Articles tagged with Computer Modeling
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.
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 ...
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 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.
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...
A new computer model uses improved artificial intelligence to predict snow and water availability more accurately, considering both time and space. This information can help farmers and water planners make better decisions about water allocation.
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
Researchers have found that plants help regulate the planet's atmosphere by trapping carbon and emitting oxygen, acting as a buffer against rapid climate changes. However, when climate shifts too fast for vegetation to adapt, it can lead to mass extinctions and extreme environmental changes.
A recent study by University of Technology Sydney has modelled the transfer and deposition of plastic particles in the human respiratory system. The results have identified hotspots where plastic particles can accumulate, particularly in the nasal cavity and lungs, posing a risk to respiratory health.
Researchers used big data to calculate per-country greenhouse gas emissions from aviation for 197 countries, revealing gaps in reporting requirements under the UNFCCC treaty. Countries like China and the US were found to emit the most aviation-related emissions.
A new AI model, DyLEMa, reduces ET prediction uncertainty by up to 30% and captures land use dynamics accurately. The model improves daily continuous ET data generation and estimation of soil erosion.
A new landscape evolution model suggests that the first humans in Australia migrated rapidly across the continent following riverine corridors and coastlines. The study identifies areas of archaeological significance and provides insights into the impact of climate-driven geography on human dispersal.
A Cleveland Clinic study uses reinforcement learning to design antibiotic regimens that minimize resistance and maximize susceptibility. The AI model predicts the most efficient treatment plans for multiple strains of E. coli, informing hospital-wide infection management.
Researchers created a simulation model to analyze the impact of coastal management activities on barrier islands' vulnerability to sea-level rise. The model shows that natural processes can create and maintain barriers, but human efforts to protect communities can disrupt these processes.
The position paper identifies eight key areas for digital neuroscience research, including near-term, middle-term, and long-term goals. It also discusses the potential of 'digital twin' approaches, ultra-high-resolution digital atlases, and neuro-derived AI and computing innovations.
A team of researchers at MIT has developed a new method to model the irrational behavior of humans, which can be used to predict their future actions. By analyzing an agent's previous decisions, the technique infers its computational constraints and adapts to human collaborators' weaknesses.
Researchers at the University of Cologne found that training can optimize word recognition, leading to improved reading efficiency. The 'Lexical Categorization Model' uses behavioral findings to predict brain activation and separates known words from unknown letter combinations.
Egg cells generate internal fluid flows to transport nutrients, but how these flows arise has been a mystery. Researchers used computational models and experiments to understand the mechanics of twister-like fluid flows, revealing their origin from microtubules and molecular motors.
DynGAN detects and resolves mode collapse by establishing thresholds on discriminator outputs and training dynamic conditional generative models. This improves the diversity of generated samples, surpassing existing GANs and their variants.
A study found that GPT-4 matched the performance of radiologists in detecting errors in radiology reports, with an accuracy rate of 82.7%. The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist.
A novel machine learning model has been developed to characterize material surfaces, accurately predicting key electronic properties. The model, which employs artificial neural networks and transfer learning, shows great promise for exploring new materials with superior properties.
Researchers created a system called Holodeck to generate interactive 3D environments, leveraging language models like ChatGPT to control it. The system outperformed earlier tools in evaluating realism and accuracy, with human evaluators preferring its outputs across various indoor environments.
Mirta Galesic has been awarded a €3 million ERC Advanced Grant to investigate collective adaptation. The project, hosted at the Complexity Science Hub, aims to understand how human groups navigate complex problem landscapes using computational models and empirical data.
Chemists develop new reactions using model systems and substrates to demonstrate versatility. A new computer-aided method reduces subjective bias by analyzing real pharmaceutical compounds' complexity and structural properties. This improves data quality and facilitates machine learning applications.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
Researchers developed a new model to predict future risk of pressure injuries, increasing accuracy by over 20%. The predictive algorithm offers improved economic efficiency and substantial savings, especially for underrepresented minorities.
A team of researchers developed an AI-powered computer vision model to detect Brazilian wild animals on roads and warn drivers in real-time. The system uses roadside cameras and portable computers to identify species such as anteaters, wolves, and tapirs, with the potential to save lives and reduce roadkill.
Researchers at Nagoya University developed a framework for evolving AI agents with diverse personality traits using large-scale language models. The study found that AI agents can switch between selfish and cooperative behaviors, mirroring human behavior.