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1,705 results for “vector”
Fig. 1 in Assessing the natural circulation of canine vector-borne pathogens in foxes, ticks and fleas in protected areas of Argentine Patagonia with negligible dog participation
Fig. 1. Map of Latin America, showing the study areas in the insert. Black circle: Bosques Petrificados National Park; grey circle: Monte León National Park.
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process.<br> A brief schema of the CoDOA-SVM approach is also provided in Figure 3.</p>
Figure 1. Maximum margin hyper-plane (Cortes & Vapnik, 1995).-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In other words, it aims to find the state in which the distance between the two classes is the maximum. The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes (Javed, Ayyaz, & Mehmood, 2007). In Figure 1, an example of support vectors and a maximum margin hyper-plane (in other words, an optimum separating hyper- plane) is shown (Cortes & Vapnik, 1995).</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</p>
Hathi Trust Library Vectorized features
<p>A smaller-resolution (and therefore more portable) version of the Stable Random Projection Hathi Trust features described in my forthcoming article. The Northeastern repository is many individual files with 1280 random dimensions; this is just 640 random dimensions. The numbers are also experimentally encoded as half-precision floats, which cuts the file size by half at the cost of only being supported by my Python module. The net result is a file 1/4 the size of the full resolution ones for the paper that has, probably, something like 60-80% of the information content.</p> <p>The full file is '<a href="https://www.zenodo.org/api/files/6d615dbd-65de-4391-93ac-91b302bb57e4/ht-640d-complete-half-precision.bin?versionId=0e9f5551-0888-454c-8bb8-0dd3f3d6d949">ht-640d-complete-half-precision.bin</a>'. You can also download 11 smaller files organized by language.</p> <p>Since these files use half-precision float encoding, to read them you must specify the precision when reading: e.g.,<br> </p> <pre><code class="language-python">from SRP import Vector_file f = Vector_file("ita.bin", precision = "half")</code></pre> <p>Code to read these files is at https://github.com/bmschmidt/pySRP. </p>
Don't count, predict! Semantic vectors
<p>Semantic vectors associated with the paper "<a href="https://www.aclweb.org/anthology/P14-1023">Don't count, predict! A systematic comparison of context-counting vs context-predicting semantics vectors</a>"</p> <p><strong>Abstract:</strong> context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.</p>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (netCDF format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of <a href="http://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. Each file contains one year of hourly geospatial data grids describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each weather front probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. These front polylines were then rasterized into geospatial data grids and stored by year into netCDF-4 files that conform to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>. The front data in each file is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded.</p> <p>There are two large groupings of the netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention merra2_[masked]_<grid>_<n>wide_<year>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The the sequence <n>wide indicates the width with which the fronts were drawn, and <year> is the year for the data stored in the file.</p> <p>The files marked as masked had a mask applied to the data grids that corresponded to the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year, as determined using the <a href="https://dx.doi.org/10.5281/zenodo.2651361">Coded Surface Bulletin </a>dataset.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the <em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are four subsets of files:</p> <ul> <li>merra2_masked_<grid>_1wide_<year>.nc</li> <li>merra2_masked_<grid>_3wide_<year>.nc</li> <li>merra2_<grid>_1wide_<year>.nc</li> <li>merra2_<grid>_3wide_<year>.nc</li> </ul>
Integrated knowledge graphs and embeddings vectors for drug-drug interaction prediction
<p>The associated Knowledge Graphs for predicting potential drug-drug interaction, which is used in our paper titled "Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network". Please consider citing the following paper if you plan or used our datasets.</p> <p>Md. Rezaul Karim, Michael Cochez, Joao Bosco Jares, Mamtaz Uddin, Oya Beyan, and Stefan Decker, "Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network", In 10th ACM Int’l Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB ’19), September 7–10, 2019, Niagara Falls, NY, USA.</p>
Interpolated maps of bird density and flight vector over Europe
<p>This dataset contains the interpolated values of bird density and bird flight speed (N-S and E-W) resulting from the methodology presented in [<em>reference</em>].The methodology is explained in less detail at <a href="https://rafnuss-postdoc.github.io/BMM/">rafnuss-postdoc.github.io/BMM</a>. The resulting interpolation is a probability distribution (define the probability of each value to occurs). Only the median, quantile 10 and 90 are given in this file. </p> <p>The spatio-temporal grid has a resolution of 0.2° in latitude (43°-68°) and longitude (-5°-30°) and 15 minutes in time (19 September to 10 October 2016), resulting in 127x176x2017 nodes. Over this large data cube, the estimation are only computed at the nodes located (1) over land, (2) within 200km of the nearest radar and (3) during nighttime.</p> <p>The same dataset can be visualised on a dedicated web interface: <a href="https://bmm.raphaelnussbaumer.com/">www.bmm.raphaelnussbaumer.com</a> and data can be queried on a API (<a href="https://github.com/Rafnuss-PostDoc/BMM-web#how-to-use-the-api">documentation</a>).</p> <p>The csv file is structured as a table with the following columns:</p> <ul> <li>density_estimation: Median bird density [bird/km^2] (quantile 50)</li> <li>density_quantile10: Quantile 10 of bird density [bird/km^2]</li> <li>density_quantile90: Quantile 90 of bird density [bird/km^2]</li> <li>speedu_estimation: Mean bird speed east(+)/west(-) [m/s]</li> <li>speedu_std: Standard deviation of bird speed east(+)/west(-) [m/s]</li> <li>speedv_estimation: Mean bird speed north(+)/south(-) [m/s]</li> <li>speedv_std: Standard deviation of bird speed north(+)/south(-) [m/s]</li> <li>latitude</li> <li>longitude</li> <li>time</li> </ul> <p> </p>
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Figure 2 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 2 Monthly mean relative abundance of medically relevant sand fly species. Please note the scale difference in the Y axis. Paracambi, RJ, Brazil, 1992-1994.
Fig. 6 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 6. Phylogenetic tree of 33 filarial nematode species constructed on the basis of partial COI sequences using the Maximum Likelihood method. The percentage of replicate trees in which the associated species clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Branch lengths is measured in the number of substitutions per site. Thelazia callipaeda was included as an outgroup. The sequence of the present study is framed in red.
Fig. 5 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 5. Phylogenetic tree of Onchocercidae species constructed on the basis of partial ITS1 sequences using the Maximum Likelihood method. The percentage of replicate trees in which the associated species clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Branch lengths is measured in the number of substitutions per site. The sequences of the present study are framed in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 3. Number of samples (blood smears) per month. Microfilaria positive samples are shown in dark blue for M. murinus and dark brown for M. ravelobensis, microfilaria negative samples in light blue for M. murinus and light brown for M. ravelobensis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere). in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere).
Figures 31–35. Culicoides peregrinus Kieffer, male pupa. 31 in Description of immature stages of Culicoides peregrinus (Diptera: Ceratopogonidae), a potent vector of bluetongue virus
Figures 31–35. Culicoides peregrinus Kieffer, male pupa. 31: Respiratory organ, 32: antennal sheath with pale spot and prothoracic extension, 33: close view of dorsal apotome, 34: palpus-clypeal/labral sensilla and ocular area, 35: segment 9. Abbreviations same as before. Scale bar 50 µm.
Figures 7–16. Culicoides peregrinus Kieffer, larva. 7 in Description of immature stages of Culicoides peregrinus (Diptera: Ceratopogonidae), a potent vector of bluetongue virus
Figures 7–16. Culicoides peregrinus Kieffer, larva. 7: Second instar, 8: head capsule of third instar, 9: body pigmentation of fourth instar, 10: fourth instar, 11: head capsule with cephalic setae and sensory pore (ventrolateral view), 12: head capsule with cephalic setae and sensory pore (dorsolateral view), 13: mouthparts, 14: mouthparts (close view), 15: epipharynx and hypopharynx, 16: setae of caudal segment. AN, antenna; GL, genital lobe; HY, hypostoma; LB, labrum; LC1, lacinial sclerite 1; LC2, lacinial sclerite 2; MD, mandible; MP, maxillary palp; MS, messors; MX, maxilla; PL, palatum; SC, scopae; ss, sensilla styloconica; st, sensilla trichoidea. Caudal segment chaetotaxy: d, dorsal setae; i, inner setae; I 1, first lateral setae; I 2, second lateral setae; o, outer setae. Head capsule chaetotaxy: o, parahypostomal setae; p, posterior perifrontal; q, post frontal; s, anterior perifrontal setae; t, prefrontal setae; u, mesolateral setae; v, posterolateral setae; w, anterolateral setae; y, ventral setae. Scale bar 50 µm except when otherwise stated.
Figure 6 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 6. Aedes aegypti larval mortality when exposed to 1000 infective juveniles (IJs) of Heterorhabditis bacteriophora at different depths of water.
Figure 2 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 2. Susceptibility of Aedes aegypti larvae to different species of EPN. Five 3rd instar larvae exposed to 1000 infective juveniles (IJs) and mortality assessed daily over 3-day period (DPI).
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.