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997 results for “Awareness”
Experiment results for the paper "Uncertainty-Aware Ship Location Estimation using Multiple Cameras in Coastal Areas" to appear in MDM'2024
<p>After decompression, there are 16 folders which corresponding to the 16 multi-camera settings in the paper.</p> <p> </p> <p>Under each folder, there are two files: trajs.csv and trajsGuess.csv.</p> <p> </p> <p>1. trajs.csv contains the trajectories of ships that are located inside the monitored area of the mult-camera setting.</p> <p> The first four columns are MMSI (ship identity), timestamp, lon, and lat.</p> <p> The following columns are the corresponding pixel of the coordinate (lon, lat) in each camera, where (-1,-1) means (lon, lat) is outside the monitored area by a camera.</p> <p> A pixel is a pair of integers. </p> <p> xPos1 and yPos1 are for the 1st camera, and xPos2 and yPos2 are for the 2nd camera, and so on so forth.</p> <p> </p> <p>2. trajsGuess.csv contains the estimated ship locations by using the proposed approach in the paper.</p> <p> There are 6 columns.</p> <p> The 1st column is timestamp.</p> <p> The 2nd column is used to distinguish between the different pixel polygon intersections.</p> <p> The 3rd column and the 4th column can be either a pixel coordinate or a spatial point coordinate in lon/lat.</p> <p> The 5th column is either the cameraID of a pixel, or the order of a boundary point for a spatial polygon. The cameraID starts from 1.</p> <p> The 6th column is the type of the record, which can be</p> <p> pixel,</p> <p> or intersection1 (a polygon),</p> <p> or center1 (center of intersection1),</p> <p> or intersection2 (a polygon),</p> <p> or center2 (center of intersection2).</p> <p> Note that intersection2 and center2 appear rarely in the 6th column.</p>
AD/AE of SC24 Submission: A sparsity-aware distributed-memory algorithm for sparse-sparse matrix multiplication
<p>AD/AE for SC24 submission: A sparsity-aware distributed-memory algorithm for sparse-sparse matrix multiplication</p> <p> </p>
COLLECE awareness assessment dataset
<p>COLLECE awareness assessment experiment data</p>
Symmetry-Aware Autoencoders
<p>This repository contains data for reconstructing the PCA, s-PCA, nlPCA and s-nlPCA results from the paper Symmetry-Aware Autoencoders: s-PCA and s-nlPCA. The codes needed for this can be found at https://github.com/simonkneer/Symmetry-Aware-Autoencoding .</p>
Processed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper
<p>Processed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper. Each directory in the archive consists of:</p> <p>- metadata.json. Metadata about a test run - tested fuzzer name, run duration, etc</p> <p>- fuzzer.json - Structured fuzzer output</p> <p>- deduplicated_cases.json - Deduplicated reported failures, when fuzzers provide it</p> <p>- sentry.json - Cleaned Sentry events for this run</p> <p>- target.json - Parsed stdout for Gitlab & Disease.sh targets that were tested without Sentry integration</p>
Datasets for "Discourse-Aware Unsupervised Summarization for Long Scientific Documents"
<p>Datasets used in <a href="https://aclanthology.org/2021.eacl-main.93.pdf">Discourse-Aware Unsupervised Summarization for Long Scientific Documents</a></p>
Distribution. If still extant, the species would likely occur in Vietnam, and possibly in neighboring Cambodia and Laos. The true distribution of the species is unknown, because there has been very little awareness of the species. Surveys have not sought it, because many have assumed that only one species, Tragulus kanchil, occurs in most of Indochina. in Tragulidae
Distribution. If still extant, the species would likely occur in Vietnam, and possibly in neighboring Cambodia and Laos. The true distribution of the species is unknown, because there has been very little awareness of the species. Surveys have not sought it, because many have assumed that only one species, Tragulus kanchil, occurs in most of Indochina.
Silver Linings in Covid-19: increased appreciation of relationships and self-awareness are associated with wellbeing at different times in the pandemic
<p>Dataset</p>
Data for injection and recovery test performed in the paper "Improved Sensitivity for Space Domain Awareness Observations with the Murchison Widefield Array"
<p>This repo contains the script and the pool of noise images used to perform the injection and recovery test in the paper titled "Improved Sensitivity for Space Domain Awareness Observations with the Murchison Widefield Array"</p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Experimental data for "sampling Configurations From Software Product Lines Via Probability-aware Diversification and SAT Solving"
<p>Experimental data for "sampling Configurations From Software Product Lines Via Probability-aware Diversification and SAT Solving" submitted to ASE. </p> <p>1. rSATJ4, PaD+rSATJ4, ProbSAT and PaD+ProbSAT are data for RQs1-2;</p> <p>2. NSbS and PaD+NSbS are data for RQ3;</p> <p>3. SATVaEA, PaD+SATVaEA, ProbSATVaEA, PaD+ProbSATVaEA are data for RQ4.</p> <p> </p> <p> </p>
Supplementary material 2 from: Cerri J, Lioy S, Porporato M, Bertolino S (2022) Combining surveys and on-line searching volumes to analyze public awareness about invasive alien species: a case study with the invasive Asian yellow-legged hornet (Vespa velutina) in Italy. NeoBiota 73: 177-192. https://doi.org/10.3897/neobiota.73.80359
Distribution of scores to the questions on the perceived impacts of V. velutina and its severity in relation to various threats for beekeeping
Data from: Anaesthetic interventions for prevention of awareness during surgery
BACKGROUND: General anaesthesia is usually associated with unconsciousness. 'Awareness' is when patients have postoperative recall of events or experiences during surgery. 'Wakefulness' is when patients become conscious during surgery, but have no postoperative recollection of the period of consciousness. OBJECTIVES: To evaluate the efficacy of two types of anaesthetic interventions in reducing clinically significant awareness: - anaesthetic drug regimens; and - intraoperative anaesthetic depth monitors. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL, ISSUE 4 2016); PubMed from 1950 to April 2016; MEDLINE from 1950 to April 2016; and Embase from 1980 to April 2016. We contacted experts to identify additional studies. We performed a handsearch of the citations in the review. We did not search trial registries. SELECTION CRITERIA: We included randomized controlled trials (RCTs) of either anaesthetic regimens or anaesthetic depth monitors. We excluded volunteer studies, studies of patients prior to skin incision, intensive care unit studies, and studies that only randomized different word presentations for memory tests (not anaesthetic interventions). Anaesthetic drug regimens included studies of induction or maintenance, or both. Anaesthetic depth monitors included the Bispectral Index monitor, M-Entropy, Narcotrend monitor, cerebral function monitor, cerebral state monitor, patient state index, and lower oesophageal contractility monitor. The use of anaesthetic depth monitors allows the titration of anaesthetic drugs to maintain unconsciousness. DATA COLLECTION AND ANALYSIS: At least two authors independently scanned abstracts, extracted data from the studies, and evaluated studies for risk of bias. We made attempts to contact all authors for additional clarification. We performed meta-analysis statistics in packages of the R language. MAIN RESULTS: We included 160 studies with 54,109 enrolled participants; 53,713 participants started the studies and 50,034 completed the studies or data analysis (or both). We could not use 115 RCTs in meta-analytic comparisons because they had zero awareness events. We did not merge 27 of the remaining 45 studies because they had excessive clinical and methodological heterogeneity. We pooled the remaining 18 eligible RCTs in meta-analysis. There are 10 studies awaiting classification which we will process when we update the review. The meta-analyses included 18 trials with 36,034 participants. In the analysis of anaesthetic depth monitoring (either Bispectral Index or M-entropy) versus standard clinical and electronic monitoring, there were nine trials with 34,744 participants. The overall event rate was 0.5%. The effect favoured neither anaesthetic depth monitoring nor standard clinical and electronic monitoring, with little precision in the odds ratio (OR) estimate (OR 0.98, 95% confidence interval (CI) 0.59 to 1.62). In a five-study subset of Bispectral Index monitoring versus standard clinical and electronic monitoring, with 34,181 participants, 503 participants gave awareness reports to a blinded, expert panel who adjudicated or judged the outcome for each patient after reviewing the questionnaires: no awareness, possible awareness, or definite awareness. Experts judged 351 patient awareness reports to have no awareness, 87 to have possible awareness, and 65 to have definite awareness. The effect size favoured neither Bispectral Index monitoring nor standard clinical and electronic monitoring, with little precision in the OR estimate for the combination of definite and possible awareness (OR 0.96, 95% CI 0.35 to 2.65). The effect size favoured Bispectral Index monitoring for definite awareness, but with little precision in the OR estimate (OR 0.60, 95% CI 0.13 to 2.75). We performed three smaller meta-analyses of anaesthetic drugs. There were nine studies with 1290 participants. Wakefulness was reduced by ketamine and etomidate compared to thiopental. Wakefulness was more frequent than awareness. Benzodiazepines reduces awareness compared to thiopental, ketamine, and placebo., Also, higher doses of inhaled anaesthetics versus lower doses reduced the risk of awareness. We graded the quality of the evidence as low or very low in the 'Summary of findings' tables for the five comparisons. Most of the secondary outcomes in this review were not reported in the included RCTs. AUTHORS' CONCLUSIONS: Anaesthetic depth monitors may have similar effects to standard clinical and electrical monitoring on the risk of awareness during surgery. In older studies comparing anaesthetics in a smaller portion of the patient sample, wakefulness occurred more frequently than awareness. Use of etomidate and ketamine lowered the risk of wakefulness compared to thiopental. Benzodiazepines compared to thiopental and ketamine, or higher doses of inhaled anaesthetics versus lower doses, reduced the risk of awareness. PLAIN LANGUAGE SUMMARY: Methods to prevent people waking during surgery and remembering surgical events KEY QUESTION: We reviewed the evidence about the use of devices to adjust the amount of drugs given during anaesthesia to prevent premature waking up. We also reviewed the evidence about the choice of drugs used during anaesthesia to prevent premature waking up. BACKGROUND: Anaesthesia is the use of drugs to render a patient unconscious for painful procedures and surgery. Being anaesthetized is not the same as being asleep. Someone sleeping may be easily awakened. Someone anaesthetized should only be allowed to awake when the surgery or procedure is completed. A very small percentage of patients may wake up during anaesthesia and surgery; this is called wakefulness. Patients usually do not remember being awake after emerging from anaesthesia. However, an even smaller percentage of patients do remember or recall events from surgery afterwards. This memory is called an awareness event. If that memory is distressing, it can impair the individual's quality of life. New devices known as anaesthetic depth monitors are being used to monitor the patient's brainwave response to anaesthetic drugs. Anaesthetic depth monitors have been compared to the usual clinical observations (e.g. fast heart rate, tearing, movement, etc.) during surgery to adjust the amount of drugs given and reduce the risk of wakefulness and awareness. Anaesthetic drugs have many different effects on brain function. Some drugs are used alone as the sole anaesthetic. Other drugs have insufficient effect to be used as a sole anaesthetic, but are used in combination with more powerful drugs. Drugs may have different risks of the patient waking up prematurely. SEARCH DATE: The evidence is current to April 2016. STUDY CHARACTERISTICS: We found 160 randomized controlled trials with 54,109 participants. Eighteen studies with 36,034 participants contributed evidence about devices and drugs to prevent premature waking up during surgery. Nine studies compared anaesthetic depth monitoring versus other methods to adjust drugs. Nine studies compared different drugs. There are 10 studies awaiting classification, which we will process when we update the review. KEY RESULTS: In the largest studies of anaesthetic depth monitors (five studies with 31,181 participants) there were 152 participants with possible or definite awareness (recall of surgery events after surgery). The use of anaesthetic depth monitors to adjust drugs during anaesthesia may have similar effects on the risk of awareness when compared with standard clinical and electrical monitoring. Wakefulness is reduced by ketamine and etomidate compared to thiopental. Benzodiazepines reduces awareness compared to thiopental, ketamine, and placebo. Also higher doses of inhaled anaesthetics versus lower doses reduced the risk of awareness. QUALITY OF EVIDENCE: The quality of the evidence was low or very low because the studies the results were not similar across studies, and there were not enough data.
Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'
<p>This record contains the 7768 lung masks <strong>manual annotations, implementation code, and pre-trained models</strong> related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'</p> <p>Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding. </p> <p>For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19</p> <p> </p>
Prototype Implementation: Uncertainty-aware Confidentiality Analysis Using Architectural Variations
<p>Dataset for the bachelor thesis "Uncertainty-aware Confidentiality Analysis Using Architectural Variations".</p> <p>The ZIP file contains the Eclipse project of the prototype with installation instructions and the models used, modeled in the Palladio Component Model.</p>
Supporting data for "KOCL: Power Self-awareness for Arbitrary FPGA-SoC-accelerated OpenCL Applications"
<p>Supporting data for "KOCL: Power Self-awareness for Arbitrary FPGA-SoC-accelerated OpenCL Applications"</p>
AMO-aware Aggregates in Answer Set Programming
<p>Dataset for the paper "AMO-aware Aggregates in Answer Set Programming" accepted at IJCAI 2024.</p>
Dataset for the paper "CoMAF: Context and Mobility-Aware Forwarding Model For V-NDN"
Open the record for dataset details and reuse information.
Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning
<p>We propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and subspace module to accurately distinguish different spatial domains by capturing tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological images prior, preserving information crucial for the clustering task. Experimental results on 8 ST datasets from 5 different platforms show that GRAS4T outperforms five state-of-the-art competing methods in spatial domain identification. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.</p>
Snakemake report for manuscript "Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing"
<p>For viewing the report, unzip the file and open index.html in your browser.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.