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174 results for “symbol”
Tshikombeni knowledge of national biodiversity symbols in South Africa
<p>Most countries have declared one or more animal or plant species to be amongst their national symbols, termed here national biodiversity symbols. National biodiversity symbols are the species formally or informally recognised by societies and countries as having meaning to one or more of national identity, values and unity.</p> <p>It has been proposed previously that national biodiversity symbols can be used as flagship species to advance habitat conservation in their respective countries. However, this assumes that the symbols are well known and revered by the citizens of the country concerned. We examined this assumption via direct interviews with 382 urban residents in four towns in South Africa, which is a mega-biodiversity country with five national biodiversity symbols (a national tree, flower, animal, bird and fish).</p> <p>We found that less than 3 % of the urban respondents could name all five species, ranging from 6 % for the national tree to 40 % for both the national flower and national animal. Knowledge of other national symbols (flag and anthem) were equally low. The number of national biodiversity symbols known increased with income and education level of respondents. Despite limited knowledge of which species were the national biodiversity symbols, almost two-thirds of respondents felt that having national biodiversity symbols was important for promoting national identity.</p> <p>These findings show that from a heritage perspective a great deal more awareness needs to be developed in South Africa around the national biodiversity symbols. From a conservation perspective, it indicates that the national biodiversity symbols are unlikely, at this stage at least, to be useful as flagship species for habitat conservation programmes.</p>
Artifacts for NDSS 2024 paper "A Unified Symbolic Analysis of WireGuard"
<p>This project gathers symbolic analyses of WireGuard protocol, with the help of Sapic+, Tamarin and ProVerif proof assistants. The following properties are verified: agreement, secrecy and anonymity.</p>
Tillytarmont 5 Pictish Symbol Stone
Tillytarmont 5 Pictish Symbol Stone, in Marischal Museum, University of Aberdeen https://canmore.org.uk/site/17838/tillytarmont-no5 Source: Objaverse 1.0 / Sketchfab
PLDI2024 Artifact: A HAT Trick: Automatically Verifying Representation Invariants Using Symbolic Finite Automata
<p>This artifact contains:</p> <ol> <li>README.md : the artifact guide.</li> <li>marple-original-submission.pdf: the original submitted paper.</li> <li>marple:pldi-2024.tar.gz: the docker image (optional, we recommend to pull from the docker hub, see README.md).</li> <li>Dockerfile: the docker file that can reproduce the docker image (optional, we recommend to pull from the docker hub, see README.md).</li> </ol>
Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music Dataset
<p>This dataset includes emotion labels for the publicly available MIDI dataset, namely Lakh MIDI Dataset and Reddit MIDI dataset. The values represent the probability of containing a particular emotion. For a single song, more than one emotion can be present, hence the values don't add up to 1.</p>
Exhaustive Symbolic Regression Function Sets
<p>ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions. This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.</p> <p>Here we provide the functions generated, the unique equations, and the mappings between all equations and unique ones using different sets of basis functions. These are:</p> <ul> <li>"core_maths": <span>\(\{x, a, {\rm inv}, +, -, \times, \div, {\rm pow} \}\)</span></li> <li>"ext_maths": <span>\(\{x, a, {\rm inv}, \sqrt{\cdot}, {\rm square}, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> <li><span>"base_e_maths": \(\{x, a, {\rm inv}, \exp, \log, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> </ul> <p>where <span>\(x\)</span> is the input variable and <span>\(a\)</span> denotes a constant.</p> <p>One can fit these functions to a data set of interest by using the <a href="https://esr.readthedocs.io">ESR package</a>.</p>
Figures of " Experimentation preceding innovation in a MIS5 Pre-Still Bay layer from Diepkloof Rock Shelter (South Africa): emerging technologies and symbols"
<p>Figures of " <strong>Experimentation preceding innovation in a MIS5 Pre-Still Bay layer from Diepkloof Rock Shelter (South Africa): emerging technologies and symbols</strong> "</p>
On-The-Fly Solving for Symbolic Parity Games using the mCRL2 toolset
<p>This artifact contains a set of mCRL2 specifications and formulas that are used to compare various on-the-fly solving techniques for symbolic parity games. The techniques are described in the paper "On-The-Fly Solving for Symbolic Parity Games" by Maurice Laveaux, Wieger Wesselink and Tim A.C. Willemse.</p>
Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science
<p>Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science</p>
seal, or ancient symbol of the Nazarene church
In the year 1990, the editor-in-chief of the Israeli magazine, Israel Update, Ludwig Schneider befriended an elderly Greek Orthodox monk, Tech Oteeoos, who was living a hermit life in Old Jerusalem. It was here that he showed the editor a secret cache of artifacts excavated from Mount Zion. near the site of the Upper Room or Nazarene Synagogue in the years before the Six Day War of 1967. Here he saw a very unique symbol etched into these artifacts that mixed a Jewish menorah on top and the Christian symbol of Ichthus the fish at the bottom. The symbiotic harmony of these two symbols gave them a third symbol: the Star of David in the middle of the two. the lines of the model are a little misaligned simulating to be an artifact made in ancient times Source: Objaverse 1.0 / Sketchfab
Golden international currency symbols
The symbols of currencies ( British Pound, US Dollar, Japanese Yen and the Euro ) made of gold.Gold texture is included. Source: Objaverse 1.0 / Sketchfab
Identifying conceptual neural responses to symbolic numerals
<p>The goal of measuring conceptual processing in numerical cognition is distanced by the possibility that neural responses to symbolic numerals are influenced by physical stimulus confounds. Here, we targeted conceptual responses to parity (even <em>vs. </em>odd), using electroencephalographic (EEG) frequency-tagging with a symmetry/asymmetry design. Arabic numerals (2–9) were presented at 7.5 Hz in 50-s sequences; odd and even numbers were alternated to target differential, "asymmetry" responses to parity at 3.75 Hz (7.5 Hz/2). Parity responses were probed with four different stimulus sets, increasing in intra-numeral stimulus variability, and with two control conditions comprised of non-conceptual numeral alternations. Significant asymmetry responses were found over the occipitotemporal cortex to all conditions, even for the arbitrary controls. The large physical-differences control condition elicited the largest response in the stimulus set with the lowest level of variability (1 font). Only in the stimulus set with the highest level of variability (20 drawn, colored exemplars/numeral) did the response to parity surpass both control conditions. These findings show that physical differences across small sets of Arabic numerals can strongly influence, and even account for, automatic brain responses. However, carefully designed control conditions and highly variable stimulus sets may be used towards identifying truly conceptual neural responses.</p>
Promoting Indonesian Batik as a Symbol of National Identity: A Bibliometric Approach
Open the record for dataset details and reuse information.
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
Supplementary Data for Paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols"
<p>This is the result data of the user study described in the paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols".</p> <p>Author information and further metadata will be added after anonymous peer review.</p> <p>27 participants, 4 displays, 6 tasks. Note that the data contains the station ID (A-D), which maps to Displays 1-4 as described in the paper: A - D2; B - D4; C - D1; D - D3;</p> <p>File description:</p> <ul> <li>all_aggregated_users.csv: Thresholds for each task and station, for each participant. One row per participant, with fields for each station/task combination (27 rows).</li> <li>all_aggregated_thresholds.csv: Thresholds for each participant, task and station. One row per threshold value, thresholds for task #2 for participants #1-3 have been discarded, due to an error in the experiment configuration (see paper). (27 x 4 x 6 - 3 x 4 = 636 rows)</li> </ul>
Data and Code: Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development
<h1><strong>Note</strong></h1> <p>Here we provide preprocessed data and analysis code used in "Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual preprocessed volumes, normalized into the MNI template, and averaged across five TRs for each block (see paper for details about the preprocessing pipeline).</p> <p>The analysis code requires Python version 3.8.8, Nilearn version 0.8.1, and Scikit-learn version 0.24.1.</p> <p>If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp. </p> <p> </p> <h1><strong>Usage</strong></h1> <pre>import PredysDecoding5ans_SearchLight_LOOCV as pdsl5 import PredysDecoding8ans_SearchLight_LOOCV as pdsl8 import PredysDecoding5to8_SearchLight as pdsl58 import PredysDecoding8to5_SearchLight as pdsl85 </pre> <h3>Within-format decoding for 5-year-olds (Figures 2A, B):</h3> <pre>pdsl5.IntraModalDec(TaskName='Dots') pdsl5.SaveNifti_PermTest(TaskName='Dots') pdsl5.IntraModalDec(TaskName='Digits') pdsl5.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding for 8-year-olds (Figure 2C, D):</h3> <pre>pdsl8.IntraModalDec(TaskName='Dots') pdsl8.SaveNifti_PermTest(TaskName='Dots') pdsl8.IntraModalDec(TaskName='Digits') pdsl8.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding, paired tests between 5- and 8-year-olds (Figures 3A, B):</h3> <pre>pdsl5.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl5.SaveNifti_Paired_PermTest(TaskName='Digits') pdsl8.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl8.SaveNifti_Paired_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding across 5- and 8-year-olds (Figures 3C, D):</h3> <pre>pdsl85.IntraModalDec(TaskName='Dots') pdsl85.SaveNifti_PermTest(TaskName='Dots') pdsl85.IntraModalDec(TaskName='Digits') pdsl85.SaveNifti_PermTest(TaskName='Digits') pdsl58.IntraModalDec(TaskName='Dots') pdsl58.SaveNifti_PermTest(TaskName='Dots') pdsl58.IntraModalDec(TaskName='Digits') pdsl58.SaveNifti_PermTest(TaskName='Digits') pdsl58.SaveNifti_PermTest_Conj(TaskName='Dots') pdsl58.SaveNifti_PermTest_Conj(TaskName='Digits')</pre> <h3>Between-format decoding for 5-year-olds (Figures 4A, 5B):</h3> <pre>pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl5.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl5.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Letters') pdsl5.SaveNifti_PermTest(TaskName='Dots2Letters') pdsl5.CrossModalDec(TaskName1='Letters', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Letters2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Letters', TaskName2='Letters2Dots')</pre> <h3>Between-format decoding for 8-year-olds (Figure 4A):</h3> <pre>pdsl8.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl8.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl8.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl8.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl8.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <h3>Between-format decoding, paired tests between 5- and 8-year-olds (Figure 4B)</h3> <pre>pdsl5.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl8.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <p> </p>
Artifacts for the IEEE Internet of Things Journal Publication: Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT
<p>Artifacts for the evaluation of the publication <em>Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT </em>which will be published in the IEEE Internet of Things journal. More information is available in the provided README.md file.</p>
A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing
<p>This is a dataset and pre-trained model for the official implementation of <a href="https://github.com/agwaBom/AsmDepictor"><strong>AsmDepictor</strong></a>, "A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing", In the 18th ACM Asia Conference on Computer and Communications Security <a href="https://asiaccs2023.org/">AsiaCCS '2023</a></p> <p> </p>
Dataset of A User-driven Hybrid Neuro-symbolic Approach for Knowledge Graph Creation from Relational Data
<p>This dataset contains the following:</p> <p>1. achieved percentage values of the generated RML rules using LXS and manually</p> <p>2. basic information about the example used and with which creation type users started</p> <p>3. all answers of users to the User Experience Questionnaire</p> <p>4. Answers to the structured part of the user interview</p>
The Wolf Head Castle Symbol
The Wolf Head carved on a wood door in the castle of Murol, France Source: Objaverse 1.0 / Sketchfab
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.