A new path-tracing method simulates event camera data from virtual 3D scenes efficiently, reducing computational costs for applications like autonomous driving and robotics. The method uses physically-based path tracing and adaptive temporal search to precisely determine event timings, enabling more efficient training-data generation.
SourceChiba University·JournalIEEE Transactions on Visualization and Computer Graphics·TypeComputational simulation/modeling·DateOct 1, 2026
Researchers are working on a four-year project to develop a personalized model that uses detailed blood flow information to detect obstructions in the aorta. The project aims to improve prenatal testing so that doctors can spot problems in the aorta before a baby is born and recommend treatments.
UVA Engineering AI researcher Chen Chen is developing trustworthy AI systems to forecast infectious disease epidemics, combining multiple data sources like reported cases, wastewater surveillance, and human travel data. Her approach aims to predict where diseases will spread and help public health officials respond effectively.
Researchers at the Stowers Institute used AlphaFold2 and evolutionary data to predict protein structures in aphids, which were previously inaccessible to AI. The study reveals a common architectural plan among 2,400 BICYCLE proteins, showcasing the evolution's role in helping AI predict protein structures.
Researchers from Tokyo Metropolitan University used evolutionary algorithms to identify optimal shapes for ultra-thin, bio-inspired corrugated airfoils. They discovered designs that minimized drag or maximized lift, with corrugations near the leading-edge reducing frictional drag and convex shapes near the trailing edge increasing lift.
Researchers are using AI to model complex turbulent flows, including wind, aerosols, and combustion, to quantify uncertainty in foundation models. The project aims to create a physics-constrained AI foundation model for complex flows important to energy systems.
Researchers will develop AI-powered tools to examine cascading wildfire impacts, including flash floods, debris flows, and infrastructure disruptions. The project aims to bolster public safety and community resilience by combining AI, infrastructure modeling, and social science.
A new AI tool has been developed to predict treatment and survival outcomes in patients with advanced non-small cell lung cancer treated with immunotherapy. The tool consistently outperformed standard clinical biomarkers, achieving high accuracy scores in predicting survival outcomes.
The new MSc Computer Science with Artificial Intelligence programme equips learners with advanced technical knowledge and practical skills in AI. The programme combines Heriot-Watt's academic expertise with flexible online delivery, enabling learners worldwide to study alongside work and other commitments.
Researchers found that AI-generated images can improve species identification and biodiversity monitoring when real images are limited. However, the synthetic images were less effective than real images overall, highlighting the importance of community science and real-world observations.
Team Kinetiz from NTU Singapore, K2, and Alp Autonomy won the world's first multi-car autonomous race in Europe, taking the lead at Imola Circuit. The team demonstrated exceptional autonomous driving skills, showcasing the potential of AI in motorsport.
Researchers at Chalmers University of Technology have developed a new method for performing advanced quantum operations significantly faster and more efficiently. This breakthrough addresses a well-known bottleneck in quantum computing and paves the way for fault-tolerant quantum computing.
A new learning mechanism uses natural variability in neural activity to understand how synapses adapt and improve the learning capabilities of brain-inspired devices. The mechanism, called Spike-based Alignment Learning, solves the weight transport problem and matches the performance of existing approaches without unrealistic assumptions.
Researchers from SUTD develop HieraScaffold, an AI framework that generates large-scale 4D LiDAR scenes more efficiently and coherently. The framework captures both static structures and moving objects, improving the realism and accuracy of autonomous systems.
Researchers at ETH Zurich have created a 3D-printed model of a rock avalanche to study the movement of mixtures of water, ice, and rock. The model, which is 1:577 in scale, was used to test various scenarios and measure parameters such as depth of runoff and impact dynamics.
Researchers developed a physics-based AI approach to predict global-scale carbon cycling in ocean sediments, resolving a long-standing challenge in climate science. The study reveals key findings on dissolved organic carbon behavior, including 11% of particulate organic carbon returning to seawater.
A machine-learning tool built from Swedish national health registry data can predict hip fracture risk with high accuracy and identify individuals at high risk without in-person assessment. The tool performed nearly seven times better than current screening methods in identifying at-risk individuals.
SourcePLOS·JournalPLOS Medicine·TypeComputational simulation/modeling·DateAug 27, 2026
Researchers developed a novel divide-and-conquer approach for model checking linear temporal properties, called DCA2MC, to address state-space explosion and long verification times. The approach divides the original model checking problem into smaller, independent tasks, reducing memory consumption and verification time.
MIT engineers develop a tool that generates plausible extreme events and worst-case scenarios without relying on extreme data, enabling planners to prepare for unprecedented scenarios. The algorithm takes a statistical approach to learn from available data, excluding implausible weather scenarios, and projects how extreme events might ...
Engineers at Texas A&M University, NASA, and Purdue University create algorithms for managing spacecraft traffic around Gateway, a lunar spaceport. The system balances fuel efficiency and operational demands to reduce the risk of collisions during space missions.
The ST-NUS HELIX Corporate Lab aims to develop new generative and embodied AI use cases at the edge through system-to-silicon innovation, reducing energy consumption and improving performance. Researchers will focus on memory-centric architecture, innovative in-memory computing, and scalable compute-and-memory systems.
The Skala AI model, developed by Microsoft Research AI for Science, is now available through the CP2K software ecosystem. CASUS and Microsoft Research collaborated to integrate Skala into CP2K, enabling more accurate quantum mechanical simulations of larger molecular systems. The collaboration aims to improve the accuracy and efficienc...
A new study models a future net-zero European power system and tests it against 80 years of historical weather data to understand how it would handle stress with periods of low wind and solar generation, combined with high demand. The study concludes that the risk is greatest during winter when cold and wind-still conditions persist, h...
A new AI model, EarlyDetect, can detect precursor signals of active region emergence in the Sun's acoustic activity and magnetic field, forecasting solar eruptions nearly nine hours in advance. This technology has the potential to allow satellite communications companies or power grid companies to prepare for solar storms.
Researchers developed Latent Seal, a watermarking framework that integrates image watermarks into latent diffusion models. The approach helps identify AI-generated content and support copyright verification without degrading image quality. Tests show the method remains accurate after common edits and distortions.
Researchers built a mathematical model to understand collective movement and rest in ant colonies. The findings show that a single 'first mover' ant activates a group, triggering a wave of coordinated movement involving hundreds or thousands of workers.
Researchers developed a method to predict how different types of breast cancer will respond to treatment using lab-grown mini tumors. The organoids mimicked a tumor's response to treatment and identified candidate combination therapies for cancers that don't respond to standard treatment.
Assistant Professor Yingxue Zhang's project aims to develop urban AI models that can efficiently process vast amounts of human-generated data to optimize commute times, traffic safety, and more. The model will utilize offline reinforcement learning to tackle spatial-temporal dynamics in urban life.
A simulation study from the University of Illinois suggests that winter canola can increase overall productivity by 18% while maintaining stable greenhouse gas intensity. The diversified system also improves net ecosystem carbon balance, with benefits increasing over time.
New York's Empire AI Beta has officially launched, providing world-class AI computing power to researchers across the state. The initiative has served as a model for the federal National Science Foundation's State and Regional AI Infrastructure Hubs, which aim to build out regional AI research infrastructure and shared research capacity.
Research at AIST, Institute of Science Tokyo, and Ritsumeikan University uncovers the impact of histone acetylation site location on liquid–liquid phase separation in gene regulation. This study provides new perspectives for therapeutic development targeting aberrant gene expression.
A research team from HKU School of Computing and Data Science has developed ClairS, a novel deep-learning algorithm that improves the detection of cancer mutations. Tested on breast, lung, and melanoma cell-line datasets, ClairS demonstrates exceptional accuracy across various cancer types and sequencing conditions.
The Smart QS Hackathon 2026 aims to explore innovative applications of Artificial Intelligence (AI) in Quantity Surveying (QS) through cross-sector collaboration, facilitating the intelligent transformation of the construction industry. Participants will have access to AI training sessions and workshops, as well as Cyberport's computin...
Researchers found that the Agulhas Current supplies heat and moisture that strengthen weather systems, causing severe floods in the Western Cape. In contrast, the Benguela Upwelling System has a minor role in intensifying rainfall.
Researchers developed three functional components for photonic microchips using inverse design algorithms. The new components are up to 500 times smaller and more efficient than traditional designs.
Researchers developed an inverse-design framework to optimize magnonic crystal design, identifying unconventional lattice structures with large band gaps. The approach enables the exploration of previously unexplored material systems and device dimensions, paving the way for high-speed spin-wave computing and energy-efficient devices
USC researchers have been selected for the U.S. Department of Energy's Genesis Mission to harness artificial intelligence for scientific discovery and innovation. Two projects led by USC will explore ways to develop faster and more energy-efficient computing hardware and better understand the natural concentration of critical minerals.
Researchers at Duke University have developed a method to systematically develop novel probiotic and prebiotic combinations to maintain gut health and treat gastrointestinal diseases. The approach uses machine learning and automation to explore complex interactions between microbes, nutritional sources, and the environment.
A team of researchers at Harvard and Max Planck Institute have developed three new functional components for photonic microchips using an inverse design algorithm. The compact designs are about 500 times smaller than conventional designs and offer a path toward higher-performance integrated light technologies.
Two Lehigh University AI projects have been selected for funding from the Department of Energy's Genesis Mission. The RIVER-AI project will improve flood- and water-level prediction, while the REACT project aims to accelerate reactor-scale fusion energy by developing an AI-enabled digital twin. These awards strengthen Lehigh University...
Researchers at ISTA develop two modeling approaches to represent extremely deformable surfaces more realistically while cutting computational costs. They take inspiration from natural systems like volcanic lava and cake batter to create a wave-based approach, which tackles long-standing graphics problems from a new angle.
A KAIST research team developed an XR comics platform, ComiXR, that enables users to read and create comics in immersive environments. The platform demonstrated increased immersion when comic elements were positioned at different depths and incorporated sensory experiences such as facial expression tracking.
A new 3D computer model developed by the University of Surrey has shown how Pseudomonas aeruginosa grows and spreads its protective layer under constant fluid flow. The model's accuracy was validated through laboratory experiments, demonstrating potential for faster and smarter ways to understand bacterial behavior.
Researchers at UCF College of Engineering and Computer Science developed CRAFTS, a plug-and-play software that simplifies engineering design. The software offers a user-friendly interface and modularity, making it accessible to users without extensive coding knowledge.
A new tool generates realistic monkey body animations, revealing an uncanny valley effect where macaques prefer less realistic avatars to highly realistic ones. The study provides the first evidence that non-human primates experience this phenomenon.
A team of geographers and climate scientists found that major drought in Samoa and Tonga forced Polynesians to migrate east into the Pacific. Climate modeling revealed increased rainfall in receiving islands and a shift in sea surface temperatures, creating powerful incentives for people to seek new opportunities.
The partnership aims to bridge the gap between university learning and real-world practice, equipping students with in-demand digital skills. Through the Octave NextGen Builders Program, students will gain practical experience using professional tools used across industry projects.
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
A new AI model called COMPASS improves the prediction of which patients are most likely to respond to cancer immunotherapy drugs, outperforming existing approaches by 8.5 percent. The model makes predictions based on tumor gene activity and provides a rationale for its output.
Digital twins are expanding rapidly, using real-time data to simulate and analyze systems before applying them in the real world. The study emphasizes the need for interoperable architectures, machine-readable metadata, and standardized trust frameworks to address challenges such as privacy, cybersecurity, and uncertainty.
MIT researchers developed a framework that allows users to apply constraints to algorithmically generated structures, making them more buildable. The approach has the potential to reduce carbon emissions in construction by up to 90% by designing structures with multiple materials and taking into account materials' properties.
Research Scientists Edward Vizy and Professor Kerry Cook analyzed a storm that caused catastrophic flooding in Central Texas. They found that warmer-than-average sea surface temperatures weakened the Great Plains low-level jet, resulting in weaker storms and less intense rainfall.
A team of MIT researchers has developed a machine-learning approach that captures the diversity of atomic environments in chemically disordered materials. This allows for more accurate predictions of material properties and opens up possibilities for creating new sustainable steels and materials for aerospace, energy, and computing.
MD4SB integrates molecular dynamics tools into three European Research Infrastructures, enabling researchers to share and reuse data, and accelerate drug discovery. The project also involves pharmaceutical companies like Almirall and Sanofi.
Researchers propose a Digital Twin Optical Computing System that reduces dependence on physical hardware for task development. The DT-OCS framework enables offline simulation, training, and optimization of computational tasks, improving research efficiency and application flexibility.
A study found that south-facing green walls can improve indoor thermal conditions by up to 1.7°C and enhance outdoor thermal comfort through albedo effects. Low albedo exterior surfaces also improved outdoor thermal comfort by approximately 1.5°C, while high albedo surfaces reduced indoor temperatures.
A new underwater mapping technique, Sonar-MASt3R, combines sonar and visual data to generate detailed 3D maps of environments in real-time. The system enables vehicles to navigate through cloudy water by quickly mapping the general shape of their surroundings using sonar.
Researchers have developed a generative AI model called Void-X that can predict protein-protein interactions with high accuracy, enabling the design of new biomolecules for drug discovery and synthetic biology. The model achieves predictive accuracies of 78.3% for intra-chain clusters and 68.2% for inter-chain clusters.
A new AI model has revolutionized molecular simulations, enabling researchers to predict molecular behavior and identify promising drug candidates more quickly. By analyzing over 12,500 organic molecules, the model has demonstrated accuracy and consistency with physical laws.
A scientific team has developed an AI-powered approach to solve a practical problem in materials science by locating missing atomic positions in otherwise known structures. By using an adapted open-source model called XtalPaint, researchers can reconstruct accurate crystal representations with a success rate of 97%.