|Robotic fish that can synchronize their swimming without any central coordination have been developed. They use blue LEDs on the back of each robotic fish and wide-angle camera "eyes" that can see the blue LEDs. By seeing other fish, algorithms can coordinate their behavior.|
|SqUID is a warehouse robot that its makers claim can be used to retrofit any warehouse. You modify the shelves so the robot can climb up and across and it can pull boxes off any shelf. How exactly it knows what box to pull, I don't know; I didn't see any obvious barcode reading or anything.|
| "Can a fruit fly learn word embeddings?" Don't worry, this time no fruit flies were hurt in the process of doing this research. In fact no fruit flies were used at all. What they did here was make a "fruit fly brain-inspired" neural network.
Apparently in the fruit fly brain, there's a pair of parts called "mushroom bodies" that has about 2,000 Kenyon cells in each one. This is a key area of the fruit fly brain's sensory input processing, input from not just sight and hearing but smell (which is actually the biggest source of input), temperature, and humidity. Mushroom bodies are something that fly brains have that human brains don't have -- only insects, other arthropods, and some annelids have them. Kenyon cells, named after F. C. Kenyon, are cells specific to the mushroom bodies that creatures such as us don't have.
The researchers replaced the usual recurrent neural networks used to make word embeddings with their own inspired by the structure of Kenyon cells, which results in a much sparser network than usual. They chose the word embedding task because it is an "unsupervised" task -- all they have to do is shovel huge amounts of text into it, but they don't have to create by hand any list of matching sets of input with the "correct" output the neural network has to learn.
The output of the neural network is sparse binary hash codes. Which is to say, instead of a list of real numbers (vector of floating-point values), it outputs a list of true/false values, and because it is sparse, most of them are false.
They tested the network on 4 tasks: static word embeddings, word clustering, context-dependent word embeddings, and document classification. The "static" test just compares the semantic similarity of word embeddings with human-generated scores. The "clustering" task uses a clustering algorithm to group words into clusters, and compares this with clustering word embeddings from traditional algorithms (like Word2Vec and GloVe) the same way. The "context-dependent" test challenges the neural network to distinguish between different meanings of the same word using a dataset designed for that purpose, for example distinguish between a bank account and a river bank, and an Apple iPhone vs an apple pie. The "document classification" task involves taking news articles and putting each one in one of 35 categories. The "fruit fly network" performed comparably to regular word embedding systems on all these tasks.
In addition to performing as well with sparse binary hash codes, the system is much more computationally efficient. Training takes only a few hours, vs 24 hours for GloVe, and even more for newer models like BERTBASE.
| During sleep, the fruit fly Drosophila will occasionally repeatedly extend and retract its proboscis, which is to say, the tube it eats from, even though it's not eating anything. This was determined to be a deep sleep stage. Simply preventing these proboscis extensions prevents the deep sleep stage and slows down the rate at which the animal can clear waste compounds from its system. If the fly is injured this will even increase the likelihood it will die.
The researchers created an automated "pixel subtraction" system to detect proboscis extensions movements. To determine whether the flies were in deep sleep or not, they resorted to pharmacological intervention. Specifically they used Gaboxadol, which binds to GABA Type A receptors, and induces deep sleep in fruit flies. In humans, the same substance induces slow wave sleep.
To test healing, they injured the flies by inducing full-body injury using the HIT assay. That's sciency-speak for using a spring-loaded device to whack the flies against a wall. (HIT stands for "high impact trauma"). To stop the proboscis from moving, they used glue. To verify that the flies' normal eating and excretion wasn't interfered with, they fed them food full of Blue #1 food coloring. "Completely immobilizing the proboscis greatly increases the number of flies that die within 24 hours after injury." Even if they reduced the proboscis extensions chemically instead of mechanically, it still increased the death of the flies. They reduced the proboscis extensions chemically by using something called UAS-NaChBac. Which, apparently is a modified gene. The "UAS" part stands for "upstream activation sequence", and "NaChBac" is the name of the gene modified. Well, apparently doing this changes another gene, NP5137-Gal4, and doing that decreases feeding behavior, which in turn decreases the proboscis extensions. Anyway, same result: dead flies.
To make the whole affair even more fun, they gave the flies firefly luciferin. As the name "luciferin" hints, this is a bioluminescent molecule. By combining the luciferin with Blue #1, they got the flies to glow blue. They called these glowing blue flies "smurfed" flies. Seriously. This made it really easy for them to see that, while the total amount of excretion of Blue #1 was the same in a 24 hour period for flies that were and were not allowed to do proboscis extensions, it was faster in the flies that were allowed to do proboscis extensions. Which is to say, the flies allowed to do proboscis extensions were "smurfed" for a shorter time. To be sure they quantified the amount of blue dye collected by collecting it in scintillation vials, which are basically glass bottles with caps.
Anyway, all this leads to the conclusion that deep sleep helps clear waste and to our headline, "Deep sleep takes out the trash". Except they say, "The researchers found that deep sleep has an ancient, restorative power to clear waste from the brain", and it seems to clear waste from more than just the brain. All this is in fruit flies anyway, so it's not clear how much of it applies to humans. I guess this concludes today's lesson in "how to write a catchy headline", to say nothing of how to screw with fruit flies with springs, glue, and food coloring. "Deep sleep serves a role in waste clearance in the fruit fly indicates that waste clearance is an evolutionary conserved core function of sleep. This suggests that waste clearance may have been a function of sleep in the common ancestor of flies and humans." Key word "may".
| "Evolving reinforcement learning algorithms". So the idea here is to take the AutoML idea and apply it to reinforcement learning. So first a brief summary of what AutoML is. Every neural network has an "architecture", which is to say, layers of various sizes, of various types, connected together in various ways. Different types of layers can be convolutional layers, pooling layers, activation layers (ReLU, logistic regression, etc), recurrent layers, and so on, and they can be fully connected, sparsely connected, connected with dropout (regularization technique), and so on. These collectively are called the "hyperparameters" of the network. The idea behind AutoML is that these hyperparameters, rather than being decided by a human guided by intuition, could themselves be learned by another neural network. Typically that neural network is a reinforcement learning system.
The problem is that AutoML is useful on supervised neural networks, but you can't use it on reinforcement learning neural networks, even though AutoML itself is a reinforcement learning system. So the idea here is to do the equivalent for reinforcement learning.
The approach here is to use genetic programming. Ordinarily the difference between "genetic algorithms" and "genetic programming" is that the "genome" that gets evolved by "genetic algorithms" represents the solution to some problem you want the genetic algorithm to solve, but with "genetic programming", the "genome" represents a *program* that you can run. In this case the "genome" represents an algorithm. The algorithm incorporates all of the rules for the reinforcement learning agent to take actions in its simulated environment and learn from the reward/punishment signal from that environment in response to its actions.
The idea is to have a population of algorithms, and select the best algorithms as "parent" algorithms, recombine their genomes, then perform mutations, then repeat the process. However, because each evaluation of a reinforcement learning algorithm is computationally expensive, they use a variation called "regularized evolution". Rather than evaluate all the algorithms, pairs of algorithms are picked at random and then compete in a "tournament". Competitors are eliminated like in a sporting competition. They further limit the mutation by using a single type of mutation which first, representing the genome as a program graph, picks one node in the graph to mutate and then replaces it with a random operation with inputs drawn uniformly from all possible inputs.
Furthermore, before evaluating an algorithm program, it first checks to see if it is functionally equivalent to any previously evaluated program. It does this by putting in 10 specific inputs and comparing the results with previous algorithms. If it matches output from a previously tested algorithm, the process of putting it in a simulated environment and evaluating it is simply skipped.
Furthermore, as the simulation is carried out, the results are watched and if it looks like an algorithm is performing poorly, it is terminated early to avoid unnecessary computation.
This is after the algorithm passes basic checks, such as accepting the right input, outputting the right type of output, and being differentiable all the way through.
If you're wondering how the initial population is formed, the system can start with algorithms handcrafted by humans, incorporating human knowledge to get the system off to a fast start, or the initial population can be completely random, in which case the system might find solutions no human ever thought of. Starting from an initially random population like this is called "learning from scratch", while starting from handcrafted human knowledge is called "bootstrapping".
In their testing of the system, they got the system to learn two algorithms which are similar to the relatively simple deep-Q-network (DQN) algorithm. They plan further experiments to evolve algorithms more similar to more complex reinforcement learning algorithms like actor-critic or policy gradient methods.
| "The Gulf of Mexico holds huge untapped offshore oil deposits that could help power the US for decades."
"The energy super basin's longevity, whose giant offshore fields have reliably supplied consumers with oil and gas since the 1960s, is the result of a remarkable geologic past -- a story that began 200 million years ago among the fragments of Pangea, when a narrow, shallow seaway grew into an ocean basin, while around it mountains rose then eroded away."
"The processes that shaped the basin also deposited and preserved vast reserves of oil and gas, of which only a fraction has been extracted. Much of the remaining oil lies buried beneath ancient salt layers, just recently illuminated by modern seismic imaging."
"Geologically, salt is important because it can radically alter how petroleum basins evolve. Compared to other sedimentary rocks, it migrates easily through the Earth, creating space for oil and gas to collect. It helps moderate heat and keeps hydrocarbon sources viable longer and deeper. And it is a tightly packed mineral that seals oil and gas in large columns, setting up giant fields."
"The Gulf of Mexico has a thick salt canopy that blankets large portions of the basin and prevented us for many years from actually seeing what lies beneath. What has kept things progressing is industry's improved ability to see below the salt."
"The bulk of the northern offshore basin's potential remains in giant, deepwater oil fields beneath the salt blanket. Although reaching them is expensive and enormously challenging, Snedden believes they represent the best future for fossil fuel energy. That's because the offshore -- where many of the giant fields are located -- offers industry a way of supplying the world's energy with fewer wells, which means less energy expended per barrel of oil produced."
Well, I guess that's bad news for people who want the price of oil to be high to incentivize the development of solar, wind, and other renewable technologies as well as electric vehicles and batteries and other technologies that don't rely on fossil fuels, and don't want fossil fuels extracted and burned and the carbon put into the atmosphere.
The northern part of the Gulf of Mexico is considered a US "federal offshore area" and it is estimated that 60 BOE out of a total estimated petroleum endowment of more than 100 BOE can be recovered. I think they mean BBOE. BOE stands for "barrel of oil equivalent". It standardizes energy from all types of oil on a single reference type of crude oil, and is used for natural gas as well. BBOE is billion barrel of oil equivalents and that's probably what they mean.
The bulk of hydrocarbon resources in these federal offshore waters is in Cenozoic sandstone reservoirs such as the Paleogene Wilcox reservoir of deep-water subsalt areas. The Cenozoic Era goes from about 66 million years ago to the present. It was the interval of time during which the continents assumed their modern configuration and geographic positions and during which Earth's flora and fauna evolved toward those of the present, approximately the same as the time the dinosaurs got whacked by an asteroid (probably), but geologically what is significant is that the continents were in their modern configuration. The Paleogene Wilcox reservoir is thought to be an area where there was a basin where water accumulated and evaporated leaving a large salt accumulation. Oil both above a salt layer (suprasalt) and below a salt layer (subsalt) tend to yield the highest flow rates and cumulative production volumes in the industry.
Along the northern border of the Yucutan peninsula, Jurassic Norphlet sandstone was found that is believed to have vast additional recoverable oil. A Norphlet formation is a formation formed from clasts, which in turn are chunks formed from detrius broken off from other geological formations.
Typically in oil fields, rock from earlier geological eras, such as the Jurrasic era (going back 145 million years) serves as top seals, but in the Gulf of Mexico super basin, Neogene and Mesozoic shales, stemming from less than 66 million years ago (with the Neogene being the more recent) and carbonate mudstones serve as the trap-sealing materials.
Salt itself forms tectonic structures, ranging from simple diapiric closures, which in this case would involve less dense salt intruding vertically into more dense salt, and extensional fault traps, which is where a type of rock that is permeable to oil is cut off by a fault line between tectonic plates, forming a trap, to complex subsalt configurations such as salt-cored compressional anticlines, formed by compression waves creating wave-shaped up and down folds, salt-cutoff traps, and bucket weld traps. A salt "weld" is formed when a fault divides a layer, but the subsequent salt movement "welds" the parts back together. It's considered a "bowl" weld if it forms a bowl shape, or a "bucket" weld if it forms a bowl shape in such a way as to cut off any flow into the bowl, any "feeders". And of course if it traps oil then it is a "trap", hence "bucket weld traps".
| "Optoacoustics can be used for monitoring skin water content." "Promising for medical applications such as tissue trauma management and in cosmetology."
"Too much or too little water in skin tissues can be a sign of various health problems, such as an edema (swelling caused by fluid accumulation) or dehydration, which can also have cosmetic impacts. Right now, electrical, mechanical and spectroscopic methods can be used to monitor water content in tissues, but there is no accurate and noninvasive technique that would also provide a high resolution and significant probing depth required for potential clinical applications."
Optoacoustic imaging is a method of imaging where energy put in in the form of light is absorbed by a biological tissue, which comes out in the form of vibration. The light is typically in the visible or near-infrared, and in this case is in the 1370 to 1650 nm range, which is in the infrared. The paper is paywalled so I can't tell you the significance of those frequencies, though they no doubt are significant as the wavelength of light chosen for optoacoustic Imaging is specific to the components within the tissue, such as hemoglobin or lipids, that you want to absorb the light energy and generate the mechanical wave that gets detected as ultrasound.
| "RNA often folds in surprising, perhaps unintuitive ways, such as tying itself into knots -- and then immediately untying itself to reach its final structure."
"Folding takes place in your body more than 10 quadrillion times a second. It happens every single time a gene is expressed in a cell, yet we know so little about it. Our movies allow us to finally watch folding happen for the first time."
"Although videos of RNA folding do exist, the computer models that generate them are full of approximations and assumptions. Julius B. Lucks' team has developed a technology platform that captures data about RNA folding as the RNA is being made."
| "Technology readiness levels for machine learning systems." So the issue here is that other domains of engineering, such as civil and aerospace, have well-defined processes and testing standards for delivering high-quality, reliable results. The idea here is to do something similar for AI, and adapted the "Technology Readiness Level" protocol shared by NASA and DARPA for spaceflight.
They're not actually inventing AI technology here, just specifying what requirements must be met for a "Technology Readiness Level" to be met. They defined 9 levels:
Level 0 "First Principles" -- Greenfield AI research, exploration of any novel idea.
Level 1 "Goal-Oriented Research" -- Experiments and mathematical foundations need to pass a review process with fellow researchers.
Level 2 "Proof of Principle Development" -- A formal research requirements document with well-specified verification and validation steps along with "testbeds" -- simulated environments and/or surrogate data that closely matches the conditions and data of real scenarios for continuous testing.
Level 3 "System Development" -- Code itself is well-designed, well-architected for its intended dataflow, has explicit and properly documented interfaces, meets style guideline requirements, and is covered by unit and integration tests.
Level 4 "Proof of Concept Development" -- Code is tested in a real scenario.
Level 5 "Machine Learning 'Capability'" -- Technology is handed off from R&D to productization.
Level 6 "Application Development" -- Code is brought up to "product caliber" for integration.
Level 7 "Integrations" -- Technology is integrated into existing production systems by both infrastructure engineers and applied AI engineers.
Level 8 "Flight-ready" -- Technology is demonstrated to work in its final form and under expected conditions, passing A/B tests, blue/green deployment tests, shadow testing, and canary testing.
Level 9 "Deployment" -- At this stage, "maintenance" takes over, with continuous monitoring, fixes to any errors or unintended consequences, performance improvements, and feature improvements.
| "A way to use chemical sensors and computer vision to determine when grilled chicken is cooked just right."
"The researchers built their own 'e-nose', with eight sensors detecting smoke, alcohol, CO and other compounds as well as temperature and humidity, and put it into the ventilation system. They also took photos of the grilled chicken and fed the information to an algorithm that specifically looks for patterns in data. To define changes in odor consistent with the various stages of a grilling process, scientists used thermogravimetric analysis (to monitor the amount of volatile particles for the 'e-nose' to detect), differential mobility analysis to measure the size of aerosol particles, and mass spectrometry."
"But perhaps the most important part of the experiment involved 16 PhD students and researchers who taste-tested a lot of grilled chicken breast to rate its tenderness, juiciness, intensity of flavor, appearance and overall doneness on a 10-point scale."
| "Humans can quickly and accurately learn new visual concepts from sparse data -- sometimes just a single example. Even 3- to 4-month-old babies can easily learn to recognize zebras and distinguish them from cats, horses and giraffes. But computers typically need to 'see' many examples of the same object to know what it is."
"The big change needed was in designing software to identify relationships between entire visual categories, instead of trying the more standard approach of identifying an object using only low-level and intermediate information, such as shape and color."
"Artificial neural networks which represent objects in terms of previously learned concepts, learned new visual concepts significantly faster."
"The anterior temporal lobe of the brain is thought to contain 'abstract' concept representations that go beyond shape."
This article is still pretty vague on what the researchers actually did. What they did is take a neural network for object recognition from 2015 called GoogLeNet (hmm I wonder what company made it?) and arbitrarily decided the last layer, which is a fully connected layer that does the categorization, is a "conceptual" layer, while the rest are "generic" layers. They then showed that this "conceptual" layer learns first when there are only a small number of training examples. All of this was predicated on the notion that GoogLeNet is a particularly good predictor of neural activity in the human brain and therefore a good model for human visual object recognition, but since that wasn't part of this research, they don't give any explanation why. That might be worth looking into.
| "As anxious passengers are often reassured, commercial aircrafts can easily continue to fly even if one of the engines stops working. But for drones with four propellers -- also known as quadcopters -- the failure of one motor is a bigger problem. With only three rotors working, the drone loses stability and inevitably crashes unless an emergency control strategy sets in."
Researchers "show that information from onboard cameras can be used to stabilize the drone and keep it flying autonomously after one rotor suddenly gives out."
Ok, so the trick here is to use something called an "event camera". With a regular camera, the problem is that when a rotor fails, the drone will start spinning (yaw rotation), and the motion coming in from the camera will become too blurred from motion blur for the drone to be able to figure out anything useful from it.
An "event camera", on the other hand, outputs *changes* in brightness on a pixel-by-pixel basis, instead of absolute brightness. It also hands over this information asynchronously, as the events it is looking at change, rather than in complete frames. Furthermore it doesn't do this at an algorithmic level -- the physical hardware of the sensor itself is optimized to do this. It outputs changes on the order of microseconds with no motion blur.
Thanks to the much tighter time resolution of the event camera, as well as great dynamic range, and algorithms developed by the researchers that combine the output from the event camera with a standard camera, the drone is able to fly -- it still spins, but can fly in a controlled manner despite the spinning -- with only three rotors. Event cameras also work well in very low light.
| A new system for connecting images and text from OpenAI. They call it CLIP (Contrastive Language-Image Pre-training). The motivation for this was that machine vision systems that learn to identify what's in images require a huge amount of computation. For example, they mention that a model called ResNeXt101-32x48d requires 19 GPU-years to train. They identified the main problem being that, outside of carefully hand-crafted datasets like ImageNet, which are too small, getting the system to match the exact words of the text accompanying each image is really hard, especially since humans out in the wild write a wide variety of descriptions and random comments, and that's after you get rid of of all the junk like camera settings.
The approach they took was instead of getting the system to try to match anyone's exact words, all it had to do is pick the best description out of the pile of all descriptions (or a sufficiently big pile of descriptions). The training process for this is called "contrastive" learning and accounts for the "contrastive" part of the "CLIP" name. Rather than try to predict exact words, it just has to "contrast" one description with another and pick the best one.
They also incorporated the "embedding" idea, which is where, instead of using a piece of data directly, you use an encoding derived from it. I can never remember why these are called "embeddings" and the term is not intuitive. But a standard example is where in natural language processing, you represent each word as a vector, and the relationships between vectors of different words corresponds to relationships between the meanings of the words. Here, they do embeddings of both the images and the descriptions. Not the individual words in the descriptions but the whole descriptions.
So what is learned, then, is to associate an embedding of an image with an embedding of a description. That is what happens in the training process. In the inference step, an image is fed in, and a bunch of descriptions of the form "photo of a" + various object names that are relevant to whatever is relevant to the setting where the system is deployed. For example, if you want a system to distinguish cats from dogs, you have "photo of a cat" and "photo of a dog" as choices. If you want a system to distinguish land use from satellite photos, you might have "a centered satellite photo of permanent crop land", "a centered satellite photo of pasture land", "a centered satellite photo of highway or road", "a centered satellite photo of annual crop land", "a centered satellite photo of brushland or scrubland", and so on.
This combination of "contrastive" learning with embeddings makes the system good at something called "zero-shot learning", a term you will see them use a lot. To understand what this strange term means, you have to think of normal learning with neural networks as gazillion-shot learning -- i.e. the neural network has to see a gazillion examples over and over a gazillion times to learn anything. Wouldn't it be great if you only needed to show a neural network something once and it could learn it? Let's call that one-shot learning. Ok, now you can see where the term "zero-shot learning" comes from. The idea is that you give the system something it has never seen in its training data -- and something that is not just a simple extrapolation of the training data -- and it gets the answer right anyway. So in this example, an example of "zero-shot" learning might be if the system correctly recognized a zebra even though no zebras were ever part of its training data. If the embedding for the image was related in the proper way to both horses and striped animals, and the description embedding system was able to place the word "zebra" in such a way as it has the right relationships between horses and striped animals, the system could work.
The system is also highly efficient to train, because the size of the embeddings it trains on are much smaller and simpler than the original images. So the end result is an efficient system, able to train on noisy data not carefully hand-curated, and capable of zero-shot image recognition. The system is able to perform on par with state-of-the-art ImageNet winners without training on the ImageNet dataset at all, and does significantly better than state-of-the-art systems on "adversarial" images -- images where tiny details in the image are manipulated on purpose to make neural nets flub.
| "The third round of 60 Starlink satellites, which SpaceX launched on 7 January 2020, included some satellites with a special anti-reflective dark coating. The Ishigakijima Astronomical Observatory astronomers set out to compare the reflectivity of this modified satellite with the 'standard' STARLINK-1113 version using the Murikabushi scope's MITSuME system, which allows for simultaneous observations in the green, red and near-infrared bands."
"The darkening paint on DarkSat certainly halves reflection of sunlight compared to the ordinary Starlink satellites, but negative impact on astronomical observations still remains." "While the mitigating effect is 'good in the UV/optical region' of the spectrum, 'the black coating raises the surface temperature of DarkSat and affects intermediate infrared observations'."
| "A machine learning algorithm capable of performing simulations for materials scientists nearly 40,000 times faster than normal" has been developed.
"Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material. A project might require thousands of simulations, which can take weeks, months or even years to run."
"The team clocked a single, unaided simulation on a high-performance computing cluster with 128 processing cores (a typical home computer has two to six processing cores) at 12 minutes. With machine learning, the same simulation took 60 milliseconds using only 36 cores -- equivalent to 42,000 times faster on equal computers. This means researchers can now learn in under 15 minutes what would normally take a year."
"Sandia's new algorithm arrived at an answer that was 5% different from the standard simulation's result, a very accurate prediction for the team's purposes."
"Machine learning previously has been used to shortcut simulations that calculate how interactions between atoms and molecules change over time. The published results, however, demonstrate the first use of machine learning to accelerate simulations of materials at relatively large, microscopic scales."
"For instance, scientists can now quickly simulate how miniscule droplets of melted metal will glob together when they cool and solidify, or conversely, how a mixture will separate into layers of its constituent parts when it melts."
Alright, well, obviously the writer of that press release is trying to sell the benefits of this research, to ensure the funding keeps coming in or whatever, but it says little about what the actual research was. It was about something called phase-field methods. I'm not going to claim to really understand what these are, but the basic idea is that instead of simulating atoms and molecules, you instead simulate liquids and solids and represent the boundary between them with partial differential equations. Well, phase-field methods started with solidification, but have since been extended to other uses. But you're looking at a situation where a material changes phase, such as from liquid to solid. You set up the equations so that one phase is represented as -1 and the other is +1 and the boundary in between is 0, something like that. Once you've gotten your system into these partial differential equations, you solve for the whole system by integrating a the partial differential equations.
The next trick involved here is to not integrate the partial differential equations, but to represent the state of the system as a time series, and ask a neural network to solve the time series. There are a variety of neural network models that work on time series, and the one they decided worked the best was LSTM (long short term memory). Unlike a simple recurrent neural network, which just gets input from the output of the previous time step as part of its input, as LSTM uses "gates" to control how far back in time it looks, and looks at multiple time steps back in time at varying time intervals.
Another trick they used was reducing the complexity the LSTM had to deal with by using a dimensionality-reduction algorithm called principle components analysis (PCA). The phase field is reduced in dimensions on the input, and then the final output of the LSTM is expanded back into the full dimensions of the phase field.
5,000 full-fledged simulations were run through this same dimensionality-reduction process to generate the training data for the neural network.
| Programmable surfaces for terahertz communication. The terahertz range is between microwaves and infrared light. 5G uses gigahertz range frequencies, and prior cellphone technologies like GSM use megahertz range frequencies.
"Unlike radio waves, which easily pass through obstructions such as walls, terahertz works best with a relatively clear line of sight for transmission. The metasurface device, with the ability to control and focus incoming terahertz waves, can beam the transmissions in any desired direction." "Metasurface" is their term for the "programmable" surface.
"This can not only enable dynamically reconfigurable wireless networks, but also open up new high-resolution sensing and imaging technologies for the next generation of robotics, cyberphysical systems and industrial automation. Because the metasurface is built using standard silicon chip elements, it is low-cost and can be mass produced for placement on buildings, street signs and other surfaces."
The metasurface "features hundreds of programmable terahertz elements, each less than 100 micrometers (millionths of a meter) in diameter and a mere 3.4 micrometers tall, made of layers of copper and coupled with active electronics that collectively resonate with the structure. This allows adjustments to their geometry at a speed of several billions of times per second. These changes -- which are programmable, based on desired application -- split a single incoming terahertz beam up into several dynamic, directable terahertz beams that can maintain line of sight with receivers."
"The Princeton researchers commissioned a silicon chip foundry to fabricate the metasurface as tiles onto standard silicon chips. In this way, the researchers showed that the programmable terahertz metasurface can be configured into low-cost, scalable arrays of tiles." "As a proof of concept, the Princeton researchers tested tile arrays measuring two-by-two with 576 such programmable elements and demonstrated beam control by projecting (invisible) terahertz holograms.These elements are scalable across larger arrays."
The 576 elements are each individually addressable and digitally programmable with 8 bits of control at GHz speed. They are fabricated at 65 nm in the industry-standard CMOS process. The amount of programmability is about 25dB of amplitude modulation, and plus or minus about 30 degrees directionally, and can change the phase as well.