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Category: mapping – Page 37

For more than half a century, researchers around the world have been struggling with an algorithmic problem known as “the single source shortest path problem.” The problem is essentially about how to devise a mathematical recipe that best finds the shortest route between a node and all other nodes in a network, where there may be connections with negative weights.

Sound complicated? Possibly. But in fact, this type of calculation is already used in a wide range of the apps and technologies that we depend upon for finding our ways around—as Google Maps guides us across landscapes and through cities, for example.

Now, researchers from the University of Copenhagen’s Department of Computer Science have succeeded in solving the single source shortest problem, a riddle that has stumped researchers and experts for decades.

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A recently released set of topography maps provides new evidence for an ancient northern ocean on Mars. The maps offer the strongest case yet that the planet once experienced sea-level rise consistent with an extended warm and wet climate, not the harsh, frozen landscape that exists today.

“What immediately comes to mind as one the most significant points here is that the existence of an ocean of this size means a higher potential for life,” said Benjamin Cardenas, assistant professor of geosciences at Penn State and lead author on the study recently published in the Journal of Geophysical Research: Planets.

“It also tells us about the ancient climate and its evolution. Based on these findings, we know there had to have been a period when it was warm enough and the atmosphere was thick enough to support this much liquid water at one time.”

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Light-pulse matter-wave interferometers exploit the quantized momentum kick given to atoms during absorption and emission of light to split atomic wave packets so that they traverse distinct spatial paths at the same time. Additional momentum kicks then return the atoms to the same point in space to interfere the two matter-wave wave packets. The key to the precision of these devices is the encoding of information in the phase ϕ that appears in the superposition of the two quantum trajectories within the interferometer. This phase must be estimated from quantum measurements to extract the desired information. For N atoms, the phase estimation is fundamentally limited by the independent quantum collapse of each atom to an r.m.s. angular uncertainty \(\Delta {\theta }_{{\rm{SQL}}}=1/\sqrt{N}\) rad, known as the standard quantum limit (SQL)2.

Here we demonstrate a matter-wave interferometer31,32 with a directly observed interferometric phase noise below the SQL, a result that combines two of the most striking features of quantum mechanics: the concept that a particle can appear to be in two places at once and entanglement between distinct particles. This work is also a harbinger of future quantum many-body simulations with cavities26,27,28,29 that will explore beyond mean-field physics by directly modifying and probing quantum fluctuations or in which the quantum measurement process induces a phase transition30.

Quantum entanglement between the atoms allows the atoms to conspire together to reduce their total quantum noise relative to their total signal1,3. Such entanglement has been generated between atoms using direct collisional33,34,35,36,37,38,39 or Coulomb40,41 interactions, including relative atom number squeezing between matter waves in spatially separated traps33,35,39 and mapping of internal entanglement onto the relative atom number in different momentum states42. A trapped matter-wave interferometer with relative number squeezing was realized in ref. 35, but the interferometer’s phase was antisqueezed and thus the phase resolution was above the SQL.

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Pictures of the sky can show us cosmic wonders; movies can bring them to life. Movies from NASA’s NEOWISE space telescope are revealing motion and change across the sky.

Every six months, NASA’s Near-Earth Object Wide Field Infrared Survey Explorer, or NEOWISE, completes one trip halfway around the Sun, taking images in all directions. Stitched together, those images form an “all-sky” map showing the location and brightness of hundreds of millions of objects. Using 18 all-sky maps produced by the spacecraft (with the 19th and 20th to be released in March 2023), scientists have created what is essentially a time-lapse movie of the sky, revealing changes that span a decade.

Each map is a tremendous resource for astronomers, but when viewed in sequence as a time-lapse, they serve as an even stronger resource for trying to better understand the universe. Comparing the maps can reveal distant objects that have changed position or brightness over time, what’s known as time-domain astronomy.

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The spatiotemporal analysis of brain activation during syllogistic reasoning, and the execution of 1 baseline task (BST) were performed in 14 healthy adult participants using high-density event-related brain potentials (ERPs). The following results were obtained: First, the valid syllogistic reasoning task (VSR) elicited a greater positive ERP deflection than the invalid syllogistic reasoning task (ISR) and BST between 300 and 400 ms after the onset of the minor premise. Dipole source analysis of the difference waves (VSR-BST and VSR-ISR) indicated that the positive components were localized in the vicinity of the occipito-temporal cortex, possibly related to visual premise processing. Second, VSR and ISR demonstrated greater negativity than BST developed at 600–700 ms. Dipole source analysis of difference waves (VSR-BST and ISR-BST) indicated that the negative components were mainly localized near the medial frontal cortex/the anterior cingulate cortex, possibly related to the manipulation and integration of premise information. Third, both VSR and ISR elicited a more positive ERP deflection than BST between 2,500 and 3,000 ms. Voltage maps of the difference waves (VSR-BST and VSR-ISR) demonstrated strong activity in the right frontal scalp regions. Results indicate that the reasoning tasks may require more mental effort to spatial processing of working memory.

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A study led by researchers from the Institute Cajal of Spanish Research Council (CSIC) in Madrid, Spain in collaboration with the Bioengineering Department of George Mason University in Virginia, U.S. has updated one of the world’s largest databases on neuronal types, Hippocampome.org.

The study, which is published in the journal PLOS Biology, represents the most comprehensive mapping performed to date between recoded in vivo and identified . This major breakthrough may enable biologically meaningful computer modeling of the full neuronal circuit of the hippocampus, a region of the brain involved in memory function.

Circuits of the mammalian cerebral cortex are made up of two types of neurons: Excitatory neurons, which release a neurotransmitter called glutamate, and inhibitory neurons, which release GABA (gamma-aminobutanoic acid), the main inhibitor of the central nervous system. “A balanced dialogue between the ‘excitatory’ and ‘inhibitory’ activities is critical for . Identifying the contribution from the several types of excitatory and inhibitory cells is essential to better understand brain operation,” explains Liset Menendez de la Prida, the Director of the Laboratorio de Circuitos Neuronales at the Institute Cajal who leads the study at the CSIC.

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Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and imitation learning. Current deep generative models, however, have shortcomings such as unstable training objectives (GANs) and low sample quality (VAEs, normalizing flows). Although recent developments in diffusion and scored-based models attain equivalent sample quality to GANs without adversarial training, the stochastic sampling procedure in these models is sluggish. New strategies for securing the training of CNN-based or ViT-based GAN models are presented.

They suggest backward ODEsamplers (normalizing flow) accelerate the sampling process. However, these approaches have yet to outperform their SDE equivalents. We introduce a novel “Poisson flow” generative model (PFGM) that takes advantage of a surprising physics fact that extends to N dimensions. They interpret N-dimensional data items x (say, pictures) as positive electric charges in the z = 0 plane of an N+1-dimensional environment filled with a viscous liquid like honey. As shown in the figure below, motion in a viscous fluid converts any planar charge distribution into a uniform angular distribution.

A positive charge with z 0 will be repelled by the other charges and will proceed in the opposite direction, ultimately reaching an imaginary globe of radius r. They demonstrate that, in the r limit, if the initial charge distribution is released slightly above z = 0, this rule of motion will provide a uniform distribution for their hemisphere crossings. They reverse the forward process by generating a uniform distribution of negative charges on the hemisphere, then tracking their path back to the z = 0 planes, where they will be dispersed as the data distribution.

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