Impaired nerve regeneration and inadequate recovery of motor and sensory function following peripheral nerve repair remain the most significant hurdles to optimal functional and quality of life outcomes in vascularized tissue allotransplantation (VCA). Neurotherapeutics such as Insulin-like Growth Factor-1 (IGF-1) and chondroitinase ABC (CH) have shown promise in augmenting or accelerating nerve regeneration in experimental models and may have potential in VCA. The aim of this study was to evaluate the efficacy of low dose IGF-1, CH or their combination (IGF-1+CH) on nerve regeneration following VCA. We used an allogeneic rat hind limb VCA model maintained on low-dose FK506 (tacrolimus) therapy to prevent rejection. Experimental animals received neurotherapeutics administered intra-operatively as multiple intraneural injections. The IGF-1 and IGF-1+CH groups received daily IGF-1 (intramuscular and intraneural injections). Histomorphometry and immunohistochemistry were used to evaluate outcomes at five weeks. Overall, compared to controls, all experimental groups showed improvements in nerve and muscle (gastrocnemius) histomorphometry. The IGF-1 group demonstrated superior distal regeneration as confirmed by Schwann cell (SC) immunohistochemistry as well as some degree of extrafascicular regeneration. IGF-1 and CH effectively promote nerve regeneration after VCA as confirmed by histomorphometric and immunohistochemical outcomes.

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}}

Diversification rates vary across species as a response to various factors, including environmental conditions and species-specific features. Phylogenetic models that allow accounting for and quantifying this heterogeneity in diversification rates have proven particularly useful for understanding clades diversification. Recently, we introduced the cladogenetic diversification rate shift model (ClaDS), which allows inferring subtle rate variations across lineages. Here we present a new inference technique for this model that considerably reduces computation time through the use of data augmentation and provide an implementation of this method in Julia. In addition to drastically reducing computation time, this new inference approach provides a posterior distribution of the augmented data, that is the tree with extinct and unsampled lineages as well as associated diversification rates. In particular, this allows extracting the distribution through time of both the mean rate and the number...

There are four datasets:

1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.

1. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.

2. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.

The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.

https://github.com/Aatlantise/syntactic-augmentation-nli/tree/master/datasets @inproceedings{min-etal-2020-syntactic, title = "Syntactic Data Augmentation Increases Robustness to Inference Heuristics", author = "Min, Junghyun and McCoy, R. Thomas and Das, Dipanjan and Pitler, Emily and Linzen, Tal", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address =… See the full description on the dataset page: https://huggingface.co/datasets/metaeval/syntactic-augmentation-nli.

runSimulationsThis file contains R scripts to simulated discrete traits and phylogenies used in the analyses

Data S2. GLIPH2 hits for all recipient pairs amongst B6 to B6D2F1 transplants with or without antibiotic exposure and 900cGy vs. 1300cGy TBI conditioning. For all TCRs within each recipient pair (12 mice total, 66 pairs for spleen; 15 pairs for SILP analysis), GLIPH2 specificity groups are generated as described in methods.

The data was constructed for detecting window and blind states. All images were annotated in XML format using LabelImg for object detection tasks. The results of applying the Faster R-CNN based model include detected images and loss graphs for both training and validation in this dataset. Additionally, the raw data with other annotations can be used for applications such as semantic segmentation and image captioning.

This directory contains R code and required data to run the full data augmentation described in, "An Imputation-Based Approach for Augmenting Sparse Epidemiological Signals." This is the updated code corresponding to the updated medRxiv manuscript. It now includes ILINet data as a predictor in the imputation.

"aug_pipeline.R" runs through all component steps and calls individual functions and data files within the directory. "plots_for_pipeline.R" uses data created during the aug_pipeline script to visualize individual steps in the augmentation process.

According to a 2025 U.S. ranking by Statista R published by Newsweek, the best plastic surgeon for breast augmentation was ******************, an MD based in Texas. This was followed by doctors *************, also in Texas, and ************** in Michigan. Breast augmentation is the ****** most popular cosmetic surgery in the United States, with over *** thousand procedures in 2024.

The Canned Goods Dataset

by Vizonix

This dataset differentiates between 4 similar object classes: 4 types of canned goods. We built this dataset with cans of olives, beans, stewed tomatoes, and refried beans.

The dataset is pre-augmented. That is to say, all required augmentations are applied to the actual native dataset prior to inference. We have found that augmenting this way provides our users maximum visibility and flexibility in tuning their dataset (and classifier) to achieve their specific use-case goals. Augmentations are present and visible in the native dataset prior to the classifier - so it's never a mystery what augmentation tweaks produce a more positive or negative outcome during training. It also eliminates the risk of downsizing affecting annotations.

The training images in this dataset were created in our studio in Florida from actual physical objects to the following specifications:

• Each item was imaged using a 360-degree horizontal rotation - imaged every 9 degrees at 0 degrees of elevation.
• Each item was imaged (per above) 3 times - using physical left lighting, right lighting, and frontal lighting.
• Backgrounds in this dataset are completely random - they do not factor into the classifier's decision-making (nor do we ever want them to). We used 100% random backgrounds generated in-house. This eliminates background bias in the dataset. Our use of random backgrounds is a newly released feature in our datasets.

The training images in this dataset were composited / augmented in this way:

• Imaged objects were randomly rotated in frame from 15 to 340 degrees.
• Imaged objects were randomly positioned in frame.
• Imaged objects were randomly sized from .33 to 1.0 of original.
• Image contrast was randomly adjusted from .7 to 1.25.
• Gaussian blur was randomly introduced at a factor from 2 to 5.
• Color channels were dropped randomly (R,G,B).
• Grayscale images were introduced randomly.
• Soft occlusions (noise, and others) in random transperencies were randomly introduced.
• Hard occlusions (noise and others) in solid transperencies were randomly introduced.
• Brightness was randomly adjusted.
• Sharpness was randomly adjusted.
• Color balance was randomly adjusted.
• Images were resized to 640x640 for Roboflow's platform.

1,600 (+) different images were uploaded for each class (out of the 25,000 total images created for each class).

Understanding our Dataset Insights File

As users train their classifiers, they often wish to enhance accuracy by experimenting with or tweaking their dataset. With our Dataset Insights documents, they can easily determine which images possess which augmentations. Dataset Insights allow users to easily add or remove images with specific augmentations as they wish. This also provides a detailed profile and inventory of each file in the dataset.

The Dataset Insights document enables the user to see exactly which source image, angle, augmentation(s), etc. were used to create each image in the dataset.

Dataset Insight Files:

• Beans: [https://176288177.s3.amazonaws.com/Dataset_Insights_Beans.xlsx]
• Olives: [https://176288177.s3.amazonaws.com/Dataset_Insights_Olives.xlsx]
• Refried Beans: [https://176288177.s3.amazonaws.com/Dataset_Insights_Refried_Beans.xlsx]
• Tomatoes: [https://176288177.s3.amazonaws.com/Dataset_Insights_Tomatoes.xlsx]

About Vizonix

Vizonix (vizonix.com) creates from-scratch datasets created from 100% in-house generated photography. Our images and backgrounds are generated in-house in our Florida studio. We typically image smaller items, deliver in 72 hours, and specialize in Manufacturer Quality Assurance (MQA) datasets.

Biogeographic history of Malesian RhododendronBiogeographic history of Malesian Rhododendron. The region was parsed into 20 discrete geographic areas following Brown et al. (2006). Each circle corresponds to a discrete area whose geographic coordinates is summarized by its location on the map. Posterior probability of being present in an area is proportional to the opacity of the circle, with the data at the tips being observed with probability one. Circles are colored according to their position relative to Wallace’s Line. We infer a continental Asian origin for Malesian rhododendrons. This figure complements the summarized results presented in FIgure 7 in the manuscript and is best explored using zoom.supp_vireya_map.pdfVireya phylogenyTime-calibrated phylogeny of Rhododendron section Vireya from Webb & Ree (2012).malaysia.55.treeVireya biogeographical coordinatesGeographical coordinates used to represent biogeographical areas of Rhododendron section Vireya as defined by Brown et ...

Context

This Dataset is created to use in a Notebook Dataset

Dataset

This dataset was created by Adwyth Darsan R

Contents

This study evaluates a web-based tool designed to augment telemedicine post-operative visits after periocular surgery. Adult, English-speaking patients undergoing periocular surgery with telemedicine follow-up were studied prospectively in this interventional case series. Participants submitted visual acuity measurements and photographs via a web-based tool prior to routine telemedicine post-operative visits. An after-visit survey assessed patient perceptions. Surgeons rated photographs and live video for quality and blurriness; external raters also evaluated photographs. Images were analyzed for facial centration, resolution, and algorithmically detected blur. Complications were recorded and graded for severity and relation to telemedicine. Seventy-nine patients were recruited. Surgeons requested an in-person assessment for six patients (7.6%) due to inadequate evaluation by telemedicine. Surgeons rated patient-provided photographs to be of higher quality than live video at the time of the post-operative visit (p < 0.001). Image blur and resolution had moderate and weak correlation with photograph quality, respectively. A photograph blur detection algorithm demonstrated sensitivity of 85.5% and specificity of 75.1%. One patient experienced a wound dehiscence with a possible relationship to inadequate evaluation during telemedicine follow-up. Patients rated the telemedicine experience and their comfort with the structure of the visit highly. Augmented telemedicine follow-up after oculofacial plastic surgery is associated with high patient satisfaction, rare conversion to clinic evaluation, and few related post-operative complications. Automated detection of image resolution and blur may play a role in screening photographs for subsequent iterations of the web-based tool.

Dataset

This dataset was created by Dr. Mohamed R. Shoaib

Contents

R code for likelihood-based analyses of horned lizard data and simulations.

Climate change mitigation necessitates increased investment in green sectors. This study proposes a methodology to predict green finance growth across various countries, aiming to encourage such investments. Our approach leverages time-series Conditional Generative Adversarial Networks (CT-GANs) for data augmentation and Nonlinear Autoregressive Neural Networks (NARNNs) for prediction. The green finance growth predicting model was applied to datasets collected from forty countries across five continents. The Augmented Dickey-Fuller (ADF) test confirmed the non-stationary nature of the data, supporting the use of Nonlinear Autoregressive Neural Networks (NARNNs). CT-GANs were then employed to augment the data for improved prediction accuracy. Results demonstrate the effectiveness of the proposed model. NARNNs trained with CT-GAN augmented data achieved superior performance across all regions, with R-squared (R2) values of 98.8%, 96.6%, and 99% for Europe, Asia, and other countries respectively. While the RMSE for Europe, Asia, and other countries are 1.26e+2, 2.16e+2, and 1.16e+2 respectively. Compared to a baseline NARNN model without augmentation, CT-GAN augmentation significantly improved both R2 and RMSE. The R2 values for the Europe, Asia, and other countries models are 96%, 73%, and 97.2%, respectively. The RMSE values for the Europe, Asia, and various countries models are 2.24e+2, 7e+2, and 2.07e+2, respectively. The Nonlinear Autoregressive Exogenous Neural Network (NARX-NN) exhibited significantly lower performance across Europe, Asia, and other countries with R2 values of 74%, 52%, and 86%, and RMSE values of 1.11e+2, 3.63e+2, and 1.8e+2, respectively.

This dataset contains images of lions and tigers sourced from the Open Images Dataset V6 and labeled specifically for object detection using the YOLO format. The dataset focuses on two classes: lion and tiger, with annotations provided for each image in a YOLO-compatible .txt file format. This dataset is ideal for training machine learning models for wildlife detection and classification tasks, particularly in distinguishing between these two majestic big cats. Key Features:

Classes: Lion and Tiger
Annotations: YOLO format, with bounding box coordinates and class labels provided in separate .txt files for each image.
Source: Images sourced from Open Images Dataset V6, which is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Application: Suitable for object detection models like YOLO, SSD, or Faster R-CNN.

Usage:

The dataset can be used for training, validating, or testing object detection models. Each image is accompanied by a corresponding YOLO annotation file, making it easy to integrate into any YOLO-based pipeline. Attribution:

This dataset is derived from the Open Images Dataset V6, and proper attribution must be given. Please credit the Open Images Dataset when using or sharing this dataset in any format.

Machine learning†based behaviour classification using acceleration data is a powerful tool in bio†logging research. Deep learning architectures such as convolutional neural networks (CNN), long short†term memory (LSTM) and self†attention mechanisms as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration†based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal†specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre†training of deep learning models with unlabelled data, ..., , , # Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

https://doi.org/10.5061/dryad.2ngf1vhwk

This repository contains the datasets of two seabird species (streaked shearwaters and black-tailed gulls) used in the following paper (Otsuka et al., 2024).

Otsuka, R., Yoshimura, N., Tanigaki, K., Koyama, S., Mizutani, Y., Yoda, K., & Maekawa, T. (2024). Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers. Methods in Ecology and Evolution.

The paper aimed to classify the behaviour of these two seabird species using tri-axial acceleration data and deep learning. It explored the effectiveness of deep learning models and related training techniques, such as data augmentation.

âš ï¸ WARNING (2025-07-02)
We found that the data collected using the BMX-055 sensor was likely not sampled consistently at the intended fre...,