Articles tagged with Algorithms
Researchers at UTSA's Department of Physics and Astronomy have used deconvolution algorithms to enhance images of galaxy NGC 5728 obtained by the James Webb Space Telescope. The study reveals a faint extended feature that could be part of an outflow from a supermassive black hole interacting with the host galaxy.
Researchers have developed a novel optical neural network architecture that achieves nonlinear optical computation by precisely controlling ultrashort pulse propagation in multimode fibers. This approach streamlines the need for energy-intensive digital processes, achieving comparable accuracy with significantly reduced parameters.
Researchers have invented a simple yet effective algorithm called SIEVE to optimize cache management. By labeling objects as 'zero' or 'one', the system efficiently evicts the least recently used items, reducing computational complexity and bugs.
Researchers developed an AI-driven algorithm that accurately predicts death and complications after percutaneous coronary intervention (PCI). The tool uses patient feedback to provide a patient-centered, individualized risk prediction score.
The Lehigh University Plasma Control Group is working on advanced controls and machine learning to improve plasma dynamics simulation capabilities and stabilize superheated gases in future reactors. The goal is to address technological issues with ITER and FPP, ensuring safe and controllable operation.
Researchers at UB have developed a new deepfake detection algorithm that reduces biases in facial recognition, with one method classifying videos based on demographics and the other relying on features not visible to the human eye. The algorithms improved fairness metrics and reduced disparities in accuracy across races and genders.
Researchers from Bar-Ilan University discover a possible mechanism underlying the brain's efficient shallow learning, enabling it to perform complex classification tasks with similar accuracy as deep learning. The study proposes a wider and higher architecture as a complementary mechanism to deep architectures.
Boleslaw Szymanski and his team developed a clustering method called SpeakEasy2: Champagne to group molecular data, which showed consistent performance across diverse applications. The method was tested on bulk gene expression, single-cell data, protein interaction networks, and large-scale human networks data, revealing its effectiven...
Gurdip Singh's project aims to develop a unified model to study cyber-physical systems and design distributed algorithms for fundamental problems in pervasive systems. The proposed model will be integrated into existing simulation tools to evaluate scenarios involving different types of physical entities and phenomena.
A computer vision system developed by University of Malaga engineers estimates vehicle speeds in real time using a single camera, reducing complexity and costs. The algorithm, published in Neurocomputing, aims to improve vehicle safety and has potential applications in autonomous driving and driver assistance.
Researchers developed a new solver algorithm for the MPAS-Ocean ocean circulation model, reducing run time by 45% and enabling semi-implicit stability. This allows for faster climate predictions and energy efficiency, as well as reduced computational power consumption.
A new commentary in JAMA Pediatrics recommends improving adolescents' media information literacy skills and promoting inclusive content to combat health misinformation spread on social media. This approach aims to harness the potential of social media as a tool for positive connections and mental well-being.
Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, decreasing energy consumption and eliminating the need for a digital twin. This approach is more biologically plausible and shows improved speed, robustness, and reduced power consumption compared to other methods.
New research from the Max Planck Institute challenges previous claims of giant exomoons around Kepler-1625b and Kepler-1708b. The study uses a computer algorithm to analyze observations, finding that 'planet-only' interpretations are more conclusive than initially thought.
A new software algorithm intelligently combines vocals and instrumentals from different songs, mimicking professional music composition techniques. The result is a unique blend of pleasing lyrics and exciting instrumentals that appeal to listeners, particularly hip-hop fans.
Researchers developed an adaptable machine learning algorithm to measure driver 'workload' using driving performance signals, enabling real-time adjustments to in-vehicle systems for enhanced safety and user experience. The system can respond to changes in the driver's behavior, status, road conditions, or characteristics.
Researchers from MIT and ETH Zurich developed a filtering technique to simplify a key intermediate step in MILP solvers, speeding up the process by 30-70% without compromising accuracy. A machine-learning model is then used to pick the best combination of algorithms for a specific optimization problem.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
A team of researchers at North Carolina State University has developed a new experiment to better understand human moral judgments in traffic scenarios. The study aims to collect data for training autonomous vehicles to make
A study led by Keck School of Medicine of USC used AI detection technology to analyze influencer content on TikTok between 2019 and 2022, finding an increase in posts that promote e-cigarettes. The prevalence of pod devices, e-juice flavor names, and nicotine warning labels increased significantly over time.
A new composite material, engineered using computer algorithms and 3D printing, can change its behavior in response to temperature changes. The material is designed to perform specific tasks depending on the environment, enabling future generations of autonomous robotics.
Researchers at ETH Zurich developed an autonomous excavator called HEAP to construct a 6-meter-high and 65-meter-long dry-stone wall. The excavator uses sensors, machine vision, and algorithms to place stones in the desired location, achieving a high level of precision and speed.
Scientists at the University of Copenhagen and University of Victoria have developed an AI formula to predict rogue waves, which can split apart ships and damage oil rigs. The new knowledge can make shipping safer by identifying the likelihood of being struck by a monster wave at sea.
A new theoretical framework evaluates the utilization efficiency of mobile power sources in power grids, considering various failure scenarios and structural resilience. The study emphasizes deploying mobile power sources to improve grid survivability and rapid recovery capabilities during disasters.
Researchers developed a novel method to model market regime dynamics using efficient frontiers. The approach outperforms benchmarks and provides interpretable results, allowing for better understanding of portfolio optimization.
A new study published in The Journal of Finance and Data Science introduces the topological tail dependence theory, a methodology for predicting stock market volatility. The approach incorporates persistent homology information, enhancing the accuracy of models and detecting complex correlations.
Scientists have developed an AI system that accurately maps the surface area and outline of giant icebergs in one-hundredth of a second. This technology surpasses manual interpretation methods, which can take several minutes to delineate an iceberg's outline, and offers insights into their impact on the polar environment.
A new smartphone app using the APRI+ALBI score increases the safety of liver surgery by providing an individualized risk assessment for patients prior to resection. The score offers a cheaper and less invasive option with comparable predictive power compared to standard preoperative tests.
A new deblurring algorithm has been developed to improve the resolution of microscopy images without amplifying noise. This breakthrough technique, called 'deblurring by pixel reassignment,' uses local gradients to sharpen images while preserving larger structures.
A team of researchers at Tampere University and Eastern Washington University used weather records to validate the effectiveness of musical characteristics in enhancing data interpretation. The study found that musical sounds can be a powerful complement to traditional data visualization methods.
A new AI-driven mapping study from the University of Copenhagen has discovered a billion tons of hidden biomass in Europe, including trees outside forested areas. The research found that countries like Denmark, Netherlands, and UK have significant tree cover outside forests, which can impact biodiversity and climate models.
A brain-computer interface (BCI) has been successfully used to restore control of home devices for a patient with ALS, allowing the individual to navigate a communication board and smart devices without recalibration. The BCI device was implanted on the surface of the brain areas responsible for speech and upper limb function, enabling...
A new study published in the University of East Anglia shows that automated insulin delivery technology can help pregnant women with type 1 diabetes better manage their blood sugars. The technology, known as Hybrid Closed-Loop or Artificial Pancreas, helps to substantially reduce maternal blood sugars throughout pregnancy.
A new asynchronous distributed PEV charging protocol has been developed to manage the charging patterns of scattered electric vehicles, ensuring network reliability and seamless user experience. The protocol preserves user privacy by not disclosing individual user-state information, addressing concerns in previous studies.
Lerrel Pinto aims to create robots that can perform a variety of tasks using algorithms and data collection methods. He seeks to address the trade-off between dexterity and generality in robotics.
A team of Japanese researchers created a big data-driven model that accurately forecasts key economic indicators in real time, eliminating the need for aggregated semi-macroeconomic data. The approach leverages search engine query data to identify highly correlated queries and provides timely insights into economic trends.
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...
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.
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 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.
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.
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.
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 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...
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.
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.
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 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.
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 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.