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1,049 results for “Robustness”
Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep<br> the capacity of watermarking fixed.<br> When a region of the watermarked image is destroyed; the whole watermark can be<br> extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1<br> shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000<br> algorithm) watermarked image.</p>
Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>A set of distortions is applied to the watermarked image and the watermark is extracted from<br> the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is<br> calculated from the following equation [6].</p>
Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>In the following experiments, two gray-level images with size of 512 by 512, “Baby” and<br> “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our<br> simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter<br> was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of<br> sub-blocks (K) were also assumed to be 2 and 16 respectively.<br> The proposed watermarking algorithm is evaluated from the point view of embedded<br> watermark transparency and robustness; the result of each is shown in next two sections.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart
<p>The methodology of our experimental method is described in Figure 3 below.</p> <p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
Data from: Automatic Definition of Robust Microbiome Sub-states in Longitudinal Data
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome datasets already published.</p> <p>Prefixes:</p> <ul> <li>David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)</li> <li>Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)</li> <li>Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)</li> <li>LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)</li> </ul> <p>Suffixes:</p> <ul> <li> <p>_All: all taxa</p> </li> <li> <p>_Dominant: only 1% most abundant taxa</p> </li> <li> <p>_NonDominant: remaining taxa after removing above dominant taxa</p> </li> <li> <p>_GenusAll: taxa aggregated at genus level</p> </li> <li> <p>_GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa</p> </li> <li> <p>_GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> </li> </ul> <p>Each folder contains 3 output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID</p> <p>Abstract of the associated paper:</p> <p>The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome's "state". Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.</p> <p> </p>
Robust and Adaptive Robot Self-Assembly Based on Vascular Morphogenesis
<p>Self-assembly is the aggregation of simple parts into complex patterns as frequently observed in nature. Following this inspiration, creating programmable systems of self-assembly that achieve similar complexity and robustness with robots is challenging. As role model we pick the growth of natural plants that adapts to environmental conditions and is robust to disturbances, such as changes due to dynamic environments and cut parts. We program a robot swarm to self-assemble into tree-like shapes and to efficiently adapt to the environment. Our approach is inspired by the vascular morphogenesis of plants, that is the patterned formation of vascular tissue to transport fluids and nutrients internally. The aggregated robots establish an internal network of resource sharing, allowing them to make rational decisions collectively about where to add and where to remove robots. As an effect, the growth is adaptive to an environmental feature (here, light) and robust to changes in a dynamic environment. The robot swarm is able to self-repair by regrowing lost parts. We successfully validate and benchmark our approach in a number of robot swarm experiments showing adaptivity, robustness, and self-repair.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart
<p>The methodology of our experimental method is described in Figure 3 below.</p> <p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.</p>
Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication
<p>All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" <em>(doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>)</em></p> <p><strong>Abstract</strong></p> <p>Geneticists have long used olfactory conditioning techniques in <em>Drosophila</em> to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.</p>
A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation
<p>Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a></p>
Research data supporting "A robust liposomal platform for direct colorimetric detection of sphingomyelinase enzyme and inhibitors"
<p>Raw research data supporting the publication: Holme, M. N. et al., ACS Nano, 2018, DOI: 10.1021/acsnano.8b03308.</p>
Data from: Robust and Automatic Definition of Microbiome States
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome dataset already published.</p> <p>Prefixes:<br> * David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)<br> * Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)<br> * Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)<br> * LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)<br> * Dam2016_: original microbiome dataset published in [Dam et al.,2016] (https://www.nature.com/articles/npjsba20167)<br> * Caporaso[Lpalm|Rpalm|Tongue]_: original microbiome dataset published in [Caporaso et al.,2011] (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2011-12-5-r50)<br> * Ravel2011_: original microbiome dataset published in [Ravel et al.,2011] (http://www.pnas.org/content/108/Supplement_1/4680)</p> <p>Sufixes:<br> _All: all taxa<br> _Dominant: only 1% most abundant taxa<br> _NonDominant: remaining taxa after removing above dominant taxa<br> _GenusAll: taxa aggregated at genus level<br> _GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa<br> _GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> <p><br> Each folder contains the following output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID<br> - robustClustering_allTogether_formatted.pdf: graph file, with the results of the robust clustering assessment.<br> - pcoa_definitiveClustering_X_kY_colorByCluster.pdf: graph file, with samples represented in Principal COordinate Analysis, with different point color associated to the assigned cluster.<br> - statesSequence_XXX.pdf (if longitudinal data): graph file, a time series diagram representing the sequence of states over time per subject.</p> <p> </p>
Expansion Cones Dataset for Robust Tetrahedral Mapping Evaluation
<p>This record stores the dataset used by the <a href="https://www.algohex.eu/publications/expansion-cones/">"Expansion Cones"</a> paper.<br>This paper presents a volumetric mapping method called <em>Shrink-And-Expand</em> (SAE for short).<br>Its robustness was evaluated by mapping ~3'000 ball-topology meshes to 4 different target boundaries (spherical, tetrahedral, "stiff" tetrahedral and random star-shaped). Other tetrahedral mapping methods can be evaluated by using the rest meshes and either of the target boundaries.</p> <p>The record is structured as follows:</p> <ul> <li>`ball_topology_tet_wild_ovm_format`: all input meshes in their original shapes, in <a href="https://www.graphics.rwth-aachen.de/software/openvolumemesh/">.ovm</a> format. These consists of the <a href="https://github.com/Yixin-Hu/TetWild">TetWild</a> dataset, restricted to ball-topology meshes. If you need to convert the meshes to another format, you can use Martin Heistermann's <a href="https://github.com/mheistermann/meshio">fork</a> of <a href="https://pypi.org/project/meshio/">meshio</a>.<br>NOTE: Those meshes were pre-processed by splitting all <strong>interior</strong> edges connecting two boundary vertices and all <strong>interior</strong> faces connecting three boundary vertices. This was done to satisfy the prerequisites of our method.</li> <li>`XXX_boundary_conditions.zip`: boundary conditions for the 4 boundary shapes: tetrahedral, "stiff" tetrahedral, ball and random star-shape. Please read the related paper for more details about the boundary types.<br>Those boundary conditions are stored as `.txt` files, each containing a list of boundary vertices indices, along with their prescribed positions.</li> </ul> <p>The other archives contain data used to compare SAE with two other volumetric mapping methods called <a href="https://github.com/duxingyi-charles/lifting_simplices_to_find_injectivity"><em>Total Lifted Conten</em>t (TLC)</a> and <a href="https://dl.acm.org/doi/abs/10.1145/3450626.3459847">FoldOver-Free (FOF)</a></p> <ul> <li> `SAE_results/expansion_data`: All `.json` files corresponding to executions of our method (SAE) on those meshes.</li> <li> `SAE_results/quality_stats`: conformal and volumetric distortion values for _all_ cells of _all_ meshes.</li> <li>`TLC_FOF_results`: For each mesh and each competitor method (and each boundary map), there are 1 to 3 files, depending on the results of running those methods. <ul> <li>`TLC/FOF_timing_s.txt` gives the number of seconds it took to run</li> <li>`TLC/FOF_stats.txt` stores the following data: `[mesh name, #vertices, #edges, #faces, #cells, #degenerate cells after running the method, #flipped cells after running the method]`. This file only exists if the method terminated within the 12hours time limit.</li> <li>`TLC/FOF_quality_stats.txt` lists the conformal and volumetric distortion of each individual cell in the resulting map produced by the competitor methods (one line per cell), This file only exists if the method produced a valid map.</li> </ul> </li> </ul> <p> </p> <p><strong>NOTE</strong>: If you want to download the codomain meshes, with prescribed boundary positions already set. you can find them in the v2.0 of this same dataset.</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.