Research using machine learning to solve scientific problems. Covers AI quality control, sea-surface temperature estimation, label-free cell analysis, and cyber defense.
Researchers developed a new method to estimate gradients and derivatives on quantum computers, enabling faster computations. This technique can be applied to various fields such as cryptography, optimization, and materials science.
Researchers developed an AI-based method to estimate BMD from plain X-ray images using a hierarchical learning framework. The approach showed high performance and reliability in estimating BMD, with correlation coefficients of 0.88 and 0.92 compared to DXA and QCT.
A research team led by HKUST developed an AI-powered model to predict glioma patients' prognosis and identify early predictors of tumor evolution under therapy. The model, CELLO2, uses genomic and transcriptomic data from 544 glioma patients to accurately predict treatment-induced hypermutation and grade progression.
Researchers found that using predictive models in healthcare can alter relationships between patient data and outcomes, leading to further degradation. Implementing a system to track individuals impacted by machine learning predictions is crucial to maintaining model performance.
A new generative model named scPoli enables multi-scale representations of cells and samples, facilitating the integration of high-quality large-scale datasets for novel biological insights and disease understanding. This model accelerates atlas building and usage, ultimately accelerating disease understanding and therapy development.
A team of New York University computer scientists created a neural network that can explain how it reaches its predictions, shedding light on the intricacies of RNA splicing. The breakthrough reveals how a small, hairpin-like structure in RNA can decrease splicing and provides new insights into the transfer of genomic information.
Researchers at the University of Texas at Austin developed an AI algorithm that accurately predicted 14 earthquakes within about 200 miles of their location and strength, with only one false warning. The system detected statistical bumps in real-time seismic data and paired them with previous earthquakes to make predictions.
The Army Research Laboratory has chosen Texas A&M University for its High-Throughput Materials Discovery for Extreme Environments Center (HTMDEC). The center aims to develop novel materials for extreme conditions, reducing experimentation costs and duration. By leveraging machine learning, physics-based simulations, and collaboration, ...
A new machine learning approach called CellOT allows for precise modeling and prediction of cell changes and drug effects. This enables personalized cancer treatments by identifying the traits of resistant cells, paving the way for more effective therapies.
Researchers from Imperial College London and the University of Nottingham used machine learning to identify 'atomic shapes' that form basic pieces of geometry in higher dimensions. The findings reveal unexpected patterns in these shapes and demonstrate the potential for machine learning to accelerate mathematical discoveries.
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.
Researchers discovered that monk parakeets possess a unique tone of voice, known as a voice print, similar to humans. This finding raises the possibility that other vocally flexible species may also have a voice print.
Researchers from FAU's College of Engineering and Computer Science employ a computer-vision deep learning technique to analyze wall-bounded turbulent flows. They successfully identify the sources of extreme events in a data-driven manner, providing new insights into non-linear relationships in fluid dynamics simulations.
Researchers discovered that users' prior beliefs about an AI chatbot's motives significantly impact their interactions with the agent. Priming users to believe certain things about the AI's empathy, neutrality, or manipulation influences their perception of its trustworthiness and effectiveness.
A new AI method leverages causal relationships in genome regulation to efficiently identify optimal genetic perturbations for cellular reprogramming. The technique reduces experimental costs by prioritizing the most informative interventions, leading to faster convergence and more effective results.
Researchers are working on EdgeRIC: Real-time radio access network intelligent control, aiming to enhance wireless communication and machine learning. The project involves developing intelligent systems that can adapt to changing conditions, prioritizing users and acquiring lower latency for applications.
A new study found that ChatGPT-4 can generate highly accurate personal narratives based on stream-of-consciousness thoughts and demographic details. The AI model was used in conjunction with therapists to guide patients toward healthier thoughts and behaviors, suggesting a potential tool for improving therapeutic approaches.
Ghent University's research team explores active machine learning to revolutionize chemical engineering. The technology offers greater flexibility and performance compared to traditional design of experiment algorithms.
A new modeling method powered by interconnected processors removed human bias from the debate over dinosaurs' demise. The study suggests that the outpouring of climate-altering gases from the Deccan Traps alone could have been sufficient to trigger global extinction, consistent with volcanic eruptions contributing to the mass extinction.
Researchers have identified a major weakness in reservoir computing, a powerful machine learning tool used to model complex dynamic systems. The tool requires a lengthy warm-up time and relies on key information about the system being predicted being built in, making it challenging to accurately predict chaotic behaviors.
Insilico Medicine has identified 9 potential dual-purpose targets against aging and 14 major age-related diseases using Microsoft BioGPT. The proposed genes include CCR5 and PTH, which have not been previously correlated to the aging process.
The University of Trento coordinates a European research network, Elias, aiming to establish Europe as a leader in AI research. The project focuses on developing new computational systems and models for sustainable innovation, social cohesion, and reliable AI.
A new clinical and research partnership has created an AI model that can predict whether cancerous tissue has been fully removed from the body during breast cancer surgery. The model performed as well as humans in identifying positive margins, especially in patients with higher breast density.
A recent study published in the Journal of Chemical Information and Modeling presents a significant breakthrough in accelerating giga-scale virtual screens using machine learning. The researchers successfully reduced processing time by 10-fold for 1.56 billion drug-like molecules, identifying top-scoring compounds in under ten days.
Researchers used machine learning to expand and accelerate work on 'atomic shapes,' fundamental pieces of geometry in higher dimensions. The breakthrough identifies shapes and their properties, such as dimension, accelerating new insights across Pure Mathematics.
Researchers at the University of Zurich developed PlantServation, a method that enables scientists to observe plants with great precision using AI and machine learning. The technique allows for the analysis of millions of images taken from various weather conditions, providing insights into how plants respond to environmental factors.
The UW team's system uses self-deploying microphones to divide rooms into speech zones and track the positions of individual speakers. The robots disperse as far from each other as possible, allowing for greater sound control and isolating specific areas or separating simultaneous conversations.
A new study uses AI to predict mortality rates in patients with hip fractures, identifying key biomarkers for poor outcomes. The model achieved accurate predictions using basic blood and lab test data as well as demographic information.
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.
The project aims to assess the operational resilience of microgrids on DoD installations and ships, using new operational resilience indexes developed by Lehigh University researcher Javad Khazaei. The team will develop a dashboard to monitor resilience indexes in real-time, providing recommendations for improving the systems.
A new study found that AI-tutored students caused less damage to healthy tissues and improved safety measures, but also showed negative outcomes such as reduced efficiency and speed. Human instructors are essential to promote both safety and efficiency in neurosurgical training.
A research team at Toyohashi University of Technology has developed a technique to create training data for robots that estimate the state of users using machine learning. The method uses a human body link model without requiring movement analysis, enabling care robots to assist elderly with reduced burden and improved safety.
Researchers found that AI/ML based programs can successfully detect PCOS with an accuracy of 80-90%, making it a promising tool for early diagnosis and reducing the burden on patients. The study suggests integrating large population-based studies with electronic health datasets to identify sensitive diagnostic biomarkers.
A new study introduces the concept of a 'noise barometer,' measuring biological age based on epigenetic changes. The researchers found that specific cytosines show increased variability with age, indicating a biomarker for aging and disease. This approach provides a novel perspective on aging research.
A UMass Amherst neuroscientist is mapping the brain of a sea slug to study how neurons are added to functional neural circuits, shedding light on how this process contributes to neurological conditions. The project aims to provide an unprecedented look at brain development and potentially inform human brain development.
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.
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.
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.
A new method of analyzing nanoscale X-ray movies reveals unprecedented insights into how lithium-ion batteries store and release charge. The study suggests ways to improve the efficiency of billions of nanoparticles in electrode materials, potentially leading to faster-charging batteries.
Researchers developed an AI foundation model for eye care that can identify sight-threatening eye diseases and predict general health. The model, RETFound, was trained on millions of eye scans from the NHS and outperforms existing state-of-the-art AI systems across complex clinical tasks.
Australian researchers analyzed over 1,300 Golden staph strains, linking specific genes to antibiotic resistance and the bacteria's ability to linger in the bloodstream. The study highlights the diagnostic power of integrating clinical and genomic data to develop targeted solutions for deadly superbug infections.
Researchers propose a reinforcement learning-based approach to optimize multi-impulse linear rendezvous trajectories, achieving faster computation times and improved fuel efficiency compared to traditional numerical optimization methods. The algorithm uses an actor-critic architecture and advantage-weighted learning to accelerate train...
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 recent study has successfully predicted potential drug outcomes and side effects by analyzing the discrepancy in gene perturbation effects between cells and humans. Researchers used machine learning to forecast drug approvals, improving reliability over conventional methods that only consider chemical properties.
A new AI tool assesses motor performance using finger taps, providing rapid and standardized ratings, comparable to expert neurologists. The test outperformed primary care physicians in accuracy, opening doors to evaluating other movement disorders.
Researchers from RIKEN Center for Quantum Computing have used machine learning to perform efficient quantum error correction using an autonomous system that can determine the best corrections despite being approximate. Machine learning plays a crucial role in addressing large-scale quantum computation and optimization challenges.
Researchers developed methods to predict CCS values using machine learning and computer models, offering a faster alternative to experimental determination. The study's findings provide a foundation for measurements using portable ion mobility spectrometers in the future.
A team of scientists from Ames National Laboratory developed a new machine learning model that predicts Curie temperatures of new material combinations. This breakthrough discovery is crucial for designing high-performance magnets with reduced critical materials.
The article explores machine learning (ML) applications in chemistry, highlighting its potential to accelerate research and provide innovative solutions. Key findings include the development of ML models for retrosynthesis, atomic simulation, and heterogeneous catalysis, as well as the need for open ML contests to nurture young talent.
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.
A team of researchers at the University of Waterloo and Dalhousie University have developed a method for forecasting short-term disease progression using limited data. The Sparsity and Delay Embedding-based Forecasting model, or SPADE4, uses machine learning to predict epidemic progressions with high accuracy.
A new study by North Carolina State University found that artificial intelligence performs better when it chooses diversity over lack of diversity. The AI was able to increase its accuracy up to 10 times more than conventional AI in solving complicated problems.
A new method can detect drivers' attention levels from their eye movements, enabling the development of more effective takeover signals. The study found that drivers who are engrossed in on-screen activities take longer to respond to warnings, highlighting a potential safety concern.
A team of researchers has discovered a particularly efficient molecular structure for solar energy storage materials, which could lead to more efficient solar energy harvesting. The new molecules were identified by screening over 400,000 molecules with the help of machine learning and quantum computing.
A team of researchers from the University of Zurich and Intel has developed an AI system called Swift that can beat human champions in drone racing. The autonomous drone achieved the fastest lap, winning multiple races against three world-class champions, but human pilots proved more adaptable to changing conditions.
A novel study from the University of South Australia identified 84 features that could signal increased cancer risk in a dataset of 459,169 UK Biobank participants. The study found several biomarkers linked to cancer risk, including urinary microalbumin and high levels of cystatin C.
A deep learning approach has unveiled a significant change in the characteristics of global daily precipitation for the first time. The research found that on more than 50% of all days, there was a clear deviation from natural variability in the daily precipitation pattern since 2015.
A machine learning model has been developed to distinguish the composition ratio of solid mixtures of chemical compounds using only photographs. The model was trained on a small dataset and achieved accuracy roughly twice that of human experts.