Articles tagged with Machine Learning
Researchers developed innovative encoding methods that simplified quantum circuits for data encoding, reducing circuit depth by a factor of 100 while maintaining accuracy. These methods showed improved resilience against adversarial attacks, paving the way for practical application of quantum machine learning on current devices.
A TU Wien-developed robot can learn to clean a sink by watching humans perform the task, adapting its knowledge to different shapes and applying the right amount of force. The technology combines machine learning and robotics, enabling robots to share their parameters through federated learning.
Researchers identify biomarker Ki67 that indicates whether treatment with vedolizumab will be successful. The biomarker can help predict which patients are more likely to respond to the form of treatment, allowing for more targeted use and potentially improving patient outcomes.
The University of Tennessee and Lockheed Martin have expanded their master research agreement to address national security challenges. The partnership will support advanced technologies such as hypersonics, materials, and energy systems, while also providing experiential learning opportunities for students.
A team of scientists leveraged machine learning to find promising compositions for sodium-ion batteries, achieving exceptional energy density. The study trained a model on a database of 100 samples to predict the optimal ratio of elements needed to balance properties like operating voltage and capacity retention.
Researchers have identified two genes, ATXN2L and MMP14, linked to both rheumatoid arthritis and osteoporosis. These genes play a role in apoptosis, immune regulation, and bone metabolism.
The AI-powered system can detect toxic gases like nitrogen dioxide in real-time, identifying the source of harmful gas leaks. The system's optimization technique ensures fewer resources are used while providing faster and more accurate gas leak detection.
The project aims to identify and fabricate optimized first-wall materials using advanced computer simulations enhanced by machine learning, accelerating the discovery of new materials by 100-fold. The research will leverage synthesis, irradiation, and testing facilities to conduct a high-impact materials discovery campaign.
Researchers developed AI to predict bloodstream infections and antimicrobial resistance in ICU patients, providing same-day assessments. This technology is cost-effective and faster than current methods, enabling quicker decision-making on antibiotic use.
The São Paulo School of Advanced Science on High-Dimensional Modeling offers minicourses and sessions to enhance data professionals' training in machine learning and finance. Key challenges related to forecasting, asset allocation, and climate econometrics will be addressed through state-of-the-art science and research.
Researchers developed a wearable ultrasound device that tracks muscle function without invasive procedures, offering high-resolution imaging and wireless monitoring capabilities. The technology has potential applications in respiratory health and human-machine interfaces.
Srisharan Shreedharan leads a collaborative effort to gain knowledge of processes that could improve seismic hazard forecasting. The research aims to identify key indicators that can help predict earthquakes and reduce seismic hazards.
Yihao Zheng and his team are developing a fiber-optic probe that analyzes artery blockages in the brain and guides procedures for blockage removal. The technology uses light and advanced calculations to determine the properties of blood clots, enabling doctors to make informed decisions about how to remove them.
A new study published in Nature found that up to 215 million hectares of land in tropical regions around the world has the potential to naturally regrow, storing 23.4 gigatons of carbon over 30 years. The study identified areas with high regrowth potential based on factors such as soil quality and proximity to existing forest.
A research team from Hokkaido University has developed a flexible multimodal wearable sensor patch that can detect arrhythmia, coughs, and falls using edge computing on a smartphone. The sensor patch generates large amounts of data that must be processed to be understood.
Researchers found that Black patients are less likely to receive medical tests, leading to inaccurate AI models. A new algorithm corrects for this bias by identifying untested patients based on race and vital signs, improving model accuracy to around 60%.
A new study by UCLA Health reveals that standard medical record surveillance methods miss youth with suicidal thoughts and behaviors in children, boys, and Black and Hispanic youths. Machine learning algorithms improved detection rates when incorporating additional data from visit notes.
Researchers analyzed correlations between fruit fly and human data to identify key metabolites impacting lifespan. Threonine was found to extend lifespan in flies and show promise as a therapeutic target for aging interventions.
Researchers have identified a genetic signature that can predict neonatal sepsis in newborns before symptoms appear, allowing for earlier recognition and life-saving treatment. The discovery has the potential to improve healthcare outcomes in lower- and middle-income countries where neonatal sepsis is most prevalent.
A machine learning algorithm developed by University of Cambridge researchers can detect and grade heart murmurs in dogs with high accuracy, similar to expert cardiologists. The technology has the potential to empower primary care veterinarians to provide early detection and treatment, improving quality of life for dogs.
A machine learning model predicts soil behavior during earthquakes, identifying areas vulnerable to liquefaction and providing contour maps for safer construction sites. The study uses geological data to create detailed 3D maps of soil layers, improving prediction accuracy by 20%.
Researchers developed a machine learning model to predict dielectric function of materials, facilitating novel dielectric material development. The model speeds up calculations by using chemical bonds between atoms and achieving accuracy close to first-principle calculations.
A new study uses PCA and Mahalanobis distance to detect early bolt loosening in wind turbines, achieving accuracy of over 95%. This technology improves operational safety and efficiency by providing early warnings based on objective data.
Researchers at UC Santa Cruz have developed a highly accurate and affordable spectrometer that can be customized for specific applications. The device uses machine learning algorithms to reconstruct images with high accuracy, enabling astronomers to study phenomena such as exoplanet atmospheres and dark matter in faint galaxies.
A new study by Penn State researchers suggests that making AI training data diversity information available can shape users' expectations of algorithmic fairness and trust. Displaying racial diversity cues in AI interfaces can enhance users' perceptions of algorithmic fairness and trust, according to the study's findings.
A new tool, SymGen, enables users to verify AI model responses more quickly and easily by displaying data citations. This speeds up the manual validation process by 20 percent, making it easier for users to spot errors in LLMs deployed in various real-world situations.
A new AI-powered model has been developed to predict kidney transplant outcomes with high accuracy, offering hope for more efficient organ allocation and improved patient outcomes. The tool, UK-DTOP, outperforms existing methods in predicting outcomes for deceased-donor kidney transplants.
A recent study reviews advancements in reinforcement learning for autonomous vehicle control, highlighting similarities and differences in DRL formulations and training algorithms. The research aims to enhance RL applications, making autonomous vehicles more capable of handling complex traffic situations under uncertain conditions.
The improved method achieves high accuracy in lithium-ion battery state of charge estimation, outperforming traditional methods such as Back propagation Neural Network and Long Short-Term Memory. The model's robustness is enhanced through periodic parameter updates based on battery operating conditions.
Researchers have created a new diagnostic tool using machine learning to detect schistosomiasis, a persistent parasitic infection affecting an estimated 250 million people. The tool can identify low levels of the infection and distinguish between active and past infections, leading to earlier treatment and improved long-term outcomes.
Researchers developed an AI model that uses machine learning and combination theory to predict suspicious skin lesions. The new C4C Risk Score has an accuracy of 69% and significantly outperformed existing methods, including 7PCL and Williams score.
A new project, 'Crowd-Assisted Human-AI Teaming with Explanations,' aims to develop an interactive AI system that leverages the collective strengths of human crowd workers and machine learning models. The researchers will use crowdsourcing platforms to recruit experts and non-experts to perform tasks, making the system more robust and ...
The new center aims to develop AI-driven tools for predicting solar eruptions, expand space science education programs, and build a long-term dataset of sun activity. It will also establish an education program providing research opportunities for students and promoting STEM education.
A study from Lehigh University and Seattle University found that making patients aware of biases in human healthcare decisions increases receptiveness to AI recommendations. By highlighting the limitations of human judgment, healthcare providers can create a more balanced relationship between patients and emerging technologies.
Researchers use machine learning to analyze optimal bike lane placement in Toronto, balancing accessibility for all with overall efficiency. Key findings include a trade-off between equity and utility, with essential routes like Bloor West's bike lanes serving neighbourhoods far from their endpoints.
Researchers have developed a new system for full-body motion capture that leverages sensors within consumer mobile devices. The app, called MobilePoser, tracks a person's full-body pose and global translation in space in real time with advanced machine learning and physics-based optimization.
Computer simulations point the way towards better solar cells by gaining crucial insights into what influences properties of 2D perovskite materials. Researchers have discovered that the choice of organic linkers can directly control how atoms in surface layers move, affecting optical properties.
Researchers found that male mice deescalate aggressive encounters by running over to a female mouse to distract the aggressive male mouse. This 'bait-and-switch' tactic reduces further conflict and helps maintain social hierarchy in groups of mice.
Researchers have found that integrating machine learning with statistical methods improves disease risk prediction model accuracy. The study highlights the potential of such integrated models in clinical diagnosis and screening practices, which could lead to better patient outcomes.
A Caltech-led team has developed a control strategy called FALCON that uses reinforcement learning to adaptively learn how turbulent wind can change over time, allowing UAVs to predict and respond to extreme turbulence in real-time. The strategy has been tested in a challenging test setup and shows promising results.
A new Multi-task Learning (MTL) model detects 85% of abusive posts originating from right-leaning individuals on social media platforms.
Researchers at Indiana University are developing next-generation ophthalmoscopes to spot early warning signs of diseases like Alzheimer's, diabetes, and heart disease with a simple eye scan. The technology uses machine learning and AI to reduce diagnosis time from days to minutes.
Researchers from Charité have shown that deep brain stimulation using electrical impulses can accelerate movement and shorten delays in Parkinson's patients. By decoding the intent preceding voluntary movement seconds before action, they discovered that dopamine significantly speeds up this process.
Researchers developed a machine learning model to predict mesenteric lymph node metastasis preoperatively in colorectal cancer patients. The XGB-based model achieved high accuracy, identifying key predictors such as perineural invasion and hematocrit levels.
Researchers used LSTM networks to detect cyber threats in SWaT plant industrial control systems, capturing complex time-dependent patterns missed by traditional methods. The study demonstrates the effectiveness of LSTM technology in safeguarding industrial control systems from cyberattacks.
Researchers have developed new AI models for plasma heating that can predict plasma behavior more accurately than existing numerical codes. The models use machine learning to analyze data generated by a computer code, enabling faster simulations without compromising accuracy.
Researchers have developed AI-enabled detection software that can accurately detect natural debris, litter, or waste blocking culverts. The system can be integrated to existing CCTV systems to provide proactive flood defense, improving safety for response teams.
Researchers developed an electronic tongue that can identify differences in liquids and detect food safety concerns. The AI-powered system achieved high accuracy when using its own assessment parameters, providing insights into the neural network's decision-making process.
Researchers tested 24 MLLMs on Raven's Progressive Matrices, finding that open-source models struggled significantly. However, closed-source models like GPT-4V performed relatively well, suggesting a need for more advanced resources and training data to improve AI's cognitive abilities.
The two-year study aims to explore biases in AI systems and develop a 'human-in-the-loop' framework for quality data discovery. It will investigate how humans can be involved as labelers, prompters, and validators to improve data sets and user interfaces.
A new study of bubbles on electrode surfaces could help improve the efficiency of electrochemical processes by understanding how blocking effects work. The findings show that only a smaller area of direct contact is blocked from its electrochemical activity, not the entire surface shadowed by each bubble.
A Kennesaw State University researcher aims to develop open-source, hands-on QML training materials to educate future researchers. The project will create nine training modules with hands-on labs covering key quantum computing concepts.
Researchers at the University of Tokyo introduce a new optical computing scheme called diffraction casting, which improves upon existing methods. The system uses light waves to perform logic operations and has shown promise in running complex calculations, including those used in machine learning.
The Endocrine Society's inaugural Artificial Intelligence in Healthcare Virtual Summit will explore AI's potential to improve medical care, advance research, and leverage big data. Key sessions will discuss predictive analytics, machine learning algorithms, and natural language processing.
The new framework, called SLIViT, has been developed by UCLA researchers and achieved accurate disease risk biomarkers detection from medical scans. It consistently achieves better performance compared to domain-specific state-of-the-art models.
Researchers developed an AI-driven approach to model complex hand movements, overcoming current limitations in neuroscience and biomedical engineering. The model achieved a 100% success rate in controlling virtual Baoding balls, showcasing its strength in various challenging situations.
Researchers at TU Graz have developed a new machine learning method that generates precise live MRI images of the beating heart using only a few MRI measurement data. This breakthrough enables faster and cheaper MRI applications, including quantitative MRI for diagnoses.
The study challenges the idea of creating artificial general intelligence (AGI) with human-level cognition, citing limitations in replicating human cognition. Researchers argue that even under ideal circumstances, it is impossible to achieve AGI due to the complexity of cognitive processes.
A team of researchers aims to improve autonomous vehicle safety by identifying and mitigating vulnerabilities in software and hardware. They plan to use knowledge gained from a $926,737 NSF award to design protection mechanisms that can be applied selectively to ensure safety while maximizing efficiency.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.