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309 results for “inspiration”
BRAIN Journal-On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex-Figure 1. Working mechanism of the VOA.
<p>As it can be seen from the algorithm steps, the VOA employs simple equations. It is an<br> advantage that the algorithm can be formed and applied within optimization problems whereas<br> alternative algorithms may contain some complex solution steps (This situation may be also an<br> disadvantage for the VOA when it is applied in more difficult optimization problems but while the<br> world is transformed into a ‘strong simplicity’, the VOA may be a practical solution approach).<br> The working mechanism of the VOA can be visualized briefly as like in Figure 1.</p>
Figure 1.Flowchart of the CoDOA.-Realizing an Optimization Approach Inspired from Piaget's Theory on Cognitive Development
<p>The objective of this paper is to introduce an artificial intelligence based optimization<br> approach, which is inspired from Piaget’s theory on cognitive development. The approach has been<br> designed according to essential processes that an individual may experience while learning<br> something new or improving his / her knowledge. These processes are associated with the Piaget’s<br> ideas on an individual’s cognitive development. The approach expressed in this paper is a simple<br> algorithm employing swarm intelligence oriented tasks in order to overcome single-objective<br> optimization problems. For evaluating effectiveness of this early version of the algorithm, test<br> operations have been done via some benchmark functions. The obtained results show that the<br> approach / algorithm can be an alternative to the literature in terms of single-objective optimization.<br> The authors have suggested the name: Cognitive Development Optimization Algorithm (CoDOA)<br> for the related intelligent optimization approach.</p>
iNSPiRe FP7 - Retrofit solutions database
<p>Energy, economic and environmental performance of a set of residential and office building retrofit measures.</p> <p>A simulation-based database organized in excel sheets collects information on the energy performance, installation and actual costs and environmental impact of different renovation packages applied to the envelope and to the HVAC system of residential and office buildings belonging to different climates and construction periods.</p> <p>The research leading to these results has received funding from the European Community's Seventh 455 Framework Programme (FP7/2007-2013) under grant agreement n° 314461.</p>
Hydroperiod maps of Donana for 2015/2016, 2016/2017 and their accompanying INSPIRE metadata XML files
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod maps for Donana are named: "Donana_Hydroperiod_from_1st_Sept_2015_to_31st_Aug_2016_using_Sentinel_2_and_Landsat_inundation_maps.tif" and "Donana_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". Their pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. They were generated by interpolating satellite-derived inundation maps falling within the period indicated in their filenames.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Hydroperiod map of Camargue for 2016/2017 and its accompanying INSPIRE metadata XML file
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod map of Camargue is named: "Camargue_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Hydroperiod map of Danube Delta for 2016/2017 and its accompanying INSPIRE metadata XML file
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical<br> ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat<br> for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod map of Danube Delta is named: "Danube_Delta_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Ecosystem of Faith-Inspired Grantmakers Sample Dataset
<p>This is a random set of 1,027 grantmakers and their grantees whose data was efiled in 2019. The dataset includes 515 public grantmakers (Form 990 filers) and 512 private grantmakers (Form 990 PF filers) and a total of 11,513 grant records. All grantmakers in the set were identified as faith-inspired by a machine learning process. All grantees in the set were identified by the same process as secular or faith-inspired. Additionally, all grantmakers and grantees in the set have been identified as much as possible according to their major religious tradition by a hand-coding process. Finally, grantees in the field of reproductive health were also hand-coded as pro-life or pro-choice. </p>
3D-PDR Orion inspired dataset
<p>This datasets contains a dataset of 8192 models of 3D-PDR in 1 dimensional mode. Each model was ran with a different configuration of initial physical parameters, namely number density, radiation field and cosmic ray ionisation. The respective ranges for these are: number densities $10^2 \leq n_\mathrm{H}(\mathrm{cm}^{-3}) \leq 10^7$ and lastly the cosmic ray ionisations $10^{-17} \leq \zeta(\mathrm{s}^{-1})\leq 10^{-15}$. Each of these parameter combinations is sampled from a Sobol sequence with the aformentioned minimum and maximum values.<br><br>Each model (grouped by `/model_1024` to `/model_9125`) consists of one parameter configuration related to its index in `model_df`, whose header is `radiation_field`, `density` and `cosmic ray ionisation`. <br><br>Each model consists of a `cool`, `heat`, `line`, `opdp`, `pdr` and `spop` dataset. All datasets but the `pdr` are saved for completeness and relate to the cooling, heating and line populations of the models. More about those parameters can be found in the references on https://uclchem.github.io/3dpdr/, or upon request to the authors.<br><br>Most important are the `pdr` datasets, they contain the evolution of the physical conditions and chemistry as a function of the visual extinction going into an 1-dimensional cloud. </p> <p> </p> <p>The first 8 columns are respectively: `time_idx`, `positions`, `visual_extinction`, `tgas`, `tdust`, `etype`, `density` and `radfield`. Followed by all the species fractional abundances, these are stored under the `/species` key. </p> <p>Surrogate models for this code would need the following features: `visual_extinction`, `tgas`, `tdust`, `density`, `radfield`, and then any subset of species.</p> <p> </p>
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments
<p>Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available.</p> <p>This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/).</p> <p>This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.</p>
SAT-Inspired Higher-Order Eliminations
<p>This is the package containing the raw evaluation data for the paper "SAT-Inspired Higher-Order Eliminations" by Jasmin Blanchette and Petar Vukmirović.</p> <p>The problems used for the evaluation are located in the "problems" directory. The seven categories are</p> <p> seventeen_th0 (called S0 in Fig. 1 of the paper)<br> seventeen_th1 (called S1 in Fig. 1)<br> tptp_th0 (called TH0 in Fig. 1)<br> tptp_th1 (called TH1 in Fig. 1)<br> tptp_cnffof (called CF in Fig. 1)<br> tptp_tf0 (called TF0 in Fig. 1)<br> tptp_tf1 (called TF1 in Fig. 1)</p> <p>The empirical results are located in the "results" directory, under the following names, corresponding to the category names above:</p> <p> seventeen_th0_results.csv<br> seventeen_th1_results.csv<br> tptp_th0_results.csv<br> tptp_th1_results.csv<br> tptp_cnffof_results.csv<br> tptp_tf0_results.csv<br> tptp_tf1_results.csv</p> <p>The CSV files were produced by StarExec. Each nonheader row gives the prover's performance on one problem. For example, the row</p> <p> 74437543,Problems/AGT/AGT036^1.p,2900058,Zipperposition---2.2pre-hoelim-v2,2410,hlbe-in,92437,complete,2.09374,0.983524,1684480.0,Theorem,Theorem,THM-Ref,Ref,THM</p> <p>in "tptp_th0_results.csv" indicates that the HLBE inprocessing mode of Zipperposition ("hlbe-in") was able to prove the TPTP problem "AGT036^1.p", as indicated by the "THM" result in the last column. "THM" and "UNS" (unsatisfiable) correspond to a successful proof; other outcomes are considered failures.</p> <p>Figure 1 was generated using the script "script/gen_figure.py", which must be run from within the "script" directory.</p> <p>The "binaries" directory contains the StarExec package used to run the evaluation. The package is called "bin" in accordance with StarExec conventions. Inside it, "zipperposition" and "eprover-ho" are the 64-bit Linux binaries for the Zipperposition prover and its E backend, and the other files are scripts used to run various configurations in time slices. When running the scripts locally, set the environment variables "STAREXEC_CPU_LIMIT" and "STAREXEC_WALLCLOCK_LIMIT" to suitable time limits in seconds.</p> <p>Zipperposition was compiled from the repositiory version with the git commit hash 2a66166453ac32c0 on the "wip_ho_elimination_techniques" branch. E was compiled with the "--enable-ho" configuration option from an unspecified repository version. The Zipperposition and E repositories are available online (https://github.com/sneeuwballen/zipperposition and https://github.com/eprover/eprover).</p>
INSPIRE-seq simultaneously selects nanobodies for immune epitopes in the complex tumor microenvironment
<p>scRNAseq of CD45 magnetic microbeads enriched cells were isolated form Py8119 bearing mice (three mice per pool/group) two hours after injection of either PBS, insertless phage display, CD45, DCs, or CD8 specific VHHs phage display libraries. </p>
dataset related to article "A NOVEL BIO-INSPIRED STRATEGY TO PREVENT AMYLOIDOGENESIS AND SYNAPTIC DAMAGE IN ALZHEIMER'S DISEASE"
<p><strong>Levels of A</strong><strong>beta40, Abeta42 and aggregated Abeta</strong></p> <p><strong>results obtained from plaque count</strong></p> <p><strong>densitometric analysis of ctf and synaptic proteins</strong></p> <p><strong>levels of antibodies against Abeta42 and Abeta1-6</strong></p>
Brain-inspired multimodal hybrid neural network for robot place recognition
<p>Brain-inspired multimodal hybrid neural network for robot place recognition</p>
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
<p>Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (<em>Prionace</em> <em>glauca</em>), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. </p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Supporting data for "Reliable interpretability of biology-inspired deep neural networks"
<p><strong>Contents</strong></p> <p><em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders:</p> <ul> <li><em>dtox</em><br> DTox results, one subfolder per seed <ul> <li><em>module_relevance.tsv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li><em>test_labels.csv</em>: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)<br> </li> </ul> </li> </ul> </li> <li><em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> </li> <li><em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li><em>node_importance.csv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li><em>predictions_test.csv</em>: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li><em>predictions_train.csv</em>: predictions for the training set (same columns as above)</li> <li><em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <p><em>v1.1.0 – 2023-06-28</em></p> <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> <p><em>v1.0.0 – 2023-03-22</em></p> <ul> <li>initial release</li> </ul>
XRF mapping of painting reconstruction, inspired by Johannes Vermeer's "Girl with a Pearl Earring"
<p>This dataset contains the XRF mapping of painting reconstruction, inspired by Johannes Vermeer’s “Girl with a Pearl Earring” produced in the Microchemistry and Microscopy Art Diagnostic Laboratory (M2ADL) of the University of Bologna (2018). The painting was obtained by applying pigments mixed with linseed oil on a commercial canvas. Pigments were selected considering their elemental signals detectable by XRF spectroscopy. In a more detail, azurite (Cu<sub>3</sub>(CO<sub>3</sub>)<sub>2</sub>(OH)<sub>2</sub>) was used for the creation of the veil, lead white ((PbCO<sub>3</sub>)<sub>2</sub>·Pb(OH)<sub>2</sub>) for the flesh tone and vermilion (HgS) for the lips. The XRF map was collected with the micro-XRF portable spectrometer (<em>ELIO</em>) produced by BRUKER. The XRF system is equipped with a rhodium-target X-ray tube. The voltage range is between 10 and 50 kV, and the anode current range is between 5 and 200 μA. The X-ray fluorescence is revealed by a Peltier-cooled silicon drift detector (SDD) with CUBE technology, with an active area of 50 mm<sup>2</sup>. The typical energy resolution for Mn-Kα radiation is <140 eV. A motorized XY stage is mounted on a tripod for mapping analysis with a total travel of 100 mm x 100 mm. The collimator used for the analysis was of 0.5 mm.</p> <p>Please, cite as:</p> <p>R. Rocha de Oliveira, C. Malegori, G. Sciutto, P. Oliveri<br><strong>PoliBrush – A user-friendly software to aid multivariate image analysis dissemination</strong><br><em>Chemometrics and Intelligent Laboratory Systems</em>, 240 (2023) 104918<br><a href="https://doi.org/10.1016/j.chemolab.2023.104918">https://doi.org/10.1016/j.chemolab.2023.104918</a></p> <p> </p> <p>You may also be interested in:</p> <p> <strong>NIR-HSI Venus dataset</strong><br> <a href="https://www.doi.org/10.5281/zenodo.8143550">10.5281/zenodo.8143550</a></p> <p> <strong>PoliBrush</strong><br> <em>A freely distributed software for exploratory multivariate analysis in RGB and spectral imaging</em><br> <a title="PoliBrush" href="https://doi.org/10.5281/zenodo.8143341" target="_blank" rel="noopener">10.5281/zenodo.8143341</a></p> <p> </p>
MarTREC Publication forBio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The link considers the data file for the MarTREC Project: Bio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi.</p> <p>PI: Dr. Sadik Khan</p> <p>Funding Agency: This material is based upon work supported by the U.S. Department of Transportation under Grant Award Number DTRT13-G-UTC50. The work was conducted through the Maritime Transportation Research and Education Center at the University of Arkansas.</p>
Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging
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Data from: Brucite-inspired ocean alkalinity enhancement alters the biogeochemistry and composition of a phytoplankton community: A Santa Barbara channel case report
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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.