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300 results for “Echo”
The free-breathing motion-corrected phase sensitive inversion recovery sequence provides improved myocardial fibrosis evaluation while significantly shortening acquisition time upon comparison to conventional gradient echo sequences: a tripartite comparison of phase-sensitive inversion recovery sequences.
<p>This article includes original research performed at a US Academic Center related to comparison of three separate Phase-Sensitive Inversion Recovery (PSIR) pulse sequences (Breath-hold Single-Shot SFFP, Breath-hold TurboFLASH, and Free-breathing Motion-Corrected SSFP) evaluating the ability of each PSIR sequence to demonstrate myocardial hyperenhancement. All three PSIR sequences were performed as a part of a cardiac MRI performed on a patient clinically referred for cardiac MRI with and without contrast. A total of 28 patients were examined with the three PSIR sequences. All three PSIR sequences were performed in the short axis 10 – 25 minutes after intravenous injection of a Gadolinium-based contrast agent. </p> <p>Evaluation of the PSIR sequences ability to detect myocardial late gadolinium enhancement (LGE) was performed by a retrospective review by two blinded, experienced cardiovascular imagers. The review was a qualitative inspection that included grading by a 5-point Likert scale for the sequence’s ability to resist motion artifact, image resolution, ability to visualize hyperenhancement, and overall satisfaction. The number of myocardial segments demonstrating LGE was also quantitated, and the acquisition time of each PSIR sequence was performed.</p> <p>To our knowledge this is the first study that compares the 3 available PSIR LGE sequences with a specific attention to acquisition time (TA). Given our initial study resulted in the conclusion that the motion-corrected SSFP PSIR sequence was superior the the TurboFLASH Gradient Echo PSIR sequence in regards to evaluator grading and acquisition time efficiency.</p>
The Free-Breathing Motion-Corrected Phase Sensitive Inversion Recovery Sequence Provides Improved Myocardial Fibrosis Evaluation while Significantly Shortening Acquisition Time Compared to Conventional Gradient Echo Sequences.
<p>This article includes original research performed at a US Academic Center related to comparison of three separate Phase-Sensitive Inversion Recovery (PSIR) pulse sequences (Breath-hold Single-Shot SFFP, Breath-hold TurboFLASH, and Free-breathing Motion-Corrected SSFP) evaluating the ability of each PSIR sequence to demonstrate myocardial hyperenhancement. All three PSIR sequences were performed as a part of a cardiac MRI performed on a patient clinically referred for cardiac MRI with and without contrast. A total of 28 patients were examined with the three PSIR sequences. All three PSIR sequences were performed in the short axis 10 – 25 minutes after intravenous injection of a Gadolinium-based contrast agent. </p> <p>Evaluation of the PSIR sequences ability to detect myocardial late gadolinium enhancement (LGE) was performed by a retrospective review by two blinded, experienced cardiovascular imagers. The review was a qualitative inspection that included grading by a 5-point Likert scale for the sequence’s ability to resist motion artifact, image resolution, ability to visualize hyperenhancement, and overall satisfaction. The number of myocardial segments demonstrating LGE was also quantitated, and the acquisition time of each PSIR sequence was performed.</p> <p>To our knowledge this is the first study that compares the 3 available PSIR LGE sequences with a specific attention to acquisition time (TA). Given our initial study resulted in the conclusion that the motion-corrected SSFP PSIR sequence was superior the the TurboFLASH Gradient Echo PSIR sequence in regards to evaluator grading and acquisition time efficiency.</p>
Stripe-Like Echoes Scattered from Nighttime F-Region Field-Aligned Irregularities at Low-Latitudes
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 221 GPS receivers came from the Crustal Movement Observation Network of China on 9 September 2017. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"
<p>This dataset is associated with the manuscript "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain", and provides the preprocessed MEG response (resampling to 100 Hz), auditory stimulus, individual quantitative observations underlying the data summarized in figures, and the analysis codes.</p>
Bow echo detection and segmentation
<p>This is a code and data repository for a CNN-based bow echo detector meant for use on NEXRAD composite reflectivity mosaics discussed in:<br>"A derecho climatology (2004-2021) in the United States based on machine learning identification of bow echoes" by Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui</p> <p><br>The repository contains the following items:</p> <p>initial_training_dataset.zip -- The dataset used to train the initial version of the segmentation model. It contains 500 positive samples with hand-drawn masks and 3,350 negative samples with no masks. The samples are stored as .png files with pixel values between 0-255 linearly mapped from dBZ values between 0-50. The negative samples are stored as single-channel images while the positive samples are RGB images with the reflectivity data stored in the blue channel and the corresponding masks stored in the red channel.</p> <p>pseudo_labeled_training_dataset.zip -- The larger training set used for the final version of the CNN with 1,199 masked positive cases and 1,978 negative cases. These are also stored as .png files. Here, the masks have been produced by a CNN and reviewed for accuracy by a human.</p> <p>testing_dataset.zip -- 217 plots of testing cases. These are radar images of MCS's from 2010 that may or may not contain bow echoes.</p> <p>test_labels.csv -- A collection of human and CNN labels for the 217 test cases.</p> <p>conus_bow_echoes.zip -- NetCDF files containing CNN segmentation results for reflectivity mosaics from 2004-2021. Bow echo masks are stored as signed integers. 0 = No bow echo, -1 = bow echo not associated with a tracked MCS, >0 bow echo associated with an MCS where each unique MCS in a year is assigned a unique integer based on PyFLEXTRKR.</p> <p>June_2010.mp4 -- An animation of composite reflectivity (coloring), MCS tracks (gray shading), and bow echoes (black contours) for the month of June 2010.</p> <p>project_code.zip -- The collection of Python scripts used to construct, train, and apply the CNN.</p> <p>cnn.h5 -- The trained Unet 3+ model. The model performs required input clipping and scaling internally and can operate directly on reflectivity data.</p> <p>sample_mosaic.npz -- Input file for 'inference_demo.py'. Contains a single composite reflectivity mosaic</p> <p>sample_output.png -- The plot produced by "inference_demo.py".</p> <p>inference_demo.py -- A Python script that demonstrates how to apply the bow echo segmentation CNN to reflectivity data, including post-processing steps.</p>
October 2019 700 kHz multibeam echo sounder data used for Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats
<p>The raw data used for the study</p> <p>Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats</p> <p>by Schulze et al.; currently under review at Frontiers in Remote Sensing. </p> <p> </p> <p>Files are stored in the s7k-Format, and sorted by date of acquisition and frequency. 200 and 400 kHz data were manufacturer-calibrated. Correct absorption values have been applied duirng the export. Refer to the paper for further dataset information.</p> <p> </p> <p>This upload stores the 700 kHz data recorded in October 2019.</p>
Dataset for the study "Can Anthipatella wollastoni be detected in Multibeam Echo Sounder multi-detect data?"
<p>Dataset for the study "Can Anthipatella wollastoni be detected in Multibeam Echo Sounder multi-detect data?", currently under review at Frontiers in Remote Sensing.</p> <p> </p> <p>The files include:</p> <p>Photos.zip: GoPro Photos with ground truthing of two ridges with the occurrence of Black Corals. Photos have been geo-located using the coordinates of the onboard Multibeam System and correcting a time offset. Refer to the paper for details.</p> <p>Sound velocities: Sound velocity casts using a Base-X shallow water profiler used to correct the multibeam echo sounder.</p> <p>MD_manual_edit: Shape file including the position of Multi-Detects after the manual cleaning. The MD point objects have been joined with information from the local bathymetry and slope. Note that associated intensity values are erroneous due to a bug in the recording MBES firmware.</p> <p>MBES: Includes Norbit s7k (version 3) raw files of the multibeam echo sounder data (Norbit iwbms-e, Serial number #12). Files in folders with "MD" were used for the Multi-Detect study. Files with "MFE" include multifrequency data. We failed to locate the black corals in multi-frequency backscatter data. Multi-detect data were recorded with a swath width of 100° split into 512 beams. The multifrequency data contains 190 and 370 kHz information. Spreading correction was set to 40, absorption set to 107 dB/km (calculated for the 390 kHz frequency with a mean water temperature of 16°C, a salinity of 35. Actual temperature according to diver information was 24°C at the surface, 23° until 50 m and 21° until 80 m). All offsets have been accounted for during the survey. The data is located using an RTK correction (refer to paper for further details).</p> <p> </p>
Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments
<p>This repo contains a complete replication package, including raw data and scripts for the statistical analysis, for the paper "Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments" submitted to the industry track of the <a href="https://conf.researchr.org/home/icsme-2024" rel="nofollow">40th International Conference on Software Maintenance and Evolution (ICSME)</a>, Flagstaff, AZ, USA, Oct 6-11, 2024.</p> <div> <h2>Authors</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#authors"></a>Markus Borg, Marwa Ezzouhri, and Adam Tornhill</div> <div> <h2>Abstract</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#abstract"></a>As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.</div> <div> <h2>Repository Content</h2> Two Jupyter Notebooks.</div> <ul> <li> <ul> <li>uc1_maintainability_prediction.ipynb: A Notebook for Use Case 1 - Maintainability Prediction.</li> <li>uc2_liability_prediction.ipynb: A Notebook for Use Case 2 - Liability Prediction.</li> </ul> </li> <li>maintainability_data.csv: The dataset covering the 404 open-source files from the Maintainability Dataset (Bertrand <em>et al.</em>, 2020) <ul> <li>Majority vote ground truth labels from Schnappinger <em>et al.</em> (2020)</li> <li>Low-level code metrics from Bertrand <em>et al.</em> (2023)</li> <li>Code Health</li> <li>SonarQube output, i.e., TD Ratio and TD Time</li> <li>Microsoft Maintainability index provided by <a href="https://plugins.jetbrains.com/plugin/13959-metricstree" rel="nofollow">MetricsTree</a></li> </ul> </li> </ul> <div> <h2>References</h2> </div> <ul> <li>Schnappinger <em>et al.</em>, <a href="https://figshare.com/articles/dataset/A_Software_Maintainability_Dataset/12801215" rel="nofollow">A Software Maintainability Dataset</a>, 10.6084/m9.figshare.12801215, 2020.</li> <li>Schnappinger <em>et al.</em>, Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability, in <em>Proc. of the 36th International Conference on Software Maintenance and Evolution</em>, pp. 278–289, 2020.</li> <li>S. Bertrand <em>et al.</em>, Replication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics, in <em>Proc. of the 27th International Conference on Evaluation and Assessment in Software Engineering</em>, pp. 241–246, 2023.</li> </ul>
MARSIS Electron Cyclotron Echo Data Set
<p>This data set contains the electron cyclotron period, T_ce, manually extracted from electron cyclotron echoes recorded in MARSIS ionograms. The local magnetic field magnitude can be derived as B = 2 pi m_e / (e * T_ce).</p>
Fig. 2 in Echoes from the past¦ rediscovering Isoscelipteron fulvum Costa, 1863 (Neuroptera¦ Berothidae) in Italy.
Fig. 2.- Sampling trap and micro-habitat in which the female specimen was collected in 2017.
Dataset for "What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?"
<p>This dataset was used for this project: "What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?". Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, Haewoon Kwak, Jeremy Blackburn. Workshop on Computational Methods in CyberSafety, Online Harassment and Misinformation, 2018. DOI: <a href="https://arxiv.org/ct?url=http%3A%2F%2Fdx.doi.org%2F10%252E1145%2F3184558%252E3191531&v=345a781d">10.1145/3184558.3191531</a>.</p> <p>In addition, this project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie ENCASE project (Grant Agreement No. 691025). The work reflects only the authors’ views; the Agency and the Commission are not responsible for any use that may be made of the information it contains.</p> <p>Using Gab’s API, we crawl the social network using a snowball methodology. Specifically, we obtain data for the most popular users as returned by Gab’s API and iteratively collect data from all their followers as well as their followings. Subsequently, for all users in our dataset we collect all of their the posts. Overall, we collect 22,112,812 posts from 336,752 users, between August 2016 and January 2018. This dataset is a .json file and each line has one .json object.</p>
Raw data, processing, and simulation scripts for "Observation of Dynamic Nuclear Polarization Echoes"
<p>Data files and processing/plotting scripts for the first observation of "dynamic nuclear polarization echoes". Also a simulation script for a semi-quantitative quantum mechanical simulation of the spin dynamics.</p>
Multi-echo resting-state fMRI networks of healthy volunteers
<p>The dataset contains the resting-state networks of 16 healthy volunteers following multi-echo combination methods: 1) optimal combination 2) tSNR-weighted combination 3) tCNR-weighted combination (PAID method) 4) second echo only (single-echo) After echo combination by one of the methods (or the second echo) 30 independent components were extracted using group ICA and dual regression.</p> <p>Image format: Gunzipped NIfTI (.nii.gz)</p> <p>Time-series format: text file (.txt)</p> <p>The structure of the uploaded folder:</p> <p>- Layer 1: PilmeyerEtAl_ICA_maps_and_timeseries - main folder</p> <p>- Layer 2 (combination method): OC - optimal combination, SE- second echo, tCNR - temporal contrast-to-noise, tSNR - temporal signal-to-noise</p> <p>- Layer 3: groupICA_desc-XX - contains the group ICA maps and time-series before dual regression, sub-YY - folders for each of the 16 subjects</p> <p>- Layer 4: sub-YY_desc_XX - contains the individual extracted ICA maps and time-series</p> <p> </p> <p> </p> <p> </p>
Accurate photon echo timing by optical freezing of exciton dephasing and rephasing in quantum dots
<p>Dataset of the publication “Accurate photon echo timing by optical freezing of exciton dephasing and rephasing in quantum dots“, ( <a href="https://doi.org/10.1038/s42005-020-00491-2">https://doi.org/10.1038/s42005-020-00491-2</a> ). The zip file includes the data on which the plots shown in figures 2-5 of the main text, and supplementary figures S1-S5 are based.</p>
Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles
<p>Dataset of the publication “Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles“, <a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11684/2576887/Controlling-the-emission-time-of-photon-echoes-by-optical-freezing/10.1117/12.2576887.short?SSO=1">Proc. SPIE 11684,116840X (2021)</a> ( <a href="https://doi.org/10.1117/12.2576887">https://doi.org/10.1117/12.2576887</a> ). The zip file includes the data on which the figures are based, the gnuplot files for the figures, and an explaining readme.txt.</p>
Detection of bow echoes in French kilometer-scale models (AROME-EPS & AROME models of Météo-France)
<p>To detect bow echoes (BE) directly in simulated reflectivity fields from French kilometer-scale models, three datasets are available. These datasets allowed for fitting and testing a univariate U-Net (convolutional neural network) using simulated reflectivity fields.</p> <p>Grid area : 717x1121 grid points, Western Europe (12°W-16°E and 37.5°N-55.4°N, resolution 0.025°)</p> <p>In each tar.gz file, files named 'input_BE' are reflectivity fields from operational models (in mm per hour), 'groundTruth' files correspond to manually labeled contours of BE (field with 1 for each grid point in BE and 0 for outside). The file format is HDF5. HDF5 files contain an array of N fields (array shape : (N,1,717,1121))</p> <p>- dataset_train.tar.gz (N=6206): input and target fields to train the U-Net (<strong>only</strong> from the AROME-EPS model)</p> <p>- dataset_validation.tar.gz (N=2620): input and target fields to validate the U-Net (<strong>only</strong> from the AROME-EPS model). Training and validation databases contain independent weather case studies.</p> <p>- dataset_det_AROME.tar.gz (N=348): input and target fields to apply U-Net to the French deterministic AROME model (same grid than one of AROME-EPS). Weather case studies are the same than those in the validation database.</p> <p>- optimal_UNet_architecture.json : architecture of the optimal U-Net configuration</p> <p>- optimal_UNet_weights.h5 : weights of the trained optimal U-Net configuration</p> <p> </p>
Etiology, characteristics and occurrence of heart diseases in rural Lesotho (ECHO-Lesotho): A retrospective echocardiography cohort study
<p><strong>Background</strong></p> <p>In 2019, 600’000 people in Africa died of heart failure and heart diseases will increase on the continent. It is crucial to understand the regional etiologies and risk factors for heart failure and underlying heart diseases. However, echocardiography data from rural Africa are scarce and from Lesotho non-existent. This study aims to examine the occurrence, characteristics and etiology of heart failure and heart diseases using echocardiography data from a referral hospital in rural Lesotho.</p> <p><strong>Methods</strong></p> <p>We conducted a retrospective cohort study at Seboche Mission Hospital, the only referral hospital in Butha-Buthe district (Lesotho) with an echocardiography department. We included data from all individuals referred to the department between January 2020 and May 2021. From non-hospitalized patients echocardiographic diagnosis, sex and age were available, from hospitalized patients additional sociodemographic and clinical data could be extracted.</p> <p><strong>Results</strong></p> <p>In the study period, a total of 352 echocardiograms were conducted; 213 had abnormal findings (among them 3 children). The majority of adult participants (130/210; 64%) were female and most frequent heart diseases were hypertensive (62/210, 30%), valvular (39/210, 19%) and chronic pulmonary (37/210, 18%). Heart failure represented 11% of hospitalizations in the same period. Among the 126 hospitalized heart failure patients, the most common etiology was chronic pulmonary heart disease (32/126; 25%). Former mine workers and people with a history of tuberculosis were more likely to have a chronic pulmonary heart disease.</p> <p><strong>Conclusions</strong></p> <p>The leading cause of heart disease in this setting is hypertension. However, in contrast to other African epidemiological studies, chronic pulmonary heart disease is unexpectedly common. There is an urgent need to improve awareness and knowledge about lung diseases, make diagnostic and therapeutic options available and increase prevention.</p>
Dataset - In-situ amplification of spin echoes within a kinetic inductance parametric amplifier
<p>Raw data and analysis for arXiv:2211.11333</p> <p> </p>
Data for: Attributes of CloudSat identified echo objects
<p>This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection.</p> <p>An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code.</p> <p>The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011).</p>
Invariant echo localization using bat pinnae
<p>This dataset works with the scripts provided in the GitHub repository in the link:<br> https://github.com/zaco87/Paper-Invariance-Localization</p> <p>matlab_variables_data.mat - should be loaded to Matlab. It has the entire data that is needed for running the Matlab programs provided with the paper's repository on GitHub</p> <p> </p>
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