Lifeboat Foundation

Isaac Newton and other premodern physicists saw space and time as separate, absolute entities — the rigid backdrops against which we move. On the surface, this made the mathematics behind Newton’s 1687 laws of motion look simple. He defined the relationship between force, mass and acceleration, for example, as $latex \vec{F} = m \vec{a}$.

In contrast, when Albert Einstein revealed that space and time are not absolute but relative, the math seemed to get harder. Force, in relativistic terms, is defined by the equation $latex \vec {F} =\gamma (\vec {v})^{3}m_{0}\,\vec {a} _{\parallel }+\gamma (\vec {v})m_{0}\,\vec {a} _{\perp }$.

Continue reading “How (Relatively) Simple Symmetries Underlie Our Expanding Universe” »

The laws of physics stay the same no matter one’s perspective. Now this idea is allowing computers to detect features in curved and higher-dimensional space.

A team of researchers – including the Max Planck Institute for Extraterrestrial Physics in Garching – have gained astounding insights into the galactic centre: The astronomers have spotted gaseous clouds which are spinning around the assumed black hole at the heart of the Milky Way at a speed of around 30 percent of the speed of light. The gas is moving in a circular orbit outside the innermost stable path and can be identified through radiation bursts in the infrared range. This discovery was made possible by the Gravity Instrument, which combines the light of all four eight-metre mirrors of the Very Large Telescope at the European Southern Observatory (ESO). Thanks to this technology, which is called interferometry, Gravity generates the power of a virtual telescope with an effective diameter of 130 metres.

This unusually compact object sits right in the middle of the Milky Way and generates radio emissions: Astronomers call it Sagittarius A*. It is highly probable that this is a black hole with the mass of approx. four million suns. But this is by no means certain, and scientists are always devising new tests to support this thesis. Researchers have now used the Gravity Instrument to take a close look at the edges of the alleged black hole.

According to this theory, the electrons in the gas approaching the event horizon should speed up and therefore increase in brightness. The region of only a few light hours around the black hole is very chaotic, in a similar way to thunderstorms on Earth or radiation bursts on the Sun. Magnetic fields also play a part here, because the gas conducts electricity making it a plasma. The latter should ultimately show up as a flickering “hot spot” circling the black hole on the final stable path.

Many philosophers and scientists believe that we need an explanation as to why the laws of physics and the initial conditions of the universe are fine-tuned for life. The standard two options are: theism and the multiverse hypothesis. Both of these theories are extravagant and arguably have false predictions. Drawing on contemporary philosophy of mind, I outline a form of panpsychism that I believe offers a more parsimonious and less problematic explanation of cosmological fine-tuning.

Researchers are busy analysing some of the final data sent back from the Cassini spacecraft which has been in orbit around Saturn for more than 13 years until the end of its mission in September 2017.

For the last leg of its journey, Cassini was put on a particularly daring orbit passing between Saturn and its rings which brought it closer to Saturn than ever before. This allowed scientists to obtain images of Saturn’s ultraviolet auroras in unprecedented resolution.

The new observations are detailed in two new studies published in Geophysical Research Letters and JGR: Space Physics.

Don’t worry, Betelguese is still there.

In a new report published on Scientific Reports, Milan M. Milošević and an international research team at the Zepler Institute for Photonics and Nanoelectronics, Etaphase Incorporated and the Departments of Chemistry, Physics and Astronomy, in the U.S. and the U.K. Introduced a hyperuniform-disordered platform to realize near-infrared (NIR) photonic devices to create, detect and manipulate light. They built the device on a silicon-on-insulator (SOI) platform to demonstrate the functionality of the structures in a flexible, silicon-integrated circuit unconstrained by crystalline symmetries. The scientists reported results for passive device elements, including waveguides and resonators seamlessly integrated with conventional silicon-on-insulator strip waveguides and vertical couplers. The hyperuniform-disordered platform improved compactness and enhanced energy efficiency as well as temperature stability, compared to silicon photonic devices fabricated on rib and strip waveguides.

Academic and commercial efforts worldwide in the field of silicon photonics have led to engineer optical data communications at the Terabit-scale at increasingly lower costs to meet the rapidly growing demand in data centers. Explosive growth in cloud computing and entertainment-on-demand pose increasingly challenging costs and energy requirements for data transmission, processing and storage. Optical interconnects can replace traditional copper-based solutions to offer steadily increasing potential to minimize latency and power consumption, while maximizing the bandwidth and reliability of the devices. Silicon photonics also leverage large-scale, complementary metal-oxide semiconductor (CMOS) manufacturing processes to produce high-performance optical transceivers with high yield at low-cost. The properties allow applications of optical transceivers (fiber optical technology to send and receive data) to be increasingly compelling across shorter distances.

More than three decades ago, physicist Richard Soref identified silicon as a promising material for photonic integration. Leading to the present-day steady development and rapid production of increasingly complex photonic integrated circuits (PICs). Researchers can integrate large numbers of massively-parallel compact energy-efficient optical components on a single chip for cloud computing applications from deep learning to artificial intelligence and the internet of things. Compared to the limited scope of commercial silicon photonic systems, photonic crystal (PhC) architectures promise smaller device sizes, although they are withheld by layout constraints imposed by waveguide requirements along the photonic crystal’s axis. Until recently, photonic band gap (PBG) structures that efficiently guide light were limited to photonic crystal platforms. Now, newer classes of PBG structures include photonic quasicrystals, hyperuniform disordered solids (HUDs) and local self-uniform structures.

The mystery of why quantum matter jumps from a wave-like state to a well-defined particle when it is observed has puzzled scientists for nearly a 100 years.

Known as ‘the measurement problem’ it is widely seen as the major complication in quantum theory and has led even well-respected scientists to suggest that the human mind may be having some kind of telepathic influence on the fabric of the universe — our thoughts actually shaping reality around us.

But physicist Jonathan Kerr, who has studied quantum mechanics for 35 years from his cottage in Surrey, believes he has solved the riddle, and the answer is more prosaic than some might have hoped.

Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.

The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.

The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.