Articles tagged with Algorithms
Critically ill children on ventilator support can experience patient-ventilator asynchrony (PVA), which worsens outcomes. Researchers are using machine learning to develop a common set of definitions and measurements for PVA in pediatric patients, aiming to minimize risks and improve clinical outcomes.
Researchers found that social media algorithms prioritize 'Prestigious, Ingroup, Moral, and Emotional' (PRIME) information, which can lead to extreme political content being amplified. To address this, the study proposes increasing user awareness of algorithmic biases and introducing more diverse content in feeds.
A Cornell University study found that encouraging fact-checking on Reddit led to a drop in story rank by an average of minus-25 spots, causing stories to be missed by readers. The research suggests that collective efforts to improve information environments can influence algorithmic recommendations.
Researchers developed a computational technique to quickly design and evaluate cellular metamaterial structures with unique properties. The new interface enables users to explore the entire space of potential shapes, allowing for faster development of complex materials.
A team of researchers has successfully tested a new asteroid detection algorithm, HelioLinc3D, which identified its first 'potentially hazardous' asteroid, 2022 SF289. The discovery confirms the algorithm's effectiveness in finding near-Earth asteroids with fewer and more dispersed observations than traditional methods.
Researchers develop new algorithm to effectively investigate long-range interacting systems, reducing runtime from quadratic to linear with system size. The new method opens up new questions and applications in nonequilibrium processes, including phase separation and structure formation in cosmology and solid state physics.
Researchers have developed a computational tool to compare large datasets and predict immune responses to disease, potentially leading to better vaccines. The new algorithm, designed by La Jolla Institute for Immunology scientists, uses machine learning to identify underlying patterns in immune system data.
The Multi-frame Moving Object Detection System enhances remote sensing applications by detecting objects as small as one pixel in low-visibility conditions. It improves signal-to-noise ratio and detects fast- and slow-moving objects with high accuracy.
A new geometric deep learning model called GFCN has been developed to detect stroke lesions in brain imaging scans. The model leverages rich geometric information to segment brain tissue and achieves higher segmentation performance than other neural network architectures.
Researchers developed a convolutional neural network to identify structures in cryo-X-Ray-microscopy data, achieving high accuracy within minutes. The AI-based analysis method enables faster evaluation of 3D X-ray data sets and has potential applications in studying cell responses to environmental influences.
A study investigates how product manufacturers can mitigate key core technology loss risks in global competition by implementing independent research and development (IR&D). The analysis reveals that enterprises investing in IR&D can create a strategic advantage, weakening the rival country's control over the situation.
Researchers create a new privacy metric, Probably Approximately Correct (PAC) Privacy, that enables the addition of minimal noise to protect sensitive data. The algorithm automatically determines the optimal amount of noise needed without requiring knowledge of the inner workings of the model or training process.
Researchers found that popular GPT detectors misclassify articles written by non-native English speakers as AI-generated. The study suggests using these detectors with caution and calls for enhancements to address biases. This could have significant implications for students, job applications, and search engines.
Researchers at the University of Liverpool have developed a mathematical algorithm that can predict the structure of any material just by knowing its atoms. This breakthrough accelerates identification of new materials and their properties, paving the way for sustainable technologies.
A new AI tool utilizing deep-learning algorithms and volumetric thresholds accurately detected splenomegaly in patients undergoing CT scans. The study analyzed a large sample of 8,901 patients, revealing that splenic volume is strongly associated with weight.
Researchers developed a soft robotic exoskeleton glove using AI to improve hand dexterity and classify song variations. The device provides real-time feedback and adjustments, making it easier for users to grasp correct movement techniques, with an accuracy of 97.13% in classifying correct and incorrect song versions.
Researchers at U.S. National Science Foundation's IceCube Neutrino Observatory reveal a galactic portrait made with particles of matter, unlike any before, by determining the origin of thousands of neutrinos. The breakthrough allows for the first 'ghost particle' image of the Milky Way galaxy.
A study found that leading firms adopt predatory strategies to move toward competitors, intensifying price competition and increasing profits despite counter-intuitive price reductions. This approach can lead to reduced product diversity and technology gaps in the market.
A new AI tool can identify sperm in severely infertile men in seconds, compared to the hours it takes scientists. The algorithm improves antiquated approaches and increases the chance of a couple conceiving their own biological children while reducing stress on sperm and increasing efficiency.
Researchers propose GREENER framework to promote sustainable research practices, reducing greenhouse gas emissions and maximizing benefits to humanity and environment. Key considerations include estimating energy consumption of algorithms, tackling embodied impacts through new collaborations, and relocating computations to low-carbon s...
A new checklist has been proposed to reduce bias in AI algorithms, promoting more accurate and fair decision-making. The checklist provides a standardized framework for identifying and addressing biases in AI systems.
A research team from Taiwan has found a way to massively speed up aerial image simulations using wavelength scaling and fast Fourier transformation. The new algorithm improves computation speed by 4000-5000 times while maintaining only a slight intensity deviation.
A new study introduces a minCluster portfolio optimization method specifically designed for highly volatile markets, including cryptocurrencies. The method consistently outperforms traditional approaches and considers downside risk to find the best asset allocation.
Generative AI raises fundamental questions about the creative process and human's role in it. Researchers highlight gaps in understanding perceptions of AI-generated content, ownership, credit, labor economics, and media ecosystem impact.
A new research paper challenges the idea that unlimited trials are needed to learn safe actions in unfamiliar environments. The team presents a fresh approach that ensures learning safe actions with complete confidence while managing tradeoffs between optimality and exposure to unsafe events.
Researchers suggest that multifactorial prediction models could enhance dementia risk estimation. Current dementia risk scores are found to have limited clinical utility for estimating 10-year dementia risk.
Researchers at University of California - San Diego developed a new model that trains four-legged robots to see more clearly in 3D, allowing them to autonomously cross complex environments. The robot uses a forward-facing depth camera to synthesize visual information from past frames and estimate its surroundings.
The CAETÊ algorithm projects the future of vegetation in the Amazon, presenting scenarios for transformation driven by climate change. It shows that a drier climate could increase biodiversity but lower carbon storage, with carbon absorption dropping between 57.48% and 57.75% compared to regular climate conditions.
A novel technique has been proposed to enhance the learning ability of robots performing repetitive tasks by using a fractional power update rule. The study demonstrates fast convergence rates and potential applications in industries such as autonomous vehicles and rehabilitation robots.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
A team of scientists has developed an automated algorithm to reconstruct the shape of each neuron inside a light microscopy image using deep learning. This breakthrough addresses the challenge of generalizing algorithms across diverse species, brain locations, developmental stages, and microscopy image sets.
Researchers at MIT have developed a new approach to match 3D shapes by mapping volumes to volumes, resulting in more accurate animations and CAD designs. This method represents shapes as tetrahedral meshes that include the mass inside a 3D object, allowing for better modeling of fine parts and avoiding common artifacts.
A collaborative study by Rutgers University found that Google Search results do not differ significantly in ideological content for Democrats and Republicans. However, the amount of partisan and unreliable news users engage with varies depending on their personal political outlook. Researchers used a custom-built browser extension to t...
Researchers have developed an algorithm that accounts for individual patient differences and validated a pump control algorithm that does not require meal announcement. This innovation simplifies the control problem, reduces delays, and increases the accuracy of insulin delivery, making diabetes management less burdensome for patients.
Researchers developed a mathematical model that accounts for variables such as helicopter resources and operational range to optimize air movement tasks. The model can perform planning functions in under an hour, saving commanders three to five hours compared to traditional methods.
A deep learning-based Transformer model, Bloomformer-1, has been developed to identify the driving factors of algal growth in the Middle Route of the South-to-North Water Diversion Project. The results reveal that total phosphorus is the most significant factor affecting algal growth, especially in the Henan section, while total nitrog...
Scientists create a subcellular omics toolkit to study organelle diversity and communication in stem cells. The tool enables the identification of similar cell types, leading to more precise therapies for various diseases.
Researchers demonstrate probabilistic computing's capabilities by simulating networks of stochastic nanodevices to solve specific NP problems. The simulations agree with theoretical solutions, indicating the potential for scaling up this approach.
A team of researchers developed a self-checking deep learning system that accurately extracts information from gravitational-wave data. The algorithm, called DINGO, has been trained to interpret real data and can cross-check its own results for accuracy.
A mobile application utilizing Python and a single-element ultrasound transducer has been developed for photoacoustic tomography (PAT) image reconstruction. The application successfully reconstructs high-quality images with signal-to-noise ratio values above 30 decibels, making it suitable for point-of-care diagnosis in low-resource se...
Computational heuristics like Grey Wolf Optimizer and Whale Optimization Algorithm offer fast solutions to complex supply chain problems, providing accurate results within a shorter time frame. The research suggests these tools can be a valuable addition to supply chain management, especially in responding to unexpected disruptions.
Researchers developed an AI-based early warning system that combines acoustic technology with artificial intelligence to classify earthquakes and determine potential tsunami risk. The system uses underwater microphones to measure acoustic radiation, which travels faster than tsunami waves and carries information about the tectonic event.
Researchers developed an algorithm to accurately extract rapeseed silique morphology from 3D imaging data, achieving high accuracy in yield estimation and phenotyping. The new approach can differentiate between various branching patterns and has the potential to support global oilseed rape breeding operations.
Researchers developed machine-learning algorithms to generate proteins with specific structural features, enabling the creation of biologically inspired materials. The models can produce millions of new protein ideas in a few days, allowing scientists to explore unique applications.
Researchers at Bar-Ilan University have discovered that efficient learning on artificial shallow architectures can achieve the same classification success rates as deep learning architectures, but with less computational complexity. This breakthrough has significant implications for the development of unique hardware and advanced GPU t...
A University of Minnesota study found that TikTok can serve as both a haven and a hindrance for users struggling with mental health. The platform offers a sense of self-discovery and community, but its algorithm can repeatedly expose users to harmful content, leading to distressing spirals.
Researchers developed an AI algorithm that allows electronic devices to express uncertainty when faced with unexpected data, improving cough detection technology. The new approach enables more precise detection with fewer sound samples per second, reducing computing power and addressing privacy concerns.
A new study published in Neurology shows that younger brain age is associated with superior post-stroke outcomes, suggesting a potential biomarker for stroke rehabilitation. The research team used multi-site data sets and 3D brain structural MRIs to analyze the relationship between brain age and stroke recovery.
Researchers developed a robotic finger with high-resolution sensors that capture data along the entire length of each finger. The three-fingered robotic hand can identify objects after just one grasp, with 85% accuracy, using tactile sensing and machine-learning algorithms.
Researchers have developed an AI algorithm that can remove atmospheric blur from astronomical images, resulting in more accurate scientific measurements and clearer data. The tool produces faster and more realistic images than current methods, producing 38.6% less error compared to classic methods.
Researchers at Pusan National University have created a new algorithm that can accurately predict ice resistance and fracture points for ships navigating through the Arctic shipping routes. The model uses an elastic material approach, allowing it to study continuous ice-breaking processes, which is essential for efficient navigation.
A new AI tool, CLEAN, can predict enzyme functions based on amino acid sequences, outperforming leading state-of-the-art tools in accuracy and reliability. The tool was developed using contrastive learning and verified experimentally with both computational and in vitro experiments.
Researchers developed a robust multiagent trajectory-planner that enables drones to generate collision-free trajectories even with delayed communications. The system achieved a 100% success rate in simulations and flight experiments, guaranteeing safe group operations.
Researchers at the University of Illinois Grainger College of Engineering have successfully integrated arrays of electrochemical random-access memory (ECRAM) onto silicon transistors, creating a practical AI accelerator. This innovation eliminates energy costs associated with data transfer and enables efficient deep learning operations.
Researchers found giant gas giants orbiting low-mass stars, challenging the widely accepted planet formation model. The study used NASA's TESS satellite data and identified 15 potential giant planets, with five confirmed as actual planets.
Researchers from Dartmouth and University Medicine Essen found that strong links between brain measures and traits can be obtained when machine learning algorithms are utilized. This approach allows for high-powered results from moderate sample sizes, opening up studies of many traits and clinical conditions previously inaccessible.
A remote hypertension program, operated by Mass General Brigham, successfully supported patients through the COVID-19 pandemic in achieving their blood pressure goals. Participants who enrolled during the pandemic reached and maintained their goal blood pressures an average of two months earlier than in the pre-pandemic period.
Researchers at the Complexity Science Hub have developed an algorithm that can be applied to different languages and expand word lists significantly better than others. The new method, called LEXpander, outperforms previous algorithms in German and English, especially in sentiment analysis tasks.
The Meta-Semi algorithm achieves competitive performance on challenging semi-supervised learning tasks, such as CIFAR-100 and STL-10, with only one additional hyper-parameter to tune. This approach naturally yields a meta-learning formulation where the correctly pseudo-labeled data has a similar distribution to labeled data.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.