Electron micrographs show how macrophages expressing girdin neutralize pathogens by fusing phagosomes (P) with the cell’s lysosomes (L) to form phagolysosomes (PL), compartments where pathogens and cellular debris are broken down (left). This process is crucial for maintaining cellular homeostasis. In the absence of girdin, this fusion fails, allowing pathogens to evade degradation and escape neutralization (right). (UC San Diego Health Sciences)
AI uncovers how a severed bond between two gut proteins sparks Crohn’s disease, and how restoring it could heal inflammation. UC San Diego researchers combined artificial intelligence with molecular biology to unravel how immune cells in the gut decide between inflammation and healing, a process gone awry in Crohn’s disease...
Over the past decades, electronics engineers have developed a wide range of memory devices that can safely and efficiently store increasing amounts of data. However, the different types of devices developed to date come with their own trade-offs, which pose limits on their overall performance and restrict their possible applications.
A research team, led by Professor Heein Yoon in the Department of Electrical Engineering at UNIST has unveiled an ultra-small hybrid low-dropout regulator (LDO) that promises to advance power management in advanced semiconductor devices. This innovative chip not only stabilizes voltage more effectively, but also filters out noise—all while taking up less space—opening new doors for high-performance system-on-chips (SoCs) used in AI, 6G communications, and beyond.
The new LDO combines analog and digital circuit strengths in a hybrid design, ensuring stable power delivery even during sudden changes in current demand—like when launching a game on your smartphone—and effectively blocking unwanted noise from the power supply.
Overview of our experiments, including examples of clean and poisoned samples, as well as benign and malicious behavior at inference time. (a)DoS pretraining backdoor experiments. Credit: arXiv (2025). DOI: 10.48550/arxiv.2510.07192
Large language models (LLMs), which power sophisticated AI chatbots, are more vulnerable than previously thought. According to research by Anthropic, the UK AI Security Institute and the Alan Turing Institute, it only takes 250 malicious documents to compromise even the largest models.
The vast majority of data used to train LLMs is scraped from the public internet. While this helps them to build knowledge and generate natural responses, it also puts them at risk from data poisoning attacks...
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