Aurelia Butler

Thanks to their visual simplicity, bar graphs are popular tools for representing data. But do we really understand how to read them? New research from Wellesley College published in the Journal of Vision has found that bar graphs are frequently misunderstood. The study demonstrates that people who view exactly the same graph often walk away with completely different understandings of the facts it represents.
“Our work reveals that bar graphs are not the clear communication tools many had supposed,” said Sarah H. Kerns, a 2019 graduate of Wellesley, research associate in its psychology department, and first author of the paper, entitled “Two graphs walk into a bar: Readout-based measurement reveals the Bar-Tip Limit error, a common, categorical misinterpretation of mean bar graphs.”
“Bar graphs that depict mean values are ubiquitous in politics, science, education, and government, and they are used to convey data over a wide range of topics including climate change, public health, and the economy,” said co-author Jeremy Wilmer, associate professor of psychology at Wellesley. “A lack of clarity in domains such as these could have far-reaching negative impacts on public discourse.”
Kerns and Wilmer’s revelation about bar graphs was made possible by a powerful new measurement technique that they developed. This technique relies upon having a person draw, on paper, their interpretation of the graph. “Drawing tasks are particularly effective at capturing visuospatial thinking in a way that is concrete, expressive, and detailed,” said Kerns. “Drawings have long been used in psychology as a way to reveal the contents of one’s thoughts, but they have not previously been used to study graph interpretation.”
The research team asked hundreds of people to show where they believed the data underlying a bar graph would be by drawing dots on the graphs themselves. A striking pattern emerged. About one in five graph readers categorically misinterpreted bar graphs that depicted averages. “These readers sketched all, or nearly all, of the data points below the average,” said Wilmer. “The average is the balanced center point of the data. It is impossible for the bulk of the data to be below-average. We call this mistake the bar-tip limit error, because the viewer has misinterpreted the bar’s tip as the outer limit of the data.” The error was equally prevalent across ages, genders, education levels, and nationalities.
Given the severity of this error, how could decades of graph interpretation research have missed it? “Previous research typically asked rather abstract, indirect questions: about predictions, probabilities, and payoffs,” said Kerns. “It is difficult to read a person’s thoughts from their answers to such questions. It is like looking through frosted glass — one may gain a vague sense of what is there, but it lacks definition. Our measurement approach is more concrete, more direct, more detailed. The drawings provide a clear window into the graph interpreter’s thinking.”
“A major lesson from this work is that simplification in graph design can yield more confusion than clarification,” said Wilmer. “The whole point of replacing individual values with a summary statistic like an average, is to simplify the visual display and make it easier to read. But this simplification misleads many viewers, and not only about the location of the individual data points that have been removed — it misleads them also about the average, which is the one thing the graph actually depicts.”
The team suggests some changes in data visualization practices based on their findings. First, they recommend that a bar be used only to convey a single number, such as a count (150 hospital beds) or quantity ($5.75): “In that case, no data is hidden,” said Kerns. “In contrast, our research shows that a bar used to depict the average of multiple numbers risks severe confusion.” Their second recommendation is to think twice before replacing concrete, detailed information (e.g., individual data points) with visually simpler yet conceptually more abstract information (e.g., an average value). “Our work provides a case-in-point that abstraction in data communication risks serious misunderstanding,” said Wilmer.
The team’s education-focused recommendations include the use of data sketching tasks to teach data literacy. “Once a student’s interpretation is made explicit and visible on paper, it is easy to discuss and, if necessary, correct,” Wilmer said. They also suggest having students work with real data. “Data is fundamentally concrete,” Kerns said. “There is value to reading about it in the abstract, but that will always be a bit like reading a book to learn how to ride a bike. There is no substitute for hands-on experience.”
Collection, visualization, and analysis of data now form a centerpiece of all of Wilmer’s courses. An enabling tool in this effort is a free-access suite of data visualization web apps he created at ShowMyData.org, which allow the user, in a matter of seconds, to build and curate attractive, high-quality graphs with individual datapoints. “Such graphs avoid the sorts of errors that our research reveals,” says Kerns. “And they are easily interpreted, even by young children,” adds Wilmer, whose children, aged 11 and 7, are “two of my most astute (and ruthless) app development and data communication consultants.”
In a political and scientific milieu where information spreads fast, and where misunderstanding can have a profound impact on popular opinion and public policy, clear data communication and robust data literacy are increasingly important. “From the grocery store to the doctors office to the ballot box, data informs our decisions,” Kerns said. “We hope our work will help to enhance data comprehension and smooth the path to informed decision-making by institutions and individuals alike.” More

People are more comfortable talking to female rather than male robots working in service roles in hotels, according to a study by Washington State University researcher Soobin Seo.
The study, which surveyed about 170 people on hypothetical service robot scenarios, also found that the preference was stronger when the robots were described as having more human features. The findings are detailed in a paper published online in the International Journal of Hospitality Management.
“People have a tendency to feel more comfort in being cared for by females because of existing gender stereotyping about service roles,” said Seo, an assistant professor of hospitality management at WSU’s Carson Business College in Everett. “That gender stereotype appears to transfer to robot interactions, and it is more amplified when the robots are more human like.”
Even before the pandemic, the hotel industry struggled with high turnover of employees, and Seo noted that some hotels have turned to robots and automation for a variety of functions from dishwashing and room cleaning to customer service such as greeting guests and delivering luggage.
Examples range from the female humanized robots named “Pepper” at the Mandarin Oriental Hotel in Las Vegas to the fully automated FlyZoo hotel chain in China where guests interact only with robots and artificial intelligence (AI) features.
For the study, survey participants were presented with one of four scenarios about interacting with an AI service robot at a hotel. In one scenario, they were greeted by a male service robot named “Alex” who was described as having a face and human-like body. A second scenario was worded exactly the same with just two changes: the robot’s gender was female, and its name was “Sara.” In two other scenarios, the robots were both gendered and named differently but described as “machine-like’ with an interactive screen instead of a face.
The respondents were then asked to rank how they felt about the interactions. The participants who were presented with the female robot scenarios rated the experience as more pleasant and satisfying than those who had scenarios with male robots. The preference for the female robot was more pronounced when the robots were described as looking more human.
Seo cautioned that replacing human hospitality workers with AI robots of any gender raises many issues that need further research. For instance, if a robot breaks down or fails in service in some way, such as losing luggage or getting a reservation wrong, customers may want a human employee to help them.
The WSU business researcher is also in the process of investigating how the personality of AI robots may impact customers’ perceptions, such as if they are extroverted and talkative or introverted and quiet.
These are important considerations for AI robot developers as well as for hospitality employers to consider as they think about adopting robots more widely, Seo said.
“We may start to see more robots as replacements of human employees in hotels and restaurants in the future, so we may find that some of the psychological relationships that we see in human-to-human interaction also implemented in robot interactions,” she said.
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Materials provided by Washington State University. Original written by Sara Zaske. Note: Content may be edited for style and length. More

Quantum science holds promise for many technological applications, such as building hackerproof communication networks or quantum computers that could accelerate new drug discovery. These applications require a quantum version of a computer bit, known as a qubit, that stores quantum information.
But researchers are still grappling with how to easily read the information held in these qubits and struggle with the short memory time, or coherence, of qubits, which is usually limited to microseconds or milliseconds.
A team of researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago have achieved two major breakthroughs to overcome these common challenges for quantum systems. They were able to read out their qubit on demand and then keep the quantum state intact for over five seconds — a new record for this class of devices. Additionally, the researchers’ qubits are made from an easy-to-use material called silicon carbide, which is widely found in lightbulbs, electric vehicles and high-voltage electronics.
“It’s uncommon to have quantum information preserved on these human timescales,” said David Awschalom, senior scientist at Argonne National Laboratory, director of the Q-NEXT quantum research center, Liew Family Professor in Molecular Engineering and Physics at the University of Chicago, and principal investigator of the project. “Five seconds is long enough to send a light speed signal to the moon and back. That’s powerful if you’re thinking about transmitting information from a qubit to someone via light. That light will still correctly reflect the qubit state even after it has circled the Earth almost 40 times — paving the way to make a distributed quantum internet.”
By creating a qubit system that can be made in common electronics, the researchers hope to open a new avenue for quantum innovation using a technology that is both scalable and cost-effective.
“This essentially brings silicon carbide to the forefront as a quantum communication platform,” said University of Chicago graduate student Elena Glen, co-first author on the paper. “This is exciting because it’s easy to scale up, since we already know how to make useful devices with this material.”
The findings were published on Feb. 2 in the journal Science Advances. More

A new drive system for flapping wing autonomous robots has been developed by a University of Bristol team, using a new method of electromechanical zipping that does away with the need for conventional motors and gears.
This new advance, published today in the journal Science Robotics, could pave the way for smaller, lighter and more effective micro flying robots for environmental monitoring, search and rescue, and deployment in hazardous environments.
Until now, typical micro flying robots have used motors, gears and other complex transmission systems to achieve the up-and-down motion of the wings. This has added complexity, weight and undesired dynamic effects.
Taking inspiration from bees and other flying insects, researchers from Bristol’s Faculty of Engineering, led by Professor of Robotics Jonathan Rossiter, have successfully demonstrated a direct-drive artificial muscle system, called the Liquid-amplified Zipping Actuator (LAZA), that achieves wing motion using no rotating parts or gears.
The LAZA system greatly simplifies the flapping mechanism, enabling future miniaturization of flapping robots down to the size of insects.
In the paper, the team show how a pair of LAZA-powered flapping wings can provide more power compared with insect muscle of the same weight, enough to fly a robot across a room at 18 body lengths per second.
They also demonstrated how the LAZA can deliver consistent flapping over more than one million cycles, important for making flapping robots that can undertake long-haul flights.
The team expect the LAZA to be adopted as a fundamental building block for a range of autonomous insect-like flying robots.
Dr Tim Helps, lead author and developer of the LAZA system said “With the LAZA, we apply electrostatic forces directly on the wing, rather than through a complex, inefficient transmission system. This leads to better performance, simpler design, and will unlock a new class of low-cost, lightweight flapping micro-air vehicles for future applications, like autonomous inspection of off-shore wind turbines.”
Professor Rossiter added: “Making smaller and better performing flapping wing micro robots is a huge challenge. LAZA is an important step toward autonomous flying robots that could be as small as insects and perform environmentally critical tasks such as plant pollination and exciting emerging roles such as finding people in collapsed buildings.”
Video: https://youtu.be/2QWoAXX9FWI
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Materials provided by University of Bristol. Note: Content may be edited for style and length. More

Bubbles aren’t just for bath time. Bubbles, specifically bubbles in foamy flows, are critical for many industrial processes, including the production of food and cosmetics and drug development and delivery. But the behavior of these foamy flows is notoriously difficult to compute because of the sheer number of bubbles involved.
Previous attempts to simulate foamy flows have relied on the time-consuming and computationally expensive process of tracking the bubbles by color-coating each individual bubble in the foam. This limited simulations to just a few dozen bubbles, instead of the thousands to millions in real foams.
Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a new way to simulate tens of thousands of bubbles in foamy flows, breaking the computational complexity of this long-standing process.
The research is published in Science Advances.
“This new method allows us for the first time to study foams with many bubbles, opening the door for simulating a wide variety of flows from the micro to the macroscale, including wet foams, turbulent flows with bubbles, suspensions and emulsions in microfluidics,” said Petros Koumoutsakos, the Herbert S. Winokur, Jr. Professor of Engineering and Applied Sciences at SEAS and senior author of the study.
Instead of color-coating each individual bubble, the researchers broke the foam down into a grid, with each cell of the grid containing at most a part of four bubbles. Each bubble inside the cell is color-coated, either yellow, green, blue or red.
“If I have four partial bubbles inside a cell, then the remaining piece of the bubbles have to be in the neighboring cells,” said Petr Karnakov, a graduate student at SEAS and first author of the paper. “We developed an algorithm that can go into other cells and find the remaining pieces of the bubble, matching green to green, blue to blue, etc. So, instead of needing millions of colors, you just need four.”
This capability allows for predictive simulations in scales ranging from microfluidics to crashing waves. “Our new approach allows for large-scale predictive simulations of flows with multiple interfaces,” said Sergey Litvinov, a postdoctoral fellow at ETH Zurich.
The difference between all previous approaches and the new approach developed by Koumoutsakos, Karnakov and Litvinov can be compared to the difference between a painting and a puzzle. A painting is painstakingly created stroke by stroke, while a puzzle relies on geometry and matching colors.
Next, the researchers aim to collaborate with experimentalists and industrial partners to see how the method can be applied in the medical field and the food industry as well as for membrane-less electrolysis for energy applications.
The research was funded by the Swiss National Science Foundation under grant CRSII5_17386. More

A team of researchers from the University of Massachusetts Amherst recently announced in the Proceedings of the National Academy of Sciences that they had engineered a new rubber-like solid substance that has surprising qualities. It can absorb and release very large quantities of energy. And it is programmable. Taken together, this new material holds great promise for a very wide array of applications, from enabling robots to have more power without using additional energy, to new helmets and protective materials that can dissipate energy much more quickly.
“Imagine a rubber band,” says Alfred Crosby, professor of polymer science and engineering at UMass Amherst and the paper’s senior author. “You pull it back, and when you let it go, it flies across the room. Now imagine a super rubber band. When you stretch it past a certain point, you activate extra energy stored in the material. When you let this rubber band go, it flies for a mile.”
This hypothetical rubber band is made out of a new metamaterial — a substance engineered to have a property not found in naturally occurring materials — that combines an elastic, rubber-like substance with tiny magnets embedded in it. This new “elasto-magnetic” material takes advantage of a physical property known as a phase shift to greatly amplify the amount of energy the material can release or absorb.
A phase shift occurs when a material moves from one state to another: think of water turning into steam or liquid concrete hardening into a sidewalk. Whenever a material shifts its phase, energy is either released or absorbed. And phase shifts aren’t just limited to changes between liquid, solid and gaseous states — a shift can occur from one solid phase to another. A phase shift that releases energy can be harnessed as a power source, but getting enough energy has always been the difficult part.
“To amplify energy release or absorption, you have to engineer a new structure at the molecular or even atomic level,” says Crosby. However, this is challenging to do and even more difficult to do in a predictable way. But by using metamaterials, Crosby says that “we have overcome these challenges, and have not only made new materials, but also developed the design algorithms that allow these materials to be programmed with specific responses, making them predictable.”
The team has been inspired by some of the lightning-quick responses seen in nature: the snapping-shut of Venus flytraps and trap-jaw ants. “We’ve taken this to the next level,” says Xudong Liang, the paper’s lead author, currently a professor at Harbin Institute of Technology, Shenzhen (HITSZ) in China who completed this research while a postdoc at UMass Amherst. “By embedding tiny magnets into the elastic material, we can control the phase transitions of this metamaterial. And because the phase shift is predictable and repeatable, we can engineer the metamaterial to do exactly what we want it to do: either absorbing the energy from a large impact, or releasing great quantities of energy for explosive movement.”
This research, which was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office as well as Harbin Institute of Technology, Shenzhen (HITSZ), has applications in any scenario where either high-force impacts or lightning-quick responses are needed.
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Data is not only the answer to numerous questions in the business world; the same applies to biomedical research. In order to develop new therapies or prevention strategies for diseases, scientists need more and better data, faster and faster. However, the quality is often very variable and the integration of different data sets often almost impossible. With the Computational Health Center at Helmholtz Munich, one of Europe’s largest research centers for artificial intelligence in medical science is now being established under the direction of Fabian Theis. In close cooperation with the Technical University of Munich (TUM), more than one hundred scientists are using artificial intelligence and machine learning to discover solutions to precisely these problems, thus enabling medical innovations for a healthier society. In the latest issue of the journal Nature Methods, they present three articles with groundbreaking new solutions.
According to Fabian Theis, Head of the Computational Health Center at Helmholtz Munich and Professor for Mathematical Modelling of Biological Systems at TUM: “It’s been a crazy 4 weeks, with many of our scientific stories and methods coming to fruition in that same time window. Our research groups focuses on using single cell genomics to understand the origin of disease in a mechanistic fashion — for this we leverage and develop machine learning approaches to better represent this complex data. In the three new paper, we worked on single cell data integration, trajectory learning and spatial resolution, respectively. Besides the applications shown in the papers, we expect to support the next generation of single-cell research towards disease understanding.”
Here are the latest solutions developed by Helmholtz Munich and TUM researchers:
Solving the data integration challenge
To see whether an observation one makes in a single dataset can be generalized, you can check whether the same can be observed in other datasets of the same system. In single-cell data, so-called batch effects complicate combining datasets in this manner. These are differences in the molecular profiles between samples as they were generated at a different time, in a different place, or from a different person. Overcoming these effects is a central challenge in single-cell genomics with more than 50 proposed solutions. But which one is the best? A group of researchers around Malte Lücken carefully curated 86 datasets and compared 16 of the most popular data integration methods on 13 tasks. After over 55,000 hours of computation time and a detailed evaluation of 590 results, they built a guide for optimized data integration. This allows for improved observations on disease processes across datasets at a population scale.
Predicting cell states with open-source software
Many questions in biology revolve around continuous processes like development or regeneration. For any cell in such a process, single-cell RNA-sequencing measures gene expression. The method, however, is destructive to cells and scientists obtain only static snapshots. Thus, many algorithms have been developed to reconstruct continuous processes from snapshots of gene expression. A common limitation: These algorithms cannot tell us anything about the direction of the process. To overcome this limitation, Marius Lange and colleagues developed a new algorithm called CellRank. It estimates directed cell-state trajectories by combining previous reconstruction approaches with RNA velocity, a concept to estimate gene up- or down-regulation. Across in-vitro and in-vivo applications, CellRank correctly inferred fate outcomes and recovered previously known genes. In a lung regeneration example, CellRank predicted novel intermediate cell states on a dedifferentiation trajectory whose existence was validated experimentally. CellRank is an open-source software package that is already used by biologists and bioinformaticians around the world to analyze complex cellular dynamics in situations like cancer, reprogramming or regeneration.
Visualizing spatial omics analysis
Recent years have seen a growing development of technologies to measure gene expression variation in tissue. The advantage of such technologies is that scientists can see cells in their context, thus being able to investigate principles of tissue organization and cellular communication. Researchers need flexible computational frameworks in order to store, integrate and visualize the growing diversity of such data. To tackle this challenge, Giovanni Palla, Hannah Spitzer, and colleagues developed a new computational framework, called Squidpy. It enables analysts and developers to handle spatial gene expression data. Squidpy integrates tools for gene expression and image analysis to efficiently manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of machine learning tools in the python ecosystem. Scientists around the world are already using it to analyze spatial molecular data. More

In a closed physical system, the sum of all angular momentum remains constant — says an important physical law of conservation. Angular momentum does not necessarily need to involve “real” bodily rotation in this context: Magnetic materials even have angular momentum when, seen from outside, they are at rest. Physicists Albert Einstein and Wander Johannes de Haas were able to prove that already in 1915.
If such a magnetized material is now bombarded with short pulses of laser light, it loses its magnetic order extremely quickly. Within femtoseconds — a millionth of a billionth second — it becomes demagnetized. The electrons’ angular momentum in the material — also called spin — thus decreases abruptly, much faster than the material can set itself in rotation. According to the conservation principle, however, the angular momentum cannot simply be lost. So, where is the spin angular momentum transferred to in such an extremely short time?
The solution to the puzzle was now published in the scientific journal Nature. In the study, a team led by Konstanz researchers investigated the demagnetization of nickel crystals using ultrafast electron diffraction — a highly precise measuring method in terms of time and space that can make the course of structural changes visible at the atomic level. They were able to show that the electrons of the crystal transfer their angular momentum to the atoms of the crystal lattice within a few hundred femtoseconds during demagnetization. Much like the passengers on a merry-go-round, the atoms are set in motion on tiny circuits and thus balance the angular momentum. It is only much later and more slowly that the macroscopic rotation effect named after Einstein and de Haas begins, which can be measured mechanically. These findings show new ways of controlling angular momentum extremely quickly, opening up new possibilities for improving magnetic information technologies or new research directions in spintronics.
Magnetism in metallic solids
Magnetic phenomena have become an indispensable part of modern technology. They play an important role especially in information processing and data storage. “The speed and efficiency of existing technologies is often limited by the comparatively long duration of magnetic switching processes,” explains Professor Peter Baum, experimental physicist at the University of Konstanz and one of the heads of the study. All the more interesting for materials research, therefore, is a surprising phenomenon that can be observed in nickel, among other things: ultrafast demagnetization caused by bombardment with laser pulses.
Just like iron, nickel physically belongs to the ferromagnetic materials. Permanent magnets as we know them from our everyday lives can be made from these materials, for example refrigerator magnets. The permanent magnetization results from a parallel arrangement of the magnetic moments of neighbouring particles of the material. “To illustrate this, we can imagine the magnetic moments as small arrows that all point in the same direction,” explains Professor Ulrich Nowak, theoretical physicist at the University of Konstanz and also one of the project leaders. Physically, the angular momentum or spin of the electrons of the ferromagnetic material mainly is the cause for these “arrows” and their direction. More