Articles tagged with Algorithms
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 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...
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.
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.
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 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.
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 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.
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.
A new algorithm predicts female patients are at higher risk for kidney damage after aneurysm repair, with those having reduced glomerular filtration rate facing a 4.7 times increased risk. The algorithm could help identify patients at risk and optimize them before surgery.
Researchers developed an algorithm that uses computer vision techniques to estimate tree diameter from a single image in realistic field conditions. The app sped up the process significantly, being about four and a half times faster than manual measurement techniques.
A new process combining human evaluation and AI optimization produces better designs than fully automated systems or manual approaches. The 'Human-Informed Topology Optimization' method reduces material usage while maintaining strength, and can be applied to various scales and applications.
Researchers created a statistical model to predict who is resistant to COVID-19, using health characteristics from electronic records. The model identified patterns in patients with similar features, suggesting that genetic and environmental differences influence their defense against the virus.
Computer scientists designed a reconstruction attack that proves US Census data can be exposed and stolen with current privacy measures. The study demonstrates risks to individual respondents' privacy, highlighting the need for differential privacy techniques to protect sensitive information.
A new algorithm uses patient-reported outcomes to identify patients with cancer who are at increased odds of having unplanned emergency department visits. The algorithm, which calculates a symptom complexity score, has shown that patients with high complexity scores are three times more likely to use the ED.
A recent study published in Human Reproduction found a significant association between prenatal alcohol exposure and changes in children's facial features. The research used AI to analyze three-dimensional images of children's faces at ages nine and thirteen, revealing that mothers' alcohol consumption before and during pregnancy can h...
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
Researchers at Brigham Young University have developed an AI algorithm that can accurately locate players and determine formations in football game footage. The system has achieved over 90% accuracy on player detection and 85% accuracy on formation identification, potentially eliminating the need for manual annotation and analysis.
Researchers at Osaka Metropolitan University developed a new method to evaluate X-ray microbeam diameter using mathematical analysis, outperforming conventional methods. The uniform evaluation method is expected to be widely adopted as an international standard.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
Researchers utilized the Chemistry42 platform to generate novel molecular structures and identified a hit molecule for CDK20, a promising target for hepatocellular carcinoma. The platform's customizable reward function and generative models enabled efficient design and optimization of molecules.
Researchers developed an ethical algorithm to distribute risk fairly in autonomous vehicles, considering multiple factors such as responsibility and traffic regulations. The new approach enables more nuanced decision-making in critical situations, weighing risks and avoiding aggressive maneuvers.
Researchers at Bar-Ilan University have developed a new type of artificial neural network that outperforms traditional deep learning architectures. By using tree architecture with single routes to output units, they achieve better classification success rates, paving the way for efficient and biologically-inspired AI hardware.
Scientists at Göttingen University have created a novel approach to produce X-ray images in color from a single exposure, eliminating the need for focusing and scanning. This breakthrough method uses an X-ray color camera and a specially structured plate to capture the intensity pattern of fluorescing atoms in a sample.
Lithuanian researchers have developed an AI system to detect early symptoms of Parkinson's disease using voice data. The study found that patients with early-stage Parkinson's often speak in a quieter, more monotonous tone, which can be difficult to notice by ear.
University of Illinois scientists developed a way to accurately map tilled land in real-time using ground, airborne, and satellite imagery. The method increased mapping accuracy by 67% compared to upscaling directly from the ground to the satellite.
A new Swedish quantum computer is being made available to the industry, accompanied by a test bed and a quantum helpdesk. The test bed will allow companies and researchers to solve problems using quantum technology at a significantly lower cost than existing commercial options.
Researchers use coherent correlation imaging to image the evolution of magnetic domains in time and space without prior knowledge. The study reveals thermal motion and pinning effects on domain boundaries, unlocking new insights into magnetism's microcosm.
Researchers use drone technology to detect changes in building facades and assess technical condition, enabling accurate classification and identification. The system can also be used for urban heritage site monitoring and planning urban development.
Researchers at Brookhaven National Laboratory have successfully discovered new materials using artificial intelligence and self-assembly. The AI-driven technique led to the discovery of three new nanostructures, expanding the scope of self-assembly's applications in microelectronics and catalysis.
Researchers developed machine learning models to accurately calculate fine particulate matter in urban air pollution using AI and traffic data. The models provide a high-resolution estimation of city street pollution surface, enabling transportation and epidemiology studies to assess health impacts.