Articles tagged with Data Sets
The Polymathic AI team has released two massive datasets for training artificial intelligence models to find and exploit transferable knowledge between seemingly disparate fields. The datasets include data from dozens of sources, covering astrophysics, biology, acoustics, chemistry, fluid dynamics, and more.
The latest report in the State of Open Data series provides quantitative analysis on the growth of open data sharing globally. Key findings show a strong growth in papers linking to data from universities, and policies for open data sharing are now more consistent globally.
A novel framework for retrieving concise entailing legal articles has been proposed, achieving state-of-the-art results across two datasets. The Retrieve–Revise–Refine framework combines small and large language models to improve precision while limiting recall loss.
The University of Rhode Island-based report card gives failing grades to over half the world's countries, with a median score of 52. Democratic countries tend to have better human rights records, but exceptions exist. The US scored a 62.5, ranking 66th, despite being a wealthy democratic country.
The National Center for Supercomputing Applications (NCSA) has received the Readers' Choice Award: Best HPC Collaboration and Editors' Choice: Best Use of HPC in Physical Sciences. This is the 14th consecutive year NCSA has been honored with an HPCwire award.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
A panel of bioethicists and experts emphasize that human accountability is crucial for healthcare decisions made by AI. The importance of diverse data sets was also stressed to avoid biases in AI-enabled medical technologies. Experts stress the need for collective liability among developers, programmers, and data scientists.
A recent machine learning study suggests that the association between gut bacteria and disease may be overstated. Instead, changes in microbial load were found to be a key factor in the presence of disease-associated microbial species. This discovery challenges current understanding of the gut microbiome's role in disease etiology.
Researchers at Lehigh University are using advanced algorithms and cross-domain data to help cities predict human movement patterns, enabling better planning and preparedness for events and emergencies. The model will account for variations in data streams from different sources, such as cell towers, GPS, and financial transactions.
The University of Washington School of Medicine has released a flagship AI-ready dataset for a type 2 diabetes study, featuring a diverse range of participants and novel measures. The dataset includes environmental sensor data, survey responses, eye-imaging scans, and traditional biologic variables.
Researchers developed a multimodal dataset, TimelyTale, to gather passenger-specific sensor data for context-relevant explanations. The approach effectively identified the timing and frequency of passenger demands for explanations, enabling the creation of a machine-learning model to predict the best time for providing an explanation.
Researchers at the University of Liverpool developed AI-driven mobile robots that can perform exploratory chemistry research tasks faster and more efficiently than humans. The robots use AI logic to make decisions, processing analytical datasets in real-time to determine the next steps in chemical synthesis.
Researchers at Radboud University Medical Center found that an extra year of education does not protect against brain aging and has no effect on brain structure. Despite positive correlations between education and cognitive benefits, the study suggests caution in assigning causation when only correlation is observed.
A new study by the Complexity Science Hub finds that firms with higher levels of information consumption outperform their peers financially and are more innovative. The research reveals an 'economy of scale' in news consumption, where larger firms read a greater number of unique pieces of news.
A new genetic analysis method called Genomic Informational Theory (GIFT) has been developed to extract more precise data than previously used methods. GIFT is capable of analyzing large datasets and extracting novel information that was previously unavailable through genome-wide association studies (GWASs).
A novel collaborative framework integrates semi-supervised learning techniques to improve MRI segmentation accuracy, even with limited labeled data. The approach achieves high Dice scores and demonstrates its potential for practical clinical application.
MIT researchers developed a versatile technique that combines diverse data from various sources into a shared language for generative AI models. This approach outperformed traditional techniques by 20% in simulation and real-world experiments.
Researchers are combining large datasets to understand the impact of environmental factors on mental health and develop new ways to reduce the burden. A 4-step approach is being used to integrate complex data across many studies, resulting in a detailed perspective of individuals within their environment.
The COP16 policy brief recommends implementing FAIR data principles to enhance biodiversity monitoring. This approach enables standardization of indicators, streamlined reporting, and improved global tracking.
A new Multi-task Learning (MTL) model detects 85% of abusive posts originating from right-leaning individuals on social media platforms.
The two-year study aims to explore biases in AI systems and develop a 'human-in-the-loop' framework for quality data discovery. It will investigate how humans can be involved as labelers, prompters, and validators to improve data sets and user interfaces.
A study reveals that shrinking Japanese cities are often medium-sized or small, and that urban policies should be tailored to their specific needs. The research found correlations between population changes and social, economic, and urban-planning factors in cities of varying sizes.
A research team led by Maria Glymour will investigate four modifiable risk factors for ADRD: lifetime alcohol use, depression, vision and hearing impairments, and social isolation. The project aims to provide comprehensive evidence on how these risk factors contribute to ADRD risk.
Scientists analyzed millions of tweets to detect early warning signs of PTSD in COVID-19 survivors, achieving an accuracy rate of 83.29%. The study highlights the potential for machine learning techniques to identify individuals at risk of PTSD through social media data.
A research team developed a computational workflow for analyzing large data sets in metabolomics, speeding up the process to capture chemical profiles of coastal environments. The tool, accessible to researchers worldwide, highlights potential sources of pollution and enables statistical insights within minutes.
A Concordia-led team developed a framework that enables crowdsourced deep reinforcement learning as a service, using blockchain technology. This allows smaller organizations to access complex AI tasks previously out of reach, reducing costs and risk.
Researchers identified known genetic variants for Alzheimer’s disease as risk factors for all-cause dementia and vascular dementia. The study also found a substantial genetic overlap with vascular dementia and cerebral small-vessel disease.
A novel approach to overcome limitations of traditional methods, NeuPh uses local conditional neural fields to reconstruct high-resolution phase information from low-resolution measurements. It provides robust resolution enhancement and outperforms existing models in accuracy.
Researchers found that control of most genes doesn't deteriorate with age, but coordination between cellular processes becomes less effective. The study suggests a more complex approach to understanding aging is needed, analyzing all genes simultaneously and their protein interactions.
Researchers developed a tool to improve data transparency in large language models, enabling practitioners to find suitable datasets for their models. The tool, Data Provenance Explorer, automatically generates summaries of dataset creators, sources, licenses, and allowable uses.
A recent study by an international team has established a link between academic freedom and innovative output for the first time. The researchers found that countries with higher levels of academic freedom tend to have more patent applications and citations, indicating a positive correlation between academic freedom and innovation. Con...
A Mass General Brigham study highlights inconsistencies in generative AI that can affect patient safety if not addressed. The researchers found 'drift' (model performance changes over time) and 'nondeterminism' (inconsistent results between runs) in their tests, emphasizing the need for repeated testing and monitoring.
Researchers at University of Bath and Technical University of Darmstadt found that large language models like ChatGPT cannot learn independently or acquire new skills, making them controllable and predictable. The study concluded that LLMs remain inherently safe, but misuse is still possible.
A new study found a significant sex bias in pain management at emergency departments, with female patients consistently receiving fewer pain medication prescriptions compared to male patients. Female patients also spend more time in the emergency department and have their pain scores less frequently recorded.
A recent study published in Frontiers in Immunology highlights the crucial role of tissue-resident memory T cells in non-small cell lung cancer. The research found that these cells can significantly impact patient outcomes and guide personalized treatment strategies, particularly those involving immunotherapy.
Researchers studied mesoscale eddy observations to estimate theoretical predictability limits of eddy trajectories. Long-lived eddies have higher predictability limits, while short-lived ones are less predictable. The study also introduced a complexity index to elucidate OME track complexity.
A recent study at Rice University found that using synthetic data to train generative AI models can lead to negative consequences, including model collapse and reduced quality. As models become increasingly dependent on self-consuming loops, they may produce warped outputs lacking diversity or quality.
A new study by Newcastle University shows that citizen scientists can accurately identify slug species with proper training and support. The research found that participants improved their identification skills throughout the project, with accuracy rates ranging from 47-70%.
Researchers found the median peak age for Olympic track-and-field athletes to be 27 years old. After 27, the probability of a peak performance drops significantly, highlighting the importance of training regimens in optimizing results.
The A4 study, a large clinical trial of pre-symptomatic Alzheimer’s disease, has yielded key insights into the condition. Researchers have collected extensive data on brain scans, blood samples, genetic information and cognitive tests from over 7,500 participants.
A new study published in Aging explores the potential of three large DNA methylation datasets to identify biological age signals in dogs. The researchers found that biological age methylation clocks are affected by population stratification and require heavy parameterization to achieve effective predictions.
Researchers developed a deep learning-based model to estimate rainfall intensity from surveillance audio, achieving a root mean absolute error of 0.88 mm h-1 and a coefficient of correlation of 0.765. This approach offers a new method for high-resolution hydrological sensing, contributing to environmental resilience and urban sensing.
Research by University of East Anglia and University of Texas found that stricter data privacy laws significantly reduced breaches, but negatively affected firms' market value. Companies compliant with GDPR invested more in data protection and were less likely to experience data breaches.
Scientists have discovered that planted mangroves can store a significant amount of carbon, reaching levels comparable to those in intact stands after just 20 years. The study used logistic models compiled from over 700 planted mangrove stands worldwide and found that the trees' carbon stock reached 71-73% of that found in intact stands.
Researchers developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model uncovered 515 previously unannotated disease subtypes.
Researchers re-analyzed historical data from a classic displacement experiment and found that young starlings migrated independently, using their own direction. Adult starlings adjusted their migratory orientation to reach their normal wintering areas, while local conspecifics influenced the route of relocated young starlings.
The e-COL+ project aims to capture and reconstruct France's natural history collections in 3D, covering nearly 6% of the world's total natural specimens. The project will provide modern equipment, create a comprehensive dataset of 3D models, and build AI tools to improve model reconstruction.
A team of researchers developed EMOKINE software to measure the objective kinematic features of movements that express emotions. The software provides movement parameters from data sets at the touch of a button, allowing scientists to analyze emotional expression and intentions.
The new interactive atlas provides a library of case studies for adapting to drought in the US Southwest. The atlas offers geospatial solutions to water scarcity, helping communities implement effective adaptation strategies.
Research by Lancaster University found that people in higher social grades, including corporate world and education sectors, are adopting each other's speech patterns to be more inclusive. This 'resonance' has increased over the past 20 years, particularly among those with high social status.
Researchers developed FairDeDup, a cost-effective method to reduce social biases in AI systems by removing redundant data and incorporating controllable diversity dimensions. The approach enables accurate and fair AI training with fewer resources.
A new method, PURPLE, estimates how often underreported health conditions occur in different demographic groups, providing a more accurate picture of intimate partner violence. The algorithm shows that women from lower-income brackets are more likely to experience violence, consistent with previous literature.
The Kids First DRC has introduced an upgraded data portal to streamline big data search and analysis, improving collaborative pediatric research outcomes. The new portal integrates diverse datasets, including genomic information from the Children's Brain Tumor Network, to foster cross-disciplinary research.
The study uses the Dermatological Vision Dataset and compares vision transformers with traditional CNNs, achieving an impressive 97.8% accuracy on validation sets. Integrating ViTs into dermatology represents a promising step toward more accurate diagnostics.
A comprehensive, user-friendly repository has been created to help study Alzheimer's disease. The ssREAD database encompasses 277 integrated datasets from 67 scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells, and includes interactive visualizations for comprehensive analysis interpretations.
Researchers developed a reliable iris recognition method by applying statistical limits to the spatial domain zero crossing technique, reducing errors to 0.022%. The algorithm uses a neural network to recognize unique features of each person's iris, achieving over 99.78% accuracy.
Portland State University has secured a nearly $1 million grant from the National Science Foundation's Campus Cyberinfrastructure program to establish the Oregon Regional Computing Accelerator (Orca) cluster. The cluster will provide free-of-cost computing resources and cyberinfrastructure to colleges in rural, regional, and minority-s...
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
A new study from the University of Notre Dame shows that repeated trips to the grocery store can be a reliable indicator of creditworthiness. The research found that buying healthier groceries and consistent shopping habits are linked to on-time credit card payments.
A new dataset integrates global Health AI research, providing a structured resource for researchers, policymakers, and practitioners. The dataset includes 96,332 Health AI documents, covering publications, open research datasets, patents, grants, and clinical trials.