Articles tagged with Learning Processes
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.
Researchers developed an AI model called GROVER that treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences. The tool has the potential to unlock the genetic code and advance personalized medicine.
The study reveals that eye-tracking technology can help identify difficulties in mathematical problem-solving, allowing teachers to suggest effective teaching methods. By analyzing data on student gaze patterns and attention shifts, educators can personalize the learning process and improve academic achievements.
Researchers at FAMU-FSU College of Engineering have developed an AI-powered tool to train surgeons by analyzing video of their surgical technique and providing feedback. The system uses deep learning models to evaluate surgical skills effectively, offering real-time feedback for aspiring surgeons.
Researchers at Pohang University of Science & Technology have developed a novel analog hardware using ECRAM devices that maximizes AI computational performance. Their technique, which uses a three-terminal structure with separate paths for reading and writing data, demonstrates excellent electrical and switching characteristics.
Researchers at UCLA have developed a wavelength-multiplexed diffractive optical processor that enables all-optical multiplane quantitative phase imaging. This approach allows for rapid and efficient imaging of specimens across multiple axial planes without the need for digital phase recovery algorithms.
Researchers propose a new approach to studying conscious cognitive information processing, focusing on eureka moments and learning curves. By analyzing performance over time, scientists can identify the timing of conscious processes and observe brain activity using imaging or electrophysiological methods.
Researchers used Game of Thrones to understand how brains recognize faces, finding that familiarity with characters increases brain activity in non-visual regions. This study provides new insights into prosopagnosia, a condition that affects facial recognition and social interactions.
A new method uses deep learning and gold nanoparticle patterns to detect tampered chips with high accuracy. The approach outperforms previous methods in detecting counterfeit chips, offering a promising solution for the $75 billion industry.
Researchers have developed PrISMa, a novel platform that seamlessly connects materials science, process design, techno-economics, and life-cycle assessment to identify effective and sustainable carbon capture solutions. The platform has been tested on over 60 real-world case studies, providing valuable insights for stakeholders.
A new study from University of Florida researchers found that quick learners rely on the visual cortex in their brains when acquiring new motor skills. The study used brain-monitoring electrodes to analyze how people learn to walk at different speeds, revealing a clear difference between fast and slow learners.
A breakthrough in brain-computer interfaces allows a patient to communicate using only the power of thought. The study, led by Dr. Ariel Tankus, enables individuals with paralysis to signal 'yes' and 'no' through electrical signals in their brain.
UCF researchers George Atia and Yue Wang received a $1.2 million DARPA grant to develop AI-based technologies that can help autonomous systems adapt to unknown variables and overcome simulation-to-real gap issues.
Leadership education should encompass both horizontal and vertical development, with skills like conflict management and wisdom being crucial for effective leaders. The authors provide a comprehensive model for training competent leaders by outlining concepts and strategies that can be embedded in coursework.
Researchers create an analog system that can learn complex tasks like XOR relationships and nonlinear regression, using local learning rules without centralized processor. The system is fast, low-power, and scalable, offering a unique opportunity for studying emergent learning.
Researchers have identified a cellular mechanism that detects when the brain needs an extra energy boost to support its activity. This discovery could lead to new therapies for maintaining brain health and longevity by targeting impaired brain energy metabolism, a process accelerated in ageing and neurodegenerative diseases.
Research reveals that pregnancy-related brain impairment affects live-bearing fish, particularly in decision-making and sensory reception. Unlike mammals, pregnant fish show decreased cell proliferation in olfactory regions, compromising scent interpretation.
Researchers developed a one-dimensional convolutional neural network (1D CNN) to compensate for errors due to sample location variations. The model achieved high accuracy, reducing mean absolute error to 0.695% and mean squared error to 0.876%.
A study by Pusan National University researchers found that repeated item development training enhances faculty members' accuracy in predicting and adjusting item difficulty levels, leading to better educational outcomes. The training improved the quality of MCQ assessments in medical education.
Researchers explore the feasibility of deep learning models in segmenting lesions on PET/CT images to improve salvage radiation therapy planning for prostate cancer. The study demonstrates promising potential to reduce inter- and intra-observer variations, leading to more accurate treatment outcomes.
A study published in Frontiers found that relying on digital devices to calm children's tantrums leads to poorer anger management skills and less effortful control. Children who used digital devices more frequently showed increased frustration and decreased ability to regulate emotions.
Researchers at U of T have developed a deep-learning model called PepFlow that can predict the full range of conformations for peptides, which are shorter than proteins but perform similar biological functions. The model combines machine learning and physics to capture precise and accurate conformations within minutes.
Nerve cells in the hippocampus region of the bat brain encode information on multiple characteristics of other individuals, including sex and dominance hierarchy. This study sheds light on how the brain operates and generates thinking processes and behavior.
Researchers at Weill Cornell Medicine discovered that before committing to cell division, cells may stay in a reversible intermediate state for many hours, potentially up to a day. This pre-commitment state allows cells to sense and integrate fluctuating input signals, reducing the chance of inappropriate division.
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 found that hospitals that implemented standardized protocols from the American Heart Association and American Stroke Association saw significant reductions in stroke treatment times. The protocols, which include specific limits on time between symptom onset and hospital arrival, helped medical teams respond more quickly to ...
Researchers at MD Anderson Cancer Center have identified a small molecule compound that restores physiological levels of telomerase reverse transcriptase (TERT), reducing cellular senescence and tissue inflammation. TERT restoration also spurred new neuron formation with improved memory and enhanced neuromuscular function.
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.
A new study at the University of Eastern Finland found that metacognitive knowledge is a key factor in the learning of mathematics. This awareness of one's cognitive processes enables students to face new challenges and adapt to changing environments.
Researchers developed a novel method to estimate modulation amplitude and determine spatial resolution in Brillouin optical correlation-domain reflectometry (BOCDR) without costly equipment. This innovation simplifies the process, reducing costs and enhancing convenience.
Researchers found that specific time cells are essential for learning complex behaviors where timing is critical in mice. The discovery could aid in early detection of neurodegenerative diseases like Alzheimer's by analyzing patterns of time cell activity.
Researchers developed a simple method to measure nano/microplastic concentrations in soil using spectroscopy, eliminating the need for separation processes. The method uses a wavelength combination of 220–260 nm and 280–340 nm to accurately quantify N/MPs in different soil types.
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.
A new study analyzed over 14,600 scientific publications funded by more than $4 billion in NIH grants, revealing key research topics such as clinical trials, vaccine distribution, and virology. The findings suggest that half of the funding went to five states: North Carolina, Washington, New York, California, and Massachusetts.
A new study reveals that praising and petting dogs enhances their learning success by reducing stress. Dogs who received a 'permissive' training style with praise and petting performed better than those in a 'controlling' style, which induced greater stress.
Scientists at Okinawa Institute of Science and Technology identify a positive glutamate-NO-glutamate feedback loop that blocks long-term potentiation and impairs learning and memory. The study suggests that this loop may explain memory loss in stroke patients and potentially offer a solution for treatment.
A new study published in Nature Neuroscience found that training exercises designed to improve cognitive control in children do not lead to any brain changes or improvements in related behavioral measures. The study tested 235 children aged six to 13 and found that while they improved on specific tasks, these gains did not carry over i...
Researchers found that infants in English-learning environments were exposed to more spoken language than music, with the gap widening as they get older. The study used daylong audio recordings collected from home environments and crowdsourced annotations through Zooniverse, closing the gap on past studies that relied on parental reports.
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 predictive model that maps soil-bearing layer distribution, enabling city planners to assess site suitability and optimize building design. The model improves prediction accuracy by combining geotechnical data and geographic coordinates.
A new virtual lab meeting program, LaMP, fosters diversity and improves undergraduate research experience. The program matches researchers with diverse students, providing valuable networking opportunities and professional development, while also promoting underrepresented groups in STEM academia.
Researchers review Silibinin's efficacy in managing inflammation, a key factor in tumour development and aging. The molecule may reduce drug-related toxicity and increase therapeutic potential in integrated cancer therapies.
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.
A recent study published in ScienceDirect found that adolescents who receive cognitive reappraisal solutions from their mothers experience more adaptive coping strategies, whereas those who receive strategizing or help-seeking solutions do not. On the other hand, children who reject or respond ambiguously to their mother's cognitive re...
The study investigates the anticancer potential of CLK kinase inhibitors 1C8 and GPS167, which inhibit CLOCK kinases and affect cancer cell proliferation. The compounds also alter the expression and alternative splicing of transcripts involved in EMT and antiviral immune response.
Researchers at Nagoya University found that cooperative hunting does not require complex cognitive processes, but rather simple rules and experience. The study used computational models and simulations to demonstrate the effectiveness of cooperation in hunting, with AI agents learning to work together through reinforcement learning.
Research from Stockholm University found that translocated pied flycatchers retain songs from their ancestral Dutch population while learning those of their new Swedish environment. This suggests that genetics play a role in shaping bird songs.
Researchers from six teams in five labs worldwide used self-driving labs to discover 21 top-performing OSL gain candidates, accelerating the discovery process by months. The decentralized workflow enabled rapid replication of experimental findings and democratized the discovery process.
Researchers found that repetitive practice leads to changes in working memory circuits, making them more stable and refined. The study's findings have implications for addressing memory-related disorders.
Researchers used D-FFOCT and deep learning to create an intraoperative diagnostic workflow for breast cancer patients. The approach achieved high accuracy and speed, reducing processing time by a factor of 10 compared to conventional histology.
A new approach uses artificial intelligence to turn low-quality images into high-quality ones, enhancing the image quality of metalens cameras. This technology could make these cameras viable for intricate microscopy applications and mobile devices.
Farny will investigate the impact of methylation on gene expression of Pseudomonas putida in soil and laboratory conditions, building gene circuits to test engineered bacteria. The project aims to advance the use of bacteria for environmental cleanups and create educational resources for undergraduate students.
Salk scientists develop new method to quantify synaptic features, revealing high precision of plasticity and up to 10 times more storage capacity. The technique uses information theory to analyze synapse pairs from a rat hippocampus, offering a scalable approach for studying brain function.
A new study finds that coaching employees is crucial in fostering inclusive strategy development processes. The research highlights the importance of employee learning and managerial coaching in unlocking innovative strategic ideas.
Researchers at UC San Francisco found that dispensing abortion pills through the mail is a safe and effective method, with nearly 98% of patients completing their abortions. The study suggests that this model can help increase access to abortion care for clinicians and patients alike.
A new approach uses neural networks to automatically determine polynomial coefficients for digital pre-distortion (DPD) in RF-PAs, reducing hardware complexity and power efficiency. This method can correct non-linearities and support emerging standards without extensive real-time processing.
A new Bayesian inference framework reduces data size by over 80% while achieving accurate modeling and control of optical power evolution. The approach enables simultaneous exploitation and exploration of a data space to identify suitable candidates for autonomous driving optical networks.
Researchers created a digital twin model that predicts and controls complex systems, achieving higher accuracy than traditional methods. The algorithm is compact, energy-efficient, and easy to implement, making it suitable for self-driving vehicles and other dynamic systems.
A study found that adult chimpanzees continue to learn and refine their tool-using skills well into adulthood, suggesting a prolonged learning capacity is key to the evolution of complex tool use in chimps and humans. Chimpanzees developed motor skills for handling sticks by age six, but continued to hone techniques until age 15.