These groups of brain cells are called “assemblies,” which Papadimitriou describes as “a highly connected, stable set of neurons which represent something: a word, an idea, an object, etc.”
Award-winning neuroscientist György Buzsáki describes assemblies as “the alphabet of the brain.”
The US Department of Energy on Thursday is officially dedicating Perlmutter, a next-generation supercomputer that will deliver nearly four exaflops of AI performance. The system, based at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory, is the world’s fastest on the 16-bit and 32-bit mixed-precision math used for AI.
The HPE Cray system is being installed in two phases. Each of Phase 1’s GPU-accelerated nodes has four Nvidia A100 Tensor Core GPUs, for a total of 6159 Nvidia A100 Tensor Core GPUs. Each Phase 1 node also has a single AMD Milan CPU.
## MATHEMATICS • MAY 24, 2021
# *Noise is commonly discarded, but identifying patterns in noise can be very useful.*
*Generalize the Hearst exponent by adding more coefficients in order to get a more complete description of the changing data. This makes it possible to find patterns in the data that are usually considered noise and were previously impossible to analyze.*
*The development of this mathematical apparatus can solve the issue of parameterisation and analysis of processes for which there is no exact mathematical description. This opens up enormous prospects in describing, analyzing and forecasting complex systems.*
*by moscow institute of physics and technology*
One of the metrics used in economics and natural sciences in time series analysis is the Hurst exponent. It suggests whether the trend present in the data will persist: for example, whether values will continue to increase, or whether growth will turn to decline. This assumption holds for many natural processes and is explained by the inertia of natural systems. For example, lake level change, which is consistent with predictions derived from analysis of the Hurst exponent value, is determined not only by the current amount of water, but also by evaporation rates, precipitation, snowmelt, etc. All of the above is a time-consuming process.
Thanks to folkstone design inc. & zoomers of the sunshine coast BC
From a purely scientific frame of reference, many quantum phenomena like non-local correlations between distant entities and wave-particle duality, the wave function collapse and consistent histories, quantum entanglement and teleportation, the uncertainty principle and overall observer-dependence of reality pin down our conscious mind being intrinsic to reality. And this is the one thing the current physicalist paradigm fails to account for. Critical-mass anomalies will ultimately lead to the full paradigm shift in physics. It’s just a matter of time.
With consciousness as primary, everything remains the same and everything changes. Mathematics, physics, chemistry, biology are unchanged. What changes is our interpretation as to what they are describing. They are not describing the unfolding of an objective physical world, but transdimensional evolution of one’s conscious mind. There’s nothing “physical” about our physical reality except that we perceive it that way. By playing the “Game of Life” we evolved to survive not to see quantum mechanical reality. At our classical level of experiential reality we perceive ourselves as physical, at the quantum level we are a probabilistic wave function, which is pure information.
No matter how you slice it, reality is contextual, the notion that immediately dismisses ‘observer-independent’ interpretations of quantum mechanics and endorses the Mental Universe hypothesis. But we have to be careful here not to throw the baby out with the bathwater, so to speak. I’d like to make a very important point at this juncture of our discussion: Mental and physical are two sides of the same coin made of information. Both should be viewed as the same substance.
Engineering A Safer World For Humans With Self Driving Cars, Drones, and Robots — Dr. Missy Cummings PhD, Professor, Duke University, Director, Humans and Autonomy Laboratory, Duke Engineering.
Dr. Mary “Missy” Cummings, is a Professor in the Department of Electrical and Computer Engineering, at the Pratt School of Engineering, at Duke University, the Duke Institute of Brain Sciences, and is the Director of the Humans and Autonomy Laboratory and Duke Robotics.
Dr. Cummings received her B.S. in Mathematics from the US Naval Academy in 1988, her M.S. in Space Systems Engineering from the Naval Postgraduate School in 1994, and her Ph.D. in Systems Engineering from the University of Virginia in 2004.
Dr… Cummings was one of the Navy’s first female fighter pilots earning the rank of lieutenant and serving as naval officer and military pilot from 1988–1999.
Dr. Cummings research interests include human-unmanned vehicle interaction, human-autonomous system collaboration, human-systems engineering, public policy implications of unmanned vehicles, and the ethical and social impact of technology.
Protocol to reverse engineer Hamiltonian models advances automation of quantum devices.
Scientists from the University of Bristol ’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems — paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.
In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them.
Artificial womb technology for extremely preterm infants — jasmijn kok, juno perinatal healthcare.
Every year, 800000 babies are born extremely preterm (defined as less than 28 weeks of age) worldwide. These infants are usually transferred to an air-based neonatal intensive care unit to support their heart and lung development. Exposure to air, however, leads to many complications, because the lungs are not fully developed yet.
An artificial uterus, or artificial womb, is a device that would allow for extra-corporeal pregnancy, by supporting the growth of a fetus outside the body of an organism that would normally carry the fetus to term.
Juno Perinatal Healthcare (https://www.junoperinatalhealthcare.com/) is a fascinating Dutch neonatal healthcare start-up which has a mission of developing a novel, alternative environment, similar to the mother’s womb, where extremely premature babies could be transferred, where the lungs remain filled with fluid and the umbilical cord will be attached to an artificial placenta to improve their organ development and ease the transition to newborn life.
Juno Perinatal Healthcare is a companion project to a interdisciplinary consortium known as the Perinatal Life Support (PLS) Project (https://perinatallifesupport.eu/), a consortium of three European universities, Aachen, Milan and Eindhoven, to establish the first ex-vivo fetal maturation system for clinical use.
Training neural networks to perform tasks, such as recognizing images or navigating self-driving cars, could one day require less computing power and hardware thanks to a new artificial neuron device developed by researchers at the University of California San Diego. The device can run neural network computations using 100 to 1000 times less energy and area than existing CMOS-based hardware.
Researchers report their work in a paper published recently in Nature Nanotechnology.
Neural networks are a series of connected layers of artificial neurons, where the output of one layer provides the input to the next. Generating that input is done by applying a mathematical calculation called a non-linear activation function. This is a critical part of running a neural network. But applying this function requires a lot of computing power and circuitry because it involves transferring data back and forth between two separate units – the memory and an external processor.
The now-familiar sight of traditional propeller wind turbines could be replaced in the future with wind farms containing more compact and efficient vertical turbines.
New research from Oxford Brookes University has found that the vertical turbine design is far more efficient than traditional turbines in large-scale wind farms, and when set in pairs the vertical turbines increase each other’s performance by up to 15%.
A research team from the School of Engineering, Computing and Mathematics (ECM) at Oxford Brookes led by Professor Iakovos Tzanakis conducted an in-depth study using more than 11500 hours of computer simulation to show that wind farms can perform more efficiently by substituting the traditional propeller-type Horizontal Axis Wind Turbines (HAWTs), for compact Vertical Axis Wind Turbines (VAWTs).
Hebrew University Researcher Introduces New Approach to Three-Body Problem, Predicts its Outcome Statistics.
The “three-body problem,” the term coined for predicting the motion of three gravitating bodies in space, is essential for understanding a variety of astrophysical processes as well as a large class of mechanical problems, and has occupied some of the world’s best physicists, astronomers and mathematicians for over three centuries. Their attempts have led to the discovery of several important fields of science; yet its solution remained a mystery.
At the end of the 17th century, Sir Isaac Newton succeeded in explaining the motion of the planets around the sun through a law of universal gravitation. He also sought to explain the motion of the moon. Since both the earth and the sun determine the motion of the moon, Newton became interested in the problem of predicting the motion of three bodies moving in space under the influence of their mutual gravitational attraction (see illustration to the right), a problem that later became known as “the three-body problem.”