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9,302 results for “DETECT”
Detection of Structural Components in Point Clouds of Existing RC Bridges
<p><em>The cost and effort </em><em>of</em><em> modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves an average detection precision of 98.8%. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters.</em></p>
Detecting Opinion Spams through Supervised Boosting Approach
<p>Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.</p>
INTEGRAL light curves of GRB150101B detected by SPI-ACS and IBIS/PICsIT
<p>We have analyzed archival INTEGRAL data of the SPI-ACS and IBIS/PICsIT in coincidence with GRB 150101B (GCN 17267, GCN 17276) due to the recent results from Troja et al. (2018, arXiv:1806.10624) in which they suggest that GRB 150101B is a kilonova event comparable to GW170817/GRB170817A, but at cosmological distance and without the observations of a gravitational-wave trigger. </p> <p>We confirm the independent detection by SPI-ACS and IBIS/PICsIT of a short duration (~0.012 sec) event, consistent with that reported in Troja et al.. The signal in SPI-ACS (75 keV-10 MeV) and in IBIS/PICsIT (200 keV-1.2 MeV) has a S/N of 5.1, and 4.2, respectively.</p>
Development of a specific PCR to detect thiocyanate-oxidizing Thioalkalivibrio strains in their environment
<p>Data repository for my thesis named 'Development of a specific PCR to detect thiocyanate-oxidizing <em>Thioalkalivibrio</em> strains in their environment' at the University of Amsterdam. The folder consists of 2 subfolders, namely the alignment, in both AA and NA forms, of the TcDH-sequences of the 10 Thioalkalivibrio strains, shown in <em>Figure 21</em> (1) and the alignment of the TcDH-sequences and names of the organisms used while making the phylogenetic tree of <em>Figure 2</em> (2).</p>
Finding an antibody to detect EZH1 protein expression – Part 2
<p>Follow up to previous post of trying to find an antibody to determine EZH1 expression in AML patient cells.</p>
[Supp. mat.] Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: initial application to the GENFI cohort
<p>Supplementary tables and figure of paper Spatio-temporal analysis for detection of pre-symptomatic shape changes in neuro-degenerative diseases: initial application to the GENFI cohort, for testing different number of clusters of the spatio-temporal regression. The supplementary materials shows results for 2 4 6 8 12 14 and 16 clusters. The 10 clusters analysis is in the paper.</p>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
Detection of genetic alterations in gastric cancer patients from Saudi Arabia using comparative genomic hybridization (CGH)
<p>Abstract</p> <p>Background: The present study was conducted to discover genetic imbalances such as DNA copy number variations (CNVs) associated with gastric cancer (GC) and to examine their association with different genes involved in the process of gastric carcinogenesis in Saudi population. </p> <p>Methods: Formalin-fixed paraffin-embedded (FFPE) tissues samples from 33 gastric cancer patients and 15 normal gastric samples were collected. Early and late stages GC samples were genotyped and CNVs were assessed by using Illumina HumanOmni1-Quad v.1.0 BeadChip. </p> <p>Results: Copy number gains were more frequent than losses throughout all GC samples compared to normal tissue samples. The mean number of the altered chromosome per case was 64 for gains and 40 for losses, and the median aberration length was 679115bp for gains and 375889bp for losses. We identified 7 high copy gain, 52 gains, 14 losses, 32 homozygous losses, and 10 copy neutral LOHs (loss of heterozygosities). Copy number gains were frequently detected at 1p36.32, 1q12, 1q22, 2p11.1, 4q23-q25, 5p12-p11, 6p21.33, 9q12-q21.11, 12q11-q12, 14q32.33, 16p13.3, 17p13.1, 17q25.3, 19q13.32, and losses at 1p36.23, 1p36.32, 1p32.1, 1q44, 3q25.2, 6p22.1, 6p21.33, 8p11.22, 10q22.1, 12p11.22, 14q32.12 and 16q24.2. We also identified 2 monosomy at chromosome 14 and 22, 52 partially trisomy and 22 whole chromosome 4 neutral loss of heterozygosities at 13q14.2-q21.33, 5p15.2-p15.1, 5q11.2-q13.2, 5q33.1-q34 and 3p14.2-q13.12. Furthermore, 11 gains and 2 losses at 1p36.32 were detected for 11 different GC samples and this region has not been reported before in other populations. Statistical analysis confirms significant association of H. pylori infection with T4 stage of GC as compare to control and other stages.</p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
<p>This link provides the data from the playback experiment to determine effective detection areas for porpoises recorded by C-POD acoustic dataloggers. The firstdata set includes the artifically created porpoises click trains captured by the C-PODs and the second is the record of the rate of re-capture for the real, recorded porpoise clicks used in the experiment. </p>
Finding an antibody to detect EZH1 protein expression – Part 3
<p>Follow up to previous post of trying to find an antibody to determine EZH1 expression in AML patient cells.</p>
OCT volumes for glaucoma detection
<p>OCT volumes used for the training and evaluation of a glaucoma detection classifier as described in</p> <p>A feature agnostic approach for glaucoma detection in OCT volumes<br> https://arxiv.org/abs/1807.04855</p> <p>Volumes are in NumPy format. A viewer is available at https://github.com/maet3608/oct-viewer</p>
Finding an antibody to detect EZH1 protein expression – Part 4
<p>Follow up to previous posts where we determine that we have an antibody to detect EZH1 expression in AML patient cells.</p>
Dataset for: "Evolutionary couplings detect side-chain interactions"
<p>This is the pre-publication dataset for "Evolutionary couplings detect side-chain interactions", a manuscript currently undergoing the review process and posted at bioRxiv (doi.org/10.1101/447409)</p>
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <p> </p>
Histological images for tumor detection in gastrointestinal cancer
<p>This is a set of 11977 image patches of hematoxylin & eosin stained histological samples of human colorectal cancer. It is a subset of the data set "100,000 histological images of human colorectal cancer and healthy tissue" which is accessible at http://dx.doi.org/10.5281/zenodo.1214456. Further information on the samples is available there.</p> <p>This set contains three classes:</p> <p>ADIMUC - adipose tissue and mucus, i.e. loose non-tumor tissue</p> <p>STRMUS - stroma and muscle, i.e. dense non-tumor tissue</p> <p>TUMSTU - colorectal cancer epithelial tissue and stomach cancer epithelial tissue, i.e. tumor tissue</p> <p>All images are 512x512 px at 0.5 µm/px</p> <p>We are using this data set to train a deep neural network to detect tumor cells in histological whole slide images of colorectal and stomach cancer.</p> <p>If you use these images in your research, please consider citing our previous [1] and upcoming publication.</p> <p>For information on ethics board approval, see [1].</p> <p>-------------</p> <p>[1] http://dx.doi.org/10.1371/journal.pmed.1002730</p>
UNICITY: A depth maps database for people detection in security airlocks
<p><strong>UNICITY: A depth maps database for people detection in security airlocks.</strong></p> <p>UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries, like for instance tailgating (when a person walks very close to another to get into a restricted area). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</p> <p><strong>Main Features:</strong></p> <ul> <li>UNICITY consists of 58k images using two depth sensors.</li> <li>65 recorded sequences with one or two people performing different behaviors such as attacks and tailgating.</li> <li>UNICITY also provides code for evaluation and visualization, and full annotation of people such as the location of head and shoulders.</li> <li>This new dataset is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</li> </ul> <p><strong>Citation</strong>:</p> <p>Please cite the following paper if you use the UNICITY dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>UNICITY: A depth maps database for people detection in security airlocks. J. Dumoulin, O. Canevet, M. Villamizar, H. Nunes, O.A. Khaled, E. Mugellini, F. Moscheni, and J.M Odobez. International Conference on Advanced Video and Signal-based Surveillance Workshop (AVSSW). November 2018.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>Joël Dumoulin, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Olivier Canévet, Idiap Research Institute, Martigny, Switzerland.</li> <li>Michael Villamizar, Idiap Research Institute, Martigny, Switzerland.</li> <li>Hugo Nunes, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Omar Abou Khaled, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Elena Mugellini, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Fabrice Moscheni, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Jean-Marc Odobez, Idiap Research Institute, Martigny, Switzerland.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The work was supported by Innosuisse, the Swiss innovation agency, through the UNICITY (3D scene understanding through machine learning to secure entrance zones) project.</p> <p><strong>Links:</strong></p> <p>Next links contain additional information about the dataset:</p> <ul> <li>Innosuisse UNICITY project: <a href="https://www.idiap.ch/en/scientific-research/projects/UNICITY">[link]</a></li> <li>Paper describing the dataset: <a href="http://publications.idiap.ch/index.php/publications/show/3939">[link]</a></li> <li>Video presenting the dataset: <a href="https://www.youtube.com/watch?time_continue=2&v=pGrnI12OhmA">[link]</a></li> <li>Paper using the dataset for counting people and detecting intrusions: <a href="http://michael-villamizar.com/avss18.html">[link]</a> <ul> <li>WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.<br> M. Villamizar, A. Martinez-Gonzalez, O. Canevet and J-M. Odobez.<br> International Conference on Advanced Video and Signal-based Surveillance (AVSS) - 2018.</li> </ul> </li> </ul> <p><strong>Contact</strong>:</p> <p>For any questions, please contact:</p> <ul> <li>Michael Villamizar, Idiap Research Institute, Martigny -Switzerland</li> </ul>
Indoor Object Detection Dataset
<p>We introduce a new fully labeled object detection dataset collected from indoor scenes. This indoor dataset consists of 2213 image frames containing seven classes. In contrast to existing indoor datasets, our dataset includes a variety of background, lighting conditions, occlusion and high inter-class differences.<br> For detail information, please refer to our paper: <a href="https://doi.org/10.1109/EUVIP.2018.8611732">10.1109/EUVIP.2018.8611732</a></p>
STORM graffiti/tagging detection dataset
<p>This object detection dataset was created by University of West Attica, by collecting images containing at least one graffiti/tagging, using the camera of a smartphone and exploiting the STORM crowdsensing application. STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu). The included images display structures, such as walls, pavements, benches and statues that are located in the municipality of Athens and its suburbs. The dataset consists of 1022 images with graffiti/tagging annotations and are split into 80% (813 images) for training and validation and 20% (209 images) for testing.</p>
ScienceDex guides
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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.