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1,506 results for “objects”
Data from: The association between subjective memory complaint and objective cognitive function in older people with previous major depression
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Data for Radial arm maze tests and Novel object recognition test
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Odor boosts visual object approach in flies
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RNA-Seq data from cortex and hippocampus (post novel object recognition test) of rats in a vagus nerve stimulation study
GEO Series GSE134309. Rattus norvegicus. 15 samples. Type: Expression profiling by high throughput sequencing.
Multi-objective Optimization Reveals Time- and Dose-Dependent Inflammatory Cytokine-Mediated Regulation of Human Stem Cell Derived T-cell Development
GEO Series GSE191086. Homo sapiens. 6 samples. Type: Other.
Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors -- Rosbag data
<p>This is the rosbag data accompanying the IROS 2014 paper "Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors"</p>
Smell object in Dutch - smell in English dictionary
<p>These four csv's each contain the Dutch names of things that smell as Key (Fruits, Flowers, Nuts and Vegetables) and what they smell like as Value.</p>
A brain-inspired architecture for cost-efficient object recognition in shallow neural networks
<p>Demo codes for "A brain-inspired architecture for cost-efficient object recognition in shallow neural networks"</p> <p><strong>1. System requirements</strong><br> - MATLAB (2019a or 2019b is recommended.)</p> <p>- Installation of Deep Learning Toolbox</p> <p>- Uploaded codes were tested using MATLAB 2019a and 2019b.</p> <p>- No non-standard hardware is required to run the codes.</p> <p><br> <strong>2. Installation guide</strong><br> - Download "LRC_code.zip" and unzip the file.</p> <p>- Download "DATASET_ori.zip","pretrainedNet.zip" and unzip on same file.</p> <p>- Choose the proper subfolder of 'fun_modifed_toolbox' which is matched to your MATLAB version and<br> Move each file to the proper directory<br> (see the comment in line 4 of each file for proper directory e.g. 'dir = C:\Program Files\MATLAB\...')<br> </p> <p><strong>3. Instructions for use</strong><br> - By running "Main.m" and selecting code option and figure option (see line 35 - 41 on the Main.m)<br> code options :<br> i) flg_ShowRes : Show the result figure of demo code using the pretrained network (in pretrainedNet.zip)<br> ii) flg_Demo : Run the demo code using a randomly initialized network<br> figure options : flg1 - Result 1 / flg2 - Result 2 / flg3 - Result 3 </p> <p> </p> <p><strong>cf. Modified MNIST datasets</strong></p> <p>To separately examine the contribution of high and low-frequency information contained in sample images, we designed three types of modified MNIST datasets. Details are as follows:</p> <p><strong>Type 1</strong><em>: shape</em>. The “<em>shape</em>” dataset was designed by arranging a hand-written digit of 8 x 8 pixels in the center of a 28 x 28 pixels image. The dataset consists of eight categories depending on the number in the center (1 to 8). For this dataset, only local information (shape) of the digits is required for classification.</p> <p><br> <strong>Type 2</strong>: <em>position</em>. The “<em>position</em>” dataset was made by the following procedure. First, two digits of 8 x 8 pixels were randomly chosen. Second, these two digits were allocated in a 28 x 28 image, with one of the following position alignments: horizontal (top, middle, bottom), vertical (left, middle, right), or diagonal (45°, 135°). This dataset also consists of eight categories depending on the position only where the digits are located. Note that the shape of each number is irrelevant for classification.</p> <p><br> <strong>Type 3</strong>: <em>shape-position</em>. The “<em>shape-position</em>” dataset was made by the following procedure. First, a 28 x 28 pixel area was divided into four 14 x 14 areas. Second, two diagonally aligned areas were selected (either 45° or 135°). Third, one of two digits, either “7” or “9”, composed of 8 x 8 pixels was inserted into each selected area. This dataset consists of eight categories depending on both the shape and position of the digits. The reason we chose “7” and “9” among ten numbers is to adjust the difficulty of the task to be similar to that of the previous tasks. Note that this dataset requires both local information (shape) and global information (position) of the digits for classification. </p>
Companion Web Page for "Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software"
<p>dataset-jars-scam2013.zip:</p> <ul> <li>Content: 3 418 Jar files, which include 382 774 different types (classes or interfaces).</li> <li>Source: All Jar files present on a machine used for performing software mining experiments for 7 years.</li> </ul> <p>type-usages-20130622.zip:</p> <ul> <li>Content: All the type-usages, filtered by type of the type-usage</li> </ul> <p>Software:</p> <ul> <li>Software to extract type-usages: <a href="https://github.com/monperrus/typeusage">https://github.com/monperrus/typeusage</a></li> </ul> <p> </p> <p>Paper:</p> <pre>@inproceedings{Mendez2013, title = {Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software}, author = {Mendez, Diego and Baudry, Benoit and Monperrus, Martin}, url = {<a href="https://hal.archives-ouvertes.fr/hal-00844753/file/article.pdf">https://hal.archives-ouvertes.fr/hal-00844753/file/article.pdf</a>}, booktitle = {{International Conference on Source Code Analysis and Manipulation (SCAM'2013)}}, pages = {10}, year = {2013}, doi = {<a href="https://doi.org/10.1109/SCAM.2013.6648183">10.1109/SCAM.2013.6648183</a>}, } </pre>
Validation data: Multi-objective support vector regression reduces systematic error in moderate resolution maps of tree species abundance
<p>Validation data used in the analysis presented by Legaard et al. (in review). Validation data are sufficient to replicate model comparisons presented in this paper. Note that model training data were provided by the USDA Forest Service, Forest Inventory and Analysis Program through a collaborative agreement, are maintained by the USDA Forest Service as confidential, and cannot be shared.</p> <p>Legaard, K., Simons-Legaard, E., Weiskittel, A., Multi-objective support vector regression reduces systematic error in moderate resolution maps of tree species abundance, Remote Sensing, in review.</p>
One Thing Leads to Another: Anticipating Visual Object Identity Based on Associative-Memory Templates
<p>Probabilistic associations between stimuli afford memory templates that guide perception through proactive anticipatory mechanisms. The current work isolates the behavioral benefits and electrophysiological signatures of memory-guided iden- tity-based anticipation, while equating anticipation of space, time, motor responses, and task relevance. Our results show that anticipation of the specific identity of a forthcoming percept impacts performance and is associated with states of attenuated alpha oscillations and the contingent negative variation, extending previous work implicating these neural substrates in spa- tial and temporal preparatory attention. Together, this work bridges fields of attention, memory, and perception, providing new insights into the neural mechanisms that support complex attentional templates.</p>
data set related to article How Attention to Faces and Objects Changes Over Time in Toddlers with Autism Spectrum Disorders: Preliminary Evidence from An Eye Tracking Study
<p>This record contains raw data related to article How Attention to Faces and Objects Changes Over Time in Toddlers with Autism Spectrum Disorders: Preliminary Evidence from An Eye Tracking Study</p>
data set related to article Visuo-haptic transfer for object recognition in children with periventricular leukomalacia and bilateral cerebral palsy
<p>This record contains raw data related to article Visuo-haptic transfer for object recognition in children with periventricular leukomalacia and bilateral cerebral palsy</p>
Acute sleep hygiene strategy improves objective sleep latency following a late-evening soccer-specific training session: A randomized controlled trial
<p>The aim of this study was to evaluate the effects of sleep hygiene (SH) education on sleep quality in soccer players after a late-evening small-sided-game (SSG) training session. Twenty-nine non-professional players were recruited and allocated to either an experimental group (EG, n=17) that received SH education, or a control group (CG, n=12). SSG consisted of 3´4 minutes in a 4vs4, with 3 minutes of recovery and was performed at 8.00 p.m. Sleep quality was monitored via actigraphy and sleep diary entries before (PRE) and two nights after (POST1, POST2) the SSG. Sleep latency (SL) differed between the two groups at POST1 (4.9±5.4 vs. 15.5±16.1 for EG and CG, respectively; p=0.017, effect size [ES]=2.0); SL values were lower at POST1 compared to PRE for the EG (-47%; p=0.021, ES=0.6). Subjective sleep quality was better in the EG than the CG at POST1 (8.6±1.0 vs. 7.1±2.0 for EG and CG, respectively; p=0.016, ES=0.9) with a significant improvement over PRE values (+11.0%, p=0.004, ES=0.8). Despite SL and subjective sleep quality were similar in PRE and POST2, no significant deterioration in such sleep indices over time was observed for EG. No other objective sleep indices were influenced by late-evening training or SH practices implemented by the EG. Soccer players may benefit from acute SH strategies to reduce the time to sleep onset after late-evening training sessions.</p>
Multi-objective Optimization of Long-run Average and Total Rewards: Supplemental Material
<p>Supplemental Material for the Paper:<br> Multi-objective Optimization of Long-run Average and Total Rewards<br> by Tim Quatmann and Joost-Pieter Katoen.</p> <p>This artifact contains our implementation for the paper "Multi-objective Optimization of Long-run Average and Total Rewards". The implementation is fully integrated into the model checker <a href="http://stormchecker.org">Storm</a>.<br> We include the sources and dependencies of Storm as well as the related tools <a href="http://qav.cs.ox.ac.uk/multigain">MultiGain</a> and <a href="http://www.prismmodelchecker.org/games">PRISM-games</a>.<br> Moreover, we include model files and scripts for replicating *all* experiments as conducted for the paper.<br> The original logfiles obtained during our experiments are included as well.</p> <p>The artifact has been tested to work with the <a href="https://zenodo.org/record/4041464">TACAS 21 Artifact Evaluation VM</a> running Ubuntu 20.04 LTS</p>
Datasets and models for Object detection
<p>This repository includes the datasets and models for object detection. </p> <p>The "Datasets" folder includes the training and testing datasets for object detection.( Both Tf-record and Images are provided)</p> <p>The models includes a pre-trained model from TensorFlow 2 model zoo and our trained model. The "Custom_model" folder includes the training log and check points.</p>
Data from: Objective estimation of sensory thresholds based on neurophysiological parameters
Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting—either by eye or by statistical means—of the threshold to the value where at least a "clear" signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.
Data from: An auditory illusion reveals the role of streaming in the temporal misallocation of perceptual objects
This study investigates the neural correlates and processes underlying the ambiguous percept produced by a stimulus similar to Deutsch's 'octave illusion', in which each ear is presented with a sequence of alternating pure tones of low and high frequencies. The same sequence is presented to each ear, but in opposite phase, such that the left and right ears receive a high–low–high … and a low–high–low … pattern, respectively. Listeners generally report hearing the illusion of an alternating pattern of low and high tones, with all the low tones lateralized to one side and all the high tones lateralized to the other side. The current explanation of the illusion is that it reflects an illusory feature conjunction of pitch and perceived location. Using psychophysics and electroencephalogram measures, we test this and an alternative hypothesis involving synchronous and sequential stream segregation, and investigate potential neural correlates of the illusion. We find that the illusion of alternating tones arises from the synchronous tone pairs across ears rather than sequential tones in one ear, suggesting that the illusion involves a misattribution of time across perceptual streams, rather than a misattribution of location within a stream. The results provide new insights into the mechanisms of binaural streaming and synchronous sound segregation.
Data from: Optimizing the genetic composition of a translocation population: incorporating constraints and conflicting objectives
Translocations of threatened species can reduce the risk of extinction from a catastrophic event. For plants, translocation consists of moving individuals, seeds, or cuttings from a native (source) population to a new site. Ideally a translocation population would be genetically diverse and consist of fit founding individuals. In practice, there are challenges to designing such a population, including constraints on the availability of material, and tradeoffs between different goals. We present an approach for designing a translocation population that identifies sets of founders that are optimized according to multiple criteria (e.g., genetic diversity), while also conforming to constraints on the representation of different founders (e.g., propagation success). It uses flexible inputs, including SNP genotypes, matrices of similarity between individuals, and vectors of phenotype data. We apply the approach to a critically endangered plant, Hibbertia puberula subsp. glabrescens (Dilleniaceae), which was genotyped at thousands of SNP loci. The goals of minimizing genetic similarity among the founding individuals and maximizing genetic diversity were largely complementary – populations optimized for one of these criteria were near-optimal for the other. We also performed analyses in which we minimized genetic similarity among founding individuals while imposing selection (against hypothetical deleterious alleles, and against undesirable phenotypes, respectively), and here characterized sharp tradeoffs. This is useful in allowing the benefits of selection to be weighed against 'costs' in terms of genetic similarity. In sum, we present an approach for designing a translocation population that allows flexible inputs, the imposition of realistic constraints, and examination of conflicting goals.
Action recognition and object detection dataset for firearm-related actions
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.