Katja Grace's apartment, in West Berkeley, is in an old machinist's factory, with pitched roofs and windows at odd angles. It has terra-cotta floors and no central heating, which can create the impression that you've stepped out of the California sunshine and into a duskier place, somewhere long ago or far away. Yet there are also some quietly futuristic touches. Nonperishables stacked in the pantry. A sleek white machine that does lab-quality RNA tests.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA.

Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 metres by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The 100-metre record builds on earlier achievements by the robot, including traversing five kilometres in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The run can also be seen on YouTube.) The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge.

A two-legged robot that can chase you down in the street may sound like something dreamed up for the latest episode of Black Mirror. But it may not be far from reality, after a two-legged robot known as'Cassie' set a new Guinness World Record for the fastest 100 metres by a bipedal robot. Cassie clocked the historic time of 24.73 seconds, starting from a standing position and returning to that position after the sprint, with no falls. While that's more than 15 seconds slower than the world record for a 100-metre sprint by a human, held by Usain Bolt at 9.58 seconds, it is faster than most humans would take to jog the same distance. Oregon State robotics professor Jonathan Hurst, who led the development of the robot, described it as'a big watershed moment'.

This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE. Then, we cover out-of-sample embedding using linear reconstruction, eigenfunctions, and kernel mapping. Incremental LLE is explained for embedding streaming data. Landmark LLE methods using the Nystrom approximation and locally linear landmarks are explained for big data embedding. We introduce the methods for parameter selection of number of neighbors using residual variance, Procrustes statistics, preservation neighborhood error, and local neighborhood selection. Afterwards, Supervised LLE (SLLE), enhanced SLLE, SLLE projection, probabilistic SLLE, supervised guided LLE (using Hilbert-Schmidt independence criterion), and semi-supervised LLE are explained for supervised and semi-supervised embedding. Robust LLE methods using least squares problem and penalty functions are also introduced for embedding in the presence of outliers and noise. Then, we introduce fusion of LLE with other manifold learning methods including Isomap (i.e., ISOLLE), principal component analysis, Fisher discriminant analysis, discriminant LLE, and Isotop. Finally, we explain weighted LLE in which the distances, reconstruction weights, or the embeddings are adjusted for better embedding; we cover weighted LLE for deformed distributed data, weighted LLE using probability of occurrence, SLLE by adjusting weights, modified LLE, and iterative LLE.

I watch most TV shows and movies on my iPad these days, and something strange happened recently. My iPad – or rather apps such as Hulu and Bravo linked via Apple TV on my iPad – started showing me commercials in Spanish. That was interesting, since I hadn't touched the language settings, watched any shows in Spanish, or done any kind of internet activity in another language. But even more curious, was what had changed when the new commercials popped up. We had just moved to a more Spanish-speaking area of Oakland, California.

This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA. We start with projection and reconstruction. Then, one- and multi-dimensional FDA subspaces are covered. Scatters in two- and then multi-classes are explained in FDA. Then, we discuss on the rank of the scatters and the dimensionality of the subspace. A real-life example is also provided for interpreting FDA. Then, possible singularity of the scatter is discussed to introduce robust FDA. PCA and FDA directions are also compared. We also prove that FDA and linear discriminant analysis are equivalent. Fisher forest is also introduced as an ensemble of fisher subspaces useful for handling data with different features and dimensionality. Afterwards, kernel FDA is explained for both one- and multi-dimensional subspaces with both two- and multi-classes. Finally, some simulations are performed on AT&T face dataset to illustrate FDA and compare it with PCA.

Fire doesn't care what it destroys or who it kills--it spreads without mercy, leaving total destruction in its wake, as California's Camp and Woolsey fires proved so dramatically this month. But fire is to a large degree predictable. It follows certain rules and prefers certain fuels and follows certain wind patterns. That means its moves with a complexity that scientists can pick apart little by little, thanks to lasers, fancy sensors, and some of the most powerful computers on the planet. We can't end wildfires altogether, but by better understanding their dynamics, ideally we can stop a disaster like the destruction of Paradise from happening again.

A Boston-based startup is hoping its state-of-the-art microphones will power the scores of voice-enabled smart devices that Silicon Valley giants and industry experts expect will soon become fixtures in homes and businesses around the globe. "People communicate by speaking, that's one of our primary methods of communicating, that's how our brains are wired," said Vesper Technologies CEO Matt Crowley, whose company is developing microphones that can be used in smartphones, speakers, and just about anything else connected to the internet that can be controlled by the human voice. Vesper microphones are made using a special kind of crystal that naturally generates an electric signal when it bends. This electric signal is then treated like any other signal from a microphone -- making the smart devices of tomorrow capable of being activated with a single command. Crowley said the reason voice-controlled devices like the soon-to-be-released Google Home are so popular is because they have the potential to make the fun and cool things people currently do with their phones -- like turning on the lights or turning up the thermostat -- easier to do.