Articles tagged with Neural Networks
Researchers at Tokyo Institute of Technology developed a tunable neural network framework that achieves high accuracy and efficiency for sparse CNNs. The new architecture employs a Cartesian-product MAC array and pipelined activation aligners to enable dense computing of sparse convolution, resulting in better resource utilization.
A team of researchers used remote sensing data and deep machine learning to identify hundreds of new shell ring sites in the southeastern US. The study provides a better understanding of how people lived in the area and offers a way to locate undiscovered shell rings.
Researchers from Skoltech and KU Leuven used machine learning to reconstruct 3D micro-CT images of fibrous materials, overcoming the difficulties faced by humans in analyzing these complex materials. The team employed GANs to fill a gap in available inpainting tools, enabling precise material analysis and simulation.
A study published in Science Advances reveals a previously unknown mechanism behind compulsive alcohol use, which may be targeted by medication. A small group of nerve cells in the central amygdala promote alcohol use despite negative consequences.
A machine learning algorithm can accurately sex Caucasian 2D footprints with up to 90 percent accuracy, surpassing expert analysis. This breakthrough has significant implications for forensic and anthropological applications.
Researchers have developed a neural network model called BiteNetPp to detect protein-peptide binding sites, enabling the design of peptide-based drugs. The model consistently outperforms existing methods and can analyze a single protein structure in under a second, making it suitable for large-scale studies.
A new study from Washington University in St. Louis shows that guided by sparsity, silicon neurons learn to pick the most energy-efficient perturbations and wave patterns, enabling an emergent phenomenon of efficient communication between neurons. This research has significant implications for designing neuromorphic AI systems.
Researchers from Skoltech and their colleagues developed a neural network that can efficiently generate IUPAC names for organic compounds in accordance with the IUPAC nomenclature system. The network, trained using the Transformer architecture, achieved an accuracy of nearly 99%, outperforming traditional rule-based solutions.
Researchers at NYU Tandon School of Engineering developed a framework called DeepReDuce that streamlines neural networks to be more adept at computing on encrypted data. The team found that DeepReDuce improved accuracy and reduced ReLU count by up to 3.5% and 3.5×, respectively.
Researchers found that bats can compress their echoes by up to 90% without losing essential information for sonar-based tasks. This efficient encoding strategy allows bats to navigate complex environments with minimal neural machinery, enabling them to detect location and movement with high accuracy.
Researchers at Université libre de Bruxelles compare popular neuro-evolutionary methods for offline robot swarm design, observing a 'reality gap' where simulated neural networks fail in the real world. To address this, they propose reducing method 'power' to adopt simpler approaches with predefined building blocks.
Researchers from Skoltech have developed a new augmentation technique called MixChannel to help train computer vision algorithms with limited data. This approach outperformed state-of-the-art solutions in testing with three neural networks and can be combined with other methods for even more training data.
Researchers introduce RoseTTAFold, a neural network approach that accurately predicts protein structures, outperforming traditional methods and rivalling DeepMind's AlphaFold2. The tool's code and public server are now accessible to the scientific community, enabling rapid solution of challenging structure determination problems.
Skoltech researchers create a neural network that can guide the controlled deformation of semiconductor crystals, enabling superior properties for next-gen chips and solar cells. The approach combines various data sources and active learning to boost accuracy and convergence.
The brain's globally sparse yet locally compact modular topological characteristics reduce resource consumption for establishing connections. The research model shows that rewiring the network to a more biologically realistic modular structure significantly reduces running consumption and building cost.
Researchers developed machine learning models that can predict daily solar radiation using only thermal data, improving upon existing methods in various geo-climatic conditions. The models have been tested in nine locations across southern Spain and North Carolina, showing significant improvements in accuracy.
A Cornell University-led team developed a machine learning tool called Correlation Convolutional Neural Networks (CCNN) to parse quantum matter and make distinctions in the data. CCNN can identify relationships among microscopic properties that are impossible to determine at the scale of quantum systems.
A team of researchers has developed an AI system that can detect COVID-19 through automatic cough analysis. The system uses spectrogram features and demonstrates improved accuracy when incorporating gender information, which is found to be a significant factor in distinguishing between male and female coughs.
University of Illinois engineers develop physics-informed neural networks to predict outcomes of complex 3D printing processes. The model accurately recreates experiments and predicts temperature and melt pool length with high accuracy.
Researchers used convolutional neural networks to analyze facial photos before and after facelift surgery in 50 patients. The AI algorithms recognized a 4.3-year reduction in age, which correlated with patient satisfaction scores, averaging 75 for facial appearance and over 80 for quality of life.
A new study reveals similarities and differences in online conversation topics between Angelenos and New Yorkers. Online, Angelenos tend to discuss healthcare, jobs, and entertainment, while New Yorkers focus on art, politics, and nightlife.
Researchers at DZNE's Dresden site develop i3D-Markers, a cutting-edge technology platform that uses high-density microelectrode arrays and 3-dimensional neuronal networks to predict the reaction of neurons to compounds. This platform aims to optimize drug candidate selection and accelerate brain disease development.
Researchers found that fluid flow vortices have high information processing capabilities before transitioning to a Karman vortex street. Virtual physical reservoir computing using numerical simulation revealed the relationship between vortices and info processing capacity.
A new study by California Institute of Technology researchers found that a computer program can accurately predict which paintings a person will like, using low-level visual attributes such as contrast, saturation, and hue. The program achieved similar accuracy to deep convolutional neural networks in predicting art preferences.
A recent study by Hebrew University researchers identified molecular factors that allow birds to fly, differing from mammals and reptiles. The ephrin-B3 molecule plays a crucial role in coordinating wing movement, enabling birds to flap and take flight.
The European Virtual Institute will study the neural basis of emotion using a Marie Sklodowska-Curie Innovative Training Network, focusing on the role of the cerebellum in controlling emotions. The network aims to develop new therapeutic strategies for emotional disorders by combining fundamental and clinical research.
Scientists at Osaka University employ machine learning algorithms to assess the remaining useful life of mechanical rolling bearings, which may lead to industrial cost savings and fewer discarded parts. The new method improves prediction accuracy by about 32%.
A new theory of dreaming proposes that our brains create strange dreamscapes to counteract overfitting by familiarizing themselves with everyday experiences. This 'overfitted brain hypothesis,' inspired by AI regularization techniques, suggests dreams serve a purpose in generalization and world understanding.
Politecnico di Milano researchers used neural networks to predict the acoustic behavior of violin plates based on geometric parameters. The results showed an accuracy close to 98%, enabling luthiers to design and build violins with optimal sound quality, exploring new designs and materials.
A scientist from HSE University has developed an image recognition algorithm that speeds up real-time processing of video-based image recognition systems by up to 40%. The algorithm uses a sequence of convolutional layers and fine-tuning to achieve accurate results while controlling loss in accuracy.
A study found that deep neural networks can accurately predict lung cancer type from CT scans, identifying new associations between genes and imaging features. This approach increases radiologists' confidence in assessing tumor types, informing individualized treatment planning.
Researchers used neural networks to simulate complex universes, reducing computation time by a thousandth. The new method allows for both high resolution and large volume simulations, holding the potential for major advances in numerical cosmology and astrophysics.
Researchers at Carnegie Mellon University have developed a technique using machine learning and high-performance computing to simulate complex universes in less than a day. The approach enables high-resolution cosmology simulations, advancing physics research and providing new insights into the universe's mysteries.
A novel auditory perception model simulates human ear dynamics to capture time dynamics of dimensional emotions. Neural networks then extract features that reflect this time dynamics, showing better emotion recognition performance than traditional acoustic-based features.
General Motors has licensed the award-winning AI software system MENNDL from Oak Ridge National Laboratory to accelerate advanced driver assistance systems technology and design. MENNDL uses evolution to design optimal convolutional neural networks, dramatically speeding up the process of recognizing patterns in datasets.
Researchers developed DeepShake, a deep spatiotemporal neural network trained on over 36,000 earthquakes, which analyzes seismic signals in real time and provides advanced warnings of strong shaking. The model was tested using the 2019 Ridgecrest earthquake, sending simulated alerts up to 13 seconds prior to high-intensity ground shaking.
A research team created a computer model that can simulate the impact of individual receptor types on brain activity. The model uses data from three imaging techniques to quantify receptor-specific modulations of brain states. By predicting changes in brain dynamics after receptor activation, the researchers hope to develop new diagnos...
A new real-time 3D motion tracking system combines transparent light detectors with advanced neural network methods to enable fast tracking speed, compact hardware, and lower cost compared to existing solutions. The technology has promising applications in automated manufacturing, biomedical imaging, and autonomous driving.
Researchers at KAUST developed a brain-on-a-chip that can learn real-world data patterns without extensive training, leveraging spiking neural networks and spike-timing-dependent plasticity model. The system is more than 20 times faster and 200 times more energy efficient than other neural network platforms.
A team of researchers has developed an AI agent called Crystallography Companion Agent (XCA) to analyze X-ray diffraction data and identify material properties faster. The agent collaborates with scientists to perform autonomous phase identifications, overcoming traditional neuronal network overconfidence.
A neural network developed by Skoltech researchers improves credit scoring using transactional banking data, surpassing existing models. The EWS-GCN model processes large-scale temporal graphs directly and aggregates information to predict target client credit ratings.
Researchers created a satellite-based map of human pressure on lands around the world using machine learning. The map reveals abrupt changes in landscapes due to deforestation, mining, and urbanization, providing insights into biodiversity conservation and sustainability.
Researchers found altered brain activity in individuals with chronic sinusitis, affecting neural networks that modulate cognition and response to external stimuli. Despite no significant clinical impairment, participants showed subtle brain region communication changes associated with attention decline and sleep disturbances.
A new deep neural network architecture can differentiate between healthy and diseased skin images with high accuracy, offering a potential screening tool for systemic sclerosis. The proposed network reached 100% accuracy in training and validation sets, outperforming traditional CNNs.
Researchers found that neural networks trained on sound files of human language reached higher performance in image recognition, identifying objects and animals correctly 92% of the time. Using sound as a training tool improved results even with limited training data, outperforming traditional binary input methods.
Researchers have created an early prototype of a medical imaging system using neural networks to analyze near-infrared images of veins and project a venous pattern onto a patient's body. The system can detect vein contours accurately, fully automatically, and independently, reducing discomfort for patients with difficult access to veins.
A new virtual diagnostic approach uses machine learning to analyze beam quality in electron microscopes, X-ray lasers, and medical accelerators. The method provides accurate information that conventional diagnostics cannot, enabling operators to optimize device performance.
Artificial neurons help decode cortical signals using a new algorithm that automates feature extraction and interpretation. The neural network architecture is automatically tuned to analyze signals from separate neural populations, providing physiologically meaningful results.
A new recurrent neural network framework enables fast and efficient 3D imaging of fluorescent samples, reducing scan times by ~30-fold. The approach uses few 2D images to reconstruct 3D images, mitigating photo-bleaching challenges in live sample experiments.
The study reveals how neural circuits balance excitation and inhibition, crucial for normal functionality of our brain. The results provide a clearer picture of how this balance is preserved and where it fails in living neural networks.
MIT researchers develop a deep-learning algorithm to optimize sensor placement on soft robots, allowing them to better interact with their environment and complete assigned tasks. The algorithm learns the most efficient sequence of movements and identifies the most important particles to improve performance.
A novel convolutional neural network, FMNet, was developed to determine the source focal mechanism of earthquakes rapidly using full waveforms. The method proves effective in calculating parameters within one second with minimal computing resources.
Researchers at UC San Diego have developed a nanoscale artificial neuron device that efficiently carries out activation functions in hardware, reducing computing power and circuitry. The device, which implements the rectified linear unit activation function, can process images and perform edge detection with high accuracy.
A new algorithm, C2FIV, uses facial motion to verify identities, providing an additional layer of security. With a success rate of over 90% accuracy in its preliminary study, the technology has broader applications beyond smartphone access, including workplace and online banking security.
Scientists have found a way to reduce energy consumption in deep neural networks, paving the way for more efficient AI hardware. The approach uses simple electrical impulses instead of complex numerical values, maintaining high accuracy.
A team of Skoltech researchers demonstrates that universal adversarial perturbations (UAPs) can be explained by classical Turing patterns. This finding can help construct a theory of adversarial examples and design defenses against pattern recognition systems.
A new deep-learning algorithm, CARRL, is designed to help machines build a healthy skepticism of their measurements and inputs. By combining reinforcement-learning algorithms with deep neural networks, researchers created an approach that outperformed standard machine-learning techniques in scenarios with uncertain and adversarial inputs.
The new project posits that deep neural networks struggle with real-world problems due to an overemphasis on neurons, which neglect the role of astrocytes. Integrating astrocytes could enhance DNN efficiency and performance.
Researchers developed a new framework to analyze massive data from thousands of individual neurons, outperforming previous models. The method captures complex dynamics and fluctuations, offering insights into animal processing information and adapting to environmental changes.
An interdisciplinary team of biologists and computational researchers designed a neural network named BPNet that can interpret regulatory code by predicting transcription factor binding from DNA sequences with unprecedented accuracy. The model revealed novel insights, including a rule governing the binding of the well-studied transcrip...