Articles tagged with Computer Modeling
A new method called D-REC uses a digital twin to predict which data users will need, improving edge caching decisions. The digital twin takes real-time data from the wireless network and conducts simulations to make accurate predictions.
A new study proposes a predictive home energy management system with a customizable bidirectional real-time pricing mechanism to promote residential demand response and reduce peak loads. The system enhances user comfort and accuracy of forecasting, while also providing cost savings.
A model simulates the group motion of fish based on visual cues, showing that each fish selects a single target and traces its motion. The model reproduces various collective patterns, including rotating vortexes and turning motions.
A new video-processing technique developed at the University of Florida uses artificial intelligence to track Parkinson's disease progression in patients, allowing neurologists to better monitor their condition and quality of life. The system analyzes video recordings of patients performing the finger-tapping test, a standard test for ...
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
UCF researchers George Atia and Yue Wang received a $1.2 million DARPA grant to develop AI-based technologies that can help autonomous systems adapt to unknown variables and overcome simulation-to-real gap issues.
Researchers at Georgia Tech developed a neural network that makes decisions similar to humans, using Bayesian neural networks and evidence accumulation processes. The model outperformed rival deterministic models and showed improved performance in high-speed scenarios, demonstrating its potential for more accurate decision-making.
A recent study predicts that lakes worldwide will experience unprecedented surface and subsurface warming, leading to severe disruptions in ecosystems. Tropical lakes are expected to be the first to emerge from natural temperature bounds, while high-latitude lakes may shield their subsurface layers from surface warming.
Researchers developed an AI model to analyze heart MRI scans, which can save NHS time and resources. The model provides a complete analysis of the entire heart using a view that shows all four chambers, leading to faster and more accurate diagnosis of heart failure and other cardiac conditions.
Researchers at the University of California - Riverside have proposed a chain of quantum magnetic objects called spin centers that can simulate exotic magnetic phases of matter. This breakthrough could lead to more efficient ways of storing and transferring information, as well as the development of room temperature quantum computers.
GenSQL integrates a tabular dataset and a generative probabilistic AI model to analyze complex tabular data. It can detect anomalies, predict outcomes, and generate synthetic data with just a few keystrokes.
The DEKR-SPrior model significantly reduces the mean absolute error in pod phenotyping compared to existing models, offering a valuable tool for enhancing crop yield predictions. It demonstrates superior accuracy in accurately detecting and counting soybean pods and seeds.
A new deep learning method, Point-Line Net, improves maize field phenotypic detection with high accuracy and efficiency. The model achieves an object detection accuracy of 81.5%, outperforming traditional methods in complex field environments.
Researchers recreated the structure of supernova remnant SN 1181 using a new computer model, explaining its double shock formation. The study also found that high-speed stellar winds may have started blowing from its surface within the past 20-30 years.
Researchers develop a method that fuses AlphaFold's strengths with computer simulations based on physics laws to predict protein structures, enabling faster drug development. The approach filters down initial hypotheses to a more manageable set of structures, increasing the effectiveness of pharmaceuticals.
Researchers found that AI models that analyze medical images can predict patient demographics with high accuracy but struggle to diagnose patients from diverse backgrounds. The models use demographic shortcuts, leading to incorrect results for women, Black people, and other groups.
A newly developed deep learning algorithm has outperformed existing methods in predicting osteoporosis risk, potentially leading to earlier diagnoses and better outcomes. The model identified key factors such as weight, age, and grip strength as significant contributors to osteoporosis risk.
A study found that large language models (LLMs) like ChatGPT underperform state-of-the-art detectors but can explain their analysis in plain language. LLMs' semantic knowledge makes them well-suited for detecting deepfakes, providing a common sense understanding of reality.
Researchers at Drexel University model the potential extent of flooding and combined sewer overflows in Camden, New Jersey, as climate change exacerbates the problem. The team's 'all-pipes' model simulates stormwater flows through every surface and pipe in the area, providing a unique solution to mitigate environmental and health risks.
Scientists developed a new approach to drive chemical reactions, generating compounds with unique pharmaceutical properties. The method uses photocatalysis and computational models to predict successful reactions, expanding the range of accessible substrates.
A new study from the University of Copenhagen reveals that AI has helped detect significantly more cases of breast cancer and reduce radiologist workloads. The AI system has been used to analyze tens of thousands of X-rays every year, resulting in a 12% increase in detected cases, including more small tumours of one centimetre or less.
A team of engineers has created a new mathematical model to accurately simulate the effects of blood flow on the adhesion and retention of nanoparticle drug carriers. The model, developed by University of Illinois professors Arif Masud and Hyunjoon Kong, was tested in vitro and demonstrated promising results.
Researchers at Boston University developed an AI model that analyzes speech patterns to predict the likelihood of Alzheimer's disease in patients with mild cognitive impairment. The model achieved an accuracy rate of 78.5% and could potentially revolutionize dementia screening, making it more accessible and efficient.
A new method, PURPLE, estimates how often underreported health conditions occur in different demographic groups, providing a more accurate picture of intimate partner violence. The algorithm shows that women from lower-income brackets are more likely to experience violence, consistent with previous literature.
Researchers developed a symbolic model checking approach to verify quantum circuits, addressing the gap between model-checking quantum programs and quantum circuits. They used Maude programming language to formally specify and verify quantum circuits, confirming their correctness and paving the way for error-free quantum computing.
A new tool, BioemuS, enables real-time emulation and hybridization of biological systems using biomimetic Spiking Neural Networks, addressing limitations of current pharmacological treatments. The system, developed through international collaboration, prioritizes cost efficiency and accessibility for closed-loop applications.
A new model developed by researchers at the University of São Paulo combines physical parameters and machine learning to predict storm tides. The model uses physics-informed machine learning, which harmonizes physical models with measured data to produce more precise forecasts.
A recent Illinois-led study found that soil moisture variability remains consistent across growing and non-growing seasons in fields across the Midwest. The research team used sensor measurements and remote sensing data to reveal a stable pattern of dry and wet areas, which can be used to estimate high-resolution soil moisture products.
Researchers from MIT and the MIT-IBM Watson AI Lab devised a navigation method that converts visual representations into pieces of language, which are then fed into one large language model. The approach utilizes purely language-based representations, generating synthetic training data to overcome challenges in visual data availability.
Researchers from MIT develop a new technique called Natural Language Embedded Programs (NLEPs) to enable large language models to solve numerical, analytical, and language-based tasks. NLEPs achieved greater than 90 percent accuracy on symbolic reasoning tasks and showed 30 percent greater accuracy than task-specific prompting methods.
Researchers have developed a computer model that sheds light on extracting renewable energy from superhot, super deep rock. The model shows the formation of microscopic cracks creating a dense 'cloud of permeability' throughout the affected rock, which can lead to higher power delivery and efficiency.
Researchers discovered that brain regions, including anterior insula and striatum, are involved in the biased response to pleasure in people with bipolar disorder. In those with bipolar disorder, these brain regions show reduced communication, leading to a 'vicious cycle' of escalating mood and risk-taking behavior.
Dr. Jeetain Mittal's NIH grant will support multiscale computational models investigating phase separation in biology, particularly heterochromatin formation and its role in neurodegenerative diseases. The research aims to elucidate the molecular origins of phase separation using innovative models and methods.
Researchers have identified two distinct types of dysbioses in cystic fibrosis, which differ in their ecological organization and response to treatment. These findings suggest that antimicrobial drugs may be more effective in hierarchically organized microbiota, potentially leading to improved treatment outcomes for patients.
Astrophysicists calculate that two million years ago, the solar system encountered a cold, harsh interstellar cloud, which may have interfered with the sun's solar wind and affected Earth's climate. The heliosphere, a protective plasma shield, was compressed in such a way that it briefly placed Earth outside its influence.
A new method developed by researchers at Florida Atlantic University and the Smithsonian Environmental Research Center improves the accuracy of acoustic tracking in marine animals. The method uses a movement model to reconstruct animal tracks, even in regions with uneven receiver coverage.
A study by Dr. Edwin Dalmaijer found that pigeons' desire for social proximity leads to improved flight paths as younger birds learn from older ones. This generational improvement in route efficiency is similar to those seen in real-life data, suggesting a key role for social factors in navigation.
Six UTA faculty members have received prestigious CAREER grants from the National Science Foundation for their innovative research. The total award amount is $3.23 million.
A new study from UCL reveals that Large Language Models exhibit irrational behavior, failing to reason logically and making simple mistakes. Despite their sophisticated capabilities, these AIs consistently fabricate information and respond inconsistently.
A new AI model developed by researchers at the University of Jyvåskilö can predict and respond to human emotions, improving user experience. The model simulates cognitive evaluation processes to assess emotional responses to events, enabling computers to preemptively predict and mitigate negative emotions.
Researchers combined robotics data from different sources using generative AI models to train better multipurpose robots. The approach, known as Policy Composition (PoCo), enables a robot to perform multiple tool-use tasks and adapt to new tasks in unfamiliar environments.
A computer model simulates dynamics of barrier island systems over two centuries, showing how human efforts to protect communities affect natural processes. The study finds that successful storm prevention strategies lead to less resilient barriers in the long term.
Researchers from Mount Sinai presented three studies on obstructive sleep apnea (OSA) at SLEEP 2024, exploring its effects on thalamic activity, overnight memory performance, and symptom improvement with CPAP treatment. The findings suggest that OSA can impair cognitive function and that hypoxic burden may predict clinical improvement.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
Researchers at Duke University developed an assistive machine learning model that greatly improves the ability of medical professionals to read EEG charts. The model, which provides visual explanations and decision support, has been shown to almost double medical professionals' accuracy in identifying seizure-like events, potentially s...
Researchers at JPMorgan Chase, Argonne National Laboratory and Quantinuum show a quantum algorithmic speedup for the QAOA algorithm on the Low Autocorrelation Binary Sequences problem. The team demonstrates a significant step towards reaching quantum advantage, laying the foundation for future impact in production.
Researchers at the University of Liège created a new type of spiking neuron, the Spiking Recurrent Cell (SRC), which combines simplicity with the ability to reproduce biological neuron dynamics. This innovation offers exciting prospects for neuro-inspired artificial intelligence, particularly in energy-efficient applications.
Researchers found that zebrafish synchronize movements by taking turns to move and responding to neighbors' timing, a two-way process known as reciprocity. Virtual reality experiments confirmed the principle, enabling the recreation of natural schooling behavior in fish and virtual conspecifics.
A new computer algorithm has been developed to enhance the management of invasive species globally, optimizing resource allocation and reducing costs. The innovative tool is adaptable to various population dynamical models and treatment methods, improving the effectiveness of environmental conservation efforts.
Researchers at Oregon Health & Science University have received a $16.4 million grant to advance mental health care for children, leveraging machine learning and novel clinical measures to improve prediction, diagnosis, and treatment of mental health conditions across childhood and adolescence. The project aims to create an actionable ...
Researchers at the University of Bonn improved 3D ocean circulation models using supercomputing resources to analyze ocean tide changes and their impact on coastal regions. The study found that a warming ocean surface enhances baroclinic tides, leading to significant energy transfer.
Warm seawater is pumping underneath Antarctica's Thwaites Glacier, causing ice to melt intensely and leading to devastating sea level rise. Researchers predict the glacier may retreat into the deeper part of the basin within 10-20 years, accelerating glacier melt and impacting coastal communities worldwide.
Researchers from MIT and the University of Basel developed a physics-informed machine-learning framework that can automatically map out phase diagrams for novel physical systems. This approach leverages generative models, making it possible to detect phase transitions without requiring huge training datasets. The technique has potentia...
RMIT's low-carbon concrete has been shown to recycle double the amount of coal ash compared to current standards, reducing cement requirements by half. The new mixture also performs exceptionally well over time, with large concrete beam prototypes meeting Australian Standards for engineering performance and environmental requirements.
Using the Hubbard model, researchers successfully re-created key features of cuprate superconductivity, which has puzzled scientists for decades. The breakthrough demonstrates the worth of simple models in understanding complex physics.
Researchers developed a computational model simulating fruit fly wing development to identify mechanisms behind organ generation. The model revealed two distinct classes of chemical signaling pathways that produce curved or flat tissues.
A new AI model developed by researchers at the University of British Columbia can accurately predict if a patient receiving cancer care will require mental health services. The AI analyzes oncologist's notes and identifies subtle clues that suggest a patient may benefit from early psychiatric or counselling interventions.
A new computer algorithm developed by University of Oxford scientist Samar Khatiwala can drastically reduce the spin-up time of Earth System Models, enabling researchers to investigate subtle changes in model parameters and define uncertainty more accurately. This breakthrough could lead to better predictions of future climate change a...