Articles tagged with Machine Learning
Researchers are working on improving the quality of high frequency wireless networks. Dr. Murat Yuksel is hoping to realize the dream of unimpeded communication at distances near and far. He is developing a smart wireless network system using machine learning, which can fine-tune the networks' efficacy.
Researchers used AI to analyze over 16,000 butterfly images, finding both males and females contribute to diversity among species. The study resolves a century-old debate between Charles Darwin and Alfred Russel Wallace on the role of natural selection in female evolution.
Researchers found that AI models that analyze medical images can predict patient demographics with high accuracy but struggle to diagnose patients from diverse backgrounds. The models use demographic shortcuts, leading to incorrect results for women, Black people, and other groups.
Researchers developed a machine learning estimator to classify charge states in quantum dots, enabling automatic tuning of qubits. The estimator achieved high accuracy with visualizations revealing decision-making patterns, paving the way for scaling up quantum computers.
Researchers developed an AI model that accurately predicts metal yield strength by combining physical theory with machine learning. The model outperforms traditional methods, which often rely on extensive experimentation.
Researchers used AI to map slush on Antarctic ice shelves and found that 57% of all meltwater is held in slush, with a significant impact on ice shelf stability and sea level rise. This discovery could lead to more accurate predictions of ice sheet melting and collapse.
A recent study by Prof. Martin Bichler suggests that dividing Germany into several price zones may not reduce total power costs as expected. In contrast, nodal pricing shows promise in reducing costs by up to 9% due to efficient resource allocation and reduced re-dispatch measures.
A Chinese research team introduced a novel two-stage framework using stacked transformers for multimodal sentiment analysis, improving the analysis of emotions expressed through modality combinations. The framework was tested on three open datasets and performed better than or as well as benchmark models.
Researchers from Florida Atlantic University developed a novel approach using wearable sensors and machine learning to assess balance. The method achieved high accuracy and strong correlation with ground truth balance scores, suggesting it is effective and reliable in estimating balance.
Researchers developed machine learning models predicting upper secondary education dropout from kindergarten age, using a 13-year longitudinal dataset. The study marks an advancement in early automatic classification, potentially leading to transformative changes in educational systems and policies.
Researchers at Boston University developed an AI model that analyzes speech patterns to predict the likelihood of Alzheimer's disease in patients with mild cognitive impairment. The model achieved an accuracy rate of 78.5% and could potentially revolutionize dementia screening, making it more accessible and efficient.
University of Texas at Dallas researchers develop AI model that can automatically reroute electricity in milliseconds to prevent power outages. The system uses machine learning to map complex relationships between entities in a power distribution network, enabling faster response times than human-controlled processes.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.
Researchers at FSU used machine learning to analyze patterns in dried salt solution drops, identifying the chemical composition of different salts with accuracy. The tool has potential applications in lab safety testing, rapid screening for suspected drugs and low-cost blood analysis.
A new system named SQUID, a computational tool created by Cold Spring Harbor Laboratory scientists, helps interpret how AI models analyze the genome. It reduces background noise and leads to more accurate predictions about genetic mutations.
Scientists at UVA and Toyota Research Institute create language representations of driving behavior to enable robots to associate words with environmental interactions. This allows cars to provide guidance and adjust speed in challenging situations, improving safety and usability.
Research suggests that large-language models could play a role in managing the energy grid, particularly in emergency response, crew assignments, and wildfire preparedness. However, significant challenges remain, including data availability, safety guardrails, and reliability, which must be addressed to ensure safe deployment.
Researchers at Bar-Ilan University have discovered a new scaling law that governs how artificial neural networks handle an increasing number of categories for identification. This law reveals how the identification error rate increases with the number of required recognizable objects, impacting AI latency and efficiency.
Researchers at Cold Spring Harbor Laboratory designed a new way for AI algorithms to move and process data more efficiently, inspired by the human brain. This design allows individual AI neurons to receive feedback and adjust on the fly, processing data in real-time.
PSICHIC uses sequence data and AI to decode protein-molecule interactions with state-of-the-art accuracy, eliminating costly processes like 3D structures. The tool effectively screens new drug candidates and performs selectivity profiling, offering a more efficient and reliable approach to drug discovery.
A new machine-learning model using serum fusion-gene levels predicts HCC with an accuracy of 83-91%, significantly improving upon current biomarkers like serum alpha-fetal protein. This breakthrough tool may help identify patients at risk and monitor cancer recurrence, leading to improved survival rates.
A new study on learning has provided insights into the balance between habitual and goal-directed behaviors, with implications for AI development. The research suggests that a balance between these two types of behavior is necessary for efficient and adaptable decision-making in AI systems.
Researchers used machine learning to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles to identify four distinct molecular profiles of Alzheimer's Disease. These profiles were associated with varying levels of cognitive function and neuropathological features.
A recent study published in Critical Care Medicine found that real-time machine learning alerts significantly improved patient outcomes by predicting clinical deterioration. The study showed that patients who received AI-generated alerts were 43% more likely to have their care escalated and had a lower risk of death.
Researchers at UC San Diego School of Medicine are developing an AI model to predict opioid addiction in high-risk patients. The model uses generative artificial intelligence to analyze genomic, social determinants of health, clinical, procedural, and demographic data to identify patients at greatest risk.
A recent study published in The Lancet Oncology found that an AI system can detect prostate cancer nearly seven percent more significantly than a group of radiologists using MRI scans. Additionally, the AI identifies suspicious areas less often, potentially reducing unnecessary biopsies by half.
A new machine-learning system can automatically produce detailed maps from satellite data to show locations of likely beetle-killed spruce trees in Alaska. This helps forestry and wildfire managers make critical decisions as the beetle infestation spreads, affecting approximately 2 million acres across Southcentral Alaska.
The article explores how AI can be applied to the electric power and energy industry, demonstrating its potential as a valuable technology for asset management. Machine learning techniques are showcased as a solution to improve efficient and sustainable energy networks.
Researchers at the Complexity Science Hub analyzed friendships and listening habits to find social networks are a crucial predictor of song popularity. The study showed that individuals with strong influence and large friend circles accelerate a song's popularity, making social connections a key factor in music trends.
Using fMRI, researchers analyzed brain activity while participants experienced sustained pain and pleasure induced by capsaicin and chocolate fluids. The study identified common brain regions activated by both experiences and developed predictive models to capture affective intensity and valence information.
A trash-sorting robot has been developed that can recognize and classify objects using tactile information and machine learning algorithms. The robot achieved a classification accuracy of 98.85% in recognizing diverse garbage objects not encountered previously.
A team of researchers at Penn has developed an artificial intelligence tool that can mine the vast and largely unexplored biological data from over 10 million molecules to discover new candidates for antibiotics. The deep learning approach identified thousands of candidates in just a few hours, with many showing preclinical potential.
A new computer vision technique developed by MIT engineers significantly speeds up the characterization of newly synthesized electronic materials. The technique automatically analyzes images of printed semiconducting samples and quickly estimates two key electronic properties: band gap and stability.
Researchers developed HypOp, a framework using unsupervised learning and hypergraph neural networks to solve combinatorial optimization problems significantly faster. The framework can also tackle certain problems that prior methods cannot effectively solve.
Researchers confirmed that elephant calls contained a name-like component identifying the intended recipient through machine learning analysis. Elephants responded affirmatively to calls addressed to them and less so to those meant for others, suggesting an ability to learn and use arbitrary vocal labels like humans.
A new study reveals that ChatGPT's automated content moderation filters can flag nearly 20% of its own generated scripts for content violations, including half of PG-rated shows. The research raises questions about the efficacy of using AI as a tool in scriptwriting and its potential impact on artistic expression.
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 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 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 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.
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.
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.
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 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 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 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 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 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 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.
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.
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.