Articles tagged with Algorithms
A new AI method can distinguish between the overall sounds of healthy and unhealthy coral reefs, making it a valuable tool for monitoring reef health. The technique uses machine learning to analyze sound recordings and track the progress of reef restoration projects.
Researchers at Cornell University have developed a new algorithm for autonomous underwater sonar imaging that significantly improves speed and accuracy for identifying objects such as explosive mines and sunken ships. The new approach, called informative multi-view planning, integrates information about object locations with sonar proc...
MIT researchers develop an interactive design pipeline enabling users to create customized robotic hands with tactile sensors. The platform streamlines the process, allowing users to adjust palm and fingers and integrate tactile sensors, resulting in complex tasks like picking delicate items or using tools being performed flawlessly.
Researchers at Sainsbury Wellcome Centre found that mice can choose the best escape route after only 10 minutes of exploration, without needing to experience threat. The study suggests that mice use innate heuristics and natural exploration to learn this information.
Researchers used big data and machine learning methods to provide insight into equity valuation over the past two decades. However, a review by Professor Doron Nissim identified crucial areas that require more research attention.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
A machine learning algorithm has outperformed astronomers in analyzing microlensing data to find new exoplanets, revealing connections hidden in complex mathematics from general relativity. The AI algorithm uncovered a degeneracy that had been missed by experts, suggesting a broader theory is likely incomplete.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
A study by Thorsten Lehnert reveals that price noise from trading activities of mutual funds explains the success of 'bets against beta' strategies. These strategies, which involve taking short positions in assets with higher betas and long positions in those with lower betas, result in significant positive returns.
Researchers at MIT developed a technique that enables an autonomous vehicle to plot a provably safe trajectory in highly uncertain situations. The algorithm considers probability of observing different environmental conditions and obstacles, and formulates trajectory planning as a probabilistic optimization problem.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
A new algorithm forecasts which hospitalized patients are at the highest risk of death from COVID-19, regardless of immune protection status or virus variant. The tool, called COVID-19 Disease Outcome Predictor (CODOP), uses blood measurements to predict prognosis and can help direct critical care resources.
Researchers at Duke University have developed a machine learning algorithm that incorporates known physics into neural networks, allowing for new insights into material properties and more efficient predictions. The approach helps the algorithm attain transparency and accuracy, even with limited training data.
A new observational study from Vanderbilt University Medical Center compares an artificial intelligence algorithm with face-to-face screening for estimating suicide risk. The ensemble learning method produced nearly always better predictions across the board compared to C-SSRS or VSAIL alone.
Researchers at North Carolina State University have developed FAXID, a hardware-based approach for detecting ransomware that is significantly faster than software-based methods. In proof-of-concept testing, FAXID demonstrated accuracy comparable to XGBoost but with speeds up to 65.8 times faster.
A new nanosensor platform uses machine learning to analyze spectral signatures of carbon nanotubes for early detection of ovarian cancer. The approach detects biomarkers and recognizes the cancer itself, offering a promising alternative to traditional methods.
Harbir Antil leads a project to create scalable algorithms for optimizing nonlinear dynamical systems with uncertainty. The research aims to enable automated design, data analysis, and optimization using randomized preconditioning and compression methods.
Researchers create a new method for detecting diagnostic markers of epilepsy using EEG and MEG, enabling precise localisation of epileptogenic cortical structures. The biomimetic algorithm improves the effectiveness of neurosurgical interventions for epilepsy patients with resistant treatment.
Researchers developed an algorithm to identify users' basic needs from their Instagram posts, images, and captions. The study analyzed 86 profiles in Spanish and Persian, achieving promising accuracy and complementary information between visual and textual cues.
Researchers developed an algorithm to quickly identify two cancer drugs that work well together, reducing testing time from three years to eight weeks. The new method uses gene expression data to prioritize potential combinations and has been confirmed in lab tests.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
A University of Auckland research team analyzed the legal documents of Spotify and Tinder to understand their algorithmic recommendations. The study found that both platforms collect and use personal data without transparency, influencing users' choices in various ways.
A new method reduces computational complexity of traffic models, making them operate more efficiently. The modified algorithm breaks down complex forecasting questions into smaller problems that can be solved in parallel, significantly reducing run time. This approach also allows for a good enough solution within an error bar, rather t...
Scientists have developed a machine learning algorithm that can accurately predict the lifetimes of different battery chemistries using as little as a single cycle of experimental data. The technique could reduce costs and accelerate the development of new battery materials, enabling researchers to quickly evaluate and test multiple ma...
Researchers at Tokyo University of Science propose two new search strategies to reduce the computational cost of rebalancing in bicycle-sharing systems. The approaches focus on finding feasible solutions more efficiently and redefine the problem to minimize solving time.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
Researchers developed a decision support program to reduce blood draws in pediatric intensive care units (PICU), leading to reduced antibiotic prescriptions without increasing sepsis risk. The program resulted in significant reductions in blood culture rates and unnecessary medication use.
James Anderson, assistant professor of electrical engineering at Columbia University, has received a National Science Foundation (NSF) CAREER award for his work on developing randomized algorithms for the analysis and control of large-scale cyber-physical systems. His goal is to provide robust, reliable, and secure autonomy to large-sc...
Researchers from Dartmouth College used artificial intelligence to draft wine and beer reviews, finding agreement between human and machine-generated reviews. The team also developed a system to write review syntheses, aggregating elements from existing reviews to provide limited but relevant information about products.
Researchers from Bar-Ilan University discovered that brain learning occurs mainly in dendritic trees, where the trunk and branches modify their strength. This finding paves the way for a new type of AI hardware and algorithms with comparable success rates to existing parallel GPUs.
Researchers have developed a novel image reconstruction method using Vision Transformer (ViT) architecture to overcome limitations of conventional methods. The proposed algorithm enables the acquisition of high-quality images in a short computing time, suitable for real-time capture and various applications.
A new algorithm developed by researchers at the University of Córdoba uses Artificial Intelligence to predict crops' water needs with greater accuracy and precision. The algorithm requires fewer meteorological variables than traditional methods, making it a more efficient tool for managing water resources.
Researchers at the University of Georgia developed a model to prioritize land conservation in the Upper Chattahoochee Watershed, which provides drinking water to 95 cities. The model considers ecosystem services like water, carbon, and habitat, and favors higher connectivity among parcels of land.
A new KAUST study uses machine learning to predict disease spread with high accuracy, dynamically incorporating latest data without human bias. This approach offers a promising alternative to conventional models, providing a more accurate story of the underlying epidemic data.
Researchers develop a new AI agent that uses four strategies to solve single-agent stochastic puzzles like Minesweeper, achieving comparable performance to state-of-the-art studies. The approach defines solvability in this context and establishes the boundary between puzzle-solving and game-playing paradigms.
A new study from George Mason University researchers highlights the use of artificial intelligence and seasonality to screen patients and identify the probability of COVID-19. The algorithm takes into account symptom clusters and age/gender differences, suggesting that fever is a stronger predictor of COVID outside of flu season.
Researchers developed a novel algorithm, 'Joint Space and Frequency Reconstruction' (JSFR-SIM), to accelerate image reconstruction in optically sectioned superresolution structured illumination microscopy. The method achieves 80 times faster execution speed without compromising image quality.
A novel method developed by the University of Tsukuba uses drones and machine learning to estimate the amount of plastic litter in rivers. The approach combines high-resolution optical and thermal images, resulting in more accurate estimates than other methods.
Researchers developed an AI model to predict side effects from new combination therapies by analyzing over 15 million records of adverse events. The model can recognize patterns between drugs and their side effects, enabling the prediction of adverse events for individual therapy profiles.
Researchers developed a procedure-specific risk calculator to encourage changes in patient behaviors before surgery. The machine learning models accurately predicted which patients were most likely to experience complications or hernia recurrence after ventral hernia repair.
Researchers from Tokyo University of Science analyzed usage patterns of bicycles in four major US cities to improve bicycle sharing system efficiency. They found temporal patterns and similarities between weekdays and weekends, contradicting previous studies.
Researchers from the University of Cambridge and Oslo identify a century-old mathematical paradox as the Achilles' heel of modern AI. The paradox limits the existence of stable and accurate neural networks, making many AI systems untrustworthy in high-risk areas.
Sandia researchers have demonstrated that neuromorphic computers can solve more complex problems than artificial intelligence and may earn a place in high-performance computing. The findings show that neuromorphic simulations can track X-rays, disease spreading, information flowing through social networks, and financial markets.
MIT engineers mapped airplane contrails over the US in 2020 and found a 20% drop in coverage compared to prepandemic years. The team's computer-vision technique can help predict where contrails form, allowing airlines to reroute planes and reduce aviation's climate impact.
A team of international researchers has developed an algorithm to translate pig grunts into actual emotions, with 92% accuracy. The study provides insight into animal welfare and could lead to improved living conditions for livestock.
Researchers at Mainz University will examine algorithm decisions on transparency, fairness, and data protection while optimizing resource use. The project aims to create workable trade-offs for applications and integrates young researcher promotion programs.
Researchers developed a machine learning model that provides good predictions for human speech recognition in noisy environments, benefiting hearing-impaired listeners. The model outperformed expectations and showed strong correlations with measured data.
Researchers at University of Illinois develop new method to accurately estimate soil organic carbon using airborne and satellite hyperspectral sensing. The study leverages machine learning algorithms with a comprehensive soil spectral library, enabling large-scale monitoring of surface soil organic carbon.
Researchers at the University of Copenhagen have developed an AI method to recognize and detect insect species based on their wingbeats, enabling easier monitoring of biodiversity. The method uses infrared sensors to measure wingbeats and group insects into different species without human input.
Researchers developed a novel method of measuring aortic growth, called vascular deformation mapping, which outperforms standard manual rating methods. The technique uses high-resolution CT imaging to calculate three-dimensional changes in the aortic wall, achieving an accuracy of less than 1 millimeter.
A recent study found that Google's image search for occupations like CEO still displays significant gender bias, even after the company claimed to have fixed the issue. The researchers proposed three algorithms to address this problem and tested them on four major search engines.
A team of researchers from Skoltech and universities developed a neural network-based solution for automated recognition of chemical formulas on research paper scans. The algorithm combines molecules, functional groups, fonts, styles, and printing defects to mimic existing molecular template depiction styles.
A study published in The Journal of Finance and Data Science found that using machine learning to estimate reward and risk improves portfolio performance. Machine learning helps provide accurate estimates by taking advantage of complex non-linear relationships between market variables, outperforming linear methods.
A new study reveals that online social network algorithms can distort and amplify biases against minority groups, leading to unequal visibility and ranking. Researchers found that homophily and the proportion of minorities in a network can contribute to this effect.
Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms. His research aims to devise algorithmic foundations and theory that will accelerate problem-solving in machine learning, information processing, and optimization.
A recent study found that protracted inflammation following COVID-19 is strongly linked to long-term changes in lung structure and function. Monitoring patients for signs of inflammation after infection may help identify those at risk of long-term lung problems.
Researchers developed an algorithm that incorporates customer behavior into recommendation systems, making more accurate and personalized suggestions. The technique, known as tensor decomposition, analyzes data in multiple dimensions to capture complex patterns and relationships.
Researchers developed an algorithm that prioritizes reliable news sources based on audience diversity, reducing the spread of misinformation. The new strategy incorporates data on web traffic and self-reported partisanship to promote diverse news sources.
Researchers have developed a system that can capture the information in speech signals similar to how humans perceive speech. The method uses a matching pursuit algorithm and psychoacoustic principles to produce high-quality resynthesized speech signals with natural accents.
Researchers developed an AI system that can analyze retinal scans to identify patients at high risk of a heart attack over the next year. The system uses deep learning techniques and achieves an accuracy of 70-80%, revolutionizing the way patients are screened for signs of heart disease.