Articles tagged with Machine Learning
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.
Researchers at the University of Cologne found that training can optimize word recognition, leading to improved reading efficiency. The 'Lexical Categorization Model' uses behavioral findings to predict brain activation and separates known words from unknown letter combinations.
Scientists have found evidence of a Bragg glass phase in a crystal using machine learning and X-ray technology. The study provides insight into the nature of glasses and their unique properties, which could inform material design.
A new AI-based approach, PERCEPTION, uses single-cell transcriptomics to predict patient response to cancer drugs and monitor resistance. The approach was validated in three clinical trials for multiple myeloma, breast, and lung cancer, with promising results.
Researchers have developed an active learning strategy to accelerate the synthesis of high-performance engineered biochar with enhanced CO2 uptake. The approach nearly doubled CO2 capture performance, showcasing its transformative impact.
Researchers from the University of Cambridge used AI to identify compounds that block alpha-synuclein aggregation, a key step in treating Parkinson's disease. This breakthrough could lead to faster development of new treatments for the condition, which affects over six million people worldwide.
Researchers have created a probabilistic computer prototype that combines CMOS with stochastic nanomagnets, achieving superior computational performance and energy-efficiency. The new technology reduces area and energy consumption by four and three orders of magnitude compared to current CMOS circuits.
Researchers at NC State University developed a fabric-based touch sensor that can control electronic devices through touch, utilizing machine learning algorithms to improve accuracy. The device, integrated into clothing, activates and controls functions like mobile apps, passwords, and video games with gestures on the sensor.
A new study developed two machine learning models to quantify CD8+ cell positivity and classify the immunophenotype of cancer specimens in patients with non-small cell lung cancer. The models hold promise for identifying patients who may benefit from immunotherapy.
Researchers found that ~60% of tissues exhibit a significant negative correlation between age and stemness score, indicating a pan-tissue decline in stemness. This study adds weight to the idea that stem cell deterioration contributes to human aging, with hematopoietic stem cells from older individuals showing higher stemness scores.
A research team has successfully created a new dimension in photonic machine learning by incorporating sound waves, enabling the creation of reconfigurable neuromorphic building blocks. This innovation has the potential to revolutionize computing tasks by providing high-speed and large-capacity solutions.
Scientists used a neural network to analyze massive particle collision data from the ATLAS detector, marking the first use of this technique in a collider experiment. The method identified an anomaly that may indicate the existence of an undiscovered particle.
Researchers at Bar-Ilan University developed a new AI confidence measure that distinguishes between high- and low-confidence decision making in deep learning architectures. This breakthrough enables the creation of safer and more reliable autonomous vehicles by prioritizing human intervention when confidence levels are lower.
A novel machine learning model has been developed to characterize material surfaces, accurately predicting key electronic properties. The model, which employs artificial neural networks and transfer learning, shows great promise for exploring new materials with superior properties.
Researchers created a system called Holodeck to generate interactive 3D environments, leveraging language models like ChatGPT to control it. The system outperformed earlier tools in evaluating realism and accuracy, with human evaluators preferring its outputs across various indoor environments.
Researchers used fMRI and predictive modeling to decode emotional dimensions of spontaneous thoughts, revealing the involvement of default mode, ventral attention, and frontoparietal networks. The study's findings hold promise for daydream decoding and potential applications in mental health.
A recent machine learning study has discovered a surprising link between wildfires in the western United States and hailstorms in the central US. The research, led by Jiwen Fan, used ML algorithms to analyze vast datasets spanning two decades, predicting hail storms with remarkable accuracy.
Lehigh University researcher A. Emrah Bayrak explores best practices for human-AI collaboration in complex design tasks, aiming to maximize productivity and job satisfaction. His project uses models that predict human decision-making and combines it with AI's training data analysis to determine strategies for division of labor.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
Chemists develop new reactions using model systems and substrates to demonstrate versatility. A new computer-aided method reduces subjective bias by analyzing real pharmaceutical compounds' complexity and structural properties. This improves data quality and facilitates machine learning applications.
A study found a significant association between hospital readmission after fracture surgery and underlying medical conditions. Gait analyses also offered valuable insights into injury impact on locomotion and recovery, optimizing rehabilitation strategies.
A new study by Carey Morewedge and colleagues found that people are more likely to recognize bias in algorithmic decisions than their own. This is because algorithms can codify and amplify human bias, but also reveal structural biases in society. The research suggests ways to increase awareness of biases and correct them.
A new machine learning method called scGHOST has been introduced to identify single-cell 3D genome subcompartments and connect them to gene expression patterns. This can reveal the spatial organization of chromosomes within the nucleus, shedding light on how DNA structure influences gene expression and disease processes.
A study from the University of Gothenburg found that patient images are severely lacking in scientific articles on atopic dermatitis. This lack of visual aids hinders patients' ability to make informed decisions about their care, as they struggle to understand complex medical terminology and figures. The absence of images also affects ...
A new statistical-modeling workflow can quickly identify molecular structures of products formed by chemical reactions, accelerating drug discovery and synthetic chemistry. The workflow also enables the analysis of unpurified reaction mixtures, reducing time spent on purification and characterization.
A novel photonic computing architecture has been developed for tens-of-task lifelong learning, surpassing existing electronic neural networks in capacity and energy efficiency. The L2 ONN demonstrates extraordinary learning capability on challenging tasks, such as vision classification and medical diagnosis.
Researchers at UMass Amherst have identified the most effective AI method for monitoring insect populations using bioacoustics, with deep learning models achieving high accuracy. The study found that machine and deep learning are becoming the gold standards for automated bioacoustics modeling.
A novel machine learning approach reveals complex relationships between hotel service attributes and customer satisfaction, providing actionable insights. The study's IML-DAA model achieves unparalleled accuracy in predicting customer satisfaction, elucidating the impact of specific service attributes on overall guest contentment.
Researchers used AI to investigate MOF-based membranes for helium extraction, revealing critical factors influencing separation performance. The study identified pore limiting diameter and void fraction as key physical features determining membrane selectivity and helium permeability.
Researchers developed an AI model to detect viable tumor cells in osteosarcoma patients, improving prognosis predictions. The model showed comparable detection performance to pathologists and reduced inter-assessor variability, enabling timely assessment.
Researchers from the University of Tokyo have developed a physics-based predictive tool that quickly identifies stable intercalated materials for advanced electronics and energy storage devices. By analyzing over 9,000 compounds, the tool uses straightforward principles from undergraduate chemistry to predict host-guest stability.
Researchers from the University of Washington created an AI algorithm to analyze infant poses using limited training data. By leveraging generative AI, they were able to produce high-quality results, enabling parents to monitor their babies' daily activities and detect potential health issues early.
Researchers analyzed over 3,500 river basins worldwide, finding that precipitation was the sole determining factor in only 25% of flood events. Soil moisture and air temperature were decisive factors in around 10% and 3% of cases, respectively. The study suggests that more extreme floods are caused by multiple factors interacting.
Researchers develop AI-powered method to rapidly predict multiple protein configurations, understanding protein dynamics and functions. This breakthrough has the potential to revolutionize drug discovery by uncovering more targets for new treatments.
Researchers found that neural networks use a similar path to chart their way from ignorance to truth when presented with images, despite varying network designs and training recipes. This commonality holds the potential for developing more efficient image classification algorithms, reducing the computational power required by AI systems.
Researchers at University of Missouri are developing software that allows drones to fly independently, perceiving and interacting with their environment while achieving specific goals. This technology has the potential to assist in mapping and monitoring applications, such as 3D or 4D advanced imagery for disaster response.
A new MIT-derived algorithm corrects coarse climate model predictions by 'nudging' them toward more realistic patterns, leading to more accurate forecasts of extreme weather events. The approach uses machine learning and dynamical systems theory to improve the resolution of large-scale climate models.
A study published in Critical Care identified eight different trauma phenotypes associated with lower in-hospital mortality when treated with tranexamic acid. The researchers used a machine learning model to analyze data from over 50,000 patients and found subgroups of patients who received no benefit from treatment.
Researchers developed a new method to predict thermoelectric materials using AI, avoiding trial-and-error and overfitting. The approach achieved remarkable accuracy in predicting newly available materials, providing guidance for experiments.
Researchers from Kobe University developed an AI image recognition algorithm that can predict mouse behavior based on brain functional imaging data, achieving 95% accuracy. The model identified critical cortical regions for behavioral classification and demonstrated near real-time speeds.
Researchers at Klick Labs developed an algorithm to detect deepfakes with 80% accuracy by analyzing speech pause patterns, offering a solution to the growing problem of AI-generated content. The study's findings suggest that vocal biomarkers can distinguish between real and fake voices, providing a novel approach to flagging deepfakes.
A team of researchers has developed a machine learning interatomic potential that predicts molecular energies and forces acting on atoms, reducing computational time and expense. This breakthrough enables scientists to study complex chemistry systems with greater accuracy and speed.
Researchers developed a novel machine learning-based depth estimation technique for satellite-derived bathymetry, improving accuracy in coastal regions with unique characteristics. The model demonstrated generalizability and potential for enhancements through incorporation of additional seabed spatial data.
Artificial intelligence has been developed to spot COVID-19 features in lung ultrasound images, combining computer-generated images with real scans to identify signs of disease. The tool holds potential for developing wearables that track illnesses like congestive heart failure and monitor fluid buildup in patients' lungs.
A new AI tool can predict which breast cancer patients are at risk of chronic arm swelling after surgery and radiotherapy. The tool, developed by international researchers, uses machine learning algorithms to analyze patient data and provides easily understandable explanations for doctors and patients.
Researchers developed machine learning models that can recognize emotions in voice recordings as short as 1.5 seconds with high accuracy comparable to humans. The study used three ML models and achieved an accuracy of over 90%, with potential applications in therapy, interpersonal communication technology and more.
A new study uses machine learning to classify fossils of extinct pollen with high accuracy, leveraging morphological features and phylogenetic data. The model successfully placed nearly all specimens within Podocarpus based on their shape and form.
The Rensselaer Polytechnic Institute researcher is working with the Tachyon Project to create surrogate machine learning models that can simulate and analyze particle physics data in real-time. This project aims to improve scientific discovery and workflow performance for scientists at Fermilab and ALCF.
Researchers used hyperspectral imaging and machine learning to classify rapeseed maturity, achieving high accuracy rates. The study identified key wavelengths and preprocessing methods that improved model performance, offering a non-destructive solution for uniform seed maturity.
A new approach to nutrient level detection in rubber leaves uses semi-supervised learning with unlabelled hyperspectral data, outperforming traditional supervised methods. The study balances class imbalance using resampling techniques, enhancing classification accuracy and reliability.
Researchers developed chronological age prediction models by analyzing gene expression changes in the prefrontal cortex, identifying genes associated with aging and potential mechanisms. The models showed high correlation with age and demonstrated female and male-specific differences.
Researchers at Rice University have developed a custom-built miniaturized chemical vapor deposition (CVD) system that can observe and record the growth of 2D MoS2 crystals in real-time. Through advanced image processing and machine learning algorithms, they were able to extract valuable insights into the growth processes of these mater...
A review published in Intelligent Computing outlines the strengths of automatic approaches to designing metaheuristics, which can lead to more successful outcomes and reduce redundant, metaphor-based algorithms. The authors encourage research that relies on automatic design, utilizing modular software frameworks and configuration tools.
Researchers have discovered new genetic mechanisms related to spinocerebellar ataxia type 37, a rare neurological disorder that affects balance and movement. The study employed advanced techniques such as CRISPR/Cas9 gene editing and machine learning to uncover the disease's underlying causes.
Researchers at ETH Zurich taught ANYmal, a quadrupedal robot, to perform parkour and navigate rubble using machine learning. The robot uses its camera and artificial neural network to determine obstacles and perform movements likely to succeed based on previous training.
A new method using multi-target regression and hyperspectral imaging enhances crop nutritional analysis, predicting multiple element concentrations with improved accuracy. The approach considers inter-element relationships, outperforming traditional single-target regression methods.
Scientists use machine learning algorithms to model atomic masses of nuclide chart, complementing research on nuclear structure and astrophysical processes. The approach enables physics-based extrapolations and provides information on 'missing physics'.
Elfi Kraka and colleagues develop a novel machine learning interatomic potential called ANI-1xnr that accurately simulates atomic-level interactions in various environments. The model has the potential to aid in understanding planetary care, drug interactions and exploring cosmic materials.
A study by Washington State University found that exposure to multiple air pollutants was associated with asthma symptoms among elementary school children. The researchers identified 25 combinations of air pollutants linked to asthma, with one group from a lower-income neighborhood experiencing higher exposure levels.
Researchers are developing a new framework to integrate renewable energy sources with the power grid using machine learning. The goal is to ensure efficient and stable operations while maximizing wind and solar power usage.