MIT physicists improve the precision of atomic clocks
A new method turns down quantum noise that obscures the “ticking” of atoms, and could enable stable, transportable atomic clocks.
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A new method turns down quantum noise that obscures the “ticking” of atoms, and could enable stable, transportable atomic clocks.
A new device concept opens the door to compact, high-performance transistors with built-in memory.
The ultrabroadband infrared frequency comb could be used for chemical detection in portable spectrometers or high-resolution remote sensors.
The flexible chip could boost the performance of current electronics and meet the more stringent efficiency requirements of future 6G technologies.
An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data.
Lincoln Laboratory cybersecurity expert Hamed Okhravi calls for a unified approach to securing computer memory, as a matter of national security.
The low-cost, scalable technology can seamlessly integrate high-speed gallium nitride transistors onto a standard silicon chip.
The results will help scientists visualize never-before-seen quantum phenomena in real space.
Preventing 3D integrated circuits from overheating is key to enabling their widespread use.
New research using computational vision models suggests the brain’s “ventral stream” might be more versatile than previously thought.
The Exo 2 programming language enables reusable scheduling libraries external to compilers.
New “Oreo” method from MIT CSAIL researchers removes footprints that reveal where code is stored before a hacker can see them.
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.