Articles tagged with Machine Learning
The team aims to create a system that can deliver items without human contact, using cables, knots, and multiple robots. They will focus on scaling up the transport of small objects like a basketball and solar panel.
Researchers have developed an AI algorithm that can track protein clumping under the microscope in real-time, revolutionizing the study of neurodegenerative disorders. The tool helps identify key characteristics of clumped proteins, which can lead to new therapies.
Scientists from TU Delft and Brown University engineer string-like resonators capable of vibrating for extended periods at room temperature, enabling sensitive sensing applications. The innovation uses advanced nanotechnology techniques and machine learning algorithms to create ultra-long strings with minimal energy loss.
Researchers have developed a machine learning model to identify high-performance multicomponent metal oxide electrocatalysts for the oxygen reduction reaction. The study found that certain features, such as itinerant electrons and configuration entropy, are critical for achieving high current density in ORR.
A new AI model developed by Cold Spring Harbor Laboratory's Benjamin Cowley and team uses a 'population code' to predict fruit fly behavior, revealing that multiple neurons combine to sculpt actions. The breakthrough enables the AI to accurately predict how real flies will behave in response to visual stimuli.
Researchers at the University of Innsbruck developed a novel method using diffusion models to generate quantum circuits. The model can produce accurate and flexible circuits, including those tailored to specific quantum hardware connections.
Engineers developed a material that mimics human bone for orthopedic femur restoration, providing optimized support and protection from external forces. This innovative approach uses machine learning, optimization, and 3D printing to create a fully controllable computational framework.
A US Army research collaboration with Boston University's KABlab used an AI machine learning robot to create a record-breaking energy-absorbing shape, breaking the known record of 71% efficiency. The shape has four points, like thin flower petals, and is taller and narrower than early designs.
Researchers applied machine learning models to predict groundwater depth in Ningxia, China, achieving better performance than traditional methods. The hybrid models outperformed multiple linear regression, and the DBO algorithm further enhanced prediction accuracy.
Researchers at Nagoya University found that cooperative hunting does not require complex cognitive processes, but rather simple rules and experience. The study used computational models and simulations to demonstrate the effectiveness of cooperation in hunting, with AI agents learning to work together through reinforcement learning.
The DGIST-Stanford joint research team successfully developed a novel medical AI model based on federated learning, which can accurately segment body organs by effectively learning medical image data from different hospitals. The technique uses shared embedding learning to enable federated learning without data breaches and leaks.
Model disgorgement is a set of techniques that force generative models to remove content leading to copyright infringement or biased responses. Researchers propose this approach to address issues like stylistic infringement, where models reproduce copyrighted works in the style of famous artists.
Researchers from MIT and the University of Basel developed a physics-informed machine-learning framework that can automatically map out phase diagrams for novel physical systems. This approach leverages generative models, making it possible to detect phase transitions without requiring huge training datasets. The technique has potentia...
A new approach uses artificial intelligence to turn low-quality images into high-quality ones, enhancing the image quality of metalens cameras. This technology could make these cameras viable for intricate microscopy applications and mobile devices.
Researchers used D-FFOCT and deep learning to create an intraoperative diagnostic workflow for breast cancer patients. The approach achieved high accuracy and speed, reducing processing time by a factor of 10 compared to conventional histology.
Researchers developed an approach combining quantum mechanical density functional theory and artificial intelligence to predict high-temperature superconducting materials. Over 120 structures with superior properties were found, including comparison to MgB2 at 39 K.
By recasting diffusion as a sum of individual contributions called 'kinosons,' researchers developed a new method to model alloy behavior. Machine learning is used to compute the statistical distribution of these contributions, allowing for fast and accurate simulation of diffusion. This breakthrough enables significant improvements in...
Insilico Medicine's lead compound demonstrates strong enzymatic activity, selectivity, and favorable ADME properties, as well as antitumor activity in various animal models. The company's generative AI-powered platform generated over 3,600 candidate molecules before identifying the promising lead compound.
Researchers at Concordia University developed a novel framework to detect counterfeit coins by analyzing image features and patterns. The method uses fuzzy association rules mining and can be applied to detect other types of counterfeit items, such as fake goods and labels.
PPPL researchers utilize machine learning to perfect plasma vessel design, optimize heating methods, and maintain stable control of fusion reactions. The team achieves significant results by predicting disruptions and adjusting settings before instabilities occur, enabling high-confinement modes in tokamaks.
Researchers at Yokohama National University utilized machine learning and AI to predict the selectivity of chemical reactions. By analyzing molecular factors such as sterics and orbitals, they developed a method to better understand reaction mechanisms, leading to more efficient synthesis of desired products.
A high school student, Michelle Du, helped develop a novel method to predict neurotransmitters from insect connectomes using neural networks. The method has been used in various neuroscience studies and provides valuable insights into brain circuit function.
Researchers created a digital twin model that predicts and controls complex systems, achieving higher accuracy than traditional methods. The algorithm is compact, energy-efficient, and easy to implement, making it suitable for self-driving vehicles and other dynamic systems.
Researchers have developed a new imaging technique that rapidly and accurately identifies cancerous tissues in breast samples. The method uses machine learning algorithms trained on hyperspectral dark-field microscopy data to pinpoint regions of invasive ductal carcinoma and invasive mucinous carcinoma.
Researchers developed ChemCrow, an AI-powered tool that integrates expertly designed software tools to autonomously perform chemical synthesis tasks. The system enables plan-and-execute approach with reduced hallucinations and practical application, accelerating research and development in pharmaceuticals and materials science.
A team at the University of Münster developed an improved method for explaining machine predictions of chemical reactions, using mechanisms such as reproduction, mutation and selection. The algorithm creates customised molecular fingerprints that predict chemical reactions with surprising accuracy, suitable for predicting quantum chemi...
A study by Albert-László Barabási and colleagues used machine learning to identify phrases representing allusions to foundational papers in the physics literature. The findings suggest that hidden citations obscure true impact in science, with influential ideas becoming so familiar that researchers stop citing their sources.
Researchers created GraSSRep and rhea, tools that outperform current methods for handling repeats and structural variants in metagenomic data. These methods use self-supervised learning and graph neural networks to analyze microbiome data, offering new insights into biological processes and potential applications in antibiotic resistance.
Researchers from GIST and MIT CSAIL develop a MultiSenseBadmpton dataset to enhance badminton performance analysis and AI-based coaching. The dataset captures joint movements, muscle signals, and gaze movements of badminton players.
Researchers at Iowa State University are developing an AI tool that can identify agricultural pests from photos, including insects and weeds. The project will use a supercomputer to train ensemble models that can analyze images quickly and provide pest-control strategies.
A new AI model developed by researchers at the University of British Columbia can accurately predict if a patient receiving cancer care will require mental health services. The AI analyzes oncologist's notes and identifies subtle clues that suggest a patient may benefit from early psychiatric or counselling interventions.
Researchers found a strong association between favorable survival outcomes and high populations of tissue-resident memory T cells in melanoma patients. The study identified 11 distinct gene signatures that correlate with T cell abundance and patient survival, suggesting a crucial role for T cells in immunomodulation.
Researchers developed a new AI algorithm called Maximum Diffusion Reinforcement Learning (MaxDiff RL) to improve robot reliability. The algorithm enables robots to learn complex skills more efficiently by encouraging exploration of their environments.
A massive open dataset, OpenDAC, has been created to accelerate direct air capture technology development while reducing costs. The database enables the training of an AI model that predicts material interactions with high accuracy, significantly faster than traditional chemistry simulations.
A new AI method developed by Swedish researchers can identify toxic substances based on their chemical structure, potentially replacing animal testing. The method has been shown to be more accurate and broadly applicable than existing computational tools, offering a promising alternative for environmental research and authorities.
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
An interdisciplinary team has published guidelines for responsible use of machine learning in science, focusing on transparency and reproducibility. The checklist includes detailed descriptions of machine learning models, data, and experimental designs to ensure integrity and validity.
A machine learning tool called PheNet can identify patients with rare, undiagnosed diseases years earlier, improving outcomes and reducing cost and morbidity. By analyzing patterns in electronic health records, the tool ranks patients by likelihood of having a disorder like common variable immunodeficiency.
A new computer model uses improved artificial intelligence to predict snow and water availability more accurately, considering both time and space. This information can help farmers and water planners make better decisions about water allocation.
FathomVerse combines real underwater images, interactive gameplay, and AI training to improve ocean research. The game engages players in labeling images for machine learning models, contributing to a broader audience in ocean exploration.
Researchers trained a quadruped robot using deep reinforcement learning to learn gait transitions on challenging terrain. The robot transitioned from walking to trotting and then to pronking to avoid falls, demonstrating the emergence of animal-like locomotion.
Researchers found that chatbots like ChatGPT can provide accurate information on climate change-related topics, but not all responses are reliable. The study compared the chatbots' answers to hazard risk indices generated by the Intergovernmental Panel on Climate Change and found more accuracy with tropical storms than droughts.
Researchers developed a device with AI algorithms to analyze suckling strength and pattern in newborns. The system showed improved accuracy over subjective clinician assessments, identifying abnormal patterns that may indicate the need for surgical intervention or improved feeding practices.
Researchers created new maps that accurately describe phosphorus levels in Amazonian soil using artificial intelligence, revealing the region's low concentration of the mineral. This information is crucial for understanding how tropical forests will react to climate change.
A new study by UNC Charlotte Professor Abigail Leavitt LaBella and colleagues reveals that yeasts do not fit the 'jack of all trades, master of none' adage. They found that yeasts with a broad ability to metabolize different carbon sources are actually efficient growers.
A machine learning system called AI-MARRVEL, developed by Baylor College of Medicine, has shown promising results in diagnosing rare Mendelian disorders. The system consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than other benchmark methods.
A new branch of AI, causal machine learning (ML), can estimate treatment outcomes better than traditional methods. This enables personalized treatment strategies and improved patient health.
Researchers developed an AI technique to estimate brain age based on EEG scans, making early screening for degenerative brain diseases more accessible. The technology uses machine learning to analyze brain waves and can be used as a low-cost screening tool for large numbers of people.
Using AI software called SLEAP, scientists at Salk Institute are designing climate-saving plants with optimized root systems that can store more carbon. This approach enables researchers to analyze plant features and connect desirable traits to targetable genes, accelerating the development of carbon-capturing plants.
Researchers from Drexel University have developed a machine-learning algorithm that can extract and recognize digital fingerprints of AI-generated video, including Stable Video Diffusion, Video-Crafter, and Cog-Video. The algorithm can be trained on just a few examples of new AI generators to detect them.
A Cleveland Clinic study uses reinforcement learning to design antibiotic regimens that minimize resistance and maximize susceptibility. The AI model predicts the most efficient treatment plans for multiple strains of E. coli, informing hospital-wide infection management.
Researchers at the University of Pennsylvania are working on a decision-support system to help clinicians navigate conflicting clinical trials. The system will use artificial intelligence to analyze study results and provide recommendations, aiming to improve patient outcomes and inform treatment decisions.
Researchers at ETH Zurich have developed an AI algorithm that can design new active pharmaceutical ingredients by analyzing protein structures. The algorithm generates blueprints for potential drug molecules that increase or inhibit protein activity, reducing the need for lengthy discovery processes and minimizing side effects.
A new study from the University of Illinois explores the use of hyperspectral imaging and explainable artificial intelligence to assess sweet potato attributes, leading to more informed decision-making and higher-quality products. The results can help industry professionals understand the significance of different features in predictin...
Researchers built more accurate cardiovascular risk models using machine learning, finding that women are underdiagnosed due to sex-neutral criteria. The study used the UK Biobank dataset and found that electrocardiogram (EKG) tests were most effective in improving detection of cardiovascular disease in both men and women.
A new framework mitigates bias in machine classification by evaluating under different fairness metrics and inferring specific bias terms from data. The framework substantially reduces bias in classification outcomes while preserving accuracy across seven datasets and 21 classifiers.
Researchers used machine learning to analyze over 1.09 million potential metabolite-receptor pairs and predict the likelihood that each interaction contributed to Alzheimer's disease. The study found a protective metabolite called agmatine interacting with a receptor called CA3R in brain cells, reducing CA3R levels and lowering phospho...
A new AI model generates realistic images of single cells, which are used as synthetic data to train an AI model for better cell segmentation. The researchers found that providing a more diverse dataset during training improves performance.
A team of researchers at MIT has developed a new method to model the irrational behavior of humans, which can be used to predict their future actions. By analyzing an agent's previous decisions, the technique infers its computational constraints and adapts to human collaborators' weaknesses.
A Wayne State University researcher is working to transform code review tools and workflows to address participants' challenges in understanding and communicating code changes. The goal is to produce high-quality feedback using empirical methods, machine learning, and natural language techniques.