Aurelia Butler

Scientists often look to nature for cues when designing robots – some robots mimic human hands while others simulate the actions of octopus arms or inchworms. Now, researchers have designed a new soft robotic gripper that draws inspiration from an unusual source: pole beans. More

Methods currently used around the world for predicting the development of COVID-19 and other pandemics fail to report precisely on the best and worst case scenarios. Newly developed prediction method for epidemics, published in Nature Physics, solve this problem.
“It is about understanding best and worst-case scenarios — and the fact that worst case is one of the most important things to keep track of when navigating through pandemics — regardless whether it be in Denmark, the EU, the USA or the WHO. If you are only presented with an average estimate for the development of an epidemic — not knowing how bad it possible can get, then it is difficult to act politically,” says Professor Sune Lehmann, one of four authors of the article Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles just published in Nature Physics.
Researchers Jonas L. Juul, Kaare Græsbøll, Lasse Engbo Christiansen and Sune Lehmann, all from DTU Compute, act as advisors to the National Board of Health in Denmark during the corona crisis. And partly based on their own experience as advisors, they have become aware that the existing methods of projecting the development of epidemics such as COVID-19 have a problem in describing the extremes possibilities of the expected development.
Epidemics are unpredictable
“Disease outbreaks are fundamentally stochastic processes. The same disease introduced in the same population can infect a large number of people or disappear quickly without having a particular prevalence. It depends in part on coincidences,” explains postdoc Jonas L. Juul.
It is precisely the unpredictability of epidemics which makes it so difficult to make the right decisions everywhere in society when it hits. How many beds and respirators will there be a need for? And how much can we reduce this demand by enforcing restrictions?

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However, the general unpredictability is just one of many problems in estimating the development of an epidemic.
“It is not just the unpredictable nature of epidemics that makes it difficult to predict their course — it is also our lack of knowledge about the disease’s characteristics and prevalence in society at any given time. Just to give a few concrete examples of this: there is typically no one who has any idea exactly when an outbreak has started, how many infected we have in an area on any given day, or in which regions the epidemic is getting a foothold right now. The only thing we know for sure is that when the health authorities discover an outbreak, it has been going on for a while, “says Sune Lehmann.
The common way to deal with the lack of information, almost everywhere in the world, is to model many scenarios based on e.g. different numbers of unknown infections and starting times and then summarize by looking at each day separately and assessing the ‘middle’ predictions as the most likely outcomes of the day. If most input parameters give infection numbers of less than 4000 on Christmas Day, more than 4000 new infected are subsequently assessed to be unlikely.
The ‘day-based’ way of making these predictions is used all over the world, and although the link between the development of an epidemic and specific dates is useful in some contexts, it systematically excludes data on how bad or mild the epidemic will be.
If all projections e.g. predict that the epidemic will peak at 4000 infected in one day, but none of the curves shows it on the same day, then on a given day it will be an extreme and therefore not included in any estimate.
“We, therefore, suggest making the summary ‘curve-based’: Instead of assessing which infection rates are probable or unlikely on individual days, we should look at one entire simulation at a time. Is the entire simulated infection curve probable or not? And based on that you can make a summary of the most likely curves for the development of the epidemic, “says Jonas L. Juul.
“By looking at entire prediction curves instead of individual days, you will get a more realistic estimate of how bad the epidemic can become. It is especially useful if you are trying to avoid the hospital system being overloaded,” concludes Sune Lehmann.

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Materials provided by Technical University of Denmark. Original written by Jesper Spangsmark Nielsen. Note: Content may be edited for style and length. More

Researchers in Japan and Italy are embracing chaos and nonlinear physics to create insectlike gaits for tiny robots — complete with a locomotion controller to provide a brain-machine interface.
Biology and physics are permeated by universal phenomena fundamentally grounded in nonlinear physics, and it inspired the researchers’ work.
In the journal Chaos, from AIP Publishing, the group describes using the Rössler system, a system of three nonlinear differential equations, as a building block for central pattern generators (CPGs) to control the gait of a robotic insect.
“The universal nature of underlying phenomena allowed us to demonstrate that locomotion can be achieved via elementary combinations of Rössler systems, which represent a cornerstone in the history of chaotic systems,” said Ludovico Minati, of Tokyo Institute of Technology and the University of Trento.
Phenomena related to synchronization allow the group to create very simple networks that generate complex rhythmic patterns.
“These networks, CPGs, are the basis of legged locomotion everywhere within nature,” he said.

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The researchers started with a minimalistic network in which each instance is associated with one leg. Changing the gait or creating a new one can be accomplished by simply making small changes to the coupling and associated delays.
In other words, irregularity can be added by making individual systems or the entire network more chaotic. For nonlinear systems, a change of output is not proportional to a change of input.
This work shows that the Rössler system, beyond its many interesting and intricate properties, “can also be successfully used as a substrate to construct a bioinspired locomotion controller for an insect robot,” Minati said.
Their controller is built with an electroencephalogram to enable a brain-computer interface.
“Neuroelectrical activity from a person is recorded and nonlinear concepts of phase synchronization are used to extract a pattern,” said Minati. “This pattern is then used as a basis to influence the dynamics of the Rössler systems, which generate the walking pattern for the insect robot.”
The researchers tap into the fundamental ideas of nonlinear dynamics twice.
“First, we use them to decode biological activity, then in the opposite direction to generate bioinspired activity,” he said.
The key implication of this work is that it “demonstrates the generality of nonlinear dynamic concepts such as the ability of the Rössler system, which is often studied in an abstract scenario,” Minati said, “but is used here as a basis to generate biologically plausible patterns.”

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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

New 2D materials have the potential to transform technologies, but they’re expensive and difficult to synthesize. Researchers used computer modeling to predict the properties of 2D materials that haven’t yet been made in real life. These highly-accurate predictions show the possibility of materials whose properties could be ‘tuned’ to make them more efficient than existing materials in particular applications. A separate paper demonstrated a way to integrate these materials into real electronic devices. More

A research team has developed a new range of strain sensors that are 10 times more sensitive when measuring minute movements. These sensors are ultra-thin, battery-free and can transmit data wirelessly, making them attractive for a wide range of applications. More

A research group is defining a new sub-area of mobile technology that they call ‘earable computing.’ The team believes that earphones will be the next significant milestone in wearable devices, and that new hardware, software, and apps will all run on this platform. More

Is it possible to read a person’s mind by analyzing the electric signals from the brain? The answer may be much more complex than most people think.
Purdue University researchers — working at the intersection of artificial intelligence and neuroscience — say a prominent dataset used to try to answer this question is confounded, and therefore many eye-popping findings that were based on this dataset and received high-profile recognition are false after all.
The Purdue team performed extensive tests over more than one year on the dataset, which looked at the brain activity of individuals taking part in a study where they looked at a series of images. Each individual wore a cap with dozens of electrodes while they viewed the images.
The Purdue team’s work is published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The team received funding from the National Science Foundation.
“This measurement technique, known as electroencephalography or EEG, can provide information about brain activity that could, in principle, be used to read minds,” said Jeffrey Mark Siskind, professor of electrical and computer engineering in Purdue’s College of Engineering. “The problem is that they used EEG in a way that the dataset itself was contaminated. The study was conducted without randomizing the order of images, so the researchers were able to tell what image was being seen just by reading the timing and order information contained in EEG, instead of solving the real problem of decoding visual perception from the brain waves.”
The Purdue researchers originally began questioning the dataset when they could not obtain similar outcomes from their own tests. That’s when they started analyzing the previous results and determined that a lack of randomization contaminated the dataset.
“This is one of the challenges of working in cross-disciplinary research areas,” said Hari Bharadwaj, an assistant professor with a joint appointment in Purdue’s College of Engineering and College of Health and Human Sciences. “Important scientific questions often demand cross-disciplinary work. The catch is that, sometimes, researchers trained in one field are not aware of the common pitfalls that can occur when applying their ideas to another. In this case, the prior work seems to have suffered from a disconnect between AI/machine-learning scientists, and pitfalls that are well-known to neuroscientists.”
The Purdue team reviewed publications that used the dataset for tasks such as object classification, transfer learning and generation of images depicting human perception and thought using brain-derived representations measured through electroencephalograms (EEGs)
“The question of whether someone can read another person’s mind through electric brain activity is very valid,” said Ronnie Wilbur, a professor with a joint appointment in Purdue’s College of Health and Human Sciences and College of Liberal Arts. “Our research shows that a better approach is needed.”

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Materials provided by Purdue University. Original written by Chris Adam. Note: Content may be edited for style and length. More