Articles tagged with Network Modeling
A study reveals that identical photons in optical circuits exhibit Hopfield Network behavior, enabling associative memory mechanisms similar to the human brain. The research finds a fundamental limit to memory capacity, with quantum coherence allowing correct retrieval but transitioning to disorder as data volume increases.
Researchers identified small clusters of interacting components that act as amplifiers, triggering outsized reactions in complex systems. These clusters can control how strongly a system reacts after a disruption, and their presence can determine the stability of entire systems.
Groups of tiny particles suspended in liquid oscillate together, keeping time as though they sense each other's motion. The surrounding fluid enables the particles to 'feel' one another at a distance, influencing their motions without direct contact.
Researchers develop Dual Perigee, a lightweight algorithm that streamlines network connections to enable secure and low-latency data sharing in IoT networks. The study reduces block-related delays by 48.54% compared to standard approaches.
A new study by Complexity Science Hub researcher Rafael Prieto-Curiel challenges the assumption that larger cities are more violent. The study shows that isolated cities, with limited highway connections, experience nearly seven times more violence against civilians per 100,000 residents than well-connected cities.
A new study from the Complexity Science Hub finds that belonging to more than one marginalized group can significantly harder forming social connections. The researchers developed a mathematical model and tested it using friendship data from around 40,000 U.S. high school students, revealing how overlapping disadvantages can interact.
A game theoretical model of cyberwarfare assesses Attacker and Defender costs, revealing that networks with fewer attack vectors benefit Defenders. Converging technological capabilities amplify conflicts, while centralized government control can provoke more aggressive attacks.
Researchers developed a cyberattack detection system that uses federated learning and cloud coordination to detect DDoS attacks in 6G-ready smart grids without exposing user energy-use data. The system achieved high accuracy and precision, but trade-offs were observed in terms of resources consumption.
Researchers developed a Commuter Metapopulation Model to capture daily mobility patterns and their impact on disease spread. The model accurately simulates rapid urban outbreaks and localized outbreaks in rural areas, providing valuable insights for targeted intervention strategies.
Researchers developed a new global flood dataset, FloodPlanet, which improves flood detection accuracy by up to 15.6% using high-resolution commercial satellite imagery and public sensor data. The dataset enables more reliable global inundation response systems and opens the door for AI-driven environmental monitoring tools.
A new analytical method reveals overlooked species at risk of extinction, providing a valuable layer of insight for conservationists. The dual-role approach captures both predator and prey interactions, identifying keystone species and vulnerabilities.
A new study from researchers at the Helmholtz Institute for Functional Marine Biodiversity offers fresh insights into why many microorganisms fail to grow in the lab. The study suggests that the survival of microbes depends on a hidden web of relationships between species, which can collapse with small structural changes.
A team of scientists at UNIST developed a data-driven structure prediction algorithm that led to the synthesis of three novel porous materials with exceptional selectivity in gas separation. The newly developed materials have significant potential for greenhouse gas separation and purification applications.
The framework utilizes consortium blockchain architecture for immutable records, minimizing data falsification and cyberattacks. AI-driven tools ensure objective data collection and analysis, reducing human bias.
Researchers have developed an algorithm to infer the structure of hypergraphs using only observed dynamics, allowing for the analysis of complex systems without prior knowledge. The approach was tested on EEG data from over 100 human subjects and accurately captured higher-order interactions in the brain.
Researchers develop a CNN-LSTM coupled deep learning model to predict the bond stress-slip constitutive relationship of grouted corrugated ducts. The model demonstrates reduced prediction errors and captures complex nonlinear interactions, achieving high consistency with experimental results.
A University of Ottawa-led study reveals that serotonin neurons are connected and interact with each other, controlling serotonin release in specific regions of the brain. This complex system has implications for understanding decision-making and developing targeted therapeutics for mood disorders.
FAU CA-AI will acquire NVIDIA infrastructure for generative physical AI and develop state-of-the-art hardware and software for computational T&E of connected AI autonomy. This funding solidifies FAU's role as a national leader in next-gen networked AI autonomous systems research.
A new study reveals promising progress toward predicting how patients with major depressive disorder will respond to antidepressant medications. Brain connectivity patterns were found to significantly improve predictions of treatment response across two large, independent clinical trials.
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.
MIT researchers have found that a computational model of the ventral stream, which processes object recognition, also performs well on spatial tasks such as determining an object's location and orientation. This challenges the dominant perspective that the ventral stream is optimized for object recognition.
A novel channel-wise cumulative spike train image-driven model (cwCST-CNN) is presented for hand gesture recognition, achieving a classification accuracy of 96.92% in recognizing 10 gestures. The method leverages HD-sEMG signals and reconstructs them into two-dimensional images to capture spatial activation patterns.
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 recent study presents a novel approach to optimizing peer-to-peer coupled electricity and carbon trading among prosumers. The proposed framework achieves optimal electricity–carbon P2P trading, outperforming traditional market schemes in terms of economic benefits and carbon-emission reduction.
A new configuration approach for radial distribution systems incorporates distributed renewable energy resources, achieving superior voltage stability, reduced system losses, and improved resilience. The study also quantifies significant environmental benefits, including reduced CO2 emissions and optimized resource utilization.
Researchers at Concordia University have developed a new approach to identifying fake news on social media using the SmoothDetector model. The model integrates probabilistic algorithms with deep neural networks to capture uncertainties and patterns in multimodal data, providing more nuanced judgments of authenticity.
A new editorial in Oncotarget discusses how artificial intelligence can improve liver imaging by recognizing when it might be wrong. The approach, called 'uncertainty quantification,' helps clinicians better detect liver cancer and other diseases by pointing out areas in medical scans that need a second look.
Researchers developed a machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. The new surrogate model maintains the same level of accuracy as traditional particle-based simulations while reducing computation time from approximately 45 minutes to just three minutes.
A new simulation of brain metabolism has revealed potential targets for future dementia treatments, according to a study published in Frontiers in Science. The model, available on the Open Brain Platform, could help scientists accelerate research into interventions promoting healthy brain aging.
Dr. David Winchester will lead the American College of Cardiology's Board of Governors, guiding chapters representing all 50 states and promoting heart health improvement in communities. His term aims to address practice challenges through advocacy and chapter support.
Göttingen research team develops infomorphic neurons that learn independently and self-organize among neighboring neurons. This allows the smallest unit in the network to control its own learning, enabling novel machine learning approaches and a deeper understanding of brain function.
Researchers at Weill Cornell Medicine developed a new AI model that harnesses whole-slide tumor imaging data and gene expression analyses to predict how patients with muscle-invasive bladder cancer will respond to chemotherapy. The model outperforms previous models using a single data type, identifying key genes and tumor characteristi...
Researchers develop AI model to predict novel mutations in protein sequences, combining grammatical and semantic changes. The method uses all available information about the sequence and mutations to create a more accurate prediction model.
Researchers at KAIST evaluated industrial microbial cell factories to identify suitable strains and optimal metabolic engineering strategies. Using genome-scale metabolic models, they calculated maximum theoretical yields and achievable yields under industrial conditions for 235 bio-based chemicals.
A new open-source model of brain metabolism has shown how altering key chemicals can restore aged cells to their youthful activity and resilience. The detailed simulation provides a glimpse into the impact of aging on brain metabolism and highlights potential drug targets.
University of Missouri researchers developed a method using lidar and AI to analyze pedestrian, cyclist, and vehicle interactions at traffic signals. The approach aims to enhance driver awareness, reduce accidents, and improve mobility.
A new real-time surface PM10 retrieval framework uses interpretable automated machine learning to provide accurate data across China. The framework demonstrates robust generalization and stability, outperforming previous studies in cross-validation and rolling iterative validation experiments.
The study systematically traces Generative AI evolution from deep learning to foundation models, highlighting four distinct stages and successful applications. Key challenges like safety concerns and theoretical breakthroughs require further attention and development in the field of Generative AI.
Researchers from Virginia Tech have published a visionary paper on fusing wireless technologies and AI to create human-like common sense. The team aims to develop a network that can think, plan, and imagine like humans, enabling seamless merging of physical, virtual, and digital dimensions.
A new study by UCLA Health has discovered a drug that fully reproduces the effects of physical stroke rehabilitation in model mice. The drug, DDL-920, excites parvalbumin neurons, leading to significant recovery in movement control after stroke.
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.
Researchers created a computational simulator to model the biochemical processes of intervertebral discs, allowing for better understanding of back pain causes. The simulator enables simulating 33 proteins and their interactions, providing valuable insights into disc degeneration.
The Open Brain Institute launches a groundbreaking platform to simulate and study digital brains, empowering researchers to explore brain complexity and diseases. With its virtual neuroscience laboratories, the OBI enables global collaboration and access to cutting-edge virtual labs.
Researchers have developed a novel technique to overcome the spurious correlations problem in AI by eliminating a small portion of the training data that contains hard-to-understand features. This approach improves performance even when conventional techniques are ineffective.
A new study suggests that artificial intelligence can effectively detect wildfires in the Amazon rainforest, using satellite imaging and deep learning. The technology achieved a 93% success rate in training models via datasets of images with and without wildfires.
The new AI model leverages hypergraphs to quickly and accurately identify therapeutic gene targets for diseases. HIT outperformed existing models in all tested metrics, demonstrating its accuracy in classifying therapeutic gene targets with great precision.
Dr. Sarah Du, an associate professor at Florida Atlantic University's College of Engineering and Computer Science, has been selected as a Senior Member of the National Academy of Inventors for her significant contributions to advancing medical technology. Her research focuses on developing point-of-care diagnostic tools and monitoring ...
Researchers have developed a new AI tool that uses sensors and real-time data to predict water quality across the US. This tool can be applied nationwide, benefiting communities by providing water quality forecasts, streamlining operations, and informing strategies for managing turbidity in basins worldwide.
Researchers created a fiber computer that can be integrated into clothing to track health conditions and physical activity. The technology achieved an average accuracy of 70% when individually operated, but increased to nearly 95% when connected collectively.
Researchers at Mizzou have developed a proactive approach using artificial intelligence to address evolving threats against smart grids. The CIBR-Fort system can predict cyberattacks with 91.88% accuracy and defend against them in real-time, enabling scalable security for power grids of the future.
A new study from USC Dornsife finds that LA's urban greenery absorbs up to 60% of daytime fossil fuel CO2 emissions in spring and summer, providing valuable insights into the impact of trees on air quality. The research provides data-driven insights for future planting efforts and informs the USC Urban Trees Initiative.
Researchers will use airborne GPR and ground-based TEM to collect rich geophysical data, estimating carbon storage and gas emissions in peatlands across a latitudinal gradient. The project aims to reduce uncertainty in these predictions and provide valuable information on how to better protect carbon stocks.
The 3D lung model can replicate realistic breathing maneuvers and offer personalized evaluation of aerosol therapeutics under various breathing conditions. The researchers detail in the paper how they built the 3D structure and what they’ve learned so far.
Research advances higher-order networks to capture multi-agent interactions, enabling accurate modeling of biological, social, and physical systems. The Dirac-Bianconi operator provides a powerful generalization of the graph Laplacian, encoding local and global interactions across different topological dimensions.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
A new study by University of Toronto researchers has developed a model that can help municipalities choose optimal locations for bike lanes, minimizing congestion and increasing cycling ridership. The model uses traffic and commuter mobility data to predict the impact of bike lane expansion on driving travel time and emissions.
Researchers developed a system to detect and decode fiducial markers in challenging lighting conditions using neural networks. The system, DeepArUco++, overcomes the limitations of classic machine vision techniques and can be applied today thanks to open availability of its code.
Researchers propose a new strategy to stabilize quantum networks by rebuilding connections after each use, which leads to an eventual stable network state. The key is finding the optimal number of links to add, determined to be the square root of the number of users.
A new hybrid machine learning model predicts ultimate axial strength of CFRP-strengthened CFST columns with high accuracy, enabling safer and more efficient designs. The model can be used to optimize construction processes and enhance the safety of structures at a lower cost.
A new study led by University of Oxford suggests that plants are more likely to be eavesdroppers than altruists when tapping into underground networks. The study found that it is unlikely that plants would evolve to warn other plants of impending attacks, instead finding that plants may signal dishonestly to harm their neighbors.