Articles tagged with Network Modeling
Researchers developed personalised brain models to simulate patient response to deep brain stimulation for depression, improving efficacy by 50%. The models use individual EEG and MRI data to replicate brain response and pave the way for tailored treatment approaches.
New research suggests the brain uses multiple strategies to process smells, employing both snapshot-like and evolving ensemble approaches. The study provides new tools for scientists to quantify and interpret brain activity patterns.
Researchers at Kaunas University of Technology improved an algorithm to detect Alzheimer's disease from MRI images, achieving over 98% accuracy. The new model uses a modified neural network and adapts to variations in data, such as differences in hospital equipment and patient positions.
Adversarially robust models capture aspects of human peripheral processing, with results showing similarity in image transformations and perception alignment. The study's findings shed light on the goals of peripheral processing in humans and could help improve machine learning models.
Researchers developed a new method for generating network layouts that allow for visualizing different information in two- and three-dimensional virtual space. This facilitates the exploration of complex protein interactions and provides more versatile, comprehensible representations of networks.
Researchers developed a machine-learning technique that can pinpoint anomalies in large datasets, such as power grid failures and traffic bottlenecks. The model uses advanced probability distributions to identify low-density values, allowing for faster and more accurate anomaly detection.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
Researchers from Tokyo Metropolitan University used a new network model to simulate the spread of COVID-19 variants. The study found that a non-linear dependence between variant infectivity and existing strain exists, leading to a more nuanced understanding of disease dynamics.
Researchers at Tokyo Institute of Technology have developed a new AI processor called Hiddenite, which achieves state-of-the-art accuracy in sparse neural networks with lower computational burdens. The chip drastically reduces external memory access for enhanced computational efficiency.
Researchers at UC3M have developed a mathematical model that analyzes the appearance of oscillations in flow networks, which may help explain how blood circulates in the brain. The model takes into account the size of the network and predicts the frequency of pressure oscillations.
A computational study finds that Omicron's spike protein has evolved to evade multiple classes of antibodies targeting SARS-CoV-2, even those from vaccinated individuals and monoclonal antibody treatments. The study suggests vaccines still offer protection due to the development of T cell immunity.
The MIT team developed a computer model that can perform sound localization tasks as well as humans, and adapts to real-world environments. The model uses convolutional neural networks and was trained on over 400 sounds, including human voices and animal sounds.
A recent study uses deep neural networks to analyze CT scans of dinosaur fossils, reducing manual segmentation time from days to minutes.
Researchers developed a school simulation model that shows how the virus spreads in different settings and calculates the effectiveness of measures against its spread. The model reveals that elementary and lower secondary schools can keep reproduction rates below 1 with classroom ventilation, wearing masks, and class size reduction.
Researchers created a computer model that simulates the way slime moulds construct their network, finding networks with improved travel time or resilience to disruption. The models were validated using three key metrics and showed good correlation with actual slime mould results.
MIT researchers develop a method to test feature-attribution methods for machine-learning models. They find that even the most popular methods often miss important features in an image and some perform as poorly as a random baseline. This has major implications for high-stakes situations like medical diagnoses.
A new study published in the Strategic Management Journal found that freemium marketing strategies can widen the revenue gap between market leaders and followers. The research used data from Apple's launch of Game Center to investigate how freemium strategies perform in markets with strong network effects.
A new project aims to improve the performance of Graph Neural Networks (GNNs) by leveraging weak supervision and additional information. The research has potential applications in fraud detection, agriculture, and cancer diagnosis.
A computer model developed by Tufts University researchers mirrors the spread of misinformation in real life. The model takes into account pre-existing beliefs, which can influence how individuals accept new information, and provides insight into strategies to counter misinformation.
A study published in Cell Reports reveals a set of critical gene interactions that are common across many cancers. By using network modeling, researchers have pinpointed functionally relevant gene networks that could offer new cancer therapies.
A study by researchers at the University of São Paulo developed a model that can predict the likelihood of politicians being convicted of corruption based on their voting histories. The model achieved 90% accuracy in identifying corrupt deputies.
Researchers propose a new model for electricity trade that balances economic interests and considers the stability of economic ties. The model suggests ways to improve cooperation between nations and prioritize transit country interests.
A new epidemiological model, the stochastic social activity (SSA) model, combines real-world observations with mathematical equations to accurately describe COVID-19 wave and plateau dynamics. The model suggests that COVID-19 may become endemic in the global population, like the common cold or flu.
A new model identifies a symptom of dangerously high levels of polarization: a group's inability to unite in the face of external threats. Researchers found that extreme polarization can lead to a tipping point, where even a shock fails to reverse the self-reinforcing dynamics of division.
A novel dynamic event-triggered scheduling approach is proposed to solve the platooning control problem, demonstrating effective trade-off between performance and efficient communication. The researchers aim to further investigate resource-efficient control strategies to preserve satisfactory operational performance.
New Cornell University research suggests that social connections can distort voting behavior, favoring minority candidates. The study found that complacency and dejectedness – conditions caused by social connections – can skew election outcomes.
A new prediction framework can forecast extreme climate events like floods and heatwaves up to two days in advance, allowing for crucial preparation time. This network-based approach analyzes large-scale connectivity patterns in observational data to improve forecasting accuracy.
Researchers create a multiscale model to track water quality indicators like nitrogen and mercury levels, incorporating biogeochemical reactions in microbially-active zones. They also develop 'stretchier' alloys by adding nano structures, which enhance strength and ductility, making them suitable for various applications. Additionally,...
A new study reveals that high-performing AI next-word prediction models resemble the function of language-processing centers in the human brain. The models' activity patterns closely match those seen in the brain during language tasks, suggesting a potential connection between AI and human language processing.
Researchers using satellite geodesy and InSAR imagery found the Arabian side of the Dead Sea Transform fault has been moving steadily northwards at around five millimeters per year. The studies suggest that large earthquakes may be less frequent near the southern end of the Gulf, but more investigations are needed for a resilient city.
A team of scientists at NAIST successfully used automatic differentiation to accelerate calculations of model parameter extraction, reducing computation time by 3.5 times compared to conventional methods. This breakthrough enables the design of more efficient power converters with increased performance and reduced energy consumption.
Researchers have developed a novel process to manufacture extreme heat-resistant carbon-carbon composites, which will be tested on a U.S. Navy rocket launching with NASA this fall. Additionally, they created a technology that more realistically emulates user activities to improve cyber testbeds and prevent cyberattacks.
Researchers developed a robust, deep neural network model to analyze automobile traffic impacts of construction zones. The model estimates hourly traffic volumes without adjustment factors, helping transportation agencies plan for efficient work zone operations.
Researchers at Nanyang Technological University and Tan Tock Seng Hospital have developed an AI-powered system to diagnose glaucoma from stereo fundus images, achieving an accuracy of 97% in diagnosing the condition. The automated method could potentially be used in less developed areas where patients lack access to ophthalmologists.
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 study at the University of São Paulo found that bird species interacting with more plant species have higher evolutionary stability. This is because they occupy central positions in seed dispersal networks, leading to longer lifespans and increased species accumulation.
A computer simulation modelled by Associate Professor David Goldbaum suggests a leader emerges through a dynamic self-reinforcing social process, even with identical attributes among group members. The study finds that building up influence and gaining popularity is key to becoming a leader.
A new algorithm can help researchers understand how simple actions lead to complex behaviors in biological systems, such as cancer growth and voting patterns. The algorithm analyzes Boolean networks, which are collections of nodes that are either on or off, to identify attractors that correspond to stable long-term behaviors.
Researchers developed a general theoretical approach to calculate the density of 5G base stations needed to achieve specific network parameters. The model shows that full isolation and mixed frequency regimes have different required densities, with the latter being more efficient but technically challenging.
The Financial Networks discipline analyzes the interconnectedness of financial actors and contracts to understand challenges like financial crises and sustainable finance. Researchers suggest models can inform policymakers and practitioners to mitigate climate-related risks and ensure a competitive economy.
A new study explores how changes in transportation networks impact disease spread, providing insights for future disease intervention strategies. The researchers developed techniques to quantify the effectiveness of different approaches in controlling disease outbreaks by analyzing network structure and hot spot placement.
Researchers apply statistical physics principles to analyze financial systems as complex networks, shedding light on early warning signals of crises and interbank linkages. Network theory reveals the impact of heterogeneity on risk propagation and systemic instability.
A new study finds that river systems exhibit lateral migration of channels, reorganizing drainage patterns into a dendritic structure. This phenomenon is not captured by existing numerical models and has significant implications for understanding river evolution and land use planning.
Researchers used a network model to study the effects of mask wearing and social distancing on the spread of airborne diseases like COVID-19. The model shows that widespread adherence to both measures can prevent viral outbreaks without mass vaccination.
Researchers at KAUST developed a new technology that increases machine learning speed on parallelized computing systems by five-fold. This 'in-network aggregation' method uses readily available programmable network hardware to provide dramatic speed improvements.
The Canadian Network for Modelling Infectious Disease (CANMOD) will inform public health decisions and prepare Canada for future pandemics. CANMOD will build and coordinate national capacity by sharing research problems, models and estimates across a broader community of researchers.
Researchers analyzed 25,766 film tropes and identified 42 cluster groups, with some associated with specific genres and high ratings. The study provides insights into the film industry's narrative patterns, potentially informing new film development and story generation.
Researchers found that native Russian speakers can precisely predict specific words and grammatical properties of words, with neural network models showing comparable precision. The study also discovered that the neural network predicts low-probability words better than humans and predicts high-probability words worse than humans.
A new paper uses a novel method to evaluate network model rules, allowing for the probabilistic reconstruction of historical data in complex systems. This enables researchers to determine the most likely network model and answer questions about who was infected first.
Researchers from SUTD developed a correlation-robust model of influence maximization, which assumes hidden correlations in influencers' behavior are detrimental to the company's interest. This model has computational benefits over traditional independent cascade models and can be used by practitioners to enhance network robustness.
Researchers found that a homogeneous population distribution reduces total infected individuals, while diverse contact frequencies can significantly decrease the number of infected. The optimal degree-based procedure involves lifting strict quarantine after high-degree nodes have acquired immunity, minimizing net infected individuals.
A recent study used real British Railway data and an artificial intelligence model to improve the ability to predict delays in railway networks. The Spatial-Temporal Graph Convolutional Network model outperformed other statistical models for forecasting delays up to 60 minutes in the future.
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.