Articles tagged with Adaptive Systems
The TRAILS AI Institute has awarded eight seed grants totaling $1.5 million to advance AI design, development and governance. The funded projects include developing AI chatbots for smoking cessation and designing animal-like robots for autism support.
Researchers developed an AI system that analyzed video recordings from therapy sessions with patients with borderline personality pathology. The system accurately detected the emotional states of patients, including fleeting micro-expressions. It also identified a predictor for therapy success: 'social' smiling at the start of a session.
A new deep learning-based detection system has been developed by INU researchers to improve the detection capabilities of autonomous vehicles. The system, aided by IoT technology, generates bounding boxes and confidence scores for visible obstacles using point cloud data and RGB images as input.
Neurons in the ventrolateral prefrontal cortex (VLPFC) work together to process social interactions by combining facial and vocal information. The study found that individual neurons did not exhibit strong responses to expressions or identities, but population-level activity could be decoded to reveal the identity and expression in vid...
The European Union's AI act could enable AI to access our subconscious minds, potentially leading to manipulation. According to Ignasi Beltran de Heredia, only 5% of brain activity is conscious, and the remaining 95% operates subconsciously, making it difficult for us to control or even be aware of.
Researchers at Rice University are developing a machine learning framework to improve decision-making processes in military communication networks. The goal is to enable rapid, adaptive action across a broad range of scenarios by combining local data in the most effective manner.
Researchers adapted a COVID-19 collaboration to monitor near-real-time trends in substance use-related hospital and emergency department visits. Detailed data shows large increases in methamphetamine- and opioid-involved hospital and ED visits among Native American, Black, and multiple-race populations.
Yu Yang's NSF-funded research aims to reduce vehicle emissions and promote the use of electric bikes and scooters by developing socially informed traffic signal control systems. The project involves a three-pronged method that uses low-cost mobile air-quality sensing, spatial-temporal graph diffusion learning, and reinforcement learnin...
A Lancaster University academic argues that AI and algorithms contribute to polarization, radicalism, and political violence, posing a threat to national security. The paper examines how AI has been securitized throughout its history, highlighting the need for better understanding and management of its risks.
Post-Acute Sequelae of COVID-19 research aims to track long-term health symptoms in survivors. A $3.7 million grant will support the development of self-supervised deep learning technologies to recognize post-COVID lung progression phenotypes.
Researchers developed Precious1GPT, a multimodal transformer-based approach for aging clock development and feature importance analysis. The model utilizes methylation and transcriptomic data to predict biological age and identify disease-related genes, providing a pathway for therapeutic drug discovery.
Researchers discovered parallels between the brain architecture of sea slugs and more complex segmented creatures with jointed skeletons and appendages. The study suggests that simpler organisms like sea slugs adapted a network of neurons to govern locomotion and posture, which was later inherited by more complex animals.
Researchers propose a 'state space' approach to reframe farming planning questions, enabling analytics and machine learning to explore optimal crop combinations and simulate different scenarios. This framework allows farmers to design diverse agricultural landscapes based on natural ecosystems, increasing crop yield and sustainability.
Researchers develop unsupervised machine learning algorithm to classify osteosarcoma at diagnosis based on gene expression modules. This approach enables personalized treatment strategies for osteosarcoma patients.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
The NERVE Center has developed test methods and metrics for various robots, identifying limitations to improve systems. The center's success grew its research capabilities through partnerships with NIST and the U.S. Army.
Researchers develop CRISPR-Cas systems associated with transposons to rewrite large chunks of DNA in organisms like E. coli. This expands the CRISPR toolbox for flexible genome editing and has significant implications for therapeutics, biotechnology, and agriculture.
Two billion people globally rely on mountain water for drinking and irrigation, which is under threat due to global heating. Researchers propose integrated water strategies that include scientists working directly with communities to drive climate adaptation and boost water security.
Researchers at North Carolina State University developed a CRISPR-based system that uses engineered bacteriophages to deliver genetic payloads to specific bacteria, even in complex environments. This technology enables precise single-letter changes to the genome without double-strand DNA breakage.
Researchers propose a broader definition of learning that includes behavioral adaptation to environmental features, enabling collaboration across fields and promoting new research. This 'mechanism-free' approach highlights the importance of system-level responses to environment in various domains.
The University of California, San Diego is part of the National Institutes of Health's Bridge to Artificial Intelligence program, aiming to create comprehensive AI-ready datasets. The program will support researchers in developing interpretable and trustworthy AI technologies to improve human health.
A new University of Illinois project aims to improve undergraduate students' ability to estimate their knowledge using artificial intelligence methods. The researchers will utilize machine learning to anticipate student performance and provide personalized feedback to enhance studying strategies.
A new study introduces a novel epigenetic predictor, PCBrainAge, that captures aging heterogeneity across multiple brain regions. The tool demonstrates stronger associations with AD dementia and pathologic AD compared to existing age predictors.
NC State researchers discovered a new way to make the difficult-to-characterize gut bacterium Bifidobacterium more responsive to antibiotics. They also found tiny changes in different strains that reflect large differences in their characteristics, highlighting the need for individualized CRISPR-based genome engineering approaches.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Homa Alemzadeh's research aims to develop safety monitoring tools for robotic surgery, enabling better training for surgeons and increasing the availability of less-invasive procedures. Her work has the potential to improve patient outcomes in underserved rural communities.
Researchers conducted wave-optics simulations to study the impact of turbulence on light beams, finding that branch point density grows non-linearly with grid resolution. The study's results could lead to more accurate modeling and improved performance in Adaptive Optics systems.
Researchers aim to develop an audio health library to enhance communication during triadic visits. The study will analyze primary care visits and conduct user-design sessions to refine the system.
Researchers developed a machine learning model that provides good predictions for human speech recognition in noisy environments, benefiting hearing-impaired listeners. The model outperformed expectations and showed strong correlations with measured data.
A team of scientists developed a soft haptic sensor that can accurately estimate contact points and forces using computer vision and deep neural networks. The sensor is sensitive enough to detect even tiny forces and detailed object shapes.
A team of scientists developed an AI-based model to predict personal thermal comfort based on spatial parameters, achieving exceptional accuracy. The study highlights the importance of incorporating architectural features in models to reduce energy consumption.
Astronomers have captured detailed images of sinuous stellar jets emanating from young stars, suggesting that their sidewinding appearances are caused by gravitational attraction from companion stars. The observations were made using the Gemini South telescope's adaptive optics system.
A new machine learning-based approach enhances student engagement in online environments. The algorithm detects when students disengage, prompting interventions to improve learning outcomes.
A new study improves AI diagnoses by penalizing algorithms for false negatives, which can be more urgent than accuracy. Researchers achieved significant improvements in precision and recall for chronic kidney disease and other conditions using cost sensitivity techniques.
Researchers used machine learning to analyze core-loss spectroscopy data, revealing connections between spectral data and material properties. The study successfully predicted intensive and extensive material properties, enabling high-throughput development of new materials.
Researchers from South Ural State University created a hybrid intelligence system that can predict pile bearing capacity with high accuracy. The system, developed by combining adaptive neuro-fuzzy inference system and group method of data handling, outperformed traditional methods and provided close to measured values.
A new warning system has been proposed to address increasing heat waves due to climate change, taking into account evolving heat thresholds and longer hot seasons. The system aims to prevent heat-related mortality outside typical heat wave periods through health alerts.
George Mason University's Lance Liotta has received a grant of $72,000 to study the adaptation mechanisms of cellular protein networks. The Australian Research Council Future Fellowship will provide an additional $784,680 over two years.
Researchers at the University of Bristol have developed a spatially adaptive broadcast system that enables broadcasters to adapt their geographical coverage in real-time, reducing energy consumption by up to 35%. This technology has the potential to improve the energy efficiency of wireless and broadcast networks.
A video analysis study by Christopher P. Bonafide and colleagues investigated factors influencing nurse response times to bedside monitor alarms. The research found that nurse experience, alarm type, and hospital environment all played significant roles in shaping response times.
The global population has more than doubled since 1960, with over 20% of people aged 60+ in developed countries by 2030. Healthcare systems must adapt to cope with the impact of ageing populations, including reducing hospital beds and reallocating budgets.
The Big Bear Solar Observatory has achieved 'first light' using a deformable mirror, resulting in the most detailed visible light image of a sunspot ever captured. This achievement marks a significant milestone for the observatory's adaptive optics system, which will enable better studies of solar complex phenomena and space weather.
Researchers discovered that capsaicin, found in chili peppers, triggers a desensitization process in pain receptors, allowing them to adapt to painful stimuli. This adaptive response enables the receptor to continuously respond to varying stimuli, leading to a shift in responsiveness threshold.
The new AO76 system, developed by the National Optical Astronomy Observatory in New Mexico, produces sharper images under worse seeing conditions than the previous AO24 system. This allows existing solar telescopes to produce higher resolution images and greatly improve their scientific output.
Adaptive optics systems are expanding ground-based astronomy capabilities, enabling unprecedented views of the universe. The technology has already produced eightfold improvements in image quality at observatories like the W.M. Keck Observatory in Hawaii.