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1,654 results for “Automation”
Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC<br> provides accurate detection of WMLs and it properly detects 195 images.</p>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>In turn, the optimized results of<br> GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results<br> of clustering models and optimization technique in terms of Se, Sp and Acc.</p>
SI: A Guide to Automated Apoptosis Detection
<p>supplementary information of the article:</p> <p>"A Guide to Automated Apoptosis Detection:</p> <p>How to Make Sense of Imaging Flow Cytometry Data"</p> <p>D. Pischel et al., 2018</p>
The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study
<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social – non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map. ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset. The findings support that the “social brain” is highly sensitive to the mere presence of social information. </p>
Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams
<p>Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ~642 thousand lines of code. We found that 36.31% of candidate streams were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.</p>
Research data supporting "Single particle automated raman trapping analysis"
<p>Research raw data supporting the publication:</p> <p>Penders J., et al., Nature Communications. (2018) 9:4256 | DOI: 10.1038/s41467-018-06397</p>
Subject source code for Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams
<p>The set of open source Java projects packaged as Eclipse projects used for assessing our refactoring. Please refer to the included README.md file for building instructions and the LICENSE.md file for licensing information.<br> </p>
Initial dataset used in SIMON, an automated machine learning approach
<p>The 7-Zip file contains raw data in the CSV file downloaded from Stanford Data Miner and used for further analysis using mulset algorithm and SIMON, as described in the publication:</p> <p>Tomic A, Tomic I, Rosenberg-Hasson Y, Dekker CL, Maecker HT, and Davis MM. SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. <em>JImmunol</em>, doi: 10.4049/jimmunol.1900033, 2019.</p> <p>File was compressed using 7-Zip available at https://www.7-zip.org/.</p>
Serial Rotation Electron Diffraction (automated continuous RED) raw data sets
<p><strong>Serial Rotation Electron Diffraction (automated continuous RED) raw data sets </strong></p> <p>Containing:</p> <p>TIFF images for particle recognition</p> <p>SMV files for XDS processing</p> <p>XDS input files (automatically generated)</p>
Datasets for 'Automated characterization of noise distributions in diffusion MRI data'
<p>Datasets we used for the manuscript 'Automated characterization of noise distributions in diffusion MRI data'.</p>
An MRI-Derived Neuroanatomical Atlas of the Fischer 344 Rat Brain for Automated Anatomical Segmentation
<p>This version of the dataset described in: <a href="https://www.biorxiv.org/content/10.1101/743583v2">https://www.biorxiv.org/content/10.1101/743583v2</a>, is outdated. Please refer to https://doi.org/10.5281/zenodo.3555556 for the current version and all future editions of the Fischer 344 neuroanatomical atlas.</p>
SmartThings - Automation Scenarios Poll
<p>The poll showed in this image was used to know how frequently the listed domotics scenarios appear in automations of members of this community. </p> <p>Source: <a href="https://community.smartthings.com/t/help-automation-scenarios-poll/171575">https://community.smartthings.com/t/help-automation-scenarios-poll/171575</a></p>
Hubitat - Automation Scenarios Poll
<p>The poll showed in this image was used to know how frequently the listed domotics scenarios appear in automations of members of this community. </p> <p>Source: <a href="https://community.hubitat.com/t/help-automation-scenarios-poll/21624">https://community.hubitat.com/t/help-automation-scenarios-poll/21624</a></p>
2nd cycle evaluation experiment ULM sim AutoMate
<p>This experiment has been conducted at the driving simulator to test the first enabler integrated version of the simulator 1 demonstrator. The data of 26 participants was collected where each participant drove through both, the baseline and the TeamMate condition in the Peter scenario. There was a special focus of the gaze behavior, therefore the included dataset involves the gaze behavior as well as the subjective rating. The description of the experiment and the results can be found in the Deliverable D6.2 “Results of Evaluation in the 2nd cycle”.</p>
3rd cycle evaluation experiment ULM sim AutoMate
<p>This experiment has been conducted at driving simulator 1 to test the final integrated version of the simulator demonstrator in the Peter scenario with all integrated and updated enablers in their final state. 18 users participated in the final experiment. The dataset consists of two files, one the zip file of the simulator logs (see Table 3) and the other file is the subjective rating of both systems. The description of the experiment and the results can be found in the Deliverable D6.3 “Results of Comparative Evaluation after 3rd cycle”.</p>
Raw Results of the paper "Automated Synthesis of Social Laws in STRIPS"
<p>The following file contains the empirical evaluation of the algorithm that was presented in the paper "Automated Synthesis of Social Laws in STRIPS" (AAAI-20) on several PDDL benchmarks. </p>
Fig. 1 in Automated size measurements of Halyomorpha halys (Stål) (Heteroptera: Pentatomidae) with simple imagebased methodology
Fig. 1. Illustration of the image-based measurement method. (A) Image of brown marmorated stink bug used for automated size estimation, shown afer CAmera aS Scanner processing. (B) Image of segmented insect, whereby the body is coded as white and the antennae and legs are shown as gray. Gray sec- tions are excluded from analysis. The red line represents the pronotal width measurement, the purple line denotes the ventral length measurement.
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Automated discovery of reprogrammable nonlinear dynamic metamaterials — Data
<p>This dataset includes optimization and experimental data complementing the paper:</p> <p><a href="https://doi.org/10.1038/s41563-024-02008-6" target="_blank" rel="noopener">G. Bordiga, E. Medina, S. Jafarzadeh, C. Boesch, R. P. Adams, V. Tournat, K. Bertoldi. Automated discovery of reprogrammable nonlinear dynamic metamaterials. <em>Nature Materials.</em> (2024)</a>.</p> <p>Optimization and post-processing data in this dataset were generated using the code <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> developed for the paper. This dataset can be loaded and visualized using <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> with the following steps:</p> <ul> <li>Install <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Download <code>data.zip</code> from this dataset.</li> <li>Extract <code>data.zip</code> and place its content in a <code>data</code> folder in the root of <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Load the data associated with the design problems shown in the paper using the <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/notebooks" target="_blank" rel="noopener">notebooks</a>.</li> </ul> <p>For more information on each problem, please refer to the <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">README</a>.</p> <p>Videos illustrating the solved design problems can be viewed at <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/videos" target="_blank" rel="noopener">DifFlexMM/videos</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.