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31 results for “Activity Classification”
1864 radio variable galaxies with classifications for activity types.
<p>1864 radio variable galaxies with classifications for activity types.</p>
Data and code used for 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'.</p> <p> </p> <h2><strong>File structure</strong></h2> <p><strong>- analysis</strong></p> <p>The analysis subfolder contains all R scripts used for the study:</p> <p>1. Sample size estimation<br>2. Synchronisation of the timestamps<br>3. Processing of the raw data<br>4. Calculating the interrater agreement<br>5. Calculating all performance metrics and producing the plots</p> <p>In addition, the two subfolders contain the plots and result tables produced when running the scripts.</p> <p> </p> <p><strong>- data</strong></p> <p>The data folder contains all raw data as well as the processed data. A subfolder for each subject contains the video annotations (.eaf), the raw acceleration data (.csv) and the SENS motion classification data (.csv).</p> <p>The ActiPASS subfolder contains the raw acceleration files in binary file format as well as the ActiPASS output.</p> <p>The shiny subfolder contains the R code used for running the shiny app during data collection as well as the logged data and timestamps.</p> <p> </p> <p><span><strong><span>- documents</span></strong></span></p> <p><span>This folder contains any additional documents used in the study.</span></p> <p> </p> <h2><strong><span>Requirements</span></strong></h2> <p>The data processing and analysis was performed in R (Version 4.3.2). To run the full analysis in R, the following packages need to be installed:</p> <ul> <li>tidyverse</li> <li>xml2</li> <li>lubridate</li> <li>here</li> <li>dygraphs</li> <li>hms</li> <li>irr</li> <li>yardstick</li> <li>cowplot</li> <li>gt</li> </ul>
Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
Prospective Study of Classification and Activity Assessment of Psoriatic Arthritis Based on Power Doppler (PD) Ultrasonography (PDUS)
ClinicalTrials.gov study NCT06730334. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Classifications of Active Galactic Nuclei According to the Traditional Unification Scheme
<p>The figure illustrates the unification scheme of active galactic nuclei (AGN). The physical appearance of galactic nuclei is sketched in the parameter space spanned by the black-hole spin (x-axis) and the accretion rate in units of the Eddington rate (y-axis). Non-jetted (radio-quiet) objects populate mainly the low-black-hole-spin regime, whereas galactic nuclei in the high-spin domain are thought to exhibit a relativistic jet and are thus called jetted AGN (or radio-loud AGN).</p> <p>If non-jetted AGN accrete matter at a low rate, the galactic nucleus is inactive, like in the case of our Milky Way's central supermassive black hole, Sagittarius A*. If they have at least a moderate accretion rate, they are active and observable as a Seyfert galaxy. Seyfert galaxies of type 1 are those objects seen at small viewing angles (more face-on) with well-known examples being Messier 77 or NGC 3147, while type 2 Seyfert galaxies, like for example NGC 1097, are those that are observed at higher viewing angle (more edge-on), such that the dusty, equatorial torus obscures the central engine for this line of sight. At very high accretion rate or very low viewing angle, low-spin AGN are sometimes called quasi-stellar objects.</p> <p>Jetted AGN with high accretion rate appear as flat-spectrum radio quasars if they are seen face-on. An archetypical example is the long-known object 3C 279. If jetted AGN with high accretion rate are not seen face-on, they are called Fanaroff-Riley type II radio galaxies, like for example the famous Cygnus A radio galaxy. These can, again depending on the viewing angle, appear as broad-line radio galaxies (type 1 objects) or as narrow-line radio galaxies (type 2 objects). At low accretion rate, jetted AGN are appearing as BL Lacertae objects if seen face-on. Famous examples for BL Lacertae objects are the eponymous BL Lacertae itself or the prominent TeV emitter Markarian 501. Jetted AGN with low accretion rate are classified as Fanaroff-Riley I radio galaxies if seen more edge-on. A typical example for this AGN subclass is Messier 84.</p> <p>Jetted AGN that are seen at small viewing angles are comprisingly called blazars.</p> <p>Remark however that it is still unclear whether the physical parameter corresponding to the x-axis is really the angular momentum of the central supermassive black hole, or perhaps another quantity like its mass or even a combination of several parameters. Notice also that in AGN classification there are far more classes and subclasses, depending on more refined observational properties, and ambiguities as well as overlaps. This figure was motivated by the sketch by Prof. Dr. Charles Dermer appearing in DOI 10.1016/j.crhy.2016.04.004.</p>
Classification of a frameshift/extended and a stop mutation in WT1 as gain of function mutations which activate cell cycle genes and promote Wilms tumor cell proliferation
GEO Series GSE54635. Homo sapiens. 6 samples. Type: Expression profiling by array.
Italian Translation and Transcultural Validation of Frenchay Activity Index and Walking Handicap Classification in Stroke
ClinicalTrials.gov study NCT04285957. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Estimation of Energy Expenditure and Physical Activity Classification With Wearables
ClinicalTrials.gov study NCT05523830. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparative Transcriptional Analysis of Canine and Human Diffuse Large B cell Lymphoma (DLBCL). Molecular Signatures of NF-κB Pathway Activation and Sub-Classification of Canine DLBCL.
GEO Series GSE30881. Canis lupus familiaris. 33 samples. Type: Expression profiling by array.
PyTAIL Benchmark of Active Learning on Social Media Text Classification
<p>PyTAIL Benchmark of Active Learning on Social Media Text Classification</p><p>Read our paper for details: https://arxiv.org/abs/2211.13786</p><ul><li>ArXiv: https://arxiv.org/abs/2211.13786</li><li>Dataset: https://doi.org/10.5281/zenodo.7236430</li><li>Code: https://github.com/socialmediaie/pytail</li><li>Video: https://www.youtube.com/watch?v=AwDu64gN8t4 </li></ul>
Activity and intensity data for UWB radar classification
<p>The task of automated activity classification has previously attracted various avenues of research, and has inspired different methodologies in solving the problem. We outline an unobtrusive method of detecting and classifying different activities and exercises using a 24 GHz UWB radar transceiver and a DNN. The radar transceiver module is used to record the data of a single individual carrying out 6 different activities within a closed environment, and the subsequently processed radar signals are used to train a CNN, which is used to classify the human activities and the intensity of the activities. </p> <p>Using a custom-designed experimental set-up, we measure 500 signal samples consisting of 6 different activities from each of the 7 participants using the UWB radar system. The dataset was recorded in a controlled environment and background noise was recorded prior to the experimentation and subsequently post-processed from the measurements. We define the methods used to record the activity data using the radar transceiver, and the techniques used to process the raw radar signals in this section using the denoising filter selection method.</p>
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