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Deep learning method enables efficient Boltzmann distribution sampling across a continuous temperature range

A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature range. The findings have been published in Physical Review Letters. The team was led by Prof. Pan Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. Li Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong Kong University of Science and Technology (HKUST).

Quantum emitter discovery in diamonds enables a new type of coupling

Researchers at The City College of New York have shown how a quantum emitter, the nitrogen-vacancy (NV) center in diamond, interacts in unexpected ways with a specially engineered photonic structure when moved around with a scanning tip.

The study, led by Carlos A. Meriles—Martin and Michele Cohen Professor of Physics in the Division of Science—and titled “Emission of Nitrogen–Vacancy Centres in Diamond Shaped by Topological Photonic Waveguide Modes,” appears in the journal Nature Nanotechnology.

What has long been considered a drawback of the NV center—its broad and messy emission spectrum—turns out to enable a new type of coupling that reshapes its light in ways not seen before. This discovery has fundamental importance for , since such coupling could help overcome longstanding challenges like spectral diffusion and open pathways toward robust spin–photon and spin–spin entanglement on a chip.

DNA-based neural network learns from examples to solve problems

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts that carries out computation through chemical reactions rather than digital signals.

An important property of any neural network is the ability to learn by taking in information and retaining it for future decisions. Now, researchers in the laboratory of Lulu Qian, professor of bioengineering, have created a DNA-based neural network that can learn. The work represents a first step toward demonstrating more complex learning behaviors in .

A paper describing the research appears in the journal Nature on September 3. Kevin Cherry, Ph.D., is the study’s first author.

A robot learns to handle bulky objects like humans do after just one lesson

For all their technological brilliance, from navigating distant planets to performing complex surgery, robots still struggle with a few basic human tasks. One of the most significant challenges is dexterity, which refers to the ability to grasp, hold and manipulate objects. Until now, that is. Scientists from the Toyota Research Institute in Massachusetts have trained a robot to use its entire body to handle large objects, much like humans do.

Super-sensitive sensor detects tiny hydrogen leaks in seconds for safer energy use

Researchers at the University of Missouri are working to make hydrogen energy as safe as possible. As more countries and industries invest heavily in cleaner, renewable energy, hydrogen-powered factories and vehicles are gaining in popularity. But hydrogen fuel comes with risks—leaks can lead to explosions, accidents and environmental harm. Most hydrogen-detecting sensors on the market are expensive, can’t operate continuously and aren’t sensitive enough to detect tiny leaks quickly.

Solar-boosted system turns wasted data center heat into clean power

When you stream a movie, back up a photo or ask ChatGPT a question, somewhere a data center is working hard—and getting hot. Cooling those facilities already consumes a huge share of their electricity, and nearly half of that energy leaves as low-temperature waste heat that’s simply vented into the air.

SeeMe detects hidden signs of consciousness in brain injury patients

SeeMe, a computer vision tool tested by Stony Brook University researchers, was able to detect low-amplitude, voluntary facial movements in comatose acute brain injury patients days before clinicians could identify overt responses.

Close friends know that I have a standing “do not unplug” order should I ever fall into an unresponsive state. If there is even a flicker of a chance that the mind is still working, I will be fine. Keep me plugged in and hang a “do not disturb” sign on whatever apparatus is keeping me alive.

It’s not like you can know in advance what it’s like, but it seems relaxed enough, with plenty of time to think, and I haven’t really gained anything useful from conversations with other humans in years (aside from my editors who always provide valuable information). If it is at all like sleeping, there might be dreams, so, perchance, that’s what I’d be doing in a comatose state. But for the friends by my bedside, how to be certain that the mind is still flickering?

Brains listen best in the ‘Goldilocks echo zone,’ says study

Macquarie University hearing researchers have discovered how our brains learn to listen, and how this can help us understand speech in noisy, echo-filled spaces.

The research, published in eLife, looks at how we can unconsciously adjust to different kinds of .

Building on earlier work that showed animals’ brains quickly adapt to changes in sound levels, the new study is the first to show how humans adapt to echoey environments to improve their speech understanding.

Polaritons enable tunable and efficient molecular charge transfer across broader spectrum of light

Polaritons are quasiparticles emerging from strong interactions between light particles (i.e., photons) and matter excitations (e.g., excitons). Over the past few years, researchers have found that these quasiparticles can alter fundamental chemical and physical processes.

Algorithms that address malicious noise could result in more accurate, dependable quantum computing

Quantum computers promise enormous computational power, but the nature of quantum states makes computation and data inherently “noisy.” Rice University computer scientists have developed algorithms that account for noise that is not just random but malicious. Their work could help make quantum computers more accurate and dependable.

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