A research team from Bar-Ilan’s Department of Computer Science, led by Dr. Dvir Samuel and Prof. Gal Chechik (also of NVIDIA), has developed OmnimatteZero, a new method for separating objects from video backgrounds without the need for heavy training or optimization.

Presented at SIGGRAPH Asia, the technology preserves complex visual details like fur, reflections, smoke, and water ripples while avoiding the huge datasets and computing power typically required. Instead, it uses advanced image-completion techniques with temporal-spatial tracking to maintain background consistency.

The system enables “visual composting,” allowing elements like a swan with its reflection to be seamlessly moved to another scene, or a background reused naturally. Unlike current methods, OmnimatteZero runs quickly and efficiently using existing video generation models, making it more practical for editors, content creators, advertisers, and everyday users.

The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications. Inspired by in-memory computing architecture of human brain, flexible memristors exhibit great application potential in emulating artificial synapses for high-efficiency and low power consumption neuromorphic computing. This paper provides comprehensive overview of flexible memristors from perspectives of development history, material system, device structure, mechanical deformation method, device performance analysis, stress simulation during deformation, and neuromorphic computing applications. The recent advances in flexible electronics are summarized, including single device, device array and integration. The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply, paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.

Ever wondered how your dinner impacts the planet? A groundbreaking study from Dr. Mohammad Fazle Rabbi at the Coordination and Research Centre for Social Sciences, Faculty of Economics and Business, University of Debrecen, Hungary, dives deep into this question—exploring how Europe can reduce its food-related carbon footprint and meet its ambitious sustainability goals. Published on July 16, 2025, in Sustainability Insights, this research evaluates eight European Union countries over a 12-year span (2010-2022) to uncover critical pathways toward a greener future.

Researchers at the UCLA Samueli School of Engineering have created a technology capable of producing novel images using photonics — employing only a fraction of the energy and computational steps per image compared with an array of current generative AI programs.

This process requires only the initial digital encoding and successive optical decoding step; many current generative AI models typically require hundreds to thousands of iterative steps.

The optical generative models use a physical “key-lock” mechanism, preventing unauthorized reconstruction of generated images and offering security measures for communication, content delivery and foiling counterfeits.