Aurelia Butler

A person who is generous and caring at home may be cutthroat at work, striving to bring in the most sales or advance up a corporate management chain. In a similar vein, a self-centered neighbor may be a model of altruism on Twitter.
It’s a widespread feature of human society: People can adopt different behaviors depending on the social context they’re in. Yet according to a new study by Penn biologists out today in Science Advances, that context-dependent behavior tends to promote the spread of cooperative behavior across a whole society.
Using models rooted in game theory, the researchers show that cooperation is particularly favored when there is room for “spillover” between domains. In other words, a worker can observe how their colleague behaves with her friends when deciding how to interact with that person and others in the workplace.
“We studied groups both small and large,” says Joshua Plotkin, a professor in Penn’s Department of Biology and senior author on the new paper, “and we find that the simple idea of conditioning behavior on the social context, while allowing imitation of behaviors across different contexts — that alone facilitates cooperation in all domains simultaneously.”
That work, along with a related study in Nature Human Behaviour, suggests that the greater the number of domains of social life, the higher the likelihood that cooperative interactions will eventually dominate.
“This shows that the structure of interactions in different aspects of our social lives can galvanize each other — for the benefit of mutual cooperation,” Plotkin says. More

Imagine a small autonomous vehicle that could drive over land, stop, and flatten itself into a quadcopter. The rotors start spinning, and the vehicle flies away. Looking at it more closely, what do you think you would see? What mechanisms have caused it to morph from a land vehicle into a flying quadcopter? You might imagine gears and belts, perhaps a series of tiny servo motors that pulled all its pieces into place.
If this mechanism was designed by a team at Virginia Tech led by Michael Bartlett, assistant professor in mechanical engineering, you would see a new approach for shape changing at the material level. These researchers use rubber, metal, and temperature to morph materials and fix them into place with no motors or pulleys. The team’s work has been published in Science Robotics. Co-authors of the paper include graduate students Dohgyu Hwang and Edward J. Barron III and postdoctoral researcher A. B. M. Tahidul Haque.
Getting into shape
Nature is rich with organisms that change shape to perform different functions. The octopus dramatically reshapes to move, eat, and interact with its environment; humans flex muscles to support loads and hold shape; and plants move to capture sunlight throughout the day. How do you create a material that achieves these functions to enable new types of multifunctional, morphing robots?
“When we started the project, we wanted a material that could do three things: change shape, hold that shape, and then return to the original configuration, and to do this over many cycles,” said Bartlett. “One of the challenges was to create a material that was soft enough to dramatically change shape, yet rigid enough to create adaptable machines that can perform different functions.”
To create a structure that could be morphed, the team turned to kirigami, the Japanese art of making shapes out of paper by cutting. (This method differs from origami, which uses folding.) By observing the strength of those kirigami patterns in rubbers and composites, the team was able to create a material architecture of a repeating geometric pattern. More

The kagome pattern, a network of corner-sharing triangles, is well known amongst traditional Japanese basket weavers — and condensed matter physicists. The unusual geometry of metal atoms in the kagome lattice and resulting electron behaviour makes it a playground for probing weird and wonderful quantum phenomena that form the basis of next-generation device research.
A key example is unconventional — such as high-temperature — superconductivity, which does not follow the conventional laws of superconductivity. Most superconducting materials exhibit their seemingly magical property of zero resistance at a few degrees Kelvin: temperatures that are simply impractical for most applications. Materials that exhibit so-called ‘high-temperature’ superconductivity, at temperatures achievable with liquid nitrogen cooling (or even at room temperature), are a tantalising prospect. Finding and synthesising new materials that exhibit unconventional superconductivity has become the condensed matter physicist’s Holy Grail — but getting there involves a deeper understanding ofexotic, topological electronic behaviour in materials.
An exotic type of electron transport behaviour that results in a spontaneous flow of charge in loops has long been debated as a precursor to high-temperature superconductivity and as a mechanism behind another mysterious phenomenon: the quantum anomalous Hall effect. This topological effect, the subject of F. Duncan M. Haldane’s 2016 Nobel Prize winning work, occurs in certain two-dimensional electronic materials and relates to the generation of a current even in the absence of an applied magnetic field. Understanding the quantum anomalous Hall effect is important not only for fundamental physics, but also for the potential applications in novel electronics and devices. Now, a PSI-led international collaboration has discovered strong evidence supporting this elusive electron transport behaviour.
Time-reversal symmetry-breaking charge ordering in the kagome superconductor KV3Sb5
The team, led by researchers from PSI’s Laboratory for Muon Spin Spectroscopy, discovered weak internal magnetic fields indicative of an exotic charge ordering in a correlated kagome superconductor. These magnetic fields break so-called time-reversal symmetry, a type of symmetry that means that the laws of physics are the same whether you look at a system going forward or backward in time.
A natural explanation of the occurrence of time-reversal symmetry-breaking fields is a novel type of charge order. The charge ordering can be understood as a periodic modulation of the electron density through the lattice and rearrangement of the atoms into a higher-order (superlattice) structure. The team focused their study on the kagome lattice, KV3Sb5, which superconducts below 2.5 Kelvin. Below a higher critical temperature of approximately 80 Kelvin, a giant quantum anomalous Hall effect is observed in the material, which was previously unexplained. The exotic charge ordering appears below this critical temperature of approximately 80 Kelvin, termed the ‘charge ordering temperature’. More

Among many of the Tsimane’ people, who live in a remote region of the Bolivian rainforest, numbers do not play an important role in their lives, and people living in this society vary widely in how high they can count.
A new study from MIT and the University of California at Berkeley has found a relationship between the counting ability of Tsimane’ individuals and their success at matching tasks that involve numbers up to about 25. The researchers found that most subjects could accurately perform tasks that require matching numbers of objects, but only up to the highest number that they could count to.
The results suggest that in order to represent an exact quantity larger than four, people may need to have a word for that number, says Edward Gibson, an MIT professor of brain and cognitive sciences.
“This finding provides the clearest evidence to date that number words play a functional role in people’s ability to represent exact quantities larger than four, and supports the broader claim that language can enable new conceptual abilities,” says Gibson, one of the authors of the new study.
Berkeley postdoc Benjamin Pitt is the lead author of the paper, which appears today in Psychological Science. Steven Piantadosi, an assistant professor of psychology at Berkeley, is the senior author of the study.
Words count
The Tsimane’ are a farming and foraging society of about 13,000 people in the Amazonian rainforest. Most Tsimane’ children start going to school around age 5, but education levels and counting ability vary considerably. The Tsimane’ language has words for numbers up to 100, and words for numbers larger than that are borrowed from Spanish. More

Can you crumple up two sheets of paper the exact same way? Probably not — the very flexibility that lets flexible structures from paper to biopolymers and membranes undergo many types of large deformations makes them notoriously difficult to control. Researchers from the Georgia Institute of Technology, Universiteit van Amsterdam, and Universiteit Leiden have shed new light on this fundamental challenge, demonstrating that new physical theories provide precise predictions of the deformations of certain structures, as recently published in Nature Communications.
In the paper, Michael Czajkowski and D. Zeb Rocklin from Georgia Tech, Corentin Coulais from Universiteit van Amsterdam, and Martin van Hecke of AMOLF and Universiteit Leiden approach a highly studied exotic elastic material, uncover an intuitive geometrical description of the pronounced — or nonlinear — soft deformations, and show how to activate any of these deformations on-demand with minimal inputs. This new theory reveals that a flexible mechanical structure is governed by some of the same math as electromagnetic waves, phase transitions, and even black holes.
“So many other systems struggle with how to be strong and solid in some ways but flexible and compliant in others, from the human body and micro-organisms to clothing and industrial robots,” said Rocklin. “These structures solve that problem in an incredibly elegant way that permits a single folding mechanism to generate a wide family of deformations. We’ve shown that a single folding mode can transform a structure into an infinite family of shapes.”
A Brief History of Metamaterials
Metamaterials rely on the use of hinges, folds, cuts, and “flexible” ingredients to display the variety of counterintuitive physics that has been steadily revealed over the past decade of intense research. Many of these new behaviors have emerged from the development of auxetics, materials that tend to shrink in all directions when they are compressed from any direction rather than bulging outward. Although the researchers’ chosen structure, “Rotating Squares,” is already one of the most heavily researched metamaterials, they uncovered entirely new and powerful physics hiding within its deformations.
“Normally complex real-world structures defy analytical physics, which made it all the more thrilling when Michael found that his conformal predictions could account for 99.9% of the variance in Corentin’s structure,” said Rocklin. “This new approach could allow us to predict and control tough, flexible structures from the size of skyscrapers to the microscale.”
Conformal Findings
The results of this paper rely on the novel observation that these maximally auxetic metamaterials deform conformally, which the researchers confirmed with a high degree of accuracy. This means that any angle drawn on the material before and after deformation will still look like the same angle. This seemingly mundane observation activates powerful mathematical structures.
This conformal insight allows for a variety of pen-and-paper analytic advances: a nonlinear energy functional, deformation fitting methods, new prediction methods etc. This culminates with a recipe to choose any of these conformal deformations in an exact, reversible, and mathematically straightforward manner via the manipulation of the boundary. By choosing how much the boundary is stretched, the overall shape can be picked from infinite possibilities.
Such deformation control is still limited by the essential nature of conformal deformations. However, the underlying principles are quite general, and researchers are working to apply these new principles to more varied and complex structures.
“Our results are very promising for the soft microscopic robotics that are being developed for non-invasive surgical purposes,” said Czajkowski. “In this effort, scalability and precise external control are two of the primary goalposts, and our style of deformation control seems perfectly suited for the job.”
The jump to more provocative applications is likely not far off, as the realm of metamaterials has steadily become populated with manipulatable faces, a variety of new grabbers and hands, and even an elastic worm that can thread a series of needles. These advances will become essential in the effort to develop soft microscopic robots, which must be externally manipulated to move through a body and perform noninvasive surgeries. More

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but they’re often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings, which appear today in Nature Communications, open up new perspectives on the use of AI to help preserve wildlife species.
Building up cross-disciplinary know-how
Wildlife research has gone from local to global. Modern technology now offers revolutionary new ways to produce more accurate estimates of wildlife populations, better understand animal behavior, combat poaching and halt the decline in biodiversity. Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count individual animals, and glean certain information, using large datasets. The generic programs currently used to process such data often work like black boxes and don’t leverage the full scope of existing knowledge about the animal kingdom. What’s more, they’re hard to customize, sometimes suffer from poor quality control, and are potentially subject to ethical issues related to the use of sensitive data. They also contain several biases, especially regional ones; for example, if all the data used to train a given program were collected in Europe, the program might not be suitable for other world regions.
“We wanted to get more researchers interested in this topic and pool their efforts so as to move forward in this emerging field. AI can serve as a key catalyst in wildlife research and environmental protection more broadly,” says Prof. Devis Tuia, the head of EPFL’s Environmental Computational Science and Earth Observation Laboratory and the study’s lead author. If computer scientists want to reduce the margin of error of an AI program that’s been trained to recognize a given species, for example, they need to be able to draw on the knowledge of animal ecologists. These experts can specify which characteristics should be factored into the program, such as whether a species can survive at a given latitude, whether it’s crucial for the survival of another species (such as through a predator-prey relationship) or whether the species’ physiology changes over its lifetime. “We used this approach to improve a bear-recognition program a few years ago,” says Prof. Mackenzie Mathis, a neuroscientist at EPFL and co-author of the study. “A researcher studying bear DNA had installed automatic cameras in bear habitats in order to recognize individual animals. But bears shed half of their body fat when they hibernate, meaning the generic programs she used were no longer able to recognize the bears once the season changed. We therefore added criteria to the program that can not only look at whether an animal has a given characteristic, but also be tweaked manually to allow for possible deviations.”
Getting the word out about existing initiatives
The idea of forging stronger ties between computer vision and ecology came up as Tuia, Mathis and others discussed their research challenges at various conferences over the past two years. They saw that such collaboration could be extremely useful in preventing certain wildlife species from going extinct. A handful of initiatives have already been rolled out in this direction; some of them are listed in the Nature Communications article. For instance, Tuia and his team at EPFL have developed a program that can recognize animal species based on drone images. It was tested recently on a seal population. Meanwhile, Mathis and her colleagues have unveiled an open-source software package called DeepLabCut that allows scientists to estimate and track animal poses with remarkable accuracy. It’s already been downloaded 300,000 times. DeepLabCut was designed for lab animals but can be used for other species as well. Researchers at other universities have developed programs too, but it’s hard for them to share their discoveries since no real community has yet been formed in this area. Other scientists often don’t know these programs exist or which one would be best for their specific research.
That said, initial steps towards such a community have been taken through various online forums. The Nature Communications article aims for a broader audience, however, consisting of researchers from around the world. “A community is steadily taking shape,” says Tuia. “So far we’ve used word of mouth to build up an initial network. We first started two years ago with the people who are now the article’s other lead authors: Benjamin Kellenberger, also at EPFL; Sara Beery at Caltech in the US; and Blair Costelloe at the Max Planck Institute in Germany.”
Story Source:
Materials provided by Ecole Polytechnique Fédérale de Lausanne. Original written by Cécilia Carron. Note: Content may be edited for style and length. More

The word “robot” would probably conjure up images of hard metallic bodies that are invulnerable to attacks. In modern day-to-day life, however, robots are hardly needed for defending against enemy attacks. Instead, they are required to perform more mundane tasks such as handling delicate objects and interacting with humans. Unfortunately, conventional robots perform poorly at such seemingly simple tasks. Moreover, they’re heavy and often noisy.
This is where “soft” robots have the upper hand. Made of materials called “elastomers” (materials with high viscosity and elasticity), soft robots absorb shocks better, can adapt better to their environments, and are safer compared to conventional robots. This has allowed for a broad range of applications, including medicine and surgery, manipulation, and wearable technology. However, many of these soft robots rely on fluidic systems, which still use pumps operated by mechanical parts (motors and bearings). As a result, they are still heavy and noisy.
One way around this problem is to use chemical reactions to drive pumps. But while such systems are definitely lightweight and quiet, they don’t perform as well as conventional pumps. Is there a way to beat this trade-off? Turns out, the answer is yes. A team of researchers from Shibaura Institute of Technology (SIT), Japan, led by Prof. Shingo Maeda, introduced an “electrohydrodynamic” (EHD) pump that uses electrochemical reactions to drive pumps. The EHD pumps have all the advantages of pumps driven by chemical reactions and none of their issues.
Now, in a recent study, the team, including Prof. Maeda, Yu Kawajima, Dr. Yuhei Yamada (all from the Department of Engineering Science and Mechanics, SIT), and Associate Professor Hiroki Shigemune (Department of Electrical Engineering, SIT) has gone one step further, designing a “self-sensing” EHD pump that uses an electrochemical dual transducer (ECDT) to sense the fluid flow, which, in turn, activates electrochemical reactions and increases current. “Self-sensing technology has attracted much attention recently for compactifying soft robots. Incorporating sensors in soft robots enhances their multifunctionality, but often make for complex wiring and bloating. Self-sensing actuation technology can help solve this issue and allow for miniaturization of soft robots,” explains Prof. Maeda. This paper was made available online on 7 January 2022 and was published in Volume 14 Issue 2 of the journal ACS Applied Materials & Interfaces on 19 January 2022.
The team based the ECDT design on the EHD pump they had previously designed. The pump consisted of a symmetrical arrangement of planar electrodes, which allowed an easy control of the flow direction by simply changing the voltage. Moreover, the arrangement enabled an obstruction-free flow and in the same amount in each direction owing to same strength of the electric field on either side.
The team evaluated sensing performance in terms of range of detectable flow, rate, sensitivity, response, and relaxation times, and also used mathematical modeling to understand the sensing mechanism. “The ECDT can easily be integrated into a fluidic system without bloating or complexity,” says Yu Kuwajima, doctoral student at the Smart Materials Laboratory (SIT) and the first author of the study. Additionally, the researchers tested its performance by using it to drive a suction cup to detect, grab, and release objects.
“The advantages of the ECDT are that it does not require any special equipment or complex processing for its fabrication. Moreover, it is small, lightweight, and demonstrates a wide range of sensitivity,” says Prof. Maeda.
However, the ECDT is more than just about soft robot miniaturization. It is a step towards a future in which humans and robots would not simply co-exist but their interaction would become fluid and natural. An exciting prospect to entertain, for sure! More