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A new study corrects an important error in the 3D mathematical space developed by the Nobel Prize-winning physicist Erwin Schrödinger and others, and used by scientists and industry for more than 100 years to describe how your eye distinguishes one color from another. The research has the potential to boost scientific data visualizations, improve TVs and recalibrate the textile and paint industries.

“The assumed shape of color space requires a paradigm shift,” said Roxana Bujack, a computer scientist with a background in mathematics who creates scientific visualizations at Los Alamos National Laboratory. Bujack is lead author of the paper by a Los Alamos team in the Proceedings of the National Academy of Sciences on the mathematics of color perception.

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Quantum computing will change everything.

“I think I can safely say that nobody really understands quantum mechanics,” renowned physicist Richard Feynman stated once. That shouldn’t come as a big surprise as quantum physics has a reputation for being exceptionally enigmatic. This was the selling point for the quantum physicist Dr. Shohini Ghose from Wilfrid Laurier University.

Having always excelled at mathematics and physics, Ghose was always interested in mysteries, detective stories, and mathematics. This led her to an intense fascination with physics, as she quickly discovered that she could use mathematics to help solve the mysteries of the universe.

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Researchers find mathematical trick to combining planetary surface data.

Researchers have discovered a method for making high-resolution maps of planetary surfaces like the moon’s by combining available imagery and topography data.

Mapping the complex and diverse surface of a world like the moon in detailed resolution is challenging because laser altimeters, which measure changes in altitudes, operate at much lower resolution than cameras. And although photographs offer a sense of surface features, it’s difficult to translate images into specific heights and depths.

One of the cornerstones of the implementation of quantum technology is the creation and manipulation of the shape of external fields that can optimize the performance of quantum devices. Known as quantum optimal control, this set of methods comprises a field that has rapidly evolved and expanded over recent years.

A new review paper published in EPJ Quantum Technology and authored by Christiane P. Koch, Dahlem Center for Complex Quantum Systems and Fachbereich Physik, Freie Universität Berlin along with colleagues from across Europe assesses recent progress in the understanding of the controllability of quantum systems as well as the application of quantum control to quantum technologies. As such, it lays out a potential roadmap for future technology.

While quantum optimal control builds on conventional control theory encompassing the interface of applied mathematics, engineering, and physics, it must also factor in the quirks and counter-intuitive nature of quantum physics.

Analysing pendulum videos, the artificial intelligence tool identified variables not present in current mathematics.

An artificial intelligence tool has examined physical systems and not surprisingly, found new ways of describing what it found.

How do we make sense of the universe? There’s no manual. There’s no prescription.

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* At Long Last, Mathematical Proof That Black Holes Are Stable * Who Gets to Work in the Digital Economy? * Mice produce rat sperm with technique that could help conservation.

* Quantum computer can simulate infinitely many chaotic particles * Radar / AI & ML: Scaling False Peaks * Cyber security for the human world | George Loukas | TEDx.

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Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.

The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.

The received view in physics is that the direction of time is provided by the second law of thermodynamics, according to which the passage of time is measured by ever-increasing disorder in the universe. This view, Julian Barbour argues, is wrong. If we reject Newton’s faulty assumptions about the existence of absolute space and time, Newtonian dynamics can be shown to provide a very different arrow of time. Its direction, according to this theory, is given by the increase in the complexity and order of a system of particles, exactly the opposite of what the received view about time suggests.

Two of the most established beliefs of contemporary cosmology are that the universe is expanding and that the direction of the arrow of time in the universe is defined by ever-increasing disorder (entropy), as described by the second law of thermodynamics. But both of these beliefs rest on shaky ground. In saying that the universe is expanding, physicists implicitly assume its size is measured by a rod that exists outside the universe, providing an absolute scale. It’s the last vestige of Newton’s absolute space and should have no place in modern cosmology. And in claiming that entropy is what gives time its arrow, physicists uncritically apply the laws of thermodynamics, originally discovered through the study of steam engines, to the universe as a whole. That too needs to be questioned.

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This research paper presents the design of a wireless power transfer (WPT) circuit integrated with magnetic resonance coupling (MRC) and harvested radio frequency (RF) energy to wirelessly charge the battery of a mobile device. A capacitor (100 µF, 16 V) in the RF energy harvesting circuit stored the converted power, and the accumulated voltage stored in the capacitor was 9.46 V. The foundation of the proposed WPT prototype circuit included two coils (28 AWG)—a transmitter coil, and a receiver coil. The transmitter coil was energized by the alternating current (AC), which produced a magnetic field, which in turn induced a current in the receiver coil. The harvested RF energy (9.46 V) was converted into AC, which energized the transmitter coil and generated a magnetic field. The electronics in the receiver coil then converted the AC into direct current (DC), which became usable power to charge the battery of a mobile device. The experimental setup based on mathematical modeling and simulation displayed successful charging capabilities of MRC, with the alternate power source being the harvested RF energy. Mathematical formulae were applied to calculate the amount of power generated from the prototype circuit. LTSpice simulation software was applied to demonstrate the behavior of the different components in the circuit layout for effective WPT transfer.