Articles tagged with Algorithms
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Researchers developed an automated method to create 3D images of leaked gas clouds, enabling precise location, volume, and concentration determination. This technology can provide early leak warnings, assess risk, or determine the best way to fix leaks in large facilities with stored toxic chemicals.
Researchers developed open-source software SHRY to find distinct substitution patterns in disordered systems, reducing computation time. The software uses group theory and canonical augmentation to efficiently analyze crystal structures with random substitutions.
A new robotic system, FuseBot, has been developed to efficiently retrieve buried objects in piles. The system uses radio frequency signals and computer vision to reason about the probable location and orientation of objects under the pile, enabling it to find more hidden items than a state-of-the-art robotics system in half the time.
Researchers found that people are less morally outraged when gender discrimination occurs due to an algorithm rather than direct human involvement. The study's findings have broader implications for efforts to combat discrimination and may affect how companies are held liable.
A newly expanded data set of brain scans from stroke patients called ATLAS now includes 1,271 MRI images with manually segmented lesions, facilitating large-scale stroke recovery research. Researchers hope to develop algorithms to automate lesion segmentation, enabling clinicians to predict patient responses to therapies.
A novel algorithm, BLIND, enables robots to navigate through environments with obstacles by incorporating human feedback. Humans provide labels to refine the robot's trajectory, avoiding obstacles efficiently.
A team of scientists has developed a novel computational approach to analyze DNA sequences of thousands of bacteria, revealing previously unknown gene clusters responsible for producing metabolites of interest. The study highlights the potential of these bacterial compounds in treating colon cancer and improving treatment options.
SeqScreen, an open-source software toolkit, accurately characterizes short DNA sequences to detect pathogenic sequences. The program uses a curated database of thousands of gene sequences representing 32 types of virulence functions.
A study by Thorsten Lehnert shows that corporate managers' behavior is linked to investor sentiment, predicting investment strategy success. The researcher found a significant relationship between market-level euphoria and investment factor performance, outperforming static strategies.
Researchers at MIT identified a flawed analysis of website-fingerprinting attacks and developed new techniques to prevent them. They found that attackers can use machine-learning algorithms to decode signals leaked between software programs, enabling them to obtain private information.
Researchers developed an algorithm to improve matching efficiency, considering user preferences and behavior. The new algorithm shows improved results in field experiments, with at least 27% more matches than the current one.
A new training algorithm for deep spiking neural networks (SNNs) uses biologically plausible spatiotemporal adjustment to improve performance and reduce energy consumption. This approach achieves competitive classification accuracy with only 3% of the energy used by traditional artificial neural networks.
A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.
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...
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.
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 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.
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.
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.
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 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 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.
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 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.
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.
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.
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.
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...
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 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.
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.
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.
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.
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 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.
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.
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.
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.