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From the best smartphone of the show to the best health-focused device, this is the cream of the crop when it comes to CES announcements and reveals. We’ve seen a tech-filled face mask that solves a lot of the problems of normal masks, as well as rollable smartphone displays.


Here are the best products we’ve seen at CES 2021, with 15 picks across several categories earning our accolades.

George Will, a political commentator for nearly half a century at The Washington Post, is known to also enjoy weighing in on sports on occasion, most notably baseball. He is fond of repeating the simple but critical observation that these games are a matter of “seconds and inches.”

In digital games, the same maxim applies, but even more so. Fractions of inches matter when targeting the enemy. And critical time is not measured in seconds but in thousandths of seconds.

With that in mind, developers at Canadian startup Brink Bionics have developed a device that promises to boost gamer proficiency by slashing the delay time between an intent to act and execution of the actual action.

Researchers develop the first nanomaterial that demonstrates “photon avalanching;” finding could lead to new applications in sensing, imaging, and light detection.

Researchers at Columbia Engineering report today that they have developed the first nanomaterial that demonstrates “photon avalanching,” a process that is unrivaled in its combination of extreme nonlinear optical behavior and efficiency. The realization of photon avalanching in nanoparticle form opens up a host of sought-after applications, from real-time super-resolution optical microscopy, precise temperature and environmental sensing, and infrared light detection, to optical analog-to-digital conversion and quantum sensing.

“Nobody has seen avalanching behavior like this in nanomaterials before,” said James Schuck, associate professor of mechanical engineering, who led the study published today (January 132021) by Nature. “We studied these new nanoparticles at the single-nanoparticle level, allowing us to prove that avalanching behavior can occur in nanomaterials. This exquisite sensitivity could be incredibly transformative. For instance, imagine if we could sense changes in our chemical surroundings, like variations in or the actual presence of molecular species. We might even be able to detect coronavirus and other diseases.”

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using to learn more about how mental illness and other disorders affect the brain.