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Category: mapping – Page 18

Skyline Robotics is disrupting the century-old practice of window washing with new technology that the startup hopes will redefine a risky industry.

Its window-washing robot, Ozmo, is now operational in Tel Aviv and New York, and has worked on major Manhattan buildings such as 10 Hudson Yards, 383 Madison, 825 3rd Avenue and 7 World Trade Center in partnership with the city’s largest commercial window cleaner Platinum and real estate giant The Durst Organization.

The machine is suspended from the side of a high-rise. A robotic arm with a brush attached to the end cleans the window following instructions from a LiDAR camera, which uses laser technology to map 3D environments. The camera maps the building’s exterior and identifies the parameters of the windows.

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New archaeological research reveals that the sea off northwestern Australia once had islands and a massive landmass. This area was so large it could support around half a million people, as reported in a study published in Quaternary Science Review.

The study maps a world that appeared and then disappeared as sea levels changed over the past seventy thousand years. People are believed to have migrated to this part of the world between forty-five thousand to sixty-five thousand years ago.

The area was part of a paleocontinent called Sahul, connecting Australia to New Guinea. The submersion of this land might have led to significant cultural and population changes in northern Australia.

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NASA has recently invested in a class of small, low-cost planetary missions called SIMPLEx, which stands for Small, Innovative Missions for PLanetary Exploration. These missions save costs by tagging along on other launches as what is called a rideshare, or secondary payload.

One example is the Lunar Trailblazer. Like VIPER, Lunar Trailblazer will look for water on the moon.

But while VIPER will land on the Moon’s surface, studying a specific area near the south pole in detail, Lunar Trailblazer will orbit the moon, measuring the temperature of the surface and mapping out the locations of water molecules across the globe.

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The release of Transformers has marked a significant advancement in the field of Artificial Intelligence (AI) and neural network topologies. Understanding the workings of these complex neural network architectures requires an understanding of transformers. What distinguishes transformers from conventional architectures is the concept of self-attention, which describes a transformer model’s capacity to focus on distinct segments of the input sequence during prediction. Self-attention greatly enhances the performance of transformers in real-world applications, including computer vision and Natural Language Processing (NLP).

In a recent study, researchers have provided a mathematical model that can be used to perceive Transformers as particle systems in interaction. The mathematical framework offers a methodical way to analyze Transformers’ internal operations. In an interacting particle system, the behavior of the individual particles influences that of the other parts, resulting in a complex network of interconnected systems.

The study explores the finding that Transformers can be thought of as flow maps on the space of probability measures. In this sense, transformers generate a mean-field interacting particle system in which every particle, called a token, follows the vector field flow defined by the empirical measure of all particles. The continuity equation governs the evolution of the empirical measure, and the long-term behavior of this system, which is typified by particle clustering, becomes an object of study.

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UCLA breaks new ground in alloy research, presenting the first 3D mapping of medium and high-entropy alloys, potentially revolutionizing the field with enhanced toughness and flexibility in these materials.

Alloys, which are materials such as steel that are made by combining two or more metallic elements, are among the underpinnings of contemporary life. They are essential for buildings, transportation, appliances and tools — including, very likely, the device you are using to read this story. In applying alloys, engineers have faced an age-old trade-off common in most materials: Alloys that are hard tend to be brittle and break under strain, while those that are flexible under strain tend to dent easily.

Advancements in Alloy Research.

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Tesla’s vehicles can now recognize speed cameras as of its latest update, along with several other navigation features that will reportedly be coming soon.

Code sleuth and Tesla update observer Greentheonly said on Sunday that Tesla software update 2023.27.12 has added the speed camera awareness feature along with other camera awareness capabilities. The update includes the Full Self-Driving (FSD) beta version 11.4.8.1, and it was first spotted in a Tesla vehicle on Saturday, according to Teslascope.

The software update also includes red light camera awareness, including those for fixed and mobile versions, and a combined awareness for red lights and speed cameras. Green also says that several other navigation features appear to be right around the corner, including U-turn control and an “avoid construction on route” setting, as found in internal code for Tesla’s maps system.

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Quantum computers promise to solve some problems exponentially faster than classical computers, but there are only a handful of examples with such a dramatic speedup, such as Shor’s factoring algorithm and quantum simulation. Of those few examples, the majority of them involve simulating physical systems that are inherently quantum mechanical — a natural application for quantum computers. But what about simulating systems that are not inherently quantum? Can quantum computers offer an exponential advantage for this?

In “Exponential quantum speedup in simulating coupled classical oscillators”, published in Physical Review X (PRX) and presented at the Symposium on Foundations of Computer Science (FOCS 2023), we report on the discovery of a new quantum algorithm that offers an exponential advantage for simulating coupled classical harmonic oscillators. These are some of the most fundamental, ubiquitous systems in nature and can describe the physics of countless natural systems, from electrical circuits to molecular vibrations to the mechanics of bridges. In collaboration with Dominic Berry of Macquarie University and Nathan Wiebe of the University of Toronto, we found a mapping that can transform any system involving coupled oscillators into a problem describing the time evolution of a quantum system. Given certain constraints, this problem can be solved with a quantum computer exponentially faster than it can with a classical computer.

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