Lifeboat Foundation

Researchers integrate terahertz vortex beam emission to advance radar target detection technology.

You may not realize it, but the Doppler effect is everywhere in our lives, from tracking the speed of cars with radar to locating satellites in the sky. It’s all about how waves change their frequency when a source (like a radar signal) and a detector are in motion relative to each other. However, traditional radar systems hit a roadblock when trying to detect objects moving at right angles to their radar signals. This limitation has driven researchers to explore an entirely new approach.

Introduction to Vortex Radar.

The results of a human study carried out by an international research team have provided valuable new insights into the activity of the brain’s noradrenaline (NA) system, which has been a longtime target for medications to treat attention-deficit/hyperactivity disorder, depression, and anxiety. The study employed what the researchers claim is a groundbreaking methodology, developed to record real-time chemical activity from standard clinical electrodes implanted into the brain routinely for epilepsy monitoring.

The results offer up new insights into brain chemistry, which could have implications for a wide array of medical conditions, and also demonstrate use of the new strategy for acquiring data from the living human brain.

“Our group is describing the first ‘fast’ neurochemistry recorded by voltammetry from conscious humans,” said Read Montague, PhD, the VTC Vernon Mountcastle research professor at Virginia Tech, and director of the Center for Human Neuroscience Research and the Human Neuroimaging Laboratory of the Fralin Biomedical Research Institute at VTC. “This is a big step forward and the methodological approach was implemented completely in humans – after more than 11 years of extensive development.” Montague is senior, and co-corresponding author of the researchers’ published paper in Current Biology, which is titled “Noradrenaline tracks emotional modulation of attention in human amygdala.” In their paper the authors concluded, “By showing that neuromodulator estimates can be obtained from depth electrodes already in standard clinical use in the conscious human brain, our study opens the door to a new area of research on the neuromodulatory basis of human health and disease.”

🚨 ALERT: A massive ad fraud botnet called PEACHPIT has been exposed. It exploited hundreds of thousands of Android and iOS devices to generate illicit profits for cybercriminals.

Scientists manipulated light to behave as if influenced by gravity using distorted photonic crystals, opening avenues for optics advancements and 6G communication.

Manipulating Light’s Behavior With Pseudogravity

A collaborative group of researchers has manipulated the behavior of light as if it were under the influence of gravity. The findings, which were published in the journal Physical Review A on September 28, 2023, have far-reaching implications for the world of optics and materials science, and bear significance for the development of 6G communications.

On Friday, researchers from Nvidia, UPenn, Caltech, and the University of Texas at Austin announced Eureka, an algorithm that uses OpenAI’s GPT-4 language model for designing training goals (called “reward functions”) to enhance robot dexterity. The work aims to bridge the gap between high-level reasoning and low-level motor control, allowing robots to learn complex tasks rapidly using massively parallel simulations that run through trials simultaneously. According to the team, Eureka outperforms human-written reward functions by a substantial margin.

“Leveraging state-of-the-art GPU-accelerated simulation in Nvidia Isaac Gym,” writes Nvidia on its demonstration page, “Eureka is able to quickly evaluate the quality of a large batch of reward candidates, enabling scalable search in the reward function space.

As the utility of AI systems has grown dramatically, so has their energy demand. Training new systems is extremely energy intensive, as it generally requires massive data sets and lots of processor time. Executing a trained system tends to be much less involved—smartphones can easily manage it in some cases. But, because you execute them so many times, that energy use also tends to add up.

Fortunately, there are lots of ideas on how to bring the latter energy use back down. IBM and Intel have experimented with processors designed to mimic the behavior of actual neurons. IBM has also tested executing neural network calculations in phase change memory to avoid making repeated trips to RAM.

Now, IBM is back with yet another approach, one that’s a bit of “none of the above.” The company’s new NorthPole processor has taken some of the ideas behind all of these approaches and merged them with a very stripped-down approach to running calculations to create a highly power-efficient chip that can efficiently execute inference-based neural networks. For things like image classification or audio transcription, the chip can be up to 35 times more efficient than relying on a GPU.

Human sensory systems are very good at recognizing objects that we see or words that we hear, even if the object is upside down or the word is spoken by a voice we’ve never heard.

Computational models known as deep neural networks can be trained to do the same thing, correctly identifying an image of a dog regardless of what color its fur is, or a word regardless of the pitch of the speaker’s voice. However, a new study from MIT neuroscientists has found that these models often also respond the same way to images or words that have no resemblance to the target.

When these neural networks were used to generate an image or a word that they responded to in the same way as a specific natural input, such as a picture of a bear, most of them generated images or sounds that were unrecognizable to human observers. This suggests that these models build up their own idiosyncratic “invariances” — meaning that they respond the same way to stimuli with very different features.

Year 2022 This is getting interesting for sure :3.

We study worldvolume actions for D-branes coupled to the worldvolume U gauge field and Ramond-Ramond (RR) potentials in nonrelativistic string theory. This theory is a self-contained corner of relativistic string theory and has a string spectrum with a Galilean-invariant dispersion relation. We therefore refer to such D-branes in nonrelativistic string theory as nonrelativistic D-branes. We focus on the bosonic fields in spacetime and also couple the D-branes to general closed string geometry, Kalb-Ramond, and dilaton background fields. We dualize nonrelativistic D-branes by performing a duality transformation on the worldvolume U gauge field and uncover novel dual D-brane actions. This generalizes familiar properties, such as the SL(2, ℤ) duality in Type IIB superstring theory and the relation between Type IIA superstring and M-theory, to nonrelativistic string and M-theory.

face_with_colon_three This looks awesome :3.

There is great interest in using quantum computers to efficiently simulate a quantum system’s dynamics as existing classical computers cannot do this. Little attention, however, has been given to quantum simulation of a classical nonlinear continuum system such as a viscous fluid even though this too is hard for classical computers. Such fluids obey the Navier–Stokes nonlinear partial differential equations, whose solution is essential to the aerospace industry, weather forecasting, plasma magneto-hydrodynamics, and astrophysics. Here we present a quantum algorithm for solving the Navier–Stokes equations. We test the algorithm by using it to find the steady-state inviscid, compressible flow through a convergent-divergent nozzle when a shockwave is (is not) present.