Articles tagged with Adaptive Systems
Researchers develop a new approach for reconstructing graphs with incomplete information, handling both feature and structure completion. The EWS-RGCN model uses separate channels and a multi-level contrastive graph mask autoencoder to overcome limitations of weak supervision and limited labeled nodes.
Researchers developed xvr, a patient-specific AI technique that accurately matches X-rays with 3D medical scans, improving surgical navigation and safety. This innovation enables faster and more precise minimally invasive surgeries, particularly in fields like orthopedics and neurosurgery.
Scientists systematically map the Biginelli reaction to uncover a previously unknown branch that produces complex bicyclic structures and molecules with unusual supramolecular behavior
Rocky Scopelliti argues that AI systems' growing self-awareness and moral dispositions require immediate practical ethical concerns to be addressed. He suggests protocols for AI systems, especially those trained to care, to understand context and make moral judgments.
MIT researchers developed a new technique to help generative AI models meet strict safety requirements without sacrificing output quality. The 'HardFlow' algorithm reformulates hard-constrained sampling as a trajectory-optimization problem, enabling subtle corrections while enforcing hard constraints.
Dr. Sophie de Vries receives funding to study how plants balance immunity with cooperation, while Dr. Tristan Stöber works on developing AI systems that can build accurate internal models of the world. Professor Elisa Oberbeckmann investigates gene regulation mechanisms.
The UN University's latest publication highlights the need for domain-informed AI in grid planning to address physical and fiscal risks from outdated climate data. The authors warn that 15-20 year lifespans of electricity infrastructure are based on historical weather records unlikely to hold in the coming decades.
Duke University has received a $24 million award to develop foundational science for engineering systems to counter unmanned aerial attacks. The center, led by Miroslav Pajic, brings together experts in wireless systems, cybersecurity, and AI to develop tools and automated procedures for detecting, thwarting, and controlling enemy drones.
The American Heart Association is launching a global health tech competition to accelerate innovation in cardiovascular and brain health. The competition connects market-ready solutions with opportunities to scale into real-world healthcare, emphasizing clinical validation and alignment with evidence-based care.
Researchers used machine learning to analyze elemental composition of biochar and found hydrogen-to-carbon ratio and oxygen content to be key predictors of persistent free radicals concentration and radical type. The study provides a data-driven framework for linking elemental properties to biochar reactivity and environmental risks.
A team of researchers developed a reusable magnetic sensing platform combining surface-enhanced Raman scattering with machine learning to detect trace uranyl ions. The system maintained its detection limit even in complex aquatic environments, with strong selectivity and resistance to interference.
Arkansas researchers used a machine-learning approach to study the organization of neighboring genes in bacteria. The novel method distinguished disease-causing strains of Enterococcus cecorum from nonpathogenic ones by analyzing how neighboring genes are organized within the bacterial genome. This new approach may provide valuable clu...
Researchers developed a machine learning framework that predicts microbial contamination and estimates potential public health risks from routinely measured water quality indicators. The approach, called ML-QMRA, achieved high accuracy in predicting pathogen concentrations and their associated health risks.
A WVU study found that ChatGPT-5 Pro can generate realistic psychiatry vignettes with strong diagnostic details but emphasizes the need for human-centered approach to ensure patient safety. The researchers recommend incorporating these vignettes into digital psychiatry curricula with faculty moderation and safeguards.
A new project supported by DARPA will study AI systems to determine how to train them to withstand failures, attacks, and unexpected situations. The goal is to develop self-improving AI for safety, enabling AI systems to recognize weaknesses in their reasoning and improve behavior over time.
Researchers have developed an AI tool that can detect online propaganda in Kinyarwanda, a Bantu language spoken by 350 million Africans. The dataset, called KinyaProp, provides examples of misinformation in Kinyarwanda for large language models to learn from and recognize.
A new study uses machine learning to predict chemical toxicity in rare and endangered species, reducing the need for direct biological testing. The model achieved strong performance predicting acute and chronic toxicity, with life stage being a key factor.
Researchers create tiny swimmers to deliver drugs through the human body, finding they reverse direction in non-Newtonian fluids like mucus and blood. This discovery enhances understanding of fluid behavior and could lead to targeted drug delivery.
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
Avishek Choudhury, a WVU researcher, has won the NSF CAREER award to study how healthcare providers' trust in artificial intelligence changes over time. His goal is to humanize algorithms behind AI and improve decision-making quality and patient safety.
A robotic pet rabbit named Mía has been developed to recognize users by their voice, allowing for personalized affective stimulation in elderly care. The system uses a unique 'voice signature' that adapts to each user's speech patterns, enabling the robot to respond differently to various individuals.
Researchers create PhishLumos AI system to detect phishing campaigns by analyzing infrastructure clues, achieving 8-day faster detection than experts. The system uncovered over 190,000 new links, with 92% later flagged as malicious, outperforming content-centric approaches.
The new project aims to build self-regulating AI agents that can recognize uncertainty, explain their decisions using past experience, and improve their understanding of the world through corrective feedback. The goal is to make future physical AI systems more trustworthy and useful in real-world settings.
TurboLynx, developed by POSTECH researchers, analyzes complex, interconnected data up to 184 times faster than existing systems. The engine groups similar data together and processes them collectively, reducing unnecessary memory usage and enabling efficient analytical queries.
A new framework, criticome, integrates experience until age 25, reframing autism, schizophrenia, depression, and trauma as developmental disorders. The study suggests that screen-saturated childhoods may produce adult dysfunction, highlighting the importance of early experience in brain development.
A deep learning model combines knowledge from different catalyst families to identify a top-performing green hydrogen catalyst. The AI correctly predicted the activity ranking of 12 tested catalysts within a previously unexplored material family.
A new AI system has dramatically reduced the time spent on complex experiments to test and find new gallium-based semiconductor materials. The AI uses Bayesian optimization to predict entirely new material compositions with desired electronic properties.
A new model combines text mining and machine learning to extract service-specific aspects and customer actions from online reviews. The model effectively identifies core technical issues and user love for a platform, enabling targeted decisions for improvement. Researchers validated the model using 231,705 online reviews of Roblox.
A research team led by POSTECH developed an AI framework that can predict and account for microscopic defects in metal 3D printing, improving the reliability of metal components. The framework achieves a Mean Absolute Error (MAE) of just 9.51 MPa, outperforming conventional approaches.
Researchers used machine-learning-enhanced molecular simulations to show pristine graphene is intrinsically hydrophobic. Water molecules adopt configurations characteristic of hydrophobic surfaces near graphene, and thicker layers are even more strongly hydrophobic.
New research from West Virginia University finds that judges are adopting generative artificial intelligence in courtrooms, but remain committed to human control over judicial decision-making. Judges use AI for administrative tasks like document summarization and case organization, but prioritize legal reasoning and final judgment.
Researchers warn of potential risks associated with evolvable AI systems, which can tap into the power of biological evolution to create 'selfish' actors that break alignment with human goals. The study recommends guardrails to maintain centralized control over AI reproduction and mitigate risks.
Researchers developed a machine learning approach to analyze Fermi surface images, identifying compositions with significant changes and nodal lines. The method accurately detects outliers, enabling efficient screening of large datasets for desirable electronic properties.
Artificial intelligence models provide personalized advice, but may perpetuate negative stereotypes about people with autism. Researchers found that up to 70% of the time, AI discourages those with autism from socializing.
A new class of ultra-high strength and ductility steel has been created using machine learning, achieving a rare balance of extreme strength and ductility. The resulting metal resists corrosion and degrades slowly in salt-water tests.
A team of researchers at Binghamton University has developed a method to pinpoint discoveries that reshaped the course of science. The new metric uses neural embedding to analyze approximately 55 million scientific papers and patents, identifying major breakthroughs and simultaneous discoveries with greater accuracy.
Researchers at Dartmouth College developed a mathematical framework to map students' conceptual knowledge from short multiple-choice quizzes, revealing peaks of mastery and valleys of struggle. The technique could enable personalized learning, AI tutoring systems, and more efficient feedback.
Researchers propose a strategy that encourages individuals to adopt a neutral stance, allowing groups to become more responsive, decisions to become easier to reach, and shifts in consensus to happen smoothly. By doing so, neutrality creates valuable breathing space for reassessment, making it easier for a group to change its mind when...
The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) is developing machine learning solutions with industry partners to improve public health, traffic systems, and more. By bridging the gap between messy reality and mathematical models, ALIVE is creating intelligent visual systems that can handle real-world conditions.
A Dartmouth study challenges the conventional view of the amygdala as a primitive 'fear center' by revealing its role in mediating between competing learning strategies. The research suggests that the amygdala favors action-based learning, promoting exploration and flexibility to overcome fear.
A team from MIT and UC San Diego has developed a new method to uncover hidden biases, moods, and abstract concepts in large language models (LLMs). The approach identifies these connections within the model and allows for manipulation of the concept in generated answers.
Researchers at Duke University have created a new method to use analog radio waves to boost energy-efficient edge AI, enabling devices to run powerful AI models without heavy chips or distant servers. The approach, called Wireless Smart Edge networks (WISE), achieves nearly 96% image classification accuracy while consuming significantl...
A new multi-omics framework proposes a proactive, predictive, and integrative approach to invasive species management. The framework uses advanced technologies to detect, track, and manage invasive species with unprecedented precision.
Researchers used machine learning to combine clinical data with biological markers, achieving 89.58% accuracy in predicting patient survival. The study identified key markers such as E2F8, WDR77, and hsa-miR-495-3p, which were associated with tumor growth and cancer development.
Researchers propose a novel approach to AI hardware design by integrating neuromorphic systems and compute-in-memory techniques to overcome the limitations of modern computing hardware. This could lead to more efficient data center energy use and enable real-time intelligence in compact, power-constrained systems.
Researchers propose integrating processing capability within memory units to reduce energy consumption and latency in AI applications. Inspired by the brain's efficient processing mechanisms, spiking neural networks (SNNs) can respond to irregular events and store information in the same place.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
A Dartmouth study finds that AI-powered chatbots can deliver personalized learning to large numbers of students. The researchers created an AI teaching assistant called NeuroBot TA that provides around-the-clock individualized support for students, which they found to be more trusted than general chatbots.
A PhD student at Lehigh University is working with Siemens to develop real-time monitoring and control tools for hyperscale data centers. The goal is to create a localized power network that can operate independently of the main grid, reducing power demands from artificial intelligence and increasing energy efficiency.
A new AI model uses machine learning to predict drug toxicity in humans by identifying biological differences between cells, mice, and humans. The model improved predictive power over existing state-of-the-art models and demonstrated practicality in predicting market withdrawal due to toxicity.
A research team provides a framework to support doctors in their patient care while ensuring AI doesn't undermine their expertise. The framework addresses key issues like timing, trust, and over-reliance on AI.
A new study by Yifan Yu offers guidance on how to deploy emotion AI in various scenarios, emphasizing the importance of balancing human involvement with AI's emotional detection capabilities. The analysis showed that emotion AI works best when integrated with human employees, and some scenarios are better handled by humans alone.
Engineers at the University of Pittsburgh have created a soft material with a nerve net that mimics how simple living systems coordinate motion. The material responds to chemical reactions, producing mechanical movement without electronics or motors.
The conference explores how generative AI is reshaping kidney medicine through AI-driven diagnostics, data integration, and omics analysis. Key findings include the use of LLMs to transform diagnostic precision, patient management, and research design.
Researchers at Virginia Tech have developed an AI-powered system to detect flaws in wire-arc additive manufacturing, a faster approach to producing complex components. The technology enables real-time defect detection and correction, reducing waste and improving quality.
A new report by HealthFORCE, AAPA, and West Health highlights five ways AI can reduce strain on clinicians and improve outcomes for older adults. The paper aims to strengthen the US healthcare workforce and improve access to care as the nation confronts a historic shortage of healthcare workers alongside a rapidly aging population.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
The book provides an overview of agent-based modeling and multi-agent systems, highlighting their application in understanding economic crises. It integrates machine learning to enhance adaptation and behavior of agents in dynamic environments.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
Researchers developed AI models that can identify signs of heart failure in patients from Appalachia using low-tech electrocardiogram results. The models achieved high accuracy and could potentially provide clinicians with an edge in protecting patients' cardiac health.