Articles tagged with Computer Modeling
Researchers developed a computer model called MANAscore to identify tumor-fighting immune cells in lung cancer patients treated with immune checkpoint inhibitors. The three-gene model helped find differences associated with patient response to immunotherapy, including the presence of stem-like memory T cells.
Researchers at King's College London have developed a complex model of molecular 'wear-and-tear' that sheds light on how proteins age. The study found that chromatin, the DNA-protein mix, is more resilient to aging than previously thought, suggesting new avenues for anti-aging treatments.
A team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The method enables agents to continually map their safety margins, allowing them to scale up to any number of agents while maintaining system safety.
The project's recordings help improve voice recognition tools by providing diverse speech patterns to train artificial intelligence models. Microsoft has seen significant improvements in recognizing non-standard English speech, with accuracy gains ranging from 18% to 60%, depending on the speaker's disability.
Researchers at the University of Surrey propose a new approach to treating inflammatory diseases using personalized probiotic therapies. By analyzing large-scale computer models and computational methodologies, they can identify potential targets and design tailored treatments.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
Vision-Language Models (VLMs) inherit biases from uncurated datasets, leading to poor group robustness and biased predictions. Researchers are exploring strategies to mitigate these biases in discriminative models, but generative tasks like image captioning and image generation require attention.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
A new hybrid machine learning model predicts ultimate axial strength of CFRP-strengthened CFST columns with high accuracy, enabling safer and more efficient designs. The model can be used to optimize construction processes and enhance the safety of structures at a lower cost.
Researchers created digital images of iridescent bird feathers that change colors at different angles, incorporating tiny variations for a glittery appearance. The new method uses specimens from the Cornell Lab of Ornithology as a reference and has potential applications in video games and animation.
A groundbreaking AI model developed by researchers at Emory University accurately predicts the likelihood of blood transfusion in non-traumatic ICU patients, addressing longstanding challenges in predicting transfusion needs. The model achieved exceptional performance metrics, including an AUROC of 0.97 and an accuracy rate of 0.93.
Scientists at the University of Lausanne used AI to simulate the last Alpine glaciation, finding ice covers 35-50% thinner than previous models. The new approach enables unprecedented accuracy and resolution, making it possible to describe complex topography numerically.
The Aging Research and Drug Discovery Meeting (ARDD2025) will feature Nobel laureates Morten Meldal and Michael Levitt. Their groundbreaking discoveries have had a lasting impact on drug development and molecular design, highlighting the conference's pivotal role in advancing healthspan and longevity science.
Researchers developed ProteinReDiff, an AI-powered method to redesign proteins for improved ligand binding. The approach uses initial protein sequences and ligand SMILES strings, reducing reliance on detailed structural data.
A new study reveals that a colossal 'megaflood' refilled the Mediterranean Sea around 5.97-5.33 million years ago, marking the end of the Messinian Salinity Crisis. The Zanclean Megaflood is believed to have had discharge rates and flow velocities dwarfing any other known floods in Earth's history.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
Anil Jain and Michael I. Jordan received the BBVA Foundation Award in Information and Communication Technologies for their pioneering work on machine learning, enabling transformative technologies like biometrics and artificial intelligence. Their research has unlocked applications of far-reaching impact on society.
Neuroscientists at WashU develop a novel method to create personalized brain models, shedding light on individual variations in brain dynamics and their impact on cognition and behavior. The study reveals new insights into the mechanisms underlying person-specific differences in brain electrophysiology.
Researchers at UVA have developed computer models to target specific bacteria in specific parts of the body, reducing the chance of antibiotic resistance. This approach could lead to more effective treatments and reduce the need for broad-spectrum antibiotics.
The University of Texas at Austin appoints Charles 'Charley' Taylor to lead a new Center for Computational Medicine, developing advanced medical applications and modeling. This move strengthens collaborations between Dell Medical School and the Oden Institute, driving innovation in health and technology.
A novel method called RESQUE predicts computational and energy costs for updating deep learning/AI models, allowing users to make informed decisions about when to update models to improve AI sustainability. The researchers conducted extensive experiments to validate the performance of RESQUE.
Researchers developed a computer model to predict which medication works best for each individual patient with CML. The approach has the potential to improve treatment outcomes for patients who previously relied on stem cell transplantation.
Researchers at Columbia University Irving Medical Center have developed an AI method that can accurately predict the activity of genes within any human cell, revealing the cell's inner mechanisms. The system can also uncover hidden biology of diseased cells and explore the role of genome's 'dark matter' in cancer and other diseases.
A new AI model developed by researchers at Penn State College of Medicine can predict the progression of autoimmune disease among those with preclinical symptoms up to 1,000% more accurately. The GPS model integrates data from large genetic studies and electronic health records to identify individuals at high risk of disease progression.
Computer models predict that climate change will lead to an increase in spongy moth outbreaks, causing widespread damage to forests. The fungus that normally curbs the moths' spread is expected to decline, allowing the moths to destroy more trees as temperatures rise.
A new study published in Nature Medicine shows that AI-based models can accurately identify ovarian cancer in ultrasound images, achieving an accuracy rate of 86.3%. The models also reduce the need for expert referrals and misdiagnosis rates by 63% and 18%, respectively.
Researchers at Seoul National University propose PV2DOC, a software that converts presentation videos into searchable, well-organized PDFs with text summaries and relevant images. The tool enhances video accessibility and reduces storage space, making it easier for users to find specific information.
The US Department of Energy is investing $179 million in three Microelectronics Science Research Centers to develop next-generation microelectronics designed for extreme environments. PNNL will lead projects on neuromorphic computing, EUV lithography, and heterogeneous computing.
Researchers developed Concurrent Dynamic Quantum Logic (CDQL) to verify quantum protocols with concurrent actions, enhancing expressiveness and speeding up verification. CDQL provides a rigorous framework for verifying both sequential and concurrent models of quantum protocols.
Researchers developed a new benchmark for health care using reinforcement learning, which shows promise in managing chronic or psychiatric diseases. However, current methods are data-hungry and fail to perform accurately when tested on real-world data.
A new tool developed by Penn State researchers uses computer vision and artificial intelligence to analyze placenta images, detecting abnormalities and risks such as neonatal sepsis. The PlacentaCLIP+ model has the potential to transform neonatal and maternal care in low- and high-resource settings.
The Mcity Test Facility's first open-source digital twin enables researchers to test autonomous algorithms in a virtual environment. The digital twin introduces real-world data and simulated safety-critical events, accelerating the development of connected and automated vehicle software.
Researchers from Florida Atlantic University developed an AI system that accurately detects and tracks American Sign Language alphabet gestures using computer vision. The model achieved a detection process with high accuracy, recognizing complex hand gestures with an F1 score of 99% and mAP of 98%.
A new AI tool called PlacentaVision can rapidly evaluate placentas at birth to detect abnormalities associated with infection and neonatal sepsis. This could lead to improved health outcomes for both mothers and babies, particularly in areas with limited medical resources.
A new study estimates that India will see over 62.4 million cases of tuberculosis in the next two decades, posing a significant burden on the country's health and economy. Scaling up existing treatment regimens could generate at least $28 billion in GDP gains, highlighting the need for increased investment in TB control measures.
A team of researchers has developed a novel technique to steal artificial intelligence (AI) models by monitoring electromagnetic signals. The method allows attackers to recreate the high-level features of an AI model with 99.91% accuracy, potentially undermining intellectual property rights and exposing sensitive data.
A research team led by USC aims to create comprehensive maps of retinal nerve connections to understand and combat retinitis pigmentosa, a progressive eye disease affecting 2 million people globally.
Scientists have developed a new technique to study faults, which can improve earthquake forecasts by determining the origins and directions of past rupture events. By analyzing curved scratches left on the fault plane, researchers can pinpoint where earthquakes start and spread, providing valuable insights for modeling future scenarios.
Researchers at Graz University of Technology developed a new database to improve speech recognition of Austrian German using speech data from 38 speakers. They found that traditional HMM-based systems are more robust for short sentences and dialectal language, while transformer-based models excel with longer sentences and context.
A new technique identifies and removes specific points in a training dataset that contribute most to a model's failures on minority subgroups. This approach maintains the overall accuracy of the model while improving its performance regarding underrepresented groups.
A new system, EXPLINGO, enables AI models to generate readable narratives explaining their predictions, helping users make better decisions. The system, developed by MIT researchers, uses large language models to transform complex explanations into plain language and automatically evaluate their quality.
A new AI model developed at the University of Georgia predicts nearby traffic movements and incorporates innovative features for planning safe vehicle movements. This approach helps reduce crashes and near-misses by consolidating two steps: predicting surrounding traffic movements and planning a self-driving car's motion.
A new research paper published in Oncotarget introduces an innovative AI tool combining CT scans and body composition data to predict severe liver problems in primary sclerosing cholangitis (PSC) patients. The model achieved impressive results, correctly identifying at-risk patients with 97% accuracy.
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
Researchers develop innovative hybrid control strategy to improve product yields in biosynthetic processes. The new approach combines model-based optimization with in-cell feedback control, outperforming traditional methods and promising reduced costs and environmental impact.
Evo, a generative AI model, uses patterns in microbial genomes to write new genetic code, expanding the length of sequences models can process and improving resolution. Researchers use Evo to understand microbial and viral genomes, fashion new proteins, and reprogram microbes for remarkable tasks.
Dartmouth researchers propose a new way of thinking about masking and social distancing rules using game theory, considering them as two distinct actions. The study finds that people respond differently to these measures based on their perception of the disease's severity, trending towards masking or no protective action over time.
Researchers observed microfiber plastics tumbling, rolling, and getting stuck in soil particles, revealing slow movement and potential trapping. The study improves understanding of exposure risks and possible health impacts of widespread plastic pollutants.
Researchers propose FTI-SLAM to maintain system performance while addressing critical privacy and communication concerns. It leverages federated learning, reducing data transmission and improving generalisation capabilities.
The Polymathic AI team has released two massive datasets for training artificial intelligence models to find and exploit transferable knowledge between seemingly disparate fields. The datasets include data from dozens of sources, covering astrophysics, biology, acoustics, chemistry, fluid dynamics, and more.
Scientists at MIT developed a fully integrated photonic processor that can perform all key computations of a deep neural network optically on the chip. The device completed machine-learning classification tasks in under half a nanosecond while achieving over 92% accuracy, similar to traditional hardware.
A new study reveals that children's awareness of famous computer scientists is dominated by white men, with few women and people from diverse backgrounds represented. The researchers argue that this lack of diversity limits the relevance and usefulness of computer science for underrepresented communities.
Experts from UTHealth Houston and Baylor College of Medicine developed a pragmatic approach to monitor and manage AI systems in healthcare organizations. The guidance emphasizes the need for robust governance systems, testing processes, and transparency with patients to ensure safe AI adoption.
The National Center for Supercomputing Applications (NCSA) has received the Readers' Choice Award: Best HPC Collaboration and Editors' Choice: Best Use of HPC in Physical Sciences. This is the 14th consecutive year NCSA has been honored with an HPCwire award.
A novel no-code prototype, Auto-DSM, generates design structure matrices (DSMs) using large language models. It improves productivity and accuracy compared to traditional methods, with speeds of up to four minutes per DSM generation.
Researchers at MIT have introduced a new algorithm that strategically selects the best tasks for training an AI agent, resulting in improved performance and reduced training costs. The technique outperforms existing methods by five to 50 times, making it more efficient and effective.
The winning team presented an exascale climate emulator that addresses the growing computational and storage requirements of high-resolution Earth System Models. This innovation enables more advanced climate modeling capabilities, holding significant potential for advancing climate research and policy-making.
A team of researchers led by Jeff Zacks used computer models to analyze over 25 hours of video footage of people performing everyday tasks. They found that the models were most accurate when responding to uncertainty, suggesting a more complex role for prediction errors in human cognition. Additionally, another researcher, Maverick Smi...
A novel deep learning model called Co-Plane Attention Across MRI Sequences (CoPAS) has been developed to assist with classifying 12 common types of knee abnormalities. The model achieved high classification accuracy comparable to that of radiologists, improving diagnostic efficiency and reducing errors.
Researchers developed an AI-powered model called FastGlioma that can detect residual tumor tissue with high accuracy in 10 seconds. The technology outperformed conventional methods, reducing the risk of missed tumors by nearly 75%. This innovation could change the field of neurosurgery and minimize reliance on radiographic imaging.