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MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.

Re relying on a weather app to predict next week’s temperature. How do you know you can trust its forecast? Scientists use statistical and physical models to make predictions about everything from weather to air pollution. But checking whether these models are truly reliable is trickier than it seems—especially when the locations where we have validation data don Traditional validation methods struggle with this problem, failing to provide consistent accuracy in real-world scenarios. In this work, researchers introduce a new validation approach designed to improve trust in spatial predictions. They define a key requirement: as more validation data becomes available, the accuracy of the validation method should improve indefinitely. They show that existing methods don’t always meet this standard. Instead, they propose an approach inspired by previous work on handling differences in data distributions (known as “covariate shift”) but adapted for spatial prediction. Their method not only meets their strict validation requirement but also outperforms existing techniques in both simulations and real-world data.

By refining how we validate predictive models, this work helps ensure that critical forecasts—like air pollution levels or extreme weather events—can be trusted with greater confidence.


A new evaluation method assesses the accuracy of spatial prediction techniques, outperforming traditional methods. This could help scientists make better predictions in areas like weather forecasting, climate research, public health, and ecological management.

Astronomer Calvin Leung was excited last summer to crunch data from a newly commissioned radio telescope to precisely pinpoint the origin of repeated bursts of intense radio waves—so-called fast radio bursts (FRBs)—emanating from somewhere in the northern constellation Ursa Minor.

Leung, a Miller Postdoctoral Fellowship recipient at the University of California, Berkeley, hopes eventually to understand the origins of these mysterious bursts and use them as probes to trace the large-scale structure of the universe, a key to its origin and evolution. He had written most of the computer code that allowed him and his colleagues to combine data from several telescopes to triangulate the position of a burst to within a hair’s width at arm’s length.

The excitement turned to perplexity when his collaborators on the Canadian Hydrogen Intensity Mapping Experiment (CHIME) turned optical telescopes on the spot and discovered that the source was in the distant outskirts of a long-dead elliptical galaxy that by all rights should not contain the kind of star thought to produce these bursts.

To see how cognitive maps form in the brain, researchers used a Janelia-designed, high-resolution microscope with a large field of view to image neural activity in thousands of neurons in the hippocampus of a mouse as it learned. Credit: Sun and Winnubst et al.

Our brains build maps of the environment that help us understand the world around us, allowing us to think, recall, and plan. These maps not only help us to, say, find our room on the correct floor of a hotel, but they also help us figure out if we’ve gotten off the elevator on the wrong floor.

Neuroscientists know a lot about the activity of neurons that make up these maps – like which cells fire when we’re in a particular location. But how the brain creates these maps as we learn remains a mystery.

We explore numerically the complex dynamics of multilayer networks (consisting of three and one hundred layers) of cubic maps in the presence of noise-modulated interlayer coupling (multiplexing noise). The coupling strength is defined by independent discrete-time sources of color Gaussian noise. Uncoupled layers can demonstrate different complex structures, such as double-well chimeras, coherent and spatially incoherent regimes. Regions of partial synchronization of these structures are identified in the presence of multiplexing noise. We elucidate how synchronization of a three-layer network depends on the initially observed structures in the layers and construct synchronization regions in the plane of multiplexing noise parameters “noise spectrum width – noise intensity”

The default mode network (DMN) is a set of interconnected brain regions known to be most active when humans are awake but not engaged in physical activities, such as relaxing, resting or daydreaming. This brain network has been found to support a variety of mental functions, including introspection, memories of past experiences and the ability to understand others (i.e., social cognitions).

The DMN includes four main brain regions: the (mPFC), the (PCC), the angular gyrus and the hippocampus. While several studies have explored the function of this network, its anatomical structure and contribution to information processing are not fully understood.

Researchers at McGill University, Forschungszentrum Jülich and other institutes recently carried out a study aimed at better understanding the anatomy of the DMN, specifically examining the organization of neurons in the tissue of its connected brain regions, which is known as cytoarchitecture. Their findings, published in Nature Neuroscience, offer new indications that the DMN has a widespread influence on the human brain and its associated cognitive (i.e., mental) functions.

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Hello and welcome! My name is Anton and in this video, we will talk about the discovery of the most massive superstructure in the nearby universe — Quipu.
https://arxiv.org/abs/2501.19236
Bohringer et al., Astronomy and Astrophysics, 2025
https://en.wikipedia.org/wiki/Sachs%E2%80%93Wolfe_effect.
Similar videos:





https://youtu.be/wp8zHG1g7bc.
#quipu #superstructure #cosmos.

0:00 Largest superstructure in the universe — Quipu.
0:45 Laniakea discovery of 2014
1:25 Shapley concentration.
2:35 Cosmological issues: Hubble Tension and S8 tension.
3:45 New study mapping galaxies and the discovery.
5:15 Additional findings and implications.
6:25 What is this though?
7:20 Confirming predictions and how this was found.
8:40 What’s next?

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Space Engine is available for free here: http://spaceengine.org.

In today’s AI news, Google launched its much-anticipated new flagship AI model, Gemini 2.0 Pro Experimental, on Wednesday. The announcement was part of a series of other AI model releases. The company is also making its reasoning model, Gemini 2.0 Flash Thinking, available in the Gemini app.

In other advancements, LinkedIn is testing a new job-hunting tool that uses a custom large language model to comb through huge quantities of data to help people find prospective roles. The company believes that artificial intelligence will help users unearth new roles they might have missed in the typical search process.

S Deep Research feature, which can autonomously browse the web and create research reports. ‘ + s up from hitting $50 million ARR, or the yearly value of last month s case for why they are the best positioned to take over TikTok And, in this episode, a16z Partner Marc Andrusko chats with Mastercard’s Chief AI and Data Officer Greg Ulrich about Mastercard’s long history of using AI, the opportunities (and potential risks) associated with integrating generative AI into fraud detection, determining what tech to employ based on use cases, and the best advice he’s ever gotten.

Then, power your AI transformation with an insightful keynote from Scott Guthrie, Executive Vice President, Cloud + AI Group at Microsoft, and other industry experts. Watch this keynote presentation from NYC stop on Microsoft’s AI Tour.

We close out with this insightful discussion with Malcolm Gladwell and Ric Lewis, SVP of Infrastructure at IBM. Learn how hardware capabilities enable the matrix math behind large language models and how AI is transforming industries—from banking to your local coffee shop.

Thats all for today, but AI is moving fast — like, comment, and subscribe for more AI news! Please vote for me in the Entrepreneur of Impact Competition today! Thank you for supporting my partners and I — it’s how I keep Neural News Network free.

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Three months after its launch from NASA’s Kennedy Space Center in Florida, the agency’s Europa Clipper has another 1.6 billion miles (2.6 billion kilometers) to go before it reaches Jupiter’s orbit in 2030 to take close-up images of the icy moon Europa with science cameras.

Meanwhile, a set of cameras serving a different purpose is snapping photos in the space between Earth and Jupiter. Called star trackers, the two imagers look for stars and use them like a compass to help mission controllers know the exact orientation of the spacecraft—information critical for pointing telecommunications antennas toward Earth and sending data back and forth smoothly.

In early December, the pair of star trackers (formally known as the stellar reference units) captured and transmitted Europa Clipper’s first imagery of space. The picture, composed of three shots, shows tiny pinpricks of light from stars 150 to 300 light-years away. The starfield represents only about 0.1% of the full sky around the spacecraft, but by mapping the stars in just that small slice of sky, the orbiter is able to determine where it is pointed and orient itself correctly.

The authors present an approach to simultaneously map local magnetization, strain, atomic structure at nanoscale. It provides direct visualization of strainmagnetic coupling in ferromagnetic materials, opening avenues for studying nanomagnetism.