Articles tagged with Algorithms
Osaka University researchers have developed an AI-driven algorithm to control indoor heating and cooling systems, achieving significant energy savings of up to 30%. The system learns the symbolic relationships between variables, including power consumption, based on a large dataset, ensuring comfortable temperatures despite winter cond...
A new study published in JAMA found that a consensus-based risk-stratification algorithm reduced unnecessary ovary removals in girls with benign masses from 16.1% to 8.4%. The algorithm accurately identified lesions highly likely to be benign, allowing for ovary-sparing surgery.
Researchers from SUTD successfully applied reinforcement learning to a video game problem, creating complex movement designs that outperformed top human players. The study's findings have the potential to impact robotics and automation, ushering in a new era of movement design.
Researchers from University of Cambridge and Cornell University have developed a method to build machine learning models that can understand complex equations using far less training data. This breakthrough enables the construction of more time- and cost-efficient models for physics, engineering, and climate modeling applications.
A study by researchers at the University of Illinois Urbana-Champaign found that YouTube's recommendation system did not promote anti-vaccine content during the COVID-19 pandemic. The study analyzed over 27,000 video recommendations and found that users were directed to longer, more popular health-related content.
Using computational topology, Brown researchers have developed an algorithm that profiles shapes and spatial patterns in embryos, enabling the study of how cells assemble into tissue-like architectures. The new approach uses persistence images to rapidly compare large datasets, reducing computation time from hours to seconds.
Researchers created a machine-learning algorithm using the largest data set of digital biomarkers for hot flashes. The predictive system delivers cooling to mitigate or fully alleviate hot flashes using Embr Wave's wearable device.
A new study from UTHealth Houston finds that AI-powered algorithm can improve detection rates of unruptured cerebral aneurysms. The study used a machine learning algorithm to analyze CT angiograms and identified 36 true aneurysms, with 24 previously not referred for follow-up.
A new study published in JAMA found that adults over 60 who spend more than 10 hours a day engaging in sedentary behaviors like sitting are at increased risk of developing dementia. The study used wearable accelerometers to track physical activity and found that the total time spent sedentary each day was a significant predictor of dem...
Researchers have developed an automated breath-by-breath measure to assess the severity of obstructive sleep apnea, predicting cardiovascular disease and mortality. The ventilatory burden tool provides a validated alternative to AHI, offering stable results and risk predictions.
A Los Alamos-developed machine learning algorithm successfully processed massive data sets exceeding a computer's available memory. The algorithm divides data into manageable batches to prevent hardware bottlenecks, enabling efficient processing of large-scale applications in various fields.
A study published in Science Advances found that exposure to harmful content on YouTube is driven by a small group of users subscribing to these channels, not algorithmic recommendations. Most viewers of these channels are also subscribers to the channel in question or similar ones, indicating a pattern of seeking out content.
Researchers from Bar-Ilan University improved AI classification tasks by choosing the most influential path to the output, rather than learning with deeper networks. This approach can enhance existing architectures and pave the way for improved AI systems without additional layers.
A hybrid system of electronic encoding and diffractive optical decoding transmits optical information with high fidelity through random, unknown diffusers. The system outperforms traditional approaches that only utilize a diffractive optical network or an electronic neural network for optical information transfer.
Researchers at National Korea Maritime and Ocean University developed a close contact identification algorithm that outperforms conventional clustering algorithms. The algorithm enables accurate tracking and physical isolation of individuals in ship environments, contributing to the health and safety of passengers.
Researchers at MIT developed a method to simplify the process of whole-body manipulation for robots, enabling them to reason efficiently about moving objects. The technique uses AI and smoothing to reduce the number of decisions required, making it possible for robots to adapt quickly in complex environments.
MIT researchers developed a new algorithm that can execute complex maneuvers like loops and rolls for tailsitter aircraft, enabling agile trajectories with fast-changing accelerations. The algorithm uses differential flatness to ensure feasibility and planning in real-time.
Researchers develop adaptive fuzzy sliding mode controller to estimate unknown parameters and control nonlinear PAMs, showing improved tracking accuracy and adaptability compared to traditional methods.
Researchers used machine learning algorithms to analyze brain imaging data and identify changes associated with autism spectrum disorder. The study found that the methodology could determine ASD diagnosis with above 95% accuracy, highlighting potential for improved diagnostic methods.
A new study shows that a rule-based natural language processing tool successfully identified patients with unstable access to transportation, food insecurity, social isolation, financial problems, and signs of abuse or exploitation. The tool performed better than deep learning algorithms in identifying these social determinants of health.
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...