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Nanoscale Acoustic Force Field Technology Developed That Isolates Submicron Particles

Acoustofluidics is the fusion of acoustics and fluid mechanics which provides a contact-free, rapid and effective manipulation of fluids and suspended particles. The applied acoustic wave can produce a non-zero time-averaged pressure field to exert an acoustic radiation force on particles suspended in a microfluidic channel. However, for particles below a critical size the viscous drag force dominates over the acoustic radiation forces due to the strong acoustic streaming resulting from the acoustic energy dissipation in the fluid. Thus, particle size acts as a key limiting factor in the use of acoustic fields for manipulation and sorting applications that would otherwise be useful in fields including sensing (plasmonic nanoparticles), biology (small bioparticle enrichment) and optics (micro-lenses).

Although acoustic nanoparticle manipulation has been demonstrated, terahertz (THz) or gigahertz (GHz) frequencies are usually required to create nanoscale wavelengths, in which the fabrication of very small feature sizes of SAW transducers is challenging. In addition, single nanoparticle positioning into discrete traps has not been demonstrated in nanoacoustic fields. Hence, there is a pressing need to develop a fast, precise and scalable method for individual nano- and submicron scale manipulation in acoustic fields using megahertz (MHz) frequencies.

An interdisciplinary research team led by Associate Professor Ye Ai from Singapore University of Technology and Design (SUTD) and Dr. David Collins from University of Melbourne, in collaboration with Professor Jongyoon Han from MIT and Associate Professor Hong Yee Low from SUTD, developed a novel acoustofluidic technology for massively multiplexed submicron particle trapping within nanocavities at the single-particle level.

Artificial intelligence can make personality judgments based on photographs

Russian researchers from HSE University and Open University for the Humanities and Economics have demonstrated that artificial intelligence is able to infer people’s personality from ‘selfie’ photographs better than human raters do. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces. The technology can be used to find the ‘best matches’ in customer service, dating or online tutoring.

The article, “Assessing the Big Five using real-life static facial images,” will be published on May 22 in Scientific Reports.

Physiognomists from Ancient Greece to Cesare Lombroso have tried to link facial appearance to personality, but the majority of their ideas failed to withstand the scrutiny of modern science. The few established associations of specific facial features with personality traits, such as facial width-to-height ratio, are quite weak. Studies asking human raters to make personality judgments based on photographs have produced inconsistent results, suggesting that our judgments are too unreliable to be of any practical importance.

A Case for Cooperation Between Machines and Humans

But Ben Shneiderman, a University of Maryland computer scientist who has for decades warned against blindly automating tasks with computers, thinks fully automated cars and the tech industry’s vision for a robotic future is misguided. Even dangerous. Robots should collaborate with humans, he believes, rather than replace them.


A computer scientist argues that the quest for fully automated robots is misguided, perhaps even dangerous. His decades of warnings are gaining more attention.

China has started a grand experiment in AI education. It could reshape how the world learns

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam.

“I used to think math was terrifying,” he says. “But through tutoring, I realized it really isn’t that hard. It helped me take the first step down a different path.”

Scale AI releases free lidar data set to power self-driving car development

High-quality data is the fuel that powers AI algorithms. Without a continual flow of labeled data, bottlenecks can occur and the algorithm will slowly get worse and add risk to the system.

It’s why labeled data is so critical for companies like Zoox, Cruise and Waymo, which use it to train machine learning models to develop and deploy autonomous vehicles. That need is what led to the creation of Scale AI, a startup that uses software and people to process and label image, lidar and map data for companies building machine learning algorithms. Companies working on autonomous vehicle technology make up a large swath of Scale’s customer base, although its platform is also used by Airbnb, Pinterest and OpenAI, among others.

The COVID-19 pandemic has slowed, or even halted, that flow of data as AV companies suspended testing on public roads — the means of collecting billions of images. Scale is hoping to turn the tap back on, and for free.

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