The Trump administration is mobilizing 700 Marines to respond to protests triggered by Immigration and Customs Enforcement raids in Los Angeles. Aerial footage of protests downtown on Saturday, Sunday, and Monday evening seemed to show crowds of a few hundred people, while another pro-immigration rally earlier on Monday reportedly drew thousands. With 4,000 members of the National Guard already deployed to the city, in addition to ICE and local police, armed law-enforcement officers appear to outnumber actual protesters, who have remained largely nonviolent (aside from the ones who set several robotic taxicabs on fire). What follows is a speculative attempt to convey the emotional truth of what these troops might encounter. We woke up at dawn, heads pounding, in a hut lit by a single bulb. We were two clicks outside the perimeter and three clicks from the nearest Lamill or Blue Bottle--a desperate goddamn distance, no no no this can't be happening.

In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory computing, it is desirable to accelerate the training as well as inference for large deep neural networks (DNNs). In the past, specialized in-memory training algorithms have been proposed that not only accelerate the forward and backward passes, but also establish tricks to update the weight in-memory and in parallel. However, the state-of-the-art algorithm (Tiki-Taka version 2 (TTv2)) still requires near perfect offset correction and suffers from potential biases that might occur due to programming and estimation inaccuracies, as well as longer-term instabilities of the device materials. Here we propose and describe two new and improved algorithms for in-memory computing (Chopped-TTv2 (c-TTv2) and Analog Gradient Accumulation with Dynamic reference (AGAD)), that retain the same runtime complexity but correct for any remaining offsets using choppers. These algorithms greatly relax the device requirements and thus expanding the scope of possible materials potentially employed for such fast in-memory DNN training.

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Low-resolution peripheral field provides coarse scene exploration to direct the eye to focus to a highly resolved fovea region for sharp imaging. Among 3D computer vision techniques, Light Detection and Ranging (LiDAR) is currently considered at the industrial level for robotic vision. LiDAR is an imaging technique that monitors pulses of light at optical frequencies to sense the space and to recover three-dimensional ranging information. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low image resolution, notably limited by the performance of mechanical or slow solid-state deflection systems. Metasurfaces (MS) are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that uses ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV and simultaneous peripheral and central imaging zones. This technology achieves MHz frame rate for 2D imaging, and up to KHz for 3D imaging, with extremely large FoV (up to 150{\deg}deg. on both vertical and horizontal scanning axes). The use of this disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve further the perception capabilities and decision-making process of autonomous vehicles and robotic systems.

NASA's Perseverance Mars rover has achieved yet another first after capturing the sounds of another spacecraft hovering on the red planet. Using the microphone on its rock-zapping SuperCam instrument, the six-wheeled robot listened to the sounds of the Ingenuity helicopter on April 30 and recorded the whirring of its fast-spinning rotors. This marked the first time a spacecraft has recorded audio of another probe on a world beyond Earth. This was the chopper's fourth flight since Perseverance and Ingenuity landed together on Feb. 18 on the floor of Mars' Jezero Crater, NASA said in a statement. A video recently released by NASA combined the footage from Perseverance's Mastcam-Z imager of the solar-powered helicopter with the recorded audio, allowing scientists to know how the robot is performing just by tuning in to the sound it makes.

From pilotless jets engaging in dogfights to huge undersea vessels ferrying troops, the Pentagon is pushing to increase the U.S. military's use of automation. Defense moves are outpacing commercial automation efforts in the air, on the ground and beneath the waves as officials seek to counter American adversaries' technological advances, according to current and former national-security and industry officials. That progress--highlighted in cockpits managed primarily by computers, totally autonomous helicopters and automated aerial-refueling tankers--is likely to show up in future civilian aircraft, advanced air-traffic-control systems and a range of drone applications. Skeptics worry automated systems sometimes reflect software developers' desire to incorporate new capabilities without full testing. They point to examples of high-profile stumbles ranging from glitch-prone radio communication systems to software problems that have deprived pilots of adequate oxygen at the controls of jet fighters.

FBI agents have arrested a Hollywood man, accusing him of recklessly operating a drone and crashing it into a Los Angeles Police Department helicopter earlier this year. The collision damaged the chopper's fuselage and required the LAPD pilot to make an emergency landing following the September encounter. The drone, which authorities say was operated by Andrew Rene Hernandez, then tumbled from the sky and crashed into a vehicle. Hernandez, 22, was arrested Thursday and charged with unsafe operation of an unmanned aircraft after an investigation by the FBI, the LAPD and the Federal Aviation Administration. The potentially deadly collision occurred Sept. 18 after Los Angeles police officers responding to a predawn burglary call at a Hollywood pharmacy requested air support.

As helicopter flights go, this one was especially boring. We took off, hovered for a bit, and maneuvered around the airport. We flew to a spot about 10 miles away, did some turns and gentle banks, then came back and landed. I've been on more exciting ferris wheels, with views more inspiring than those of rural Connecticut. Still, the flight was impressive for at least one reason: The pilot controlling the 12,000-pound Sikorsky S-76 had never before operated a helicopter.

If there was ever something worthy of being called a chopper, this crazy contraption is truly it. The radio-controlled helicopter not only has rotary blades, but it also has a chainsaw attached to it. The three friends from Finland who made it have nicknamed the invention their'killer drone', and used the test flight to hack at trees, icicles and snowmen. Test-run: The three friends from Finland who made it have nicknamed the invention their'killer drone, and used the test flight to hack at trees, icicles and snowmen Beheaded: The snowmen's heads were immediately reduced to nothing when the chainsaws hit them, sending a cloud of ice and slush flying through the air Miika Ullakko, Henri Kiviniemi and drone pilot Antti Junnari came up with the idea as a joke after seeing videos of drones delivering mail. Miika said: 'Anyone who's ever flown a drone knows how ridiculous the idea of a drone delivering mail is.

An electrmic model can be especially helpful, when the parameters of the model can be easily controlled, and when the model will operate in real time.

Detectim of the periodicity of amplitude modulatim is a major step in the determinatim of the pitch of a SOODd. In this article we will present a silicm model that uses synchrroicity of spiking neurms to extract the fundamental frequency of a SOODd. It is based m the observatim that the so called'Choppers' in the mammalian Cochlear Nucleus synchrmize well for certain rates of amplitude modulatim, depending m the cell's intrinsic chopping frequency. Our silicm model uses three different circuits, i.e., an artificial cochlea, an Inner Hair Cell circuit, and a spiking neuron circuit