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Category: mapping – Page 22

Researchers from Carnegie Mellon University have developed a new technique that could lead to faster and more efficient drone exploration.

A team of researchers from Carnegie Mellon University has successfully developed a new dual-mapping technique that could help robots explore areas faster and more efficiently. By producing both a site’s high-and low-resolution map, this new technique enables robots to explore areas using only a fraction of the computing power typically needed for a similar task.

ROBOTICS INSTITUTE, CARNEGIE MELLON UNIVERSITY

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During routine navigation in daily life, our brains use spatial mapping and memory to guide us from point A to point B. Just as routine is making a mistake in navigation that requires a course correction.

Now, researchers at Harvard Medical School have identified a specific group of neurons in a region involved in that undergo bursts of activity when running a maze veer off course and correct their error.

The findings, published July 19 in Nature, bring scientists a step closer to understanding how navigation works, while raising new questions. These questions include the specific role these neurons play during navigation, and what they are doing in other brain regions where they are found.

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Companion robots enhanced with artificial intelligence may one day help alleviate the loneliness epidemic, suggests a new report from researchers at Auckland, Duke, and Cornell Universities.

Their report, appearing in the July 12 issue of Science Robotics, maps some of the ethical considerations for governments, , technologists, and clinicians, and urges stakeholders to come together to rapidly develop guidelines for trust, agency, engagement, and real-world efficacy.

It also proposes a new way to measure whether a companion is helping someone.

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Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.

RNNs essentially leverage recurrent connections between their different processing units to process sequential data and make accurate predictions. While RNNs have been found to perform well on numerous tasks, optimizing their performance by identifying parameters that are most relevant to the task they will be tackling can be challenging and time-consuming.

Jason Kim and Dani S. Bassett, two researchers at University of Pennsylvania, recently introduced an alternative approach to design and program RNN-based reservoir computers, which is inspired by how programming languages work on computer hardware. This approach, published in Nature Machine Intelligence, can identify the appropriate parameters for a given network, programming its computations to optimize its performance on target problems.

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GPS is now a mainstay of daily life, helping us with navigation, tracking, mapping, and timing across a broad spectrum of applications. But it does have a few shortcomings, most notably not being able to pass through buildings, rocks, or water. That’s why Japanese researchers have developed an alternative wireless navigation system that relies on cosmic rays, or muons, instead of radio waves, according to a new paper published in the journal iScience. The team has conducted its first successful test, and the system could one day be used by search and rescue teams, for example, to guide robots underwater or to help autonomous vehicles navigate underground.

“Cosmic-ray muons fall equally across the Earth and always travel at the same speed regardless of what matter they traverse, penetrating even kilometers of rock,” said co-author Hiroyuki Tanaka of Muographix at the University of Tokyo in Japan. “Now, by using muons, we have developed a new kind of GPS, which we have called the muometric positioning system (muPS), which works underground, indoors and underwater.”

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Summary: Researchers mapped neural activity in an octopus’s visual system, revealing striking similarities to humans.

The team observed neural responses to light and dark spots, thereby creating a map resembling the organization of the human brain. Interestingly, octopuses and humans last shared a common ancestor around 500 million years ago, suggesting independent evolution of such complex visual systems.

These findings contribute greatly to our understanding of cephalopod vision and brain structure.

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Lot’s of science news, stay till the end for the climate stuff.

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Today we’ll talk about plants that use quantum mechanics, the first data from a new galaxy survey, quantum utility, online hate groups, photonic computing, the most sensitive power measurement ever, how to map a tunnel with muons, bad climate news that I don’t want to talk about, and you don’t want to hear, but that we need to talk about anyway. And of course, the telephone will ring.

Continue reading “Record Temperatures in the North Atlantic” | >

A key challenge in neuroscience is to understand how the brain can adapt to a changing world, even with a relatively static anatomy. The way the brain’s areas are structurally and functionally related to each other—its connectivity—is a key component. In order to explain its dynamics and functions, we also need to add another piece to the puzzle: receptors.

Now, a new mapping by Human Brain Project (HBP) researchers from the Forschungszentrum Jülich (Germany) and Heinrich-Heine-University Düsseldorf (Germany), in collaboration with scientists from the University of Bristol (UK), New York University (U.S.), Child Mind Institute (U.S.), and University of Paris Cité (France) had made advances on our understanding of the distribution of receptors across the .

The findings were published in Nature Neuroscience, and the data is now freely available to the neuroscientific community via the HBP’s EBRAINS infrastructure.

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Superfast, subatomic-sized particles called muons have been used to wirelessly navigate underground for the first time. By using muon-detecting ground stations synchronized with an underground muon-detecting receiver, researchers at the University of Tokyo were able to calculate the receiver’s position in the basement of a six-story building.

As GPS cannot penetrate rock or water, this new technology could be used in future search and rescue efforts, to monitor undersea volcanoes, and guide autonomous vehicles underground and underwater. The findings are published in the journal iScience.

GPS, the , is a well-established navigation tool and offers an extensive list of positive applications, from safer air travel to real-time location mapping. However, it has some limitations. GPS signals are weaker at and can be jammed or spoofed (where a counterfeit signal replaces an authentic one). Signals can also be reflected off surfaces like walls, interfered with by trees, and can’t pass through buildings, rock or water.

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Early Dark Energy Spectroscopic Instrument (DESI) release holds nearly two million objects, including distant galaxies, quasars and stars in our own Milky Way.

Dark Energy Spectroscopic Instrument (DESI), the most robust multi-object survey spectrograph, capable of mapping more than 40 million galaxies, quasars, and stars, recorded an 80-terabyte data set this Tuesday.

The data was collected after 2,480 exposures taken over six months during the experiment’s “survey validation” phase in 2020 and 2021, said Lawrence Berkeley National Lab.

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