Articles tagged with Machine Learning
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Researchers at Osaka Metropolitan University developed a machine learning-based deicer that offers higher performance while minimizing environmental harm. The new mixture of propylene glycol and sodium formate solution shows improved ice penetration capacity, reducing the need for substance use.
A team of researchers from Princeton University and the US Department of Energy's PPPL have successfully deployed machine learning methods to suppress harmful edge instabilities in fusion devices. Their approach optimizes the system's suppression response in real-time, maintaining high plasma performance without sacrificing stability.
A new study uses machine learning to search for antibiotics in a vast dataset of microbial genomes, identifying 863,498 candidate antimicrobial peptides. Promising results are observed in initial tests against disease-causing bacteria and preclinical animal models.
A new study by the Society for Risk Analysis explores the impact of AI-driven cyberattacks on global economies, supply chains, and trade. The research found that these attacks can cause significant declines in real GDP, trade prices, and volumes, as well as disruptions to trade routes, particularly among heavily reliant digital economies.
Researchers found human infants use 'helpless' period to pre-train brain, leading to rapid learning and high performance, similar to machine learning models. This study challenges classic explanation for infant helplessness and could inspire next gen AI models.
A comprehensive study led by Dr. Luan Shenghua of the Chinese Academy of Sciences found a general factor of impulsivity that is stable and predictive of behaviors, contradicting claims of its demise as a personality trait.
A new open-source platform called CheckMate allows users to interact with and evaluate the performance of large language models (LLMs) like ChatGPT. Researchers found that while LLMs can be helpful, they also make mistakes and provide incorrect information.
A new method for detecting defects in additively manufactured components uses deep machine learning, generating synthetic defects for training and testing on physical parts. The algorithm accurately identifies hundreds of defects, even those unseen by the model before.
The team created a prediction model that generates sustainable products with high accuracy and explores the vast design space of aerogel assembly. Their strong and flexible aerogels have programmable mechanical and electrical properties, opening up new possibilities for green technologies.
Portland State University has secured a nearly $1 million grant from the National Science Foundation's Campus Cyberinfrastructure program to establish the Oregon Regional Computing Accelerator (Orca) cluster. The cluster will provide free-of-cost computing resources and cyberinfrastructure to colleges in rural, regional, and minority-s...
Researchers at La Jolla Institute for Immunology developed a computational method to link gene activity to molecular marks on DNA, potentially aiding in the detection of solid tumors and more accurate cancer diagnoses. This new approach utilizes machine learning tools to identify connections between genes and enhancers in the genome.
Researchers developed a machine learning model that predicts ideal oxygen levels for individual patients based on characteristics such as age, sex, and heart rate. The results suggest personalized oxygenation targets could reduce mortality rates, offering new hope for critical care patients.
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
A new study from Chalmers University of Technology shows that AI-controlled charging stations can offer personalized prices to electric vehicle users, minimizing both price and waiting time. However, the researchers highlight the importance of addressing ethical issues related to data exploitation by motorists.
Researchers at Texas A&M University are investigating the historical effects of strain on shape-memory alloys to improve predictive capabilities. They will use a synergistic experimental and numerical approach to understand and predict history effects in these alloys, with potential applications in heart stents and airplane wing flaps.
A team of researchers from Rice University and the University of Michigan found that some neurons not only replay recent past experiences but also anticipate future experience during sleep. The discovery provides an unprecedented view of how individual neurons in the hippocampus stabilize and tune spatial representations during periods...
Researchers developed a new method to enhance thermal image super-resolution by employing synthetic imagery, significantly improving detail and utility of thermal imaging across various applications. The approach utilizes high-resolution images from the visible spectrum to guide the super-resolution of low-resolution thermal images.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
A team of researchers from Japan, China, and Finland created a system called generative content replacement (GCR) that uses AI to replace parts of images that might threaten confidentiality with visually similar but AI-generated alternatives. In tests, 60% of viewers couldn't tell which images had been altered.
Researchers have developed a system combining bio-inspired cameras with AI to quickly detect obstacles around cars, using less computational power. The hybrid system detects objects up to one hundred times faster than current systems while reducing data transmission and processing needs.
Researchers at Duke University developed an assistive machine learning model that greatly improves the ability of medical professionals to read EEG charts. The model, which provides visual explanations and decision support, has been shown to almost double medical professionals' accuracy in identifying seizure-like events, potentially s...
A new deep learning AI model, Dev-ResNet, identifies embryonic developmental events in pond snails using video analysis. This breakthrough enables the detection of key features, such as heart function and hatching, with unprecedented sensitivity.
Researchers have developed a method to detect microplastics in marine and freshwater environments using porous metal substrates and machine learning. The system can identify six types of microplastics with high accuracy, offering a cost-effective solution for environmental monitoring.
Researchers at Oregon Health & Science University have received a $16.4 million grant to advance mental health care for children, leveraging machine learning and novel clinical measures to improve prediction, diagnosis, and treatment of mental health conditions across childhood and adolescence. The project aims to create an actionable ...
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 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 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.
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 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.
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.
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.
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.
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...
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.
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.
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 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.
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.
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.
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 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...
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.
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 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.
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 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.
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.
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.