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96 results for “Affordances”
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</p>
Unclean but affordable solid fuels effectively sustained household energy equity
<p>This dataset contains the data used in preparation for the paper "Unclean but affordable solid fuels effectively sustained household energy equity" by Ke Jiang et al, describing the inequity of household energy consumption, cost and burden in mainland China in 2017.</p>
CAD 120 affordance dataset
<p>% ==============================================================================<br> % CAD 120 Affordance Dataset<br> % Version 1.0<br> % ------------------------------------------------------------------------------<br> % If you use the dataset please cite:<br> %<br> % Johann Sawatzky, Abhilash Srikantha, Juergen Gall.<br> % Weakly Supervised Affordance Detection.<br> % IEEE Conference on Computer Vision and Pattern Recognition (CVPR'17)<br> %<br> % and<br> %<br> % H. S. Koppula and A. Saxena.<br> % Physically grounded spatio-temporal object affordances.<br> % European Conference on Computer Vision (ECCV'14)<br> %<br> % Any bugs or questions, please email sawatzky AT iai DOT uni-bonn DOT de.<br> % ==============================================================================</p> <p>This is the CAD 120 Affordance Segmentation Dataset based on the Cornell Activity<br> Dataset CAD 120 (see http://pr.cs.cornell.edu/humanactivities/data.php).</p> <p>Content</p> <p>frames/*.png:<br> RGB frames selected from Cornell Activity Dataset. To find out the location of the frame<br> in the original videos, see video_info.txt.</p> <p>object_crop_images/*.png<br> image crops taken from the selected frames and resized to 321*321. Each crop is a padded<br> bounding box of an object the human interacts with in the video. Due to the padding,<br> the crops may contain background and other objects.<br> In each selected frame, each bounding box was processed. The bounding boxes are already<br> given in the Cornell Activity Dataset.<br> The 5-digit number gives the frame number, the second number gives the bounding box number<br> within the frame.</p> <p>segmentation_mat/*.mat<br> 321*321*6 segmentation masks for the image crops. Each channel corresponds to an<br> affordance (openabe, cuttable, pourable, containable, supportable, holdable, in this order).<br> All pixels belonging to a particular affordance are labeled 1 in the respective channel,<br> otherwise 0. </p> <p>segmentation_png/*.png<br> 321*321 png images, each containing the binary mask for one of the affordances.</p> <p>lists/*.txt<br> Lists containing the train and test sets for two splits. The actor split ensures that<br> train and test images stem from different videos with different actors while the object split ensures<br> that train and test data have no (central) object classes in common.<br> The train sets are additionally subdivided into 3 subsets A,B and C. For the actor split,<br> the subsets stem from different videos. For the object split, each subset contains<br> every third crop of the train set.</p> <p>crop_coordinate_info.txt<br> Maps image crops to their coordinates in the frames.</p> <p>hpose_info.txt<br> Maps frames to 2d human pose coordinates. Hand annotated by us.</p> <p>object_info.txt<br> Maps image crops to the (central) object it contains.</p> <p>visible_affordance_info.txt<br> Maps image crops to affordances visible in this crop</p> <p> </p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55<br> The crops contain the following object classes:<br> 1.table<br> 2.kettle<br> 3.plate<br> 4.bottle<br> 5.thermal cup<br> 6.knife<br> 7.medicine box<br> 8.can<br> 9.microwave<br> 10.paper box<br> 11.bowl<br> 12.mug</p> <p>Affordances in our set:<br> 1.openable<br> 2.cuttable<br> 3.pourable<br> 4.containable<br> 5.supportable<br> 6.holdable</p> <p>Note that our object affordance labeling differs from the Cornell Activity Dataset:<br> E.g. the cap of a pizza box is considered to be supportable.</p> <p> </p>
Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)
<p><strong>Author Information</strong><br> - Diego S. Cardoso, Department of Agricultural Economics - Purdue University<br> - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future</p> <p><strong>Corresponding Author:</strong> Casey J. Wichman: wichman@gatech.edu</p> <p><strong>Period of data collection:</strong> 2017--2018</p> <p><strong>License: </strong>This data set can be used under terms of the <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International license</a>. </p> <p><strong>Recommended citation for the research paper</strong> <br> Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206</p> <p><strong>Recommended citation for the data:</strong> <br> Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563</p> <p> </p> <p> </p> <p>Please see the README.txt file for the data dictionary and additional information.</p>
A Computational Analysis of Telegram's Narrative Affordances
<p><strong>Overview</strong></p> <p>Anonymized message classification data and actantial analyses of public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances". </p> <p><strong>Message classification data</strong></p> <p>All files are included in the zipped folder 'narrative_affordances_data.zip'</p> <p>Each file contains the message classification data for a single Telegram channel. Numbered files are included for each of the six datasets (1 combined, 5 thematic) discussed in the paper. </p> <p><strong>Actantial analysis</strong></p> <p>Frequency lists of retrieved actants are included in the zipped folder 'overview_of_actants.zip'</p> <p> </p>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)
<p>This video is the tenth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022.</p> <p>"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)</p> <p>Bio: Al Edwards has a background in fundamental immunology combined with expertise in biochemical engineering, he is an interdisciplinary researcher focussed on solving current and future healthcare challenges using an engineering science approach that combines a range of fields from biology, biochemistry, chemistry and physics. He works at the interface between academic technology discovery and industrial development and have experience of both fundamental research and the commercialisation of new technology. The two main challenges he currently works on are the development of affordable microfluidics for clinical diagnostics and microbiology, and the engineering science of complex biologic therapeutics such as vaccines. Alexander's research is funded from a wide range of sources, including NIHR , EPSRC, SBRI Healthcare, the Wellcome Trust, Innovate UK and industry</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/21a78Vql8b0</p>
Figure 1. Affordance in Relation with Environment and Agent
<p>In figure 1, relevance emerges in a third dimension, as a result of the interaction between<br> perception/activity (through affordance and agent /environment relationship; environment is full of<br> meaning potential; the agent has certain abilities; aptitude, effectiveness, fitness, or whatever<br> psychologists, biologists or anthropologists might call it; affordance fuels perception and activity,<br> and brings about meaning.</p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</p>
Figure 12 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 12. Right manual ungual phalanx of digit I in A,C, ventral, and B,D, lateral views. A-B, Megaraptor; C-D, Australovenator and schematic representation in E,G, ventral, and F,H, lateral views. E-F, Megaraptor; G-H, Australovenator. Scale bar: 2 cm. Abbreviations: ff, flexor facets.
Figure 10. Right manual phalanx 1 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 10. Right manual phalanx 1 of digit I in ventral view and schematic representations of Megaraptor (A, C), Australovenator (B,D). Scale bar: 2 cm. Note the well-developed longitudinal ventral furrow.
Figure 9 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 9. Proximal end of right phalanx I.1 of A, Megaraptor; B, Australovenator; C, Allosaurus; D, Tyrannosaurus (modified from Brochu, 2003); and E, Deinonychus (modified from Ostrom, 1969). Not to scale.
Figure 6 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 6. Left manus in dorsal view of A, Dilophosaurus (modified from Welles, 1980); B, Allosaurus; C, Megaraptor; D, Sinocalliopteryx; E, Tanycolagreus (modified from Carpenter et al., 2005); F, Deinonychus (modified from Ostrom, 1969); G, Scipionyx (modified from Dal Sasso and Maganuco, 2011); H, Guanlong (modified from Xu et al., 2009); and I, Sinosauropteryx (modified from Currie and Chen, 2001). Not to scale.
Figure 11 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 11. Right manual ungual phalanx of digit I in ventral view and schematic representation of Megaraptor (A,C); and Australovenator (B,D). Not to scale.
Figure 8. A-C in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 8. A-C, left first metacarpal in dorsal view of A, Megaraptor, B, Australovenator, and C, Rapator; D-F, proximal view of left metacarpus of D, Guanlong (modified from Xu et al.,2009), E, Tanycolagreus (modified from Carpenter et al., 2005), and F, Deinonychus (modified from Ostrom, 1969); G-H, proximal view of right first metacarpal of G, Rapator, and H, Australovenator. Not to scale. Abbreviations: pdp, proximomedial process; vpI, ventral process of metacarpal I; vpII, ventral process of metacarpal II.
Figure 7. Right metacarpals II and I in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 7. Right metacarpals II and I in dorsal view of A, Acrocanthosaurus (modified from Currie and Carpenter, 2000); B, Torvosaurus (modified from Galton and Jensen, 1979); C, Megaraptor; D, Deinonychus (modified from Ostrom, 1969); E, Guanlong (modified from Xu et al., 2009). Not to scale. Abbreviations: ep, extensor pit; pdp, proximomedial process; ps, proximolateral surface.
Figure 5 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 5. Left "semilunate" carpal in proximal (upper row) and dorsal (lower row) of A, Allosaurus, B, Acrocanthosaurus (modified from Currie and Carpenter, 2000); C, Megaraptor; D, Guanlong (modified from Xu et al., 2014); E, Ornitholestes (mofiied from Carpenter et al., 2005); F, Tanycolagreus (modified from Carpenter et al., 2005); G, Alxasaurus (modified from Xu et al., 2014); H, Deinonychus (modified from Ostrom, 1969); and I, Australovenator (modified from White et al., 2012). Not to scale. Abbreviations: ag, anterior groove; dp, distal projections.
Figure 3 in Phylogenetic relationships of the Cretaceous Gondwanan theropods Megaraptor and Australovenator: the evidence afforded by their manual anatomy
Figure 3. Left manus of Megaraptor namunhuaiquii (MUCPv 341) in dorsal view (A) and schematicrepresentation (B). Scale bar: 1 cm.
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