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1,940 results for “fusion”
fusion-jena/befchina-test-collection: Major service release
<p>This repository provides a test collection for dataset search in biodiversity. The test collections consists of 14 questions collected in different biodiversity research related projects and reflecting real user informations needs, a corpus of 372 datasets created in the scope of the <a href="https://bef-china.com">BEF-China project</a> and human assessments evaluating which dataset is relevant to a question.</p> <p>Further information on the BEF-China project can be obtained from the website: <a href="https://bef-china.com">https://bef-china.com</a>.</p> <p><em>version 2.0:</em> This service release provides the relevance judgments in the proper TREC format: <TOPIC><ITERATION><DATASET NUMBER><RELEVANCE> and duplicate entries are removed.</p> <p><a href="https://github.com/fusion-jena/befchina-test-collection">https://github.com/fusion-jena/befchina-test-collection</a></p>
Analytical expressions for thermophysical properties of solid and liquid aluminum relevant for fusion applications
<p>Aluminum is being actively employed by the fusion community as a non-toxic chemical proxy to beryllium, since both materials form covalent hydrides, high-melting oxides as well as alloys with tungsten [1]. Characteristic examples include studies of in situ cleaning of diagnostic first mirrors [2,3], investigations of hydrogen retention or deposited layer formation [4,5] and experiments dedicated to sputtered material transport in diagnostic ducts [6]. Aluminum has also served as a surrogate for beryllium in high heat flux tests, given its low melting point and low mass density. Characteristic examples concern experiments on the interaction of adhered Al dust with transient and stationary plasmas carried out in Magnum-PSI [7] and the controlled melting of Al blocks exposed in the DIII-D divertor under steady L-mode discharge conditions using the DiMES manipulator [8]. In order to reliably model the macroscopic metallic melt motion realized in the sloped geometry Al L-mode exposures in the DIII-D divertor, the material library of the MEMENTO melt dynamics code, that previously concerned tungsten [9], beryllium [10], niobium [11,12] and iridium [11,12], has to be extended to aluminum.</p> <p>Reliable experimental data have been analyzed for the specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, latent heat of fusion, enthalpy of vaporization, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of aluminum as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid aluminum measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] L. Marot, C. Linsmeier, B. Eren, L. Moser, R. Steiner and E. Meyer, "Can aluminium or magnesium be a surrogate for beryllium: A critical investigation of their chemistry", Fus. Eng. Des. 88 (2013) 1718.<br> [2] A. Maffini, L. Moser, L. Marot, R. Steiner, D. Dellasega, A. Uccello, E. Meyer and M. Passoni, "In situ cleaning of diagnostic first mirrors: an experimental comparison between plasma and laser cleaning in ITER-relevant conditions", Nucl. Fusion 57 (2017) 046014.<br> [3] A. Litnovsky, V. S. Voitsenya, R. Reichle et al., "Diagnostic mirrors for ITER: research in the frame of International Tokamak Physics Activity", Nucl. Fusion 59 (2019) 066029.<br> [4] A. Kreter, T. Dittmar, D. Nishijima, R. P. Doerner, M. J. Baldwin and K. Schmid, "Erosion, formation of deposited layers and fuel retention for beryllium under the influence of plasma impurities" Phys. Scr. T159 (2014) 014039.<br> [5] C. Quirós, J. Mougenot, G. Lombardi, M. Redolfi, O. Brinza, Y. Charles, A. Michau and K. Hassouni, "Blister formation and hydrogen retention in aluminium and beryllium: A modeling and experimental approach", Nucl. Mater. Energy 12 (2017) 1178.<br> [6] N. A. Babinov, A. G. Razdobarin, I. M. Bukreev et al, "Three-dimensional modeling of sputtered materials transport in diagnostic ducts of fusion devices", Nucl. Fusion 62 (2022) 126004.<br> [7] S. Ratynskaia, P. Tolias, M. De Angeli, D. Ripamonti, G. Riva, D. Aussems and T. W. Morgan, "Interaction of adhered beryllium proxy dust with transient and stationary plasmas", Nucl. Mater. Energy 17 (2018) 222.<br> [8] D. L. Rudakov, T. Abrams, I. Bykov et al., "Controlled low-Z metal melting in the DIII-D divertor", Abstract submitted for the 19th International Conference on Plasma-Facing Materials and Components for Fusion Applications, 22-26 May 2023, Bonn, Germany.<br> [9] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [10] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.<br> [11] P. Tolias, S. Ratynskaia and K. Paschalidis, "Thermophysical properties for the published article - Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Zenodo. https://doi.org/10.5281/zenodo.6778824.<br> [12] S. Ratynskaia, K. Paschalidis, P. Tolias et al., "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> </p>
Data of hybrid vesicles fusion for Nano Letters' journal article
<p>Dataset to accompany the manuscript "Thermoplasmonic induced vesicle fusion for investigating membrane protein phase affinity"</p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data
<p>Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation.</p> <p>Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.</p>
The Spitzer Data Fusion Astronomical Photometric Filter Database
<p>The Spitzer Data Fusion Astronomical Photometric Filter Database - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a></p> <p>A collection of photometric filters from a variety of astronomical observatories by</p> <p>Lucia Marchetti (University of Cape Town) & Mattia Vaccari (University of Cape Town)</p> <p>The complete collection of filters is also available at: <a href="https://www.mattiavaccari.net/df/filters">https://www.mattiavaccari.net/df/filters</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Lucia Marchetti and Mattia Vaccari acknowledge financial support from the Inter-University Institute for Data Intensive Astronomy (IDIA), a partnership of the University of Cape Town, the University of Pretoria, the University of the Western Cape and the South African Radio Astronomy Observatory, and from the South African Department of Science and Innovation's National Research Foundation under the ISARP RADIOSKY2020 Joint Research Scheme (DSI-NRF Grant Number 113121) and the CSUR HIPPO Project (DSI-NRF Grant Number 121291).</p>
Urban Traffic Simulation Data from Real Fusion Estimates
<p>The datasets contain vehicle data from the simulation of the city center of Guimarães. The simulation was created using data collected from real sensor data, thus, providing an accurate view of the traffic flows. The data contains route information, fuel consumption, emissions, driving distance, and the amount of time each vehicle is stopped.</p>
Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors
<p>Raw dataset for "<strong>Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors</strong>" by T.Zhang, T.B.Britton.</p> <ul> <li>ArXiv: https://doi.org/10.48550/arXiv.2306.14167</li> </ul> <p>An excel file with metadata of the patterns is included. </p> <p> </p> <p>Details will be updated after acceptance.</p> <p>Processing with the proposed methodology in the paper above requires the AstroEBSD toolbox in MATLAB. This is available on GitHub at https://zenodo.org/record/8078806</p>
The Spitzer Data Fusion
<p>We present the Spitzer Data Fusion, a database incorporating far-ultraviolet to far-infrared flux measurements as well as photometric and spectroscopic redshifts for 4.4 million IRAC-selected sources detected over 8 extragalactic fields covering 65 deg^2 observed by the Spitzer Space Telescope in all its seven IRAC1234 and MIPS123 bands during its cryogenic mission.</p> <p>The catalogs are publicly available at:</p> <p><a href="https://cdsarc.cds.unistra.fr/viz-bin/cat/II/377">https://cdsarc.cds.unistra.fr/viz-bin/cat/II/377</a></p> <p><a href="https://zenodo.org/record/6120913">https://zenodo.org/record/6120913</a></p> <p><a href="https://www.mattiavaccari.net/df">https://www.mattiavaccari.net/df</a></p>
Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.
Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu
CC20 Artifact - Automatic Fusion
<p>1. Getting started</p> <p>The title of our paper submitted to CC20 is</p> <p><strong>Improving Database Query Performance with Automatic Fusion</strong></p> <p>This repository is created for showing the reproducibility of our experiments in this paper. We provide the details of scripts and original data used in the experiments. There are mainly two systems: <em>HorsePower</em> and <em>RDBMS MonetDB</em>. We supply step-by-step instructions to configure and deploy both systems in the experiments.</p> <p>On this page, you will see:</p> <ul> <li>how to run experiments (Section 2); and</li> <li>the results used in the paper (Section 3);</li> </ul> <p>2. Experiments</p> <p>All experiments were run on a server called <code>sable-intel</code> equipped with</p> <ul> <li>Ubuntu 16.04.6 LTS (64-bit)</li> <li>4 Intel Xeon E7-4850 2.00 GHz</li> <li>total 40 cores with 80 threads</li> <li>128GB RAM</li> </ul> <p>Docker setup</p> <p>Download the docker image: cc20-docker.tar (About 13GB)</p> <pre><code>docker load < cc20-docker.tar </code></pre> <p>Generate a named container (then exit)</p> <pre><code>docker run --hostname sableintel -it --name=container-cc20 wukefe/cc20-docker exit </code></pre> <p>Then, you can run the container</p> <pre><code>docker start -ai container-cc20 </code></pre> <p>Open a new terminal to access the container (optional)</p> <pre><code>docker exec -it container-cc20 /bin/bash </code></pre> <p>Introduction to MonetDB</p> <p>Work directory for MonetDB</p> <pre><code>/home/hanfeng/cc20/monetdb </code></pre> <p>Start MonetDB (use all available threads)</p> <pre><code>./run.sh start </code></pre> <p>Login MonetDB using its client tool, <code>mclient</code></p> <pre><code>mclient -d tpch1 ## ... MonetDB version v11.33.3 (Apr2019) sql> SELECT 'Hello world'; +-------------+ | L2 | +=============+ | Hello world | +-------------+ 1 tuple </code></pre> <p>Show the list of tables in the current database</p> <pre><code>sql> \d TABLE sys.customer TABLE sys.lineitem TABLE sys.nation TABLE sys.orders TABLE sys.part TABLE sys.partsupp TABLE sys.region TABLE sys.supplier </code></pre> <p>Leave the session</p> <pre><code>sql> \q </code></pre> <p>Stop MonetDB before we can continue our experiments</p> <pre><code>./run.sh stop </code></pre> <p>Reference: <a href="https://www.monetdb.org/Documentation/Guide/Installation">How to install MonetDB and the introduction of server and client programs.</a></p> <p>Run MonetDB with TPC-H queries</p> <p>MonetDB: server mode</p> <p>Invoke MonetDB with a specific number of threads (e.g. 1)</p> <pre><code>mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=1 </code></pre> <p>Open a new terminal</p> <pre><code>docker exec -it container-cc20 /bin/bash cd cc20/monetdb </code></pre> <p><strong><em>Note</em></strong>: Type <code>\q</code> to exit the server mode.</p> <p>Run with a specific number of threads (Two terminals required)</p> <p>1 thread</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=1 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_1.log" </code></pre> <p>2 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=2 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_2.log" </code></pre> <p>4 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=4 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_4.log" </code></pre> <p>8 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=8 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_8.log" </code></pre> <p>16 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=16 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_16.log" </code></pre> <p>32 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=32 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_32.log" </code></pre> <p>64 threads</p> <pre><code>## terminal 1 mserver5 --set embedded_py=true --dbpath=/home/hanfeng/datafarm/2019/tpch1 --set monet_vault_key=/home/hanfeng/datafarm/2019/tpch1/.vaultkey --set gdk_nr_threads=64 ## terminal 2 (time ./runtest | mclient -d tpch1) &> "log/log_thread_64.log" </code></pre> <p>Post data processing - MonetDB</p> <p>Fetch average execution time (ms)</p> <pre><code>grep -A 3 avg_query log/log_thread_1.log | python cut.py 699.834133333 // q1 85.9178666667 // q4 65.0172 // q6 101.730666667 // q12 58.212 // q14 60.1138666667 // q16 248.926466667 // q19 77.6482 // q22 grep -A 3 avg_query log/log_thread_2.log | python cut.py grep -A 3 avg_query log/log_thread_4.log | python cut.py grep -A 3 avg_query log/log_thread_8.log | python cut.py grep -A 3 avg_query log/log_thread_16.log | python cut.py grep -A 3 avg_query log/log_thread_32.log | python cut.py grep -A 3 avg_query log/log_thread_64.log | python cut.py </code></pre> <p><strong><em>Note</em></strong>: The above numbers can be copied to an Excel file for further analysis before plotting figures. Details can be found in Section 3.</p> <p>Run with HorseIR</p> <p>The HorsePower project can be found on GitHub. In the docker image, it has been placed in <code>/home/hanfeng/cc20/horse</code>.</p> <pre><code>https://github.com/Sable/HorsePower </code></pre> <p>Execution time</p> <p>We then run each query 15 times to get the average execution time (ms).</p> <pre><code>(cd /home/hanfeng/cc20/horse/ && time ./run_all.sh) </code></pre> <p>The script <code>run_all.sh</code> runs over three versions of generated C code based on different levels of optimizations.</p> <pre><code>- naive : no optimization - opt1 : with optimizations - opt2 : with automatic fusion </code></pre> <p>In each version, it first compiles its C code and runs the generated binary with a different number of threads (i.e. 1/2/4/8/16/32/64). Each run computes a query 15 times and returns the average.</p> <p>As a result, all output is saved into a log file, for example, <code>log/naive/log_q6.log</code> contains the result of query 6 in the naive version with all different number of threads.</p> <p>Log file structures</p> <pre><code>log/naive/*.txt log/opt1/*.txt log/opt2/*.txt </code></pre> <p>Fetch a brief summary of execution time from a log file</p> <pre><code>cat log/naive/log_q6.txt | grep -E 'Run with 15 times' q06>> Run with 15 times, last 15 average (ms): 266.638 | 278.999 266.134 266.417 <12 more> # 1 thread q06>> Run with 15 times, last 15 average (ms): 138.556 | 144.474 137.837 137.579 <12 more> # 2 threads q06>> Run with 15 times, last 15 average (ms): 71.8851 | 75.339 72.102 72.341 <12 more> # 4 threads q06>> Run with 15 times, last 15 average (ms): 73.111 | 75.867 72.53 72.936 <12 more> # 8 threads q06>> Run with 15 times, last 15 average (ms): 56.1003 | 59.263 56.057 56.039 <12 more> # 16 threads q06>> Run with 15 times, last 15 average (ms): 56.8858 | 59.466 56.651 57.109 <12 more> # 32 threads q06>> Run with 15 times, last 15 average (ms): 53.4254 | 55.884 54.457 52.878 <12 more> # 64 threads </code></pre> <p>It may become verbose when you have to extract information for all queries over three different kinds of versions. We provide a simple solution for it.</p> <pre><code>./run.sh fetch log | python gen_for_copy.py </code></pre> <p>Output data in the following format</p> <pre><code>// query id | naive | opt1 | opt2 | ----------------------- | ... | ... | ... | # 1 thread | ... | ... | ... | # 2 threads ... ... ... | ... | ... | ... | # 64 threads </code></pre> <p>Note that we copy the generated numbers into an Excel described in Section 3. Within an Excel file, we compare the performance difference in MonetDB and different versions of the generated C code.</p> <p>Compilation time</p> <p>Work directory</p> <pre><code>/home/hanfeng/cc20/horse/codegen </code></pre> <p>Fetch compilation time for different kinds of C code</p> <pre><code>./run.sh compile naive &> log_cc20_compile_naive.txt ./run.sh compile opt1 &> log_cc20_compile_opt1.txt ./run.sh compile opt2 &> log_cc20_compile_opt2.txt </code></pre> <p>Let's look into the result of query 1 in the log file <code>log_cc20_compile_naive.txt</code>.</p> <pre><code>Time variable usr sys wall GGC phase setup : 0.00 ( 0%) 0.00 ( 0%) 0.01 ( 5%) 1266 kB ( 18%) phase parsing : 0.07 ( 54%) 0.07 ( 88%) 0.14 ( 64%) 3897 kB ( 55%) phase opt and generate : 0.06 ( 46%) 0.01 ( 12%) 0.07 ( 32%) 1899 kB ( 27%) dump files : 0.00 ( 0%) 0.00 ( 0%) 0.02 ( 9%) 0 kB ( 0%) df reg dead/unused notes : 0.01 ( 8%) 0.00 ( 0%) 0.00 ( 0%) 31 kB ( 0%) register information : 0.00 ( 0%) 0.00 ( 0%) 0.01 ( 5%) 0 kB ( 0%) preprocessing : 0.03 ( 23%) 0.02 ( 25%) 0.08 ( 36%) 1468 kB ( 21%) lexical analysis : 0.00 ( 0%) 0.03 ( 38%) 0.05 ( 23%) 0 kB ( 0%) parser (global) : 0.04 ( 31%) 0.02 ( 25%) 0.01 ( 5%) 2039 kB ( 29%) tree SSA other : 0.00 ( 0%) 0.01 ( 12%) 0.00 ( 0%) 3 kB ( 0%) integrated RA : 0.01 ( 8%) 0.00 ( 0%) 0.01 ( 5%) 726 kB ( 10%) thread pro- & epilogue : 0.02 ( 15%) 0.00 ( 0%) 0.00 ( 0%) 41 kB ( 1%) shorten branches : 0.00 ( 0%) 0.00 ( 0%) 0.01 ( 5%) 0 kB ( 0%) final : 0.00 ( 0%) 0.00 ( 0%) 0.01 ( 5%) 56 kB ( 1%) initialize rtl : 0.01 ( 8%) 0.00 ( 0%) 0.01 ( 5%) 12 kB ( 0%) rest of compilation : 0.01 ( 8%) 0.00 ( 0%) 0.00 ( 0%) 62 kB ( 1%) TOTAL : 0.13 0.08 0.22 7072 kB </code></pre> <p>The whole compilation time is split into many parts. We take the total wall time as the actual time spent on the code compilation. In this query, it needs 0.22 seconds to complete the whole compilation. (Note that manual work is required for retrieving the compilation time.)</p> <p>3. Results</p> <p>We have a lot of numbers generated by our experiments. We use R and Excel to process these performance numbers and R for plotting figures. There are two kinds of data: (1) the data can be used in R directly, such as the execution time of various versions of C code with different number of threads; and (2) the data needs to pre-processed in Excel before it is sent to R. In order to make it easy for further data analysis, we intentionally provide scripts to make an Excel-friendly format that allows us to copy and paste numbers easily.</p> <p>Work directory</p> <pre><code>/home/hanfeng/cc20/plot </code></pre> <p>For example, the R script for generating figure 10</p> <pre><code>fig10/plot-gmeans.R </code></pre> <p>The Excel file</p> <pre><code>result-book.xlsx </code></pre> <p>Note: We use RStudio Version 1.2.1335 to generate figures from our R scripts. Since RStudio is a GUI-based software, you can install it on another machine with GUI and run scripts from it.</p>
Photochromic fluorophores enable imaging of endogenous fusion constructs in Candida albicans
<p>10. Gcn5_Stat : Main dataset for figure 2 stationary phase of the manuscript.</p> <p>5., 6. and 7.Gcn5_Stat are additional datasets for figure 2 stationary phase.</p> <p>11. SC5314_Stat : main negative control dataset for figure 2 depicted in supplementary figure 1.</p> <p>14. Gcn5_Exp : Main dataset for figure 2 exponential phase of the manuscript.</p> <p>12., 13. Gcn5_Exp are additional datasets for figure 2 exponential phase.</p> <p>15. SC5314_Exp : main negative control dataset for figure 2 depicted in supplementary figure 1.</p> <p>___________________________________________________________________________________________________</p> <p>2. Erg11 and 7. Erg11 are main datasets for figure 3 of the manuscript.</p> <p>15. SC5314 (Erg11 control) : negative control dataset for figure 3 depicted in supplementary figure 3.</p>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Figures 1–4 in Fusion of Pectinal Teeth in Scorpio kruglovi Birula, 1910 (Scorpiones: Scorpionidae)
Figures 1–4: Scorpio kruglovi, female. Figures 1–2. Dorsal (1) and ventral (2) views. Figures 3–4. Right pecten (3), fused 7th and 8th teeth in the right pecten (4). Scale bar: 10 mm (1–2).
Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?
<p>Socioecological theory attributes social variation in female-bonded species to differences in within- and between-group competition, shaped by food distribution. Strong between-group contests are expected over large, monopolisable resources, but not when low-quality food is distributed across large, undefended home ranges. Within-group contests are expected to be more frequent with increasing heterogeneity in feeding sites. We tested these predictions in female Asian elephants, which show traits associated with infrequent contests – predominant graminivory, overlapping home ranges, and high fission-fusion. We examined how agonistic interactions within and between female elephant clans (social groupings) vary with food distribution and competitor density. We found stronger between-clan contests than that known from neighbouring forests and more frequent agonism between females between clans than within clans. Such strong between-clan contest is attributable to food patchiness as the Kabini grassland in the study area had three times the grass biomass as adjacent forests. Within-clan agonism was also frequent but was not influenced by food distribution, contradicting socioecological predictions. Contrary to recent claims, increasing within-clan agonism with group (party) size showed that ecological constraints operate despite high fission-fusion in Asian elephants. Thus, despite graminivory and fission-fusion, within-clan and between-clan agonism can be frequent, especially at high population density.</p>
Data for: Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation
<p>This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: <a href="https://doi.org/10.1021/acs.jcim.4c00316">10.1021/acs.jcim.4c00316</a></p>
Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
<p>Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video.</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
Paired Fusion Augmented Dataset for Vehicle Extraction and Counting (Domino Dataset)
<p>This dataset try to expedite the deep learning researcher's task of a model training to extract vehicles from aerial images in an urban environment. Vehicles included in the dataset are motorcycles and cars of any type, number of wheels and color. The specific process of acquisition, enhancing, fusion and augmentation is presented. Inclusion of height of cars using a Digital Surface Model (DSM) is described and comparison of the application of a U-net segmentation model over non height and height dataset is shown.</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.