoor working environment. Specifically, the camera was placed at a height of 3 meters, and it rotates along the sequence. Regarding the room (whose distribution is shown in figure the next figure), its dimensions are 8.56 × 5.02m, and it has a height of 3.84m. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2Fa1ebe9f4de9d06e508b3105bfd9973f9%2Fdistribution.png?generation=1578588287335433&alt=media" alt="">

5) File Formats

5.1) Depth data

The depth information (distance to the camera plane) in stored as a .png image, in which each pixel represent the depth value in millimeters as a (little endian) unsigned integer of two bytes. Its values range from 0 to 15000.

5.2) Position Ground Truth Data

The ground truth information is also provided as a .png file, with the same dimensions that the gener- ated images (320 × 240 pixels). The ground truth files have in their names the same number than the corresponding depth files. For labeling people positions, there have been placed Gaussian functions over the centroid of the head of each person in the scene, so that the centroid corresponds to the 2D position of the center of the head and has a normalized value of one. The standard deviation has a value of 15 pixels for all the Gaussians, regardless of the size of each head and the distance from the head to the camera. This value has been calculated based on an estimated value of the average diameter of a person head, taking into account anthropometric considerations. It is worth to highlight that, when two heads are very closely or overlapping with each other, instead of adding both Gaussian functions, the maximum value of them prevail. That modification provides a set of Gaussians that are always separated, so that the CNN can learn to generate that separation between Gaussians in its output. The next figure shows an example of two Gaussian functions. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2F935aa3e17689e9183fe181fc50b76239%2Fgaussian.png?generation=1578588393522470&alt=media" alt="">

6) Disclaimer, Licensing, Request and Contributions This document and the data provided are work in progress and provided as is. The GEINTRA Synthetic Depth People Detection (GESDPD) Database (and accompanying files and documentation) by David Fuentes-Jiménez, Roberto Martín-López, Cristina Losada-Gutiérrez, Javier Macías-Guarasa and Carlos A. Luna is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

If you make use of this database and/or its related documentation,...

Context

The aim of this dataset is to provide a simple way to get started with 3D computer vision problems such as 3D shape recognition.

Accurate 3D point clouds can (easily and cheaply) be adquired nowdays from different sources:

• RGB-D devices: Google Tango, Microsoft Kinect, etc.
• Lidar.
• 3D reconstruction from multiple images.

However there is a lack of large 3D datasets (you can find a good one here based on triangular meshes); it's especially hard to find datasets based on point clouds (wich is the raw output from every 3D sensing device).

This dataset contains 3D point clouds generated from the original images of the MNIST dataset to bring a familiar introduction to 3D to people used to work with 2D datasets (images).

In the 3D_from_2D notebook you can find the code used to generate the dataset.

You can use the code in the notebook to generate a bigger 3D dataset from the original.

Content

full_dataset_vectors.h5

The entire dataset stored as 4096-D vectors obtained from the voxelization (x:16, y:16, z:16) of all the 3D point clouds.

In adition to the original point clouds, it contains randomly rotated copies with noise.

The full dataset is splitted into arrays:

• X_train (10000, 4096)
• y_train (10000)
• X_test(2000, 4096)
• y_test (2000)

Example python code reading the full dataset:

 with h5py.File("../input/train_point_clouds.h5", "r") as hf:  
   X_train = hf["X_train"][:]
   y_train = hf["y_train"][:]  
   X_test = hf["X_test"][:] 
   y_test = hf["y_test"][:] 

train_point_clouds.h5 & test_point_clouds.h5

5000 (train), and 1000 (test) 3D point clouds stored in HDF5 file format. The point clouds have zero mean and a maximum dimension range of 1.

Each file is divided into HDF5 groups

Each group is named as its corresponding array index in the original mnist dataset and it contains:

• "points" dataset: x, y, z coordinates of each 3D point in the point cloud.
• "normals" dataset: nx, ny, nz components of the unit normal associate to each point.
• "img" dataset: the original mnist image.
• "label" attribute: the original mnist label.

Example python code reading 2 digits and storing some of the group content in tuples:

with h5py.File("../input/train_point_clouds.h5", "r") as hf:  
  a = hf["0"]
  b = hf["1"]  
  digit_a = (a["img"][:], a["points"][:], a.attrs["label"]) 
  digit_b = (b["img"][:], b["points"][:], b.attrs["label"]) 

voxelgrid.py

Simple Python class that generates a grid of voxels from the 3D point cloud. Check kernel for use.

plot3D.py

Module with functions to plot point clouds and voxelgrid inside jupyter notebook. You have to run this locally due to Kaggle's notebook lack of support to rendering Iframes. See github issue here

Functions included:

• array_to_color Converts 1D array to rgb values use as kwarg color in plot_points()

• plot_points(xyz, colors=None, size=0.1, axis=False)

• plot_voxelgrid(v_grid, cmap="Oranges", axis=False)

Acknowledgements

• Website of the original MNIST dataset
• Website of the 3D MNIST dataset

Have fun!

The Arbuckle-Simpson aquifer covers an area of about 800 square miles in the Arbuckle Mountains and Arbuckle Plains of South-Central Oklahoma. The aquifer is in the Central Lowland Physiographic Province and is composed of the Simpson and Arbuckle Groups of Ordovician and Cambrian age. The aquifer is as thick as 9,000 feet in some areas. The aquifer provides relatively small, but important, amounts of water depended on for public supply, agricultural, and industrial use (HA 730-E). This product provides source data for the Arbuckle-Simpson aquifer framework, including: Georeferenced images: 1. i_46ARBSMP_bot.tif: Digitized figure of depth contour lines below land surface representing the base of fresh water in the Arbuckle-Simpson aquifer. The base of fresh water is considered to be the bottom of the Arbuckle-Simpson aquifer. The original figure is from the "Reconnaissance of the water resources of the Ardmore and Sherman Quadrangles, southern Oklahoma" report, map HA-3, page 2, prepared by the Oklahoma Geological Survey in cooperation with the U.S. Geological Survey (HA3_P2). Extent shapefiles: 1. p_46ABKSMP.shp: Polygon shapefile containing the areal extent of the Arbuckle-Simpson aquifer (Arbuckle-Simpson_AqExtent). The extent file contains no aquifer subunits. Contour line shapefiles: 1. c_46ABKSMP_bot.shp: Contour line dataset containing depth values, in feet below land surface, across the bottom of the Arbuckle-Simpson aquifer. This dataset is a digitized version of the map published in HA3_P2. This dataset was used to create the rd_46ABKSMP_bot.tif raster dataset. This map generalized depth values into zoned areas with associated ranges of depth. The edge of each zone was treated as the minimum value of the assigned range, thus creating the depth contour lines. This interpretation was favorable as it allowed for the creation of the resulting raster. This map was used because more detailed point or contour data for the area is unavailable. Altitude raster files: 1. ra_46ABKSMP_top.tif: Altitude raster dataset of the top of the Arbuckle-Simpson aquifer. The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data. This raster was interpolated from the Digital Elevation Model (DEM) dataset (NED, 100-meter). 2. ra_46ABKSMP_bot.tif: Altitude raster dataset of the bottom of the Arbuckle-Simpson aquifer. The altitude values are in meters referenced to NAVD88. Depth raster files: 1. rd_46ABKSMP_top.tif: Depth raster dataset of the top of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data. 2. rd_46ABKSMP_bot.tif: Depth raster dataset of the bottom of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). This raster was interpolated from the contour line dataset c_46ABKSMP_bot.shp.

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

These results are from the rail demo of 5G-PICTURE (www.5g-picture-project.eu). For more details see Deliverable D6.3 where there are also plotted figures. Dataset 4-1 This dataset is generated by a computer model. The modulation and coding scheme (MCS) of a mmWave link between an access point (AP) and a station (STA) mounted on the roof of a train is plotted as a function of the distance between AP and STA. The IEEE 802.11ad single-carrier technology is assumed, and typical conditions when the range is approximately 350 m – in other words the lowest MCS, MCS1 can be supported up to this distance. The MCS takes integer values in range 1 to 12. Dataset 4-2 This dataset is generated by the same computer model as dataset 1. In this case we plot the predicted data rate (at the application layer in Gbps) and SNR (in dB). In the simulation we assume SNR requirements of an ideal AWGN channel and adjust the link budget to align with the typical range observed in the field. The SNR is also capped at a maximum value of 25dB commensurate with a real device. Datasets 4-5 to 4-12 This is a measured dataset from field testing of the Rail Demo. In the field test the train drives from one end of the test network to the other (over approximately 2km). Traffic (TCP iperf3) is generated within each trackside mmWave AP and sent to the train STAs when an association has been established. The datasets include measurement performed by the two STA of a single train node (TN), labelled ‘Train-1’. One STA has a radio facing forwards and one is facing backwards (see deliverable D6.3). These form the two datasets for each parameter. When a STA is not associated (i.e. has no mmWave link) the parameter is not recorded since no data packets are received. The following parameters are captured: Datasets 4-5 and 4-6 The modulation and coding scheme (MCS) of a mmWave link between an AP and each STA is logged. Datasets 4-7 and 4-8 The SNR is logged. SNR is measured in dB. Datasets 4-9 and 4-10 The sector ID here indicates which beam has been chosen by the TN radios when receiving packets. A STA maintains a beambook of 13 directional beams, and a beamforming protocol identify the best beam to use. The Sector ID is an integer from 1 to 13. Low beam numbers are close to boresight, whilst the highest numbers (up to 13) imply beam steering up to 45 degrees away from boresight. Odd numbers represent pointing to the left and even number point to the right. Datasets 4-11 and 4-12 This plots the received data rate by each STA at the application layer (TCP iperf3). Unit Mbps.

A simple table time series for school probability and statistics. We have to learn how to investigate data: value via time. What we try to do: - mean: average is the sum of all values divided by the number of values. It is also sometimes referred to as mean. - median is the middle number, when in order. Mode is the most common number. Range is the largest number minus the smallest number. - standard deviation s a measure of how dispersed the data is in relation to the mean.

Overview

This dataset contains numbers from 25 up to 1 quadrillion (10¹⁵) that are smooth relative to the gap to the preceding prime. More precisely, we list all numbers n so that

r + p^a ≤ n

where r is the largest prime smaller than n - 1, and p^a is the largest prime-power divisor of n. The dataset is the result of a 10 day computation using 15 cores on an Intel Xeon system, running code hosted at GitHub (see "Related identifiers"). The GitHub code checks additional conditions when r is n - 2 and n - 1 is a power of 2, but it is easy and quick to check that when (up to 10¹⁵) n = 2^k + 1, the second largest prime r₂ satisfies r₂ + p^a > n. Thus, this additional check makes no difference in the output.

Our motivations for computing this data are described in our paper On invariable generation of alternating groups by elements of prime and prime power order (arXiv:2201.12371). Any number n in the range which is not of the given form has the associated alternating group A_n generated by any element of order r together with any element having a certain cycle structure (and of order p^a).

Description / specification

The data is stored as compressed text-based input to a computer algebra system, specifically in gzipped GAP format. The file out-k.g.gz holds numbers in the range from (k - 1)⋅10¹² to k⋅10¹². The first line of each file sets the variable invgen_oversmooth_range to be the range (thus, [(k - 1)⋅10¹² .. k⋅10¹²]). The subsequent lines set invgen_oversmooth to a list of pairs of numbers [n, p^a], where n is a smooth number as described above, and p^a is the largest prime-power of n. The largest prime preceding n - 1 is given in a GAP comment.

Thus, the first few lines of out-0.g.gz (when uncompressed) appear as

invgen_oversmooth_range:=[25..1000000000000];
invgen_oversmooth := [
 [ 30, 5 ], # bp 23 
 [ 60, 5 ], # bp 53 
 [ 126, 9 ], # bp 113 
 [ 210, 7 ], # bp 199 
 [ 252, 9 ], # bp 241 
 [ 308, 11 ], # bp 293 
 [ 330, 11 ], # bp 317 
 [ 420, 7 ], # bp 409 
...

where [25 .. 1000000000000] is the range considered, and for example "[ 30, 5 ], # bp 23" represents that 23 is the largest prime preceding 30 - 1, 5 is the largest prime-power divisor of 30, and 23 + 5 ≤ 30.

We created the data in GAP files for ease of inputting into a GAP program in our own use of the data. It is easy to convert the GAP files to another format via standard technique such as regular expression-based search and replace. For example, on macOS or Linux, the following command will convert the list in out-0.g.gz to a CSV file, which it will display on the terminal.

zcat out_quadrillion/out-0.g.gz | sed -En 's/ \[ ([0-9]+), ([0-9]+) \], # bp ([0-9]+)/\1,\2,\3/gp' | less