Ultrafast real-time nanoplasmonic polymerase chain reaction for rapid diagnostics   Light incidence in plasmonic nanostructures creates light-to-heat conversion and dramatic rises in local temperature. A research group led by Ki-Hun Jeong has demonstrated an ultrafast, real-time, and on-chip nanoplasmonic polymerase chain reaction offering rapid and quantitative molecular diagnostics by using gold nan...

Emergence of number sense in untrained neural networks   A study using an artificial deep neural network shows that visual number sense can arise spontaneously in the complete absence of learning, providing new insight into the origin of innate cognitive functions in the brain.   Article | Fall 2021     Researchers have found that higher cognitive functions can arise spontaneously in untra...

Microbial production of multiple short-chain primary amines via retrobiosynthesis   Short-chain primary amines (SCPAs) are industrially important compounds. To achieve sustainable production of SCPAs, the authors report a combination of retrobiosynthesis and a precursor selection step for designing biosynthetic pathways leading to the production of SCPAs, using valine decarboxylase-expressing E. coli strains....

  Automatic Correction of Internal Units in Generative Neural Networks   Automatic correction of internal units is a retraining-free framework to improve the quality and reliability of the deep generative neural networks which are models trained to generate various images. This method is a crucial element of the generative model for mission-critical applications.   Article | Fall 2021   &...

Extremely small AI system that answers factual questions about the world   The retrieve-and-read approach is widely adopted for open-domain question answering due to its accuracy, interpretability, and flexibility, while suffering from a large memory footprint. MS student Sohee Yang and Professor Minjoon Seo explored approaches to make the system footprint extremely small.   Article | Fall 2021 &nbs...

Super-fast and accurate monocular 3D object detection for Autonomous Driving   Professor Dongsuk Kum and his team have developed a super-fast and accurate 3D object detection method using a single monocular camera. Their method achieved 1st place on the KITTI monocular 3D object detection leaderboard (most widely adopted perception benchmark for autonomous driving) while running real-time, outperforming ...