Articles tagged with Computer Modeling
Researchers found that LLMs are better at representing non-sensorimotor concepts but struggle with sensory and motor concepts. Incorporating sensory input improves LLM performance and leads to more human-like representation, consistent with previous human studies on multimodal learning.
Hypergraph research aims to improve network resilience by analyzing multi-way relationships, enabling more accurate representations of complex systems. By modeling projects within Texas A&M University as hypergraphs, researchers can depict interconnectedness and how individual work affects the entire system.
A new design method, SPADE, evaluates UAV video surveillance system quality without real-world manipulations. It assesses the impact of image resolution on object detection performance., The SPADE method can be applied to various UAV systems and will aid in designing complex large-scale systems involving multiple UAVs.
Researchers have developed a world-first method to simulate specific types of error-corrected quantum computations, a significant leap forward in the quest for robust quantum technologies. The new algorithm tackles a long-standing challenge in quantum research and enables accurate simulation using conventional computers.
Researchers at the Wyss Institute have identified vorinostat as a promising treatment for Rett Syndrome using an AI-driven drug discovery process and innovative disease modeling. The findings demonstrate disease-modifying abilities across multiple tissues, offering hope for a potentially curative treatment.
Scientists have developed a new multiscale modeling approach that can simulate how microscopic changes affect macroscale brain activity. The framework successfully predicted the effects of anesthesia on synaptic receptors, bridging the gap between molecular and whole-brain levels.
Researchers at Cornell University have developed a process that transforms short videos of rooms into highly accurate, interactable 3D simulations. The technology can be used to create more realistic video games and train robots to operate within specific real-world spaces.
Researchers developed an AI tool called AAnet to characterize cancer cell diversity, identifying five distinct cell groups with different gene expression profiles. This could lead to more targeted therapies and improved patient outcomes.
A novel 3D modeling method accurately quantifies the shading potential of over 800 microalgal species, affecting underwater light conditions and ecosystem balance. The study provides a comprehensive Projected Area Database for freshwater microalgae, enabling researchers to estimate the ecological impact of algal blooms.
Researchers at ETH Zurich have created a living material that can absorb CO2 from the air through photosynthesis and store it in a stable mineral form. The material, made with cyanobacteria, can be shaped using 3D printing and requires sunlight, water, and nutrients to grow.
Researchers Mostafa Bedewy and Ahmed Aziz Ezzat are advancing nanomanufacturing by using machine learning to control the formation of nanoparticles and grow carbon nanotubes. The team aims to reveal which nanoparticles act as seeds for growing nanotubes, a key step towards creating ideal high-density structures.
A new study published in Frontiers in Robotics and AI explores the use of artificial intelligence to detect live oysters. While the AI model ODYSSEE shows promise, it lags behind humans in accuracy, correctly identifying live oysters at a rate of 63% compared to 74% for expert annotators.
A new AI tool predicts infectious disease risk using large language modeling, consistently outperforming existing methods. The model uses real-time information to predict disease patterns and hospitalization trends, filling a gap in pandemic prediction tools.
A newly developed AI model, crossNN, accurately diagnoses brain tumors in 99.1% of cases and differentiates between over 170 tumor types from all organs with 97.8% accuracy. This technology enables non-invasive diagnostics using cerebrospinal fluid samples, providing a stress-free alternative to traditional biopsy methods.
A new AI tool developed by University of Missouri researchers can predict the 3D shape of chromosomes inside individual cells, providing a new view of how genes work. The tool helps identify unique differences in chromosome folding between cells, which controls gene activity and can lead to diseases like cancer.
Researchers have successfully modeled the synaptic vesicle cycle with unprecedented detail, shedding new light on how our brains function. The model predicts parameters of synaptic function that could not be tested experimentally, opening new avenues for neuroscience investigations.
Researchers propose therapeutic robots exhibit autonomy, responding only when users are calm and emotionally regulated, to foster trust, empathy, and fine-tuning. This approach has the potential to transform robotic therapy and human-robot interaction in fields like social skills development and emotional coaching.
Researchers have developed an image-analysis tool called SeaSplat that cuts through the ocean's optical effects and generates images of underwater environments with accurate colors. The team paired SeaSplat with a computational model to convert images into three-dimensional underwater worlds, allowing for virtual exploration.
Researchers created over 3,800 anatomically accurate digital hearts to investigate how age, sex, and lifestyle affect heart disease. They found that age and obesity cause changes in the heart's electrical properties, which could explain why these factors are linked to a higher risk of heart disease.
Kobe University researchers uncover a new phenomenon in bismuth that masks its surface conductivity, relevant to topological materials suitable for quantum computing and spintronics. The study breaks the principle of bulk-edge correspondence, suggesting 'topological blocking' in other systems.
Researchers at Texas A&M University developed a new AI model to speed up building damage assessments and predict recovery times after a tornado. The model uses remote sensing, deep learning, and restoration models to generate damage assessments in under an hour, providing actionable intelligence for first responders and policymakers.
A new AI-driven tool can forecast acute child malnutrition in Kenya up to six months in advance with high accuracy, enabling timely interventions. The model integrates clinical data and satellite information to identify emerging risk areas, providing a game-changing solution to address public health emergency in the country.
Researchers, Mollie Brewer and Kevin Childs, are exploring how coaches use data and technology to maximize player performance and safety. They found that coaches analyze data from wearable sensors to determine when players need rest or additional training, and that AI-powered analytics can improve team strategy.
A new generative AI model, DiffSMol, has been developed to generate realistic 3D structures of small molecules with promising drug properties. The study achieved a 61.4% success rate in creating novel molecules, outperforming prior research attempts.
The 4th Annual MPS World Summit brings together over 1,500 international experts to explore advancements in Microphysiological Systems (MPS) research. This event focuses on drug and chemical safety, disease modeling, and regulatory testing.
Dr Constantine Evans, a Maynooth University researcher, has won the Robert Dirks Molecular Programming Prize for his work on molecular self-assembly and its applications in biological systems. His research continues to make important strides in the scientific community.
A team of researchers used machine learning to analyze changes in astrocyte cells' structure, shedding light on heroin addiction and relapse. The study, published in Science Advances, found that specific subpopulations of astroglia exhibit more pronounced morphological changes during drug use.
César A. Uribe, a Rice University professor, has received an NSF CAREER Award to develop mathematical tools for decentralized learning in AI and data science. His research aims to create more efficient networks of computers that can process massive amounts of data without relying on centralized coordination.
Researchers have developed a neuro-quantum leap in finding optimal solutions, leveraging Fowler-Nordheim annealers to discover new and unknown solutions. NeuroSA's structure is neuromorphic, with a search behavior determined by FN annealer, making it powerful for solving complex optimization problems.
DSAPS achieves dual scalability by manipulating ∆ E blocks using two structures: a high-capacity structure for increasing spins and a high-precision structure for increasing interaction bit width. The system has shown promising results in solving complex COPs, with validation tests achieving over 99% accuracy.
A new study suggests that adding particles to the atmosphere at a lower altitude near the polar regions can effectively cool the planet. Commercial jets like Boeing 777F could reach this altitude.
Researchers at Rice University developed a new machine learning algorithm that excels in interpreting light signatures of molecules, materials and disease biomarkers. The tool can detect subtle signals in optical spectroscopy, enabling faster medical diagnoses and sample analysis.
A study by Cornell University finds that AI-based writing assistants can generate generic language that makes non-Western users write in a more American style, leading to cultural stereotyping and language homogenization. Indian participants showed a smaller productivity boost compared to their American counterparts.
Researchers developed a biomimetic COF membrane that selectively separates lithium ions from complex brines, achieving selectivity comparable to biological ion channels. The membrane's unique structure and design enable efficient single-step purification of lithium, making it a promising route for sustainable extraction.
Researchers developed an AI model called Odor Generative Diffusion (OGDiffusion) to automate fragrance creation, generating essential oil blends based on user input of scent descriptors. The system produced fragrances that met people's expectations in human sensory tests.
Researchers develop a new approach to automatically guide large language models (LLMs) towards generating accurate, properly structured outputs for various programming languages and formats. This probabilistic approach boosts computational efficiency, enabling small LLMs to outperform larger models in generating accurate code.
MIT researchers have created a unifying framework that combines existing ideas to improve AI models or create new ones. The 'periodic table of machine learning' categorizes classical algorithms based on the approximate relationships they learn, allowing for fusion of strategies and discovery of new algorithms.
A team of researchers developed Lp-Convolution, a novel method that uses multivariate p-generalized normal distribution to reshape CNN filters dynamically. This breakthrough improves the accuracy and efficiency of image recognition systems while reducing computational burden.
Biomass is crucial for Europe's ability to reach its climate targets, providing both energy and negative emissions. Excluding biomass from the European energy system would increase costs by 169 billion Euros per year.
Researchers analyzed massive audio recordings to create a dictionary of short melodies in English-language prosody, assigning functions and meanings. They discovered hundreds of basic patterns with linguistic functions, including conveying attitudes such as curiosity or surprise, and identified syntactic rules governing their order.
Researchers developed a machine learning model that analyzes over 100 variables to predict an athlete's risk of lower-extremity musculoskeletal injury after concussion. The model achieved 95% accuracy in identifying vulnerable athletes, highlighting the importance of tracking individual performance and medical histories.
A Lehigh University team developed a novel machine learning method to predict abnormal grain growth in materials, enabling the creation of stronger, more reliable materials. The model successfully predicted abnormal grain growth in 86% of cases, with predictions made up to 20% of the material's lifetime.
A team of geoscientists from Rice University and partners have discovered a sharp, volatile-rich cap just 3.8 kilometers beneath Yellowstone's surface. This cap helps trap pressure and heat below it, suggesting the Yellowstone magma reservoir is actively releasing gas while remaining in a stable state.
Researchers propose universe may rotate with one rotation every 500 billion years, resolving Hubble tension paradox and explaining discrepancies in astronomical measurements. The theory is compatible with current models and doesn't break any known laws of physics.
A recent study by Concordia researchers uses reinforcement learning to maximize online polarization on Twitter, a platform prone to echo chambers and malicious manipulation. The approach confirms the effectiveness in intensifying disagreements across social networks.
A new numerical computer model tracks how pollution travels through Galveston Bay, helping scientists understand water movement in estuaries. The model is critical for evaluating climate variability and sea level fluctuation impacts on coastal communities, guiding better decisions to keep water clean and prevent flooding.
A University of Michigan-led team is developing a method to guarantee the quality of 3D-printed metal parts using digital twins, fatigue models, and multisensor integration. The technique aims to predict when these parts will fail under repeated stresses.
A Lehigh University researcher is developing a new platform to enhance flu forecasting through a human judgment temporal forecast system, which aims to improve evidence-based public health decision making. The system will collect unbiased opinions and forecasts from public health experts and the general public.
A new study reveals that 81% of O’ahu's coastline could experience erosion by 2100, with a further 40% loss happening by 2030. The research used computer models incorporating satellite imagery to predict the seasonal movement of sand, resulting in more severe erosion projections than previous studies.
Researchers created a promising approach that augments LLMs with graph-based models to generate molecules with desired properties. The method generates molecules with better matching user specifications and valid synthesis plans, outperforming existing LLM-based approaches.
Researchers at Florida Atlantic University developed an innovative real-time ASL interpretation system using AI and deep learning. The system achieved a 98.2% accuracy rate with minimal latency, enabling fast and reliable performance for applications such as live video processing and interactive technologies.
Dr. Wei Li is creating a virtual lunar welding platform to simulate welding in the moon's harsh environment, addressing temperature fluctuations and extreme vacuum conditions. The project aims to enable reliable large structure assembly on the moon, a crucial step for human colonization.
A recent study from UTSA researchers reveals that large language models (LLMs) can pose a serious threat to programmers who use them to help write code. The study found that up to 97% of software developers incorporate generative AI into their workflow, and 30% of code written today is AI-generated.
Researchers at Concordia University have developed a new approach to identifying fake news on social media using the SmoothDetector model. The model integrates probabilistic algorithms with deep neural networks to capture uncertainties and patterns in multimodal data, providing more nuanced judgments of authenticity.
A new artificial intelligence-based method detects genetic markers of antibiotic resistance in bacteria, potentially leading to faster and more effective treatments. The method, called Group Association Model, uses machine learning to identify key mutations linked to drug resistance, reducing false positives and misdiagnoses.
Researchers developed a machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. The new surrogate model maintains the same level of accuracy as traditional particle-based simulations while reducing computation time from approximately 45 minutes to just three minutes.
A new framework helps clinicians choose phrases that more accurately reflect the likelihood of certain pathologies in X-rays, improving the reliability of critical clinical information. The approach treats certainty phrases as probability distributions, capturing nuances of what each word means.
A team of NYU computer scientists has developed a new blockchain system called Bounce that leverages satellites to ensure security, reduce energy consumption, and achieve low response times. Bounce processes over five million transactions per second, outperforming its nearest competitor by 30-100 times.
A new computer modeling tool suggests that Bronze Age people may have traveled directly over the open ocean between Denmark and Norway. The simulations indicate that such trips were possible, but required a boat with specific capabilities and good weather forecasting.
Cleveland Clinic researchers successfully tested quantum computing's ability to simulate proton affinity, a fundamental chemical process critical to life. The study used machine learning applications on quantum hardware, achieving higher accuracy than classical computing in predicting proton affinity.