Mathematicians say they proved reality is real. Researchers say it's impossible to use algorithmic computation to generate everything in our universe. Breakthroughs, discoveries, and DIY tips sent every weekday. Despite how it may feel some days, we probably aren't stuck in a computer simulation . An international team of mathematicians says that they have once-and-for-all determined that our reality is, in fact, .

Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector.

The landscape of low-energy effective field theories stemming from string theory is too vast for a systematic exploration. However, the meadows of the string landscape may be fertile ground for the application of machine learning techniques. Employing neural network learning may allow for inferring novel, undiscovered properties that consistent theories in the landscape should possess, or checking conjectural statements about alleged characteristics thereof. The aim of this work is to describe to what extent the string landscape can be explored with neural network-based learning. Our analysis is motivated by recent studies that show that the string landscape is characterized by finiteness properties, emerging from its underlying tame, o-minimal structures. Indeed, employing these results, we illustrate that any low-energy effective theory of string theory is endowed with certain statistical learnability properties. Consequently, several learning problems therein formulated, including interpolations and multi-class classification problems, can be concretely addressed with machine learning, delivering results with sufficiently high accuracy.

A quantum computer has been used to simulate a holographic wormhole for the first time. In this case, the word "holographic" indicates a way to simplify physics problems involving both quantum mechanics and gravity, not a literal hologram, so simulations like this could help us understand how to combine those two concepts into a theory of quantum gravity – perhaps the toughest and most important problem in physics right now. Both quantum mechanics, which governs the very small, and general relativity, which describes gravity and the very large, are extraordinarily successful in their respective realms, but these two fundamental theories do not fit together. This incompatibility is particularly apparent in areas where both theories should apply, such as in and around black holes. These areas are extraordinarily complicated, and that is where holography comes in.

Summary: Study suggests quantum processes are part of cognitive and conscious brain functions. Scientists from Trinity College Dublin believe our brains could use quantum computation after adapting an idea developed to prove the existence of quantum gravity to explore the human brain and its workings. The brain functions measured were also correlated to short-term memory performance and conscious awareness, suggesting quantum processes are also part of cognitive and conscious brain functions. If the team's results can be confirmed – likely requiring advanced multidisciplinary approaches –they would enhance our general understanding of how the brain works and potentially how it can be maintained or even healed. They may also help find innovative technologies and build even more advanced quantum computers.

Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a $79\%$ efficiency gain compared with matched filtering at an equal error rate of $0.2\%$. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection.

The holographic universe is a scientific concept discovered by Einstein. It's based on string theories that are related to concepts of quantum gravity. The holographic universe says that the mind and five senses project physical reality holographically. A black hole is a place in space where gravity pulls so much that even light cannot get out. The gravity is so strong because matter has been squeezed into a tiny space.

The U.S. Department of Energy (DoE) recently announced the names of 83 scientists who have been selected for their 2021 Early Career Research Program. The list includes four faculty members from MIT: Riccardo Comin of the Department of Physics; Netta Engelhardt of the Department of Physics and Center for Theoretical Physics; Philip Harris of the Department of Physics and Laboratory for Nuclear Science; and Mingda Li of the Department of Nuclear Science and Engineering. Each year, the DoE selects researchers for significant funding the "nation's scientific workforce by providing support to exceptional researchers during crucial early career years, when many scientists do their most formative work." The quantum technologies of tomorrow –– more powerful computing, better navigation systems, and more precise imaging and magnetic sensing devices –– rely on understanding the properties of quantum materials. Quantum materials contain unique physical characteristics, and can lead to phenomena like superconductivity.

Time travel has been a beloved science-fiction idea at least since H.G. Wells wrote The Time Machine in 1895. The concept continues to fascinate and fictional approaches keep coming, prodding us to wonder whether time travel is physically possible and, for that matter, makes logical sense in the face of its inscrutable paradoxes. Remarkably, last year saw both a science-fiction film that illuminates these questions, and a real scientific result, spelled out in the journal, Classical and Quantum Gravity,1 that may point to answers. The film is writer-director Christopher Nolan's attention-getting Tenet. Like other time travel stories, Tenet uses a time machine.

Physicists have wondered for decades whether infinitely dense points known as singularities can ever exist outside black holes, which would expose the mysteries of quantum gravity for all to see. Singularities--snags in the otherwise smooth fabric of space and time where Albert Einstein's classical gravity theory breaks down and the unknown quantum theory of gravity is needed--seem to always come cloaked in darkness, hiding from view behind the event horizons of black holes. The British physicist and mathematician Sir Roger Penrose conjectured in 1969 that visible or "naked" singularities are actually forbidden from forming in nature, in a kind of cosmic censorship. But why should quantum gravity censor itself? Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.