Month: April 2020

Machine Learning to Reduce the Recalibration Needs of Brain-Computer Interfaces

Historically, just one of the major hurdles in the field of brain-computer system interfaces (BCIs) has been the frequent require for recalibration which forces buyers to prevent what they are undertaking and reset the connection concerning their mental commands and the process at hand.

This could be likened to a hypothetical situation where every instance of making use of your smartphone would demand prior calibration to allow the display to “know” which pieces of it you are pointing at.

Device learning will come to the rescue and solves the trouble of variation in recorded brain indicators which could enormously cut down the require for recalibrating brain-computer system interfaces all through or concerning experiments. Impression: pxfuel.com, CC0 Community Area

“The present-day condition of the art in BCI technological know-how is sort of like that. Just to get these BCI products to get the job done, buyers have to do this repeated

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MTU Junior Kaylee Meyers Awarded Goldwater Scholarship

For the second consecutive year, a Michigan Tech student has been awarded one of the
nation’s most prestigious undergraduate study scholarships.

Kaylee Meyers, a third-year student at Michigan Technological University, has gained
one of the nation’s highest accolades for faculty undergraduates. 
The biomedical engineering major from Essexville, Michigan is the twelfth Michigan Tech student to receive the
prestigious Barry M. Goldwater Scholarship.

Established in 1989 by Congress to honor the late Arizona senator and administered
by the
Barry M. Goldwater Scholarship and Excellence in Instruction Foundation, Goldwater scholarships are awarded to faculty sophomores and juniors. The awards
are based on educational merit, study knowledge and intent to go after a vocation in
science, engineering or mathematics.

As part of the assortment method, schools nominate up to four students who intend
to go after a vocation in study and have at the very least a three. GPA. As one of

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MTU Oceanographer Addresses 80 Years of Lake Levels at High Water Summit

Engineer Dude Meadows will discuss Michigan’s lake amounts from 1938 to present and inform
coastal communities, property house owners and neighborhood planners at Michigan’s Substantial Water
Summit April 28.

A 2nd Michigan Substantial Water Summit webinar city hall will focus on Great Lakes shoreline
erosion and permitting. Registration is open and minimal to 1,000 attendees. The webinar
is from 5-six:30 p.m. on April 28. Visit the Michigan Department of Natural environment, Great
Lakes and Power (EGLE) high water amounts website to sign up for the webinar and for further information.

A map depicting the shoreline near Benton Harbor upon which are overlaid lines which show how the shoreline has changed since 1938.
Bluff retreat hazard projections around Benton Harbor. The blue line farthest offshore
signifies the shoreline in 1938, whilst the orange line farthest offshore signifies
the place the bluff was in that 12 months. The next blue and orange lines are the place the shoreline
and bluff ended up in 2016. The hashed red line signifies the bluff retreat hazard area,
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Automating the search for entirely new “curiosity” algorithms

Driven by an innate curiosity, children pick up new skills as they explore the world and learn from their experiences. Computers, by contrast, often get stuck when thrown into new environments.

To get around this, engineers have tried encoding simple forms of curiosity into their algorithms with the hope that an agent pushed to explore will learn about its environment more effectively. An agent with a child’s curiosity might go from learning to pick up, manipulate, and throw objects to understanding the pull of gravity, a realization that could dramatically accelerate its ability to learn many other things.

Image credit: MIT CSAIL

Engineers have discovered many ways of encoding curious exploration into machine learning algorithms. A research team at MIT wondered if a computer could do better, based on a long history of enlisting computers in the search for new algorithms.

In recent years, the design of deep neural networks,

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