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284 results for “reasoner”
Brain Training to Enhance Frontal Lobe Reasoning
ClinicalTrials.gov study NCT01552473. IPD Sharing: NO. Countries: 1. Publications: 1.
Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife
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Data set from: Inferential reasoning in wild bumblebees
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Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
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Data from: Too hot to reason? Experimental heatwaves affect cognitive traits in male guppies
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Signage interventions for stair climbing at work: more than 700,000 reasons for caution
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Figure 5 in Goliath frogs build nests for spawning - the reason for their gigantism?
Figure 5. Conraua goliath at nest sites; (a) adult sitting at the edge of nest 16 (compare text and Supplementary Material); (b) eggs attached to cleaned nest ground; (c) tadpole in advanced developmental stage, the white dots above its head and at the left margin, are new eggs.
Figure 2 in Goliath frogs build nests for spawning - the reason for their gigantism?
Figure 2. Study site at Mpoula River near Penja in West Cameroon (inset figure), and Goliath Frog nest sites (red dots) along the river; the river discharges southwards.
Experimental Repository for "Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning"
<p>Code and Data supplement to "Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning"</p>
Automatic Method of Flow Direction Reasoning for Bridging Tributaries
<p>Please see the detailed description of the data in the Read me.txt file. </p>
Data from: Screening primary-care patients forgoing health care for economic reasons
Background: Growing social inequities have made it important for general practitioners to verify if patients can afford treatment and procedures. Incorporating social conditions into clinical decision-making allows general practitioners to address mismatches between patients' health-care needs and financial resources. Objectives: Identify a screening question to, indirectly, rule out patients' social risk of forgoing health care for economic reasons, and estimate prevalence of forgoing health care and the influence of physicians' attitudes toward deprivation. Design: Multicenter cross-sectional survey. Participants: Forty-seven general practitioners working in the French–speaking part of Switzerland enrolled a random sample of patients attending their private practices. Main Measures: Patients who had forgone health care were defined as those reporting a household member (including themselves) having forgone treatment for economic reasons during the previous 12 months, through a self-administered questionnaire. Patients were also asked about education and income levels, self-perceived social position, and deprivation levels. Key Results: Overall, 2,026 patients were included in the analysis; 10.7% (CI95% 9.4-12.1) reported a member of their household to have forgone health care during the 12 previous months. The question "Did you have difficulties paying your household bills during the last 12 months" performed better in identifying patients at risk of forgoing health care than a combination of four objective measures of socio-economic status (gender, age, education level, and income) (R2=0.184 vs. 0.083). This question effectively ruled out that patients had forgone health care, with a negative predictive value of 96%. Furthermore, for physicians who felt powerless in the face of deprivation, we observed an increase in the odds of patients forgoing health care of 1.5 times. Conclusion: General practitioners should systematically evaluate the socio-economic status of their patients. Asking patients whether they experience any difficulties in paying their bills is an effective means of identifying patients who might forgo health care.
ORE 2015 Reasoner Competition Corpus
<p>This is the OWL Reasoner Evaluation Workshop Competition Dataset. This dataset contains the competition corpus of the ORE competition 2015. The files are intended purely for use in reasoner benchmarking activities. The open access extends only to this purpose. All individual ontologies in the dataset have their own license restrictions which still apply when a use other than reasoner benchmarking is intended. If you are the copyright holder of one of the ontologies in the set and want your ontology removed, please contact us here (http://owl.cs.manchester.ac.uk/contact/).</p> <p>Details about its creation can be found in the following link.</p> <p>http://mowlrepo.cs.manchester.ac.uk/datasets/ore-2015-reasoner-competition-dataset/</p>
ORE 2015 Reasoning Systems
<p>This dataset contains the executable versions of all reasoning systems that participated in the ORE reasoner competition in 2015. The files are intended purely for use in reasoner benchmarking activities. The open access extends only to this purpose. All individual reasoners in the dataset have their own license restrictions which still apply when a use other than reasoner benchmarking is intended. If you are a reasoner developer and want your reasoner removed, please contact us here (http://owl.cs.manchester.ac.uk/contact/).</p> <p>More information can be found at </p> <p>http://owl.cs.manchester.ac.uk/publications/supporting-material/ore-2015-report/</p>
Citation reasons and their impact on knowledge
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Common Reasons for Conflicts and Common Conflict Resolution Techniques Used: Findings of 8 Years of Secondary and Primary Research Studies
<p>The paper, a summary of 8 years of research (2015-2023) on conflict management, presents the common reasons for conflicts and common conflict resolution techniques used by employees at all levels in an organization based on secondary research and also an empirical investigation. The Macro Objective of the Research is to Generalize the Reasons for Conflicts and Conflict Resolution Techniques Used at Employee, Manager and Top Management Level using Author’s Previous 3 Secondary Research Studies and Current Primary Research Study by finding the Top-3 Common Reasons for Conflicts and Top-3 Common Conflict Resolution Techniques Used at all levels of Employees. The 3 different secondary research studies are conducted (2015-2023) at Employee level, Manager Level, Top Management Level to identify Top-5 Reasons for Conflicts and Top-5 Conflict Resolution Techniques used by Employees, Managers and Top Management Respectively. Top-5 items are identified based on reference count (number of citations) in literature in each of these research works. Later on, in 4th Paper (2023) Common Top-3 Reasons for Conflicts, Common Top-3 Conflict Resolution Techniques are Identified based on the 3 published secondary research works. This current work is primary study conducted on 68 respondents; and Primary Research Findings are Compared with Secondary Research findings; further top-3 common reasons for conflicts and top-3 conflict resolution techniques used are identified at all levels of employees in an organization. Overall, the paper presents the Top-3 common reasons for conflicts among employees, project managers, and top management; and top-3 common conflict resolution techniques used by employees, project managers and top management. Based on Secondary & Primary Research Studies, the Top-3 common reasons for conflicts identified are Resources, Value Differences, Personality Differences; and Top-3 conflict resolution techniques used are Avoidance, Compromise, Confrontation/Problem Solving at all levels of employees. </p>
PWB Muscle SR - Supplementary Table S2 (Reasons for Exclusion)
<p>Supplementary material for the manuscript entitled ' Skeletal Muscle Deconditioning During Partial Weight-Bearing in Rodents – A Systematic Review and Meta-Analysis'</p>
Subspecies and Distribution. C.s.simumBurchell,1817—SEAngola,NENamibia,Botswana,Zimbabwe,SMozambique,Swaziland,SouthAfrica,andperhapsformerlyinextremeSWZambia. C. s. cottoni Lydekker, 1908 — historically present in S Chad, Central African Republic, S Sudan, NE DR Congo, and Uganda; last individuals occurred in DR Congo, but possibly extinct in the wild. The "Southern White Rhino," race simum, has been introduced into Kenya, Uganda, and Zambia. Especially vague range map for conservation reasons. in Rhinocerotidae
Subspecies and Distribution. C.s.simumBurchell,1817—SEAngola,NENamibia,Botswana,Zimbabwe,SMozambique,Swaziland,SouthAfrica,andperhapsformerlyinextremeSWZambia. C. s. cottoni Lydekker, 1908 — historically present in S Chad, Central African Republic, S Sudan, NE DR Congo, and Uganda; last individuals occurred in DR Congo, but possibly extinct in the wild. The "Southern White Rhino," race simum, has been introduced into Kenya, Uganda, and Zambia. Especially vague range map for conservation reasons.
Subspecies and Distribution. D.b.bicornisLinnaeus,1758—Namibia,W&SESouthAfrica. D.b.longipesZukowsky,1949—historicallyinNigeria,Cameroon,Chad,Sudan,andCentralAfricanRepublic(couldbeextinct). D. b. michaeli Zukowsky, 1965 — Kenya, Rwanda (could be extinct by now), and N Tanzania; historically also in Sudan, Ethiopia, and Uganda. D. b. minor Drummond, 1876 — C Tanzania, Zambia, Zimbabwe, Malawi, Botswana, and Mozambique (possibly extinct), to N and CE South Africa. Especially vague range map for conservation reasons. in Rhinocerotidae
Subspecies and Distribution. D.b.bicornisLinnaeus,1758—Namibia,W&SESouthAfrica. D.b.longipesZukowsky,1949—historicallyinNigeria,Cameroon,Chad,Sudan,andCentralAfricanRepublic(couldbeextinct). D. b. michaeli Zukowsky, 1965 — Kenya, Rwanda (could be extinct by now), and N Tanzania; historically also in Sudan, Ethiopia, and Uganda. D. b. minor Drummond, 1876 — C Tanzania, Zambia, Zimbabwe, Malawi, Botswana, and Mozambique (possibly extinct), to N and CE South Africa. Especially vague range map for conservation reasons.
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
<p>A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): 1) few-shot concept learning, and 2) context-dependent reasoning. We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few-shot instances, from partial to no overlaps. Bongard-HOI presents a substantial challenge to today's visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy. With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning.</p>
AURORA: Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
<p>Dataset for the publication "Learning Action and Reasoning-Centric Image Editing from Videos and Simulations".</p> <p>Details to fully use the dataset: https://github.com/McGill-NLP/AURORA</p> <p> </p> <p>Abstract:</p> <p>An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing actions or movement, which require many forms of reasoning. Current <em>general</em> instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the <strong>A</strong>U<strong>RO</strong>R<strong>A</strong> Dataset (<strong>A</strong>ction-<strong>R</strong>easoning-<strong>O</strong>bject-<strong>A</strong>ttribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., <em>truly minimal</em> changes between source and target images. To demonstrate the value of our dataset, we evaluate an <strong>A</strong>U<strong>RO</strong>R<strong>A</strong>-finetuned model on a new expert-curated benchmark (<strong>A</strong>U<strong>RO</strong>R<strong>A-Bench</strong>) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.</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.