Articles tagged with Learning Processes
Researchers propose a reinforcement learning-based approach to optimize multi-impulse linear rendezvous trajectories, achieving faster computation times and improved fuel efficiency compared to traditional numerical optimization methods. The algorithm uses an actor-critic architecture and advantage-weighted learning to accelerate train...
A new study by Hebrew University researchers reveals that conformity impairs group performance in temporally variable settings. In contrast, lower conformity facilitates more efficient adaptability in groups, shedding light on the nuanced relationship between conformity and group outcomes.
Researchers tested memory abilities of bonobos and humans in a series of experiments, finding that humans can learn to distinguish short sequences nearly immediately, while bonobos struggle to retain information and exhibit difficulty learning sequences.
The CDC's Adapting Clinical Guidelines for the Digital Age (ACG) initiative introduces a new integrated process for developing written and computable clinical practice guidelines. This approach allows for feedback from clinicians and can save time and costs. The goal is to achieve a seamless, efficient, accurate, and consistent transla...
A new UCLA study reveals that subjects consistently perceive players in jerseys numbered from 10 to 19 as thinner than those in jerseys numbered from 80 to 89, even when body sizes are the same. This finding suggests that learned associations between numbers and sizes influence perception of body size.
A Brazilian study evaluated the impact of quick and accessible SEL Kernels on children aged 3-6. The program showed significant reductions in conduct problems and improvements in prosocial behavior, indicating improved sociability and reduced hyperactivity.
Researchers at EPFL developed a novel system integrating 2D semiconductors and ferroelectric materials to create faster, more efficient electronics with brain-inspired operations. The technology enables significant energy reduction and advanced functionalities, including synaptic neuron function within the same device.
Researchers from Bar-Ilan University improved AI classification tasks by choosing the most influential path to the output, rather than learning with deeper networks. This approach can enhance existing architectures and pave the way for improved AI systems without additional layers.
Researchers found that insects like American cockroaches use the mushroom body to encode behavioral decision-making based on sensory information. The study challenges the prevailing view of insect cognition, suggesting a more complex brain function than previously thought.
A recent MU study found that health and healthcare organizations are increasingly offering basic social services, such as transportation, housing, food, and mental health support, to address disparities in health outcomes. The research team explored innovative solutions implemented by care coordinators and social services to help overc...
A hybrid system of electronic encoding and diffractive optical decoding transmits optical information with high fidelity through random, unknown diffusers. The system outperforms traditional approaches that only utilize a diffractive optical network or an electronic neural network for optical information transfer.
A study found that veterinary students' attitudes towards dog breeds' pain sensitivity and trustworthiness change as they progress through their coursework, becoming more in line with those of practicing clinicians. The study suggests that cultural transmission, not just data transmission, plays a role in shaping these attitudes.
The WVU team evaluated Code Interpreter's features, finding it accessible to students but limited for scientists working with biological data. The plugin breaks down barriers for coding, but lacks internet access and parallel processing capabilities.
Research by Kessler Foundation team found that processing speed plays a role in benefit from the modified Story Memory Technique on verbal list learning but not on prose memory tasks. This suggests that treating deficits in processing speed may lead to optimal outcomes for individuals with traumatic brain injury.
The South Carolina Clinical and Translational Research Institute's program provides early-career clinicians with the time and training needed to establish a sustainable portfolio of clinical trials. Scholars attend 'meet the expert' presentations, discuss strategies with mentors, and select suitable trials.
Researchers use surface normal nonlinear photodetector to improve speed and energy efficiency of diffractive optical neural networks. The new device can perform high-speed image and video processing at the speed of light in an extremely energy efficient manner.
Morphological awareness plays a crucial role in deaf children learning to read and expand their vocabulary. The study found that this knowledge is more important for deaf students than phonology or phonics, offering opportunities for them to actively construct words.
Researchers developed an AI tool called DECIMER that can translate chemical structural formulae into machine-readable codes. This allows for the automatic search and processing of scientific articles containing chemical information.
Researchers developed a deep-learning model to assess CXR images for probable COVID-19 severity. The model achieved an area under the receiver operating characteristic curve of 0.78 when predicting intensive care need within 24 hours.
A recent study published in Neuron reveals that Noelin proteins play a crucial role in learning and memory formation in the mammalian brain. The study found that these proteins act as 'universal anchors' controlling the distribution and dynamics of AMPA receptors, which are essential for synaptic plasticity.
Researchers found that natural forgetting can be reversed in certain circumstances, with memories stored in engram cells still intact but inaccessible. Competition between engrams affects recall, and forgotten memory traces can be reactivated by cues or updated with new information.
A team led by Dr. Zixiang Xiong at Texas A&M University aims to understand the fundamental limits of learned source coding, a machine learning-based data compression method. They hope to develop more powerful compression methods for efficient use of wireless communication and less energy consumption by mobile devices.
Researchers discovered intricate neural connections in Octopus vulgaris, a model organism for studying memory acquisition networks. The findings challenge traditional models of neural network functionality, revealing an evolutionary adaptation that underlies the octopus's unique cognitive prowess.
Scientists at Max-Planck-Institut für Eisenforschung developed a machine learning model that enhances predictive accuracy in alloy design, uncovering new corrosion-resistant compositions. The model combines numerical and textual data, enabling the identification of optimal alloy formulas.
WVU linguists analyze speech recordings to determine how intensity and duration shape pronunciation in American English and Spanish. The two-year study aims to redefine sound patterns across languages.
A new machine-learning method, BigMHC, can accurately predict cancer-related protein fragments that may trigger an immune system response. By leveraging massive data through transfer learning, BigMHC enables scientists to develop personalized immunotherapies and vaccines by identifying the most likely to provoke an immune response.
A WVU team is developing technology to detect and track lethal non-trackable space debris. The project aims to identify objects that can cause mission-ending damage to satellites and other space assets. The researchers will use a digital twin and simulation environment to test and evaluate their findings.
Researchers found that dopamine and acetylcholine hormones follow constant 'ebb and flow' cycles in the brain, enabling self-driven learning and memory formation. This discovery challenges current understanding of how dopamine and acetylcholine work together in the brain.
Researchers used a mathematical theory called the free energy principle to predict how real neural networks learn and organize themselves. The study successfully mimicked this process in rat embryo neurons grown in a culture dish, demonstrating the principle's guiding force behind biological neural network learning.
Researchers developed an innovative optical tool, the Schistoscope, to capture microscopy images of urine samples for efficient detection of Schistosoma haematobium eggs. A two-stage diagnostic framework using deep learning accurately identified and counted eggs in field settings with high sensitivity, specificity, and precision.
Researchers have discovered that Heliconius butterflies can learn spatial information at large scales, suggesting complex learning skills may be more common in insects. The findings provide the first experimental evidence of spatial learning in any butterfly or moth species.
Deep learning methods significantly improved protein design success rates by 10-fold using AI-augmented pipelines and machine learning software tools AlphaFold 2 and RoseTTA fold. The study successfully generated accurate models of protein structures, paving the way for new discoveries in fields like cancer and COVID-19 research.
Researchers discovered that stimulating dopamine neurons in mice's midbrain triggers orofacial movements, which are linked to brain-wide neuronal activities. The study identified two distinct orofacial movements: transient actions upon reward expectation and active, sustained movements upon receiving a reward.
Researchers developed a generative AI tool, AniFaceDrawing, to assist users in creating high-quality anime portraits. The tool uses a sketch-to-image framework and employs stroke-level disentanglement to match raw sketches with latent vectors of the generative model.
Researchers have discovered a way to utilize nonlinear scattering media for optical computing and machine learning. They created a novel theoretical framework involving third-order tensors, which can represent the complex relationships between input and output signals. This breakthrough has potential applications in real-world settings...
A study reveals that spirit mediums can teach tourism researchers to challenge traditional Western approaches to creating knowledge. By adopting a selfless epistemology, researchers can foster a more inclusive approach to knowledge production and promote the inclusion of marginalized knowledge systems.
Researchers at UCLA found that GPT-3 performs well on analogy problems, similar to human undergraduates, but struggles with understanding physical space. The AI model's ability to mimic human reasoning is unclear, raising questions about its underlying cognitive processes.
Researchers developed a reinforcement learning model that incorporates human decision-making criteria to improve AI accuracy in skin cancer diagnoses. The study showed increased sensitivity for melanoma and basal cell carcinoma, leading to more accurate diagnoses and optimal treatment decisions.
A new study published in the Journal of Urology Open Plus reveals that only 7 saw palmetto products meet the identity and potency standards to effectively address urinary tract symptoms associated with an enlarged prostate. The study highlights the need for physicians and industry to verify the quality of supplements.
Researchers at MPFI discovered Protein Kinase C delta's (PKCd) role in regulating cell-wide gene expression through synaptic plasticity. The study found that PKCd activates biochemical reactions that spread throughout the neuron, influencing gene transcription and memory formation.
A study by University of Toronto researchers found that child language development and language evolution share a common cognitive foundation, based on a core knowledge base. The team built a computational model that predicts word meaning extension patterns across languages and time scales, highlighting the role of visual, associative,...
A new complex-domain neural network enhances large-scale coherent imaging by exploiting latent coupling information between amplitude and phase components. The technique reduces exposure time and data volume significantly while maintaining high-quality reconstructions.
A new study uses machine learning to analyze data from DrugAge, a database of chemical compounds modulating lifespan in model organisms. The researchers create four types of datasets to predict whether or not a compound extends the lifespan of C. elegans, using features such as compound-protein interactions and Gene Ontology terms.
Researchers at WVU have developed a way to view synthetic DNA at the atomic level, enabling them to understand how to change its structure for enhanced scissor-like function. This breakthrough could lead to new technology for medical diagnoses and treatments, including potential therapies for diseases like retinal degeneration and cancer.
A new algorithm has been created to search fungal genomes for clusters of genes likely to result in interesting biological compounds. The algorithm, trained on a newly described type of chemistry in fungi, has discovered over 1,300 fungal species with biosynthetic gene clusters centered on isocyanide chemistry.
A new study published in Aging (Albany NY) suggests that prior training can rescue cognitive decline in aging by improving task performance and strengthening memory processes. The research, conducted on rats, found that prior training enhanced short-term and intermediate memory, while also enabling encoding-boosted long-term memory.
A mobile app is being tested to help students with disabilities such as autism, down syndrome, and learning disorders. The app, I-Connect, will enable select students to set goals, track progress, and receive prompts to improve their behavior and engagement in class.
Researchers found that migrant orangutan males use observational social learning to learn new ecological knowledge from local individuals after dispersing to a new area. They observed males peering at role models for at least five seconds, indicating attentive interest and learning how to process new foods.
Human metacognitive behavior is distinct from AI's reward-punishment system, with humans adjusting memory retention in response to gain and learning speed in response to loss. This asymmetric property may provide insights into the neural mechanisms underlying human metacognition.
Researchers demonstrate that AI language models like ChatGPT can generate high-quality fraudulent medical articles with standard sections and references. The study highlights the need for increased vigilance and enhanced detection methods to combat potential misuse of AI in scientific research.
Researchers found that migrant male orangutan males use 'peering' behavior to observe local individuals and learn about new ecological knowledge, including how to process difficult-to-eat foods. This ability provides a survival advantage and is likely ancestral in the hominin lineage.
Researchers at Georgetown University Medical Center have discovered a new location for the brain's spoken word lexicon, which is located in the front of the primary auditory cortex. This finding has significant implications for recovery and rehabilitation following a stroke or other brain injury.
A soft robotics glove with integrated sensors and AI can aid patients in relearning daily tasks after neurotrauma, including playing music. The glove provides hand guidance, amplifying dexterity and motor skills.
Researchers found that deaf mice have nearly normal neural activity in the auditory system for two weeks after birth, suggesting a molecular mechanism for improved outcomes with early cochlear implants. This spontaneous activity helps train the brain to process sound before the ear canal opens.
Researchers discovered that a diet rich in the bacteria Lactobacillus reuteri can maintain associative learning ability in older nematodes. This finding suggests potential ways to use diet to reduce age-related cognitive decline in other animals, including humans.
A Virginia Tech study found that hellbender fathers are more likely to eat their eggs in deforested areas, leading to a drastic increase in filial cannibalism. The research suggests that protecting and re-establishing forest cover around streams is crucial for the species' survival.
A new rapid viral plaque detection system enabled by holography and deep learning can help accelerate vaccine and drug development. The system reduces the detection time of traditional viral plaque assays by up to 48 hours, eliminating chemical staining and manual counting.
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.
Leading researchers discuss challenges associated with return to school for children and adolescents with TBI, highlighting the need for improved educator training and communication. Strategies for improving quality of life and outcomes are recommended by experts in this themed issue.
A new machine learning algorithm analyzed high-resolution digital images of herbarium specimens, revealing that factors other than climate have a strong effect on leaf size within a plant species. The study also demonstrates how AI can be used to transform static specimen collections and quickly document climate change effects.