Articles tagged with Neural Networks
A new deep learning model, CNN-SENet, leverages GNSS-R data to improve wind speed retrieval. The model outperforms conventional models in both speed and precision, offering promising tool for global ocean wind monitoring.
Researchers identified a key brain region, the ventrolateral periaqueductal gray matter (vIPAG), that modulates pain and emotional responses in threatening situations. By inhibiting specific cells within this region, scientists discovered a potential pathway for better pain relief.
The study proposes an event-triggered asymptotic composite neural tracking control scheme for intelligent vehicles, addressing system nonlinearities and uncertainties. It enhances tracking precision and reduces communication traffic through variable threshold-based triggering conditions.
The Rice University team created a soft robotic arm capable of performing complex tasks using smart materials, machine learning, and an optical control system. The arm is guided and powered remotely by laser beams without any onboard electronics or wiring.
A revolutionary AI model has been developed to diagnose lung cancer without relying on costly GPU servers or massive datasets. The ultra-lightweight model achieved a discrimination performance corresponding to an AUC value of 0.92, outperforming state-of-the-art large-scale AI systems.
Researchers found that AI chatbots have difficulty detecting adverse drug reactions and providing personalized advice. The study suggests that improving AI for mental health concerns could be life-changing for communities with limited access to healthcare.
Researchers employed Bayesian neural networks to fit photonuclear cross-sections with remarkable reliability, outperforming traditional methods like TENDL-2021. The approach demonstrated superior accuracy in describing low-energy thresholds and high-energy tails, particularly for sparse or biased data.
Researchers at Johns Hopkins University found that AI systems struggle to understand social dynamics and context necessary for human interaction. Human participants were able to accurately rate features important for understanding social interactions, while AI models failed to match human brain and behavior responses across the board.
The AChemS 47th Annual Meeting features cutting-edge research on chemosensory perception, including taste and smell dysfunction in cancer patients and potential associations with learning and memory decline. The conference also highlights the impact of GLP-1 Receptor Agonists on human taste ability.
A team of researchers developed Lp-Convolution, a novel method that uses multivariate p-generalized normal distribution to reshape CNN filters dynamically. This breakthrough improves the accuracy and efficiency of image recognition systems while reducing computational burden.
A Lehigh University team developed a novel machine learning method to predict abnormal grain growth in materials, enabling the creation of stronger, more reliable materials. The model successfully predicted abnormal grain growth in 86% of cases, with predictions made up to 20% of the material's lifetime.
Researchers have created a breakthrough photonic chip that can train nonlinear neural networks using light, accelerating AI training while reducing energy use. The chip uses a special semiconductor material to reshape how light behaves, enabling reconfigurable systems with wide mathematical function expression.
Scientists have developed an all-optical activation function based on sound waves for photonic computing, enabling the creation of energy-efficient artificial intelligence systems. This breakthrough could potentially facilitate the scaling up of physical computing systems and pave the way for more efficient optical neural networks.
A new hardware platform for AI accelerators capable of handling significant workloads with reduced energy requirement has been developed. The platform leverages III-V compound semiconductors to create photonic integrated circuits, which operate at the speed of light with minimal energy loss.
A new USC-led study using fMRI reveals the neural mechanisms that contribute to urinary incontinence in stroke survivors. The research found significant differences in brain activity during voluntary versus involuntary bladder contractions, presenting potential pathways for targeted therapies.
Researchers have identified specific brain structures that regulate political passion, finding damage to the prefrontal cortex increases intensity and amygdala decrease it. The study suggests emotion plays a role in shaping expressed political beliefs rather than determining ideology.
A new study from USC Dornsife's Brain and Creativity Institute found that nostalgic music engages the brain's default mode network linked to memory and self-reflection, as well as its reward circuitry. This discovery could support emotional well-being and cognitive function in individuals with memory impairments.
Distributed acoustic sensing systems face data processing speed limitations; researchers leverage photonic neural networks to overcome these challenges. The TWM-PNNA system achieves high recognition accuracy above 90% with low power consumption, outperforming electrical GPUs by orders of magnitude.
Researchers at Saarland University are developing leaner, customized AI models and techniques like knowledge distillation to reduce energy consumption. These smaller models enable small and medium-sized businesses to access powerful AI technology without a large technical infrastructure.
A new neural network can identify fish activity on coral reefs by sound, faster than human experts, enabling real-time monitoring of fish populations, species identification, and disaster response. This technology has the potential to revolutionize ocean monitoring and research.
Researchers at Technical University of Munich developed a new AI training method that significantly reduces energy consumption. The approach uses probabilities to determine parameters, making the training process 100 times faster while maintaining accuracy comparable to existing procedures.
Researchers have identified three cell types in the median raphe nucleus that control decisions on perseverance, exploration, and disengagement. These findings may help understand neuropsychiatric conditions such as OCD, autism, and major depressive disorder.
Researchers found differences in genes and brain wiring between forest and desert flies, explaining how climate change impacts insects. Forest flies show increased avoidance of heat, while desert flies are attracted to warmer temperatures.
Researchers discovered a horizontally distributed and modular organization of cortical movement units, with different types of neurons forming functional clusters in distinct regions. The study also found that the brain re-networks and adapts to learn new motor skills.
Researchers develop a novel adaptive nonlinear PID controller integrated with radial basis function neural network for enhanced ballbot functionality. The proposed NPID-RBFNN controller demonstrates superior stability and robustness, outperforming traditional PID and NPID controllers.
Researchers created a computational method to track brain cell development over time, capturing unlabeled cells and fine structures in live cultures. The algorithm achieved high precision rates for detecting individual neurons, paving the way for studying neurological diseases and developing therapies.
A new study led by researchers at Mass General Brigham suggests that different brain regions activated by creative tasks are part of one common brain circuit. People with brain injuries or neurodegenerative diseases may have increased creativity due to changes in this circuit.
A study by Nagoya University researchers found that excessive neuronal activation over time leads to brain function decline, contradicting previous theories. Interventions targeting reduced neuronal hyperactivation, such as dietary changes, may mitigate age-related cognitive decline in humans.
Harvard researchers have developed a silicon chip capable of recording small yet telltale synaptic signals from a large number of neurons. The chip has successfully mapped over 70,000 synaptic connections from approximately 2,000 rat neurons.
The study compared the performance of seven AI models with that of 400 humans in comprehension tasks, revealing a significant difference in accuracy. Human accuracy reached 89%, while AI models struggled to achieve more than 70% correct answers.
A new optical encryption system uses holograms and neural networks to encode information, making it virtually unbreakable. The system achieves an exceptional level of encryption by utilizing a neural network to generate the decryption key.
A recent study published in JAMA Network Open found that heavy cannabis users exhibited reduced brain activity during working memory tasks, associated with worse performance. Abstaining from cannabis before cognitive tasks may help improve performance.
The new model, based on a PV-RNN framework, achieves compositionality by combining language with vision, proprioception, working memory, and attention. It requires less computing power than large language models (LLMs) and makes mistakes similar to humans.
A new tool called EpiScalp uses algorithms trained on dynamic network models to map brainwave patterns and identify hidden signs of epilepsy from a single routine EEG. This tool has ruled out 96% of false positives, cutting potential misdiagnoses among cases by nearly 70%, according to a Johns Hopkins University study.
Researchers introduced a novel approach to enhance reservoir computing, incorporating a generalized readout that offers improved accuracy and robustness compared to conventional methods. The new method uses a nonlinear combination of reservoir variables to uncover deeper patterns in input data.
Researchers at the University of Bonn have developed a new training technique for highly efficient AI methods, inspired by biological neurons that use short voltage pulses to communicate. This approach enables spiking neural networks to be trained using conventional methods, resulting in improved accuracy and reduced energy consumption.
Researchers developed a cutting-edge method leveraging Graph Neural Networks (GNNs) to predict mesozooplankton community dynamics and visualize their interactions. The study achieved remarkable improvements in forecasting accuracy by integrating inter-series relationships and temporal dependencies among input-variables.
The study reveals that directional connections propagate signals in a downstream flow, leading to more complex activity patterns. Mathematical models also suggest that modularity and connectivity interact to foster dynamical complexity.
A recent study demonstrates how DNNs can predict fragrance profiles from essential oil chemical compositions, validating sensory evaluations. The model achieved high accuracy in predicting floral scents and showed promise for generating new and unique combinations.
Researchers developed a laser-based artificial neuron that emulates biological graded neuron functions, achieving a signal processing speed of 10 GBaud. This enables fast AI decision-making in time-critical applications with high accuracy.
A new study has identified a specific connectivity pattern of brain atrophy in schizophrenia, distinct from brain networks associated with other psychiatric disorders. The findings suggest that this network may be a core characteristic of schizophrenia and could inform treatment plans.
Researchers developed a new method, k* distribution method, to visualize and assess how well deep neural networks categorize related items together. The model reveals clustered, fractured or overlapping arrangements of data points, indicating accuracy and reliability issues.
Researchers at Pusan National University developed a hybrid model to predict metal wear in magnesium alloys, enabling safer, lighter designs. The model combines machine learning and physics to improve fatigue life prediction, offering greater predictive reliability for enhanced safety and longevity.
Studies have shown that the lateral habenula regulates stress-related respiratory responses via the monoaminergic system, which includes dopaminergic and serotoninergic pathways. Researchers found that electrical stimulation of the LHb mimicked a stress state in rats, significantly increasing respiratory frequency.
Researchers at the University of Tokyo discover that the patterns of spontaneous activity and stimulus-evoked response are similar in lower visual areas of the cerebral cortex but gradually become independent as one moves to higher visual areas. This orthogonal relationship helps explain how sensory perception remains stable despite co...
Researchers at Karolinska Institutet and Columbia University identified a mini-brain within the heart with its own nervous system that controls the heartbeat. This discovery challenges current views on how the heartbeat is controlled and may lead to new insights into heart diseases and treatments.
Scientists at MIT developed a fully integrated photonic processor that can perform all key computations of a deep neural network optically on the chip. The device completed machine-learning classification tasks in under half a nanosecond while achieving over 92% accuracy, similar to traditional hardware.
A new computational model called Multi-Stage Residual-BCR Net (m-rBCR) uses a unique frequency representation to solve deconvolution tasks with fewer parameters and faster processing times. The model demonstrates high performance on various microscopy datasets, outperforming traditional methods.
Researchers discovered that NMDA receptors set the baseline level for neural network activity, helping maintain stable brain function. The study's findings suggest potential innovative treatments for diseases linked to disrupted neural stability.
Researchers at Tel Aviv University found that a special protocol of hyperbaric oxygen therapy can improve the condition of PTSD sufferers, reducing typical symptoms such as flashbacks, hypervigilance, and irritability. The study showed improvements in brain connectivity and clinical symptoms, offering new hope for millions of PTSD suff...
Researchers at Linköping University have developed a new version of AlphaFold that can predict the shape of very large and complex protein structures, integrating experimental data. This breakthrough aims to improve the development of new proteins for medical drugs.
Researchers developed a novel neural network model to reconstruct 3D digital images of relief-type cultural heritage objects from old photos. The model improves the accuracy of depth estimation and soft-edge detection, enhancing the preservation of cultural heritage.
A new training algorithm called ternarized gradient BNN (TGBNN) enables learning capabilities for binarized neural networks (BNNs) on IoT edge devices. The proposed MRAM-based CiM architecture achieves faster convergence and matching accuracy with regular BNNs.
Researchers develop Knowledge-enhanced Bottlenecks (KnoBo) method to emulate human physicians' education, resulting in more accurate and interpretable AI models for medical image recognition. KnoBo-based models outperform existing best-in-class models on accuracy and robustness, especially in handling confounded data.
Researchers found that positive expectations lead to increased activity in pleasure-related brain regions, while negative expectations prime pain processing. The study suggests a dissociable impact of hedonic information, with positive expectations facilitating reward processing and negative expectations heightening anxiety.
A Princeton-led research team has built the first neuron-by-neuron and synapse-by-synapse roadmap through the brain of an adult fruit fly. The map reveals connections within the brain at every scale, enabling researchers to better understand its underlying logic and potentially develop tailored treatments for brain diseases.
A new method called Clio allows robots to make task-relevant decisions by identifying the parts of a scene that matter. In real experiments, Clio successfully mapped scenes at different levels of granularity based on natural-language prompts and enabled robots to grasp objects of interest.
A new study reveals how psilocybin alters brain connectivity to alleviate symptoms of body dysmorphic disorder, potentially aiding treatment. Psilocybin strengthens neural connections between executive functions and emotionally salient stimuli, leading to improved BDD symptoms.
Researchers at Brigham and Women's Hospital have identified a specific brain circuit that may protect against post-traumatic stress disorder (PTSD) in veterans with traumatic brain injury. The study suggests using neurostimulation therapies on this circuit could treat PTSD, offering a new potential non-invasive treatment option.
A Concordia-led team developed a framework that enables crowdsourced deep reinforcement learning as a service, using blockchain technology. This allows smaller organizations to access complex AI tasks previously out of reach, reducing costs and risk.