Articles tagged with Network Modeling
Researchers developed an artificial neural network model that can simulate brain processes involved in grasping movements. The model was trained with data from rhesus monkeys and accurately reproduced their grasping movements, providing insights into neuronal dynamics.
Researchers have discovered subnetworks within BERT that can complete the same task more efficiently, reducing computing costs and increasing accessibility to state-of-the-art natural language processing. The 'lottery ticket hypothesis' identifies these leaner subnetworks, which can be repurposed for multiple tasks.
A KAUST modeling study shows RISs can dramatically enhance wireless communication in areas with blind spots. Six RISs per kilometer can significantly improve coverage at a density of 300 blockages per square kilometer.
Researchers developed a geometric network model to study the multiscale organization of the brain, finding that layers at different resolutions exhibit self-similar structure and efficient decentralized communication. This discovery has implications for understanding brain functioning and may lead to advanced tools for brain simulation.
A new model of human brain networks offers a new tool for exploring individual differences in brain networks, critical for classifications of brain disorders and disease. The model highlights ongoing conversations between brain structures, providing new insights into human behavior and cognition.
Texas A&M researchers have created a contagious model to accurately forecast flood water spread and recession process on urban road networks. The findings provide valuable insights into the universality of network spread processes across various systems, which can help better manage cities.
Researchers used violin synchronization to study complex network dynamics, finding that players can tune their playing periods and delete connections to achieve stability. This approach may inform better strategies for human networking in fields such as traffic management and epidemic control.
Liang Zhao at George Mason University has been awarded a $102,873 NSF CAREER Award to develop transformative frameworks for spatial network generative modeling. The project aims to learn complex generation processes from massive datasets and create more interpretable models.
Researchers have created a distributed sensor fault diagnosis algorithm to detect and isolate multiple sensor faults in large-scale HVAC systems. The algorithm can be applied to both existing Building Management Systems and plug-in IoT systems, notifying users and operators about faulty measurements and sensor locations.
Researchers from North Carolina State University and the Army Research Office have developed a new model for how competing pieces of information spread in online social networks and IoT. The findings suggest that network size plays a significant role in displacing old data with new, accurate information.
Researchers demonstrated mathematically that low-dimensional embeddings fail to capture important properties of social networks and other complex networks. This flaw leads to significant structural aspects being lost in the embedding process, resulting in inaccurate predictions and recommendations.
A new tool, designed for both planning and operation phases, aims to optimize evacuation routes and supply logistics in near-real time. Researchers focus on noncombatant evacuation operations in the Republic of Korea, but it can be applied to various scenarios.
Researchers developed a model to study online polarization and echo chambers, finding that discourse becomes less stable when like-minded groups form. The model accurately replicated Twitter data on polarization and debates, providing insight into social network dynamics.
A deep neural network model developed by Finnish researchers can predict healthcare visits among elderly people with high accuracy. The model has the potential to save millions of dollars in unnecessary funding for healthcare services.
Researchers have developed a theoretical mechanical model to study how spider orb-webs detect prey through vibrations. The model reveals that web dynamics are crucial in localizing prey, and it has potential applications for bioinspired materials.
Researchers at Argonne National Laboratory have developed domain-aware neural networks to replace expensive parameterizations in the Weather Research and Forecasting (WRF) model. These algorithms can predict environmental data more accurately with significantly less training data, enabling faster and higher-resolution simulations.
Researchers used a dataset to model primary care networks and found that removing critical fractions of PCPs can lead to significant weakening of the network. This can result in inadequate coverage for patients, even when critical thresholds are not reached.
Researchers developed lipid-based memcapacitors that mimic biological synapses, accelerating routes to neuromorphic computing. The discovery could support the emergence of biology-inspired computing networks for sensory approaches to machine learning.
A recent study suggests that BRICS countries can curb the spread of invasive species by forming networks of researchers dedicated to invasion science. The study proposes a 'facilitated' network model that would allow for rapid responses to emerging alien species and substantial increases in capacity to tackle existing invasions.
Researchers used AI techniques to analyze patterns in fish schools, discovering a solution that balances simplicity and accuracy. The model, inspired by 'attention networks', can predict the behavior of individual fish with 90% accuracy, offering new insights into collective behaviors.
Researchers developed a 'risk-aware' mathematical model inspired by financial risk theories to improve cloud-computing networks. The model allocates traffic through optimal paths to minimize loss and maximize usage, reducing waste of energy and resources.
A new model, called the Military Logistics Network Planning System (MLNPS), draws on logistical data and operational information to assess risk and forecast logistical outcomes. The MLNPS can help military leaders identify efficient means of meeting logistical needs and account for uncertainty during expeditionary operations.
Researchers have found that faults in the Imperial Valley are mechanically linked, forming a continuous fault structure. This could increase the likelihood of larger earthquakes in Los Angeles and surrounding counties.
Nanoengineers developed new graph network-based models that accurately predict material properties, outperforming existing AI technology in complex tasks. The MEGNet models can learn relationships between elements and overcome data limitations in materials science, enabling rapid discovery of transformative materials.
A team of researchers developed CosmoGAN, a deep learning network that generates high-fidelity convergence maps for weak gravitational lensing. The model achieves high statistical agreement with fully simulated maps, paving the way for building emulators out of deep neural networks.
A new model reveals 'tipping points' where small localized rain increases lead to widespread road disruptions, even isolating entire regions. The model was validated using Hurricane Harvey data and shows a 90.6% prediction rate for reported road closures.
A team of researchers found that as the temperature increases, the ergodization time-scale becomes huge, but the time-scales necessary for chaoticity remain practically unchanged. This phenomenon is known as dynamical glass, which emerges from the fragmentation of frozen and seemingly inert regions.
A new mathematical model developed by NYU Tandon researchers takes into account the influence of bursty social interactions on disease spread. The model's discovery could improve predictive models and provide a more nuanced understanding of how diseases spread in globally connected populations.
Researchers develop a time-varying network model that factors in individual dynamics and burstiness to better explain the relationship between social activity and disease spread. The model shows that self-excitement mechanisms lower the epidemic threshold, increasing disease communicability.
Researchers at Colorado State University have developed a new model that quantifies the vulnerability of communities to fire spread. The AGNI-NAR model assesses the risk of fires spreading to homes and groups of homes, providing practical guidance for urban planners and emergency managers.
Researchers employed a concrete interplay model in quenched multiplex networks to study the connection between adaptive human behavior and epidemic spread. The model accurately describes actual epidemic spread in complex networks while characterizing interactions between transmission and human behaviors.
Researchers have developed a novel audio-visual model that can isolate and enhance speech in videos, even in challenging real-world scenarios. The model uses both visual cues, such as lip movements, and auditory signals to focus on the speaker's voice and improve speech quality.
A new index helps utilities balance electricity distribution with lower consumer costs by accounting for the variability of decentralized energy sources like solar and wind. The index suggests deployment of flexible loads according to market conditions, potentially leading to lower costs and grid stability.
A team of physicists has calculated the size of scale-free and small-world networks, which have six degrees of separation. This method can be applied to modeling complex networks and predicting their capacity to tolerate faults.
Researchers at The University of Tokyo Institute of Industrial Science describe a new method for creating one mini neuron network model, using microscopic plates to connect neurons together one cell at a time. This approach guides neurons to grow in a defined way and form functional communication hubs.
Researchers have developed comprehensive computer models of cortical neurons that accurately replicate their activity. These models can be used to understand how different cell types differ from one another and may eventually be applied to model neurological disorders such as epilepsy or Alzheimer's disease.
Researchers created asset management framework to streamline maintenance and reduce costs by 33% in Kelowna, B.C. Improved coordination between city departments can lead to better budget allocation and enhanced service delivery.
Four NYU faculty members have been awarded Sloan Research Fellowships for their outstanding contributions to science and engineering. Miranda Holmes-Cerfon develops mathematical tools to study mesoscale materials, while Tianning Diao works on sustainable synthetic methods for pharmaceutical products.
Researchers developed a novel probabilistic approach to mine big data, enabling the creation of an 'Optimal Information Network' (OIN) for assessing health outcomes in U.S. cities. The OIN identified poor and favorable health metrics, with some cities showing high variability over time.
A mathematical model has been developed to explain how new ideas emerge, using Heaps' law and complex networks to analyze the discovery process. The study reveals that innovations often arise in clusters and are strongly correlated, providing insights for effective interventions to nurture sustainable growth.
Researchers at Carnegie Mellon University have developed a new dynamic statistical model to visualize changing patterns in networks, including gene expression during developmental periods of the brain. The model, Persistent Communities by Eigenvector Smoothing (PisCES), combines information across multiple networks over time to identif...
A new method predicts when sudden change occurs in complex systems like bee colonies, which could help prevent colony collapse disorder. The model uses dimension reduction to accurately predict tipping points and informs control strategies to delay or prevent their occurrence.
A team of Caltech engineers has developed an empirical model of the California reservoir network, allowing managers to anticipate and respond to drought conditions more effectively. The model uses statistical techniques to learn from past data and shed light on how reservoirs will respond to different climate patterns in the future.
A new study led by Colorado State University found that faculty mentorship and supportive networks play a key role in retaining women in geoscience majors. The program PROGRESS connects students with female mentors and helps them expand their networks of support.
The article investigates the 1-good-neighbor diagnosability of alternating group graph networks under the PMC model, revealing that it has a high degree of fault-tolerance. The authors prove that the 1-good-neighbor diagnosability is higher than classical diagnosability when considering faulty neighbors.
Researchers developed a predictive model of inflammatory bowel disease by integrating DNA variation, gene expression, and clinical information. The model identifies key regulators of the immune component and may lead to new treatments for patients.
Researchers create model to test existing theories on electron transport mechanism in nanomaterials, laying groundwork for AI-powered machine learning devices. They also explore neural networks inspired by the human brain and nervous system.
A new AI system developed by MIT researchers reduces online video rebuffering and improves quality, adapting to different network conditions. The system achieves higher-quality streaming with less rebuffering than existing approaches.
A new study led by Princeton University researchers finds that cells must move around and change shape to gain a meaningful understanding of their environment. The typical cell's environment is highly varied in stiffness or flexibility, making it difficult for the cell to determine its surroundings through mechanosensing.
A study published in Frontiers analyzed traditional cuisines' flavor networks to develop a new principle behind cooking, called food-bridging. The research found regional clusters with distinct flavor patterns, and its mathematical representation, semi-metricity, may be applied to predict successful ingredient combinations.
A new brain network model suggests that understanding brain connections and structure can help predict how brain function changes after injury. The study identified key white matter pathways and lesions responsible for network disruptions, which could lead to more accurate treatment plans and therapeutic targets.
Researchers from MIT's Computer Science and Artificial Intelligence Laboratory present a system called Flexplane that tests new traffic management protocols at realistic speeds without altering network hardware. The system uses a compact, efficient computational model of a network to schedule transmissions on the real network.
Researchers from the University of Granada found a close association between magnetic systems and certain brain activity states, including 'spin-glass' states that can lead to frozen neuronal activity. This study provides a novel theoretical framework for understanding biological mechanisms behind destabilization of these states.
A new study from Intermountain Medical Center found that using longitudinal data improves predictive accuracy for kidney transplant patients. Four predictive models were developed by layering available data, achieving accuracy improvements of nearly 16% and up to 83.8% in predicting kidney loss or death.
Researchers have applied physics models to understand economic systems, but results show that universal features may be the exception rather than the rule. Econophysics has led to discoveries like the inverse cubic law describing stock price fluctuations.
A new theoretical model shows that dense networks evolve differently depending on the rate of second-neighbor connections. Networks with high copying probabilities exhibit densifying behavior, growing faster than themselves, and an unlimited number of growth transitions related to copying are discovered.
Researchers have designed a model network of marine reserves to protect spiny lobsters and increase fishing yields in Honduras. The study resolves the 'conserve or catch' conflict by showing that it's possible to balance conservation and economic goals.
Researchers found that network structure determines success in group problem-solving strategies, and vice versa. Teams should be designed around their learning style to strike the right balance between exploration and exploitation.
Mathematical biologist Samuel Scarpino and colleagues found that replacing sick individuals with healthy ones can accelerate the spread of infection for certain diseases. The study analyzed data from two diseases, influenza and dengue, and revealed a stronger correlation between human exchange and outbreak acceleration in influenza.
Researchers developed a theoretical framework to understand cooperation in competing populations, applying it to consensus votes and epidemic models. The model shows that long-range spatial correlations affect switching between cooperative and non-cooperative behavior.