Articles tagged with Machine Learning
Researchers have developed an AI-powered system to diagnose autism spectrum disorder (ASD) in children using a single flash of light to the eye. The system uses electroretinography (ERG) to identify specific features that classify ASD, providing a faster and more accurate method for diagnosis than existing tests.
Researchers have developed an AI-powered algorithm that can detect the risk of atrial fibrillation (AFib) with high accuracy, even in people with infrequent AFib episodes. This new tool has the potential to reduce the incidence of stroke and heart failure by identifying patients at increased risk and guiding targeted interventions.
The São Paulo School of Advanced Science on Quantum Materials will select and support 100 graduate students and young researchers to focus on fundamental, theoretical, and experimental aspects of quantum materials. The program will cover topics like superconductivity, electronic topology, and complex magnetic configurations.
Researchers developed a method to measure microvascular changes in the skin using AI and optoacoustic imaging technology, enabling non-invasive assessment of diabetes severity. The study identified 32 significant changes in blood vessels, which can be used to monitor disease progression.
Researchers from the University of Technology Sydney have developed a portable, non-invasive system that can decode silent thoughts and turn them into text. The technology has been shown to achieve state-of-the-art performance in EEG translation, with an accuracy score of around 40% on BLEU-1.
Researchers developed a background-resistant model to predict wheat Leaf Area Index (LAI) across diverse soil backgrounds, showing substantial improvement in prediction accuracy. The model demonstrated good estimation accuracy for different soil backgrounds and reliably captured seasonal LAI dynamics under various treatments.
Researchers developed an adaptable machine learning algorithm to measure driver 'workload' using driving performance signals, enabling real-time adjustments to in-vehicle systems for enhanced safety and user experience. The system can respond to changes in the driver's behavior, status, road conditions, or characteristics.
Researchers create Automatic Surface Reconstruction framework to estimate all possible variations of material surfaces, providing detailed information on catalysts, semiconductors, and battery components. The method reduces human intuition and provides dynamic information on surface properties over time.
Researchers developed a geoknowledge-guided GPT model that extracted location data from Hurricane Harvey tweets with an accuracy rate 76% better than default models. The model recognized complete location descriptions, enabling first responders to reach victims more quickly and potentially saving lives.
Researchers analyzed 13,000 hours of audio data from Okinawan forests before, during, and after typhoons, finding that ecosystems responded differently than expected. The study suggests that developed sites were more resilient to extreme weather than anticipated, but climate change may push these ecosystems to their limits.
A new AI-powered satellite analysis technique reveals the economic conditions of regions with limited data, such as North Korea. The approach combines human input with machine learning to provide detailed economic maps and monitor progress towards Sustainable Development Goals.
Researchers developed a weeklong high school curriculum that teaches color chemistry and AI, improving students' knowledge and motivation. The curriculum uses machine learning to analyze pH levels, showing the connection between chemistry and AI in a practical application.
Researchers use AI to develop dynamic modeling of brain graphs, capturing dynamics in continuous time for more accurate predictions and personalized treatment of brain diseases. The project aims to track disease development in individual patients and identify biomarkers associated with brain disorders.
Novel Dice loss functions, t-vMF Dice loss and Adaptive t-vMF Dice loss, have been developed to improve image segmentation accuracy in medical images. These new functions outperform conventional formulations and show great potential for critical fields like medical imaging and diagnosis.
Researchers from MIT and ETH Zurich developed a filtering technique to simplify a key intermediate step in MILP solvers, speeding up the process by 30-70% without compromising accuracy. A machine-learning model is then used to pick the best combination of algorithms for a specific optimization problem.
Scientists developed an AI method to track neurons in moving and deforming animals using convolutional neural networks with targeted augmentation. This breakthrough reduces manual annotation efforts by three times, enabling faster analysis of brain activity in model organisms like Caenorhabditis elegans.
Researchers used brain imaging and machine learning to identify distinct patterns of brain connectivity in people with autism spectrum disorder (ASD), taking into account individual differences. The study reveals that certain brain features are shared across subtypes, while others are unique to specific individuals.
A new study reveals AI tools are more vulnerable than thought to targeted attacks that force AI systems to make bad decisions. Researchers developed a software called QuadAttac K to test for vulnerabilities in deep neural networks.
Researchers developed two models to evaluate personal learning performance, combining biometric responses with demographic data. The simplified forecast model showed higher accuracy and reduced overfitting than the full forecast model.
Scientists at National University of Singapore developed a hybrid generative machine learning model to explore structural disorders in complex materials. The model unveiled pathways to material disorder, shedding light on factors affecting piezoelectric response. It also found evidence that domain boundaries maximize entropy.
Researchers used machine learning and mobile mapping systems to analyze the town of Sabbioneta's streets and pavements, identifying accessible trajectories and paths for citizens with motor disabilities. The study demonstrated the effectiveness of AI in assessing physical accessibility in historic urban contexts.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
This review article surveys existing deep active learning approaches, applications, and challenges in the context of foundation models. Effective query strategies and model training methods are essential for optimizing joint performance.
A researcher has developed a chatbot with expertise in nanomaterials, leveraging document-retrieval method to provide accurate context. The bot uses embedding to categorize and link information quickly, generating factual responses sourced from trusted documents.
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.
The contribution grows the open-access resource that scientists use to invent new materials for future technologies. Researchers can now focus on promising materials with improved fuel economy in cars, more efficient solar cells, or faster transistors.
A recent study has uncovered a remarkable connection between individuals' musical preferences and their moral values, shedding light on the influence of music on morality. The research found that specific lyrics and audio features from favorite songs can predict moral values such as Care and Fairness and Loyalty.
Researchers analyzed seismic data from the region since 2014, detecting a 8-month long crustal seismicity transient suggesting a preparation process before the M 7.8 Kahramanmaraş earthquake. This highlighted high and increasing seismic hazard in the area.
Researchers developed GraphNovo, a program that provides accurate understanding of peptide sequences in cells, improving immunotherapy for unique cases. The AI model enhances de novo peptide sequencing accuracy, filling gaps with precise mass data.
A novel contamination-detection method enables faster and safer T-cell therapy production, reducing the risk for patients and speeding up treatment. The method uses cutting-edge technology to identify harmful microorganisms within 24 hours.
A team of researchers used AI to optimize thermal aging schedules for nickel-aluminum alloys, resulting in stronger materials at high temperatures. By analyzing unconventional heat treatment patterns, the team discovered a two-step schedule that outperformed conventional methods.
Researchers have developed a novel approach using tensor networks to bridge quantum concepts with machine learning, enabling efficient construction of probabilistic models from quantum states. This framework offers enhanced interpretability comparable to classical probabilistic machine learning.
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...
Researchers developed three diffractive deep neural networks using orbital angular momentum to recognize objects in images, achieving accuracy comparable to wavelength and polarization-based models. The technology has potential for real-time processing applications like image recognition and data-intensive tasks.
A study conducted at Carnegie Mellon University suggests that combining human curation with automated recommender technology can improve user engagement in online news outlets. The research found that algorithmic recommendations outperformed human-curated choices on average, but the human editor did better under certain conditions.
Researchers have successfully mapped the entire HLA class II landscape, predicting how pathogens are displayed on cell surfaces. The mapping reveals that multiple HLA variants play essential roles in autoimmune disorders and organ rejection, highlighting their potential for developing immunotherapy treatments.
Researchers developed an AI model to optimize the macronutrient content of pooled human donor milk recipes, decreasing production time by 60%. The model improved protein and fat levels in milk bank products without compromising bacterial safety, benefiting preterm and sick babies.
A new AI program created by researchers at UF and NVIDIA can generate medical records so well that human physicians couldn't tell the difference. The GatorTronGPT model uses a large language model to mimic natural human language, overcoming hurdles such as protecting patients' privacy and being highly technical.
Researchers at Universitat Oberta de Catalunya used machine learning to simulate patient conditions based on questionnaire answers, predicting stress test performance and identifying symptoms. This approach provides a warning for early diagnosis and referral to specialized units.
A novel machine learning model, FIREANN, accurately simulates system-field interactions for complex chemical, biological, and material systems. The model correlates response properties like dipole moment and polarizability with potential energy changes under external fields.
A team of researchers, including York University, used a mouse model to test how the brain learns new sensory input patterns. They found that the brain's response to image patterns that violate expectations evolves differently over time, suggesting a distinct role in sensory learning.
The study used a machine learning approach called FUN-PROSE to predict how fungi react to different environmental conditions. The model was able to accurately predict the expression of genes in baker's yeast and two less studied fungi, with limitations noted for organisms with more complex gene regulation.
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.
Scientists at PNNL introduced a new way to evaluate AI system recommendations by incorporating human experts' insights. Human expertise improved the accuracy of predictions and boosted confidence scores, indicating better decision-making capabilities for machine learning systems.
A recent study published in Nature reveals that machine learning algorithms designed to diagnose bacterial vaginosis in women show diagnostic bias among ethnic groups. The research found that Hispanic women were more likely to receive false-positive diagnoses, while Asian women received the most false-negative diagnoses.
A recent study by researchers at Charité – Universitätsmedizin Berlin highlights the limitations of large language models like ChatGPT in precision medicine. Human experts were found to be more accurate in identifying personalized treatment options for fictitious cancer patients.
Scientists at the University of Copenhagen and University of Victoria have developed an AI formula to predict rogue waves, which can split apart ships and damage oil rigs. The new knowledge can make shipping safer by identifying the likelihood of being struck by a monster wave at sea.
A new tool called Facemap uses deep neural networks to relate mouse facial movements to neural activity in the brain. This allows researchers to track and quantify movements and correlate them with brain activity, bringing them one step closer to understanding how the brain uses persistent, widespread signals.
Researchers argue for a 'human-centered AI' approach to co-creativity, balancing automation with human control. They emphasize the need for interdisciplinary research on creativity, ethics, and intellectual property rights in human-AI collaboration.
Researchers used machine learning to guide high-throughput experimental screening of small molecules, finding ones that improve vaccine response and reduce inflammation. The team discovered a molecule that outperforms the best immunomodulators on the market, with potential applications in cancer treatment.
Researchers developed a deep learning model that can identify previously unknown quasicrystalline phases in multiphase crystalline samples. The model achieved a prediction accuracy of over 92% and successfully detected an unknown phase in Al-Si-Ru alloys.
A new USC study identifies two metabolites that may predict which young Latino people are most likely to develop prediabetes. The research found that allylphenol sulfate and caprylic acid were the most predictive of prediabetes when combined with other risk factors.
The Python code library snnTorch, developed by UC Santa Cruz's Jason Eshraghian, has surpassed 100,000 downloads and is used in various projects. A new paper published in the Proceedings of the IEEE documents the library and offers a candid educational resource for students and programmers interested in brain-inspired AI.
A novel robotic system developed by USC researchers can help clinicians accurately assess a patient's rehabilitation progress. The method generates an 'arm nonuse' metric using machine learning and a socially assistive robot to track how much a patient is using their weaker arm spontaneously.
Researchers developed a new 3D inkjet printing system that works with a wider range of materials, including slower-curing materials. The system utilizes computer vision to automatically scan the print surface and adjust the amount of resin deposited in real time.
A team of researchers has developed an atom-predicting model similar to the GPT models that support applications like ChatGPT. The new model focuses on small organic molecules with relevance to energy storage and conversion applications.
Researchers developed DIRFA, an AI-based program that generates realistic videos with facial animations synchronized to spoken audio, showcasing improvements over existing approaches. The tool has potential applications in healthcare, education, and entertainment, enhancing user experiences.
Researchers found that smaller subsets of data can be just as effective in training AI models, reducing the need for massive computing power. The study suggests that information richness is more important than dataset size.
Researchers at NC State University developed an autonomous system called SmartDope to synthesize 'best-in-class' materials for specific applications in hours or days. It uses a self-driving lab to manipulate variables, characterize optical properties, and update its understanding of the synthesis chemistry through machine learning.
Researchers developed a deep convolutional neural network to pinpoint cardiac catheter tip locations in photoacoustic images, achieving high precision and recall. The approach has the potential to replace fluoroscopy during cardiac interventions, leading to safer procedures.