Articles tagged with Computer Modeling
The two-year study aims to explore biases in AI systems and develop a 'human-in-the-loop' framework for quality data discovery. It will investigate how humans can be involved as labelers, prompters, and validators to improve data sets and user interfaces.
Researchers developed a new AI model that predicts optical properties across a wide range of light frequencies using only a material's crystal structure as input. This enables highly precise predictions, making it suitable for screening materials for high-performance solar cells and detecting quantum materials.
Researchers at Lehigh University are developing predictive models for gene editing with CRISPR to improve outcomes and expand medical applications. The team is using AI and advanced computer models to simulate the effects of altering a single gene on the entire genome, enabling them to predict and avoid unintended consequences.
Researchers at MIT developed a security protocol that leverages quantum mechanics to guarantee secure data transmission during deep-learning computations. The protocol encodes data into laser light, making it impossible for attackers to copy or intercept information without detection.
Researchers developed a model to project Italy's energy storage needs for a renewable energy system, accounting for daily and seasonal fluctuations. The model suggests that increasing short-term energy storage capacity is critical for decarbonizing the power sector.
Researchers trained an automatic speech recognizer on recordings from people with dysarthria related to Parkinson's disease, achieving a 30% accuracy improvement. The study, led by Mark Hasegawa-Johnson, provides valuable data for improving voice recognition devices.
A Kennesaw State University researcher is developing secure AI models that not only protect personal information but also reduce energy use. Her goal is to create a federated learning system that overcomes specific vulnerabilities of current systems, improving data transmission security and confining AI training to individual devices.
Researchers at UT Austin developed AI model EvoRank to design protein-based therapies and vaccines by leveraging nature's evolutionary processes. The model identifies useful mutations in proteins, offering a new approach to biomedical research and biotechnology.
Researchers from Kyushu University found that reducing plastic waste entering oceans by 32% by 2035 would result in more than 50% less plastic in the oceans by 2050. The study aims to provide a clear numerical target for global efforts to tackle marine plastic pollution.
A study reveals that the gender gap in physics has remained stable for over a century due to established scientists' adoption patterns and network inclusion. The gap can be closed by adjusting parameters, but interventions such as funding and promotion opportunities are more challenging.
A new study warns that future climate change will intensify wildfires in the Arctic region, leading to an abrupt switch from no fires to very intense ones within just a few years. The accelerated permafrost thawing is expected to increase soil water and vegetation biomass, exacerbating fire fuel.
Researchers at Mizzou have developed Cryo2Struct, a computer program that uses AI to build the three-dimensional atomic structure of large protein complexes from cryo-electron microscopy images. This breakthrough enables scientists to better understand protein interactions, critical for developing effective treatments for diseases like...
Researchers apply computational technique to understand the 'pseudogap', a long-standing puzzle in quantum physics with ties to superconductivity. The discovery helps scientists in their quest for room-temperature superconductivity, enabling lossless power transmission and faster MRI machines.
Researchers found that large language models used in home surveillance can make inconsistent decisions about calling the police, even when videos show no crime. Models often disagreed with each other and exhibited inherent biases influenced by neighborhood demographics.
A Concordia-led team developed a framework that enables crowdsourced deep reinforcement learning as a service, using blockchain technology. This allows smaller organizations to access complex AI tasks previously out of reach, reducing costs and risk.
A team of researchers from Shibaura Institute of Technology developed a moving particle simulation-aided soil plasticity analysis for earth pressure balance shield tunnelling. The study found that earth pressure is a reliable indicator for analyzing soil plasticity and proposed a computer-aided analysis system that precisely reflects e...
Researchers analyzed COVID-19 cases in US states, finding oscillating waves every six months that start near the southern border in July-August. The data suggests a larger pattern sweeping up and down North America, but further research is needed to understand the mechanisms behind these seasonal oscillations.
A team of researchers at Pusan National University developed a pricing formula for perpetual American strangle options (PASOs) using a stochastic volatility model. The formula is accurate and provides a better understanding of the risks and returns associated with PASOs, especially in low-volatility environments.
The new Corporate Lab aims to advance collaboration between industry and public sector researchers to address real-world challenges in sustainable living and digital technologies. It will develop and trial novel digital technologies prioritizing sustainable ecosystems and lifestyles, including energy-efficient AI algorithms, green clou...
Researchers have found a new mechanism for the formation of impermeable horizontal ice layers below the surface of ice sheets, which could improve estimates of sea level rise. This process is critical for determining the contribution of ice sheet meltwater to sea level rise.
Researchers found that coastal dams can either increase or decrease flood risks, depending on the duration of a surge event and friction from water flow. The study, published in the Journal of Geophysical Research: Oceans, analyzed data from Charleston Harbor and other estuaries to make this surprising discovery.
A Dartmouth-led study projects that Antarctica's glaciers will rapidly retreat and potentially collapse by 2200, increasing global sea levels by up to 5.5 feet by 2300. The researchers used 16 ice-sheet models to refine the projection of ice loss over the next 300 years.
A novel network computer model, DiWANN, allows for efficient searches of cancer genetic data, identifying co-occurring mutations and similarities among DNA sequence elements across several types of cancer. The model provides a scalable solution to prioritize possible treatment targets.
A research team at the University of Würzburg has achieved electrically controlled modulation of light antennas, paving the way for ultra-fast active plasmonics. This breakthrough could lead to significantly faster computer chips and new insights into energy conversion and storage technologies.
A new AI model generates detailed images of cancer tissue that imitate what its staining would look like, reducing the need for resource-intensive lab analyses. The VirtualMultiplexer uses contrastive unpaired translation to create accurate virtual pictures of diagnostic tissue colorations.
Researchers found that sustained neck exertions alter the cervical spine's curvature, leading to muscle fatigue and increased risk of neck pain. The study provides new insights into the mechanical changes that occur in the spine and muscles when performing repetitive tasks.
Researchers found that control of most genes doesn't deteriorate with age, but coordination between cellular processes becomes less effective. The study suggests a more complex approach to understanding aging is needed, analyzing all genes simultaneously and their protein interactions.
Researchers at Duke University have developed a computer model that simulates nerve responses to electrical stimulation, enabling the efficient design of more effective and targeted neuromodulation therapies. The new tool, called S-MF, runs thousands of times faster than current industry standards without sacrificing accuracy or detail.
A new editorial in Oncoscience explores the application of functional information to adapt radiation therapy to individual patients. This approach aims to reduce uncertainty and improve outcomes in cancer treatment by analyzing signal data uncertainty.
Researchers at the University of South Australia have developed an AI sensor that can accurately measure the orientation of the Milky Way in low light, using a technique inspired by the dung beetle. This system could improve navigation for drones and satellites in difficult lighting conditions.
Researchers at the University of Vienna developed a software tool called MolCompass to identify areas of 'chemical space' where machine learning models are weak. This helps toxicologists and regulatory authorities increase confidence in these models, reducing the need for animal testing.
Researchers developed a framework called SigLLM that uses large language models to detect anomalies in time-series data. The approach converts time-series data into text-based inputs and can be deployed right out of the box, offering an efficient anomaly detection solution for complex systems.
Researchers at University of Bath and Technical University of Darmstadt found that large language models like ChatGPT cannot learn independently or acquire new skills, making them controllable and predictable. The study concluded that LLMs remain inherently safe, but misuse is still possible.
The UFZ forest condition monitor tracks dynamic changes in Germany's forests due to climate change extreme events. The index displays seasonal and annual maps of forest condition, highlighting areas with severe damage, such as the Harz region.
Researchers at Carnegie Mellon University propose guidelines for using interpretable machine learning methods in computational biology to tackle complex problems. The guidelines address pitfalls such as relying on a single method and cherry-picking results, emphasizing the need for multiple approaches and human-centric considerations.
A new interpretable deep learning modeling architecture, LACG, is proposed to handle complex variable interactions in chemical processes. It achieves high-accuracy modeling results via the coupling of three sub-modules based on underlying logics. The model outperforms widely used neural networks in terms of interpretability.
Researchers found that powerful waves triggered deep within the Earth can cause continental surfaces to rise by over a kilometre. The study explains why parts of continents experience substantial uplift and erosion, forming sweeping elevated regions known as plateaus.
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.
A new study from the University of Illinois Chicago proposes an alternative theory for the formation of Earth's continents, challenging the long-held leading theory. The researchers used computer models to investigate the origin of Archaean zircons, which date back to 2.5-4 billion years ago.
A research team at Iowa State University has developed artificial intelligence technology that can model and understand complex chemical reactions, including those involved in ammonia production. The technology uses reinforcement learning to identify the optimal reaction pathway, promising to reduce production costs and emissions.
Researchers have introduced a new AI calibration method called Thermometer, which enables efficient calibration of large language models for various tasks. This technique leverages temperature scaling to adjust a model's confidence and can generalize to new tasks without requiring additional labeled data.
Researchers developed a computer model to examine the dynamics underlying suicide contagion after the suicides of Robin Williams in 2014 and Kate Spade and Anthony Bourdain in 2018. The findings provide a framework for quantifying suicidal contagion, allowing for better understanding, prevention, and containment of its spread.
Researchers investigated the effects of initial microbiota on microbial succession during eggplant fermentation, finding two distinct patterns of LAB dominance. They also identified Lactiplantibacillus plantarum as a primary contributor to lactic acid production and nutrient content.
Researchers developed a machine-learning framework that can predict phonon dispersion relations up to 1,000 times faster than other AI-based techniques, with comparable or even better accuracy. This method could help engineers design more efficient power generation systems and develop faster microelectronics.
The Special Report explores expert perspectives on deploying AI in radiology, emphasizing the need for trust, reproducibility, explainability, and accountability. Radiologists must be able to trust in AI systems' design and receive adequate training, while establishing clear guidelines regarding clinical accountability.
A new AI model can identify certain stages of ductal carcinoma in situ (DCIS), a type of pre-invasive breast cancer, that are likely to progress to invasive cancer. The model uses imaging and machine learning to analyze tissue samples and determine the stage of DCIS based on cell arrangement and organization.
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.