Computers Math

Cornell researchers used magnetic imaging to obtain the first direct visualization of how electrons flow in a special type of insulator, and by doing so they discovered that the transport current moves through the interior of the material, rather than at the edges, as scientists had long assumed.
The finding provides new insights into the electron behavior in so-called quantum anomalous Hall insulators and should help settle a decades-long debate about how current flows in more general quantum Hall insulators. These insights will inform the development of topological materials for next-generation quantum devices.
The team’s paper, “Direct Visualization of Electronic Transport in a Quantum Anomalous Hall Insulator,” published Aug. 3 in Nature Materials. The lead author is Matt Ferguson, Ph.D. ’22, currently a postdoctoral researcher at the Max Planck Institute for Chemical Physics of Solids in Germany.
The project, led by Katja Nowack, assistant professor of physics in the College of Arts and Sciences and the paper’s senior author, has its origins in what’s known as the quantum Hall effect. First discovered in 1980, this effect results when a magnetic field is applied to a specific material to trigger an unusual phenomena: The interior of the bulk sample becomes an insulator while an electrical current moves in a single direction along the outer edge. The resistances are quantized, or restricted, to a value defined by the fundamental universal constant and drop to zero.
A quantum anomalous Hall insulator, first discovered in 2013, achieves the same effect by using a material that is magnetized. Quantization still occurs and longitudinal resistance vanishes, and the electrons speed along the edge without dissipating energy, somewhat like a superconductor.
At least that is the popular conception.
“The picture where the current flows along the edges can really nicely explain how you get that quantization. But it turns out, it’s not the only picture that can explain quantization,” Nowack said. “This edge picture has really been the dominant one since the spectacular rise of topological insulators starting in the early 2000s. The intricacies of the local voltages and local currents have largely been forgotten. In reality, these can be much more complicated than the edge picture suggests.”
Only a handful of materials are known to be quantum anomalous Hall insulators. For their new work, Nowack’s group focused on chromium-doped bismuth antimony telluride — the same compound in which the quantum anomalous Hall effect was first observed a decade ago. More

In prehistoric societies, humans tended to learn from members of our ingroup or from more prestigious individuals, as this information was more likely to be reliable and result in group success. However, with the advent of diverse and complex modern communities — and especially in social media — these biases become less effective. For example, a person we are connected to online might not necessarily be trustworthy, and people can easily feign prestige on social media. In a review published in the journal Trends in Cognitive Science on August 3rd, a group of social scientists describe how the functions of social media algorithms are misaligned with human social instincts meant to foster cooperation, which can lead to large-scale polarization and misinformation.
“Several user surveys now both on Twitter and Facebook suggest most users are exhausted by the political content they see. A lot of users are unhappy, and there’s a lot of reputational components that Twitter and Facebook must face when it comes to elections and the spread of misinformation,” says first author William Brady, a social psychologist in the Kellogg School of Management at Northwestern.
“We wanted to put out a systematic review that’s trying to help understand how human psychology and algorithms interact in ways that can have these consequences,” says Brady. “One of the things that this review brings to the table is a social learning perspective. As social psychologists, we’re constantly studying how we can learn from others. This framework is fundamentally important if we want to understand how algorithms influence our social interactions.”
Humans are biased to learn from others in a way that typically promotes cooperation and collective problem-solving, which is why they tend to learn more from individuals they perceive as a part of their ingroup and those they perceive to be prestigious. In addition, when learning biases were first evolving, morally and emotionally charged information was important to prioritize, as this information would be more likely to be relevant to enforcing group norms and ensuring collective survival.
In contrast, algorithms are usually selecting information that boosts user engagement in order to increase advertising revenue. This means algorithms amplify the very information humans are biased to learn from, and they can oversaturate social media feeds with what the researchers call Prestigious, Ingroup, Moral, and Emotional (PRIME) information, regardless of the content’s accuracy or representativeness of a group’s opinions. As a result, extreme political content or controversial topics are more likely to be amplified, and if users are not exposed to outside opinions, they might find themselves with a false understanding of the majority opinion of different groups.
“It’s not that the algorithm is designed to disrupt cooperation,” says Brady. “It’s just that its goals are different. And in practice, when you put those functions together, you end up with some of these potentially negative effects.”
To address this problem, the research group first proposes that social media users need to be more aware of how algorithms work and why certain content shows up on their feed. Social media companies don’t typically disclose the full details of how their algorithms select for content, but one start might be offering explainers for why a user is being shown a particular post. For example, is it because the user’s friends are engaging with the content or because the content is generally popular? Outside of social media companies, the research team is developing their own interventions to teach people how to be more conscious consumers of social media.
In addition, the researchers propose that social media companies could take steps to change their algorithms, so they are more effective at fostering community. Instead of solely favoring PRIME information, algorithms could set a limit on how much PRIME information they amplify and prioritize presenting users with a diverse set of content. These changes could continue to amplify engaging information while preventing more polarizing or politically extreme content from becoming overrepresented in feeds.
“As researchers we understand the tension that companies face when it comes to making these changes and their bottom line. That’s why we actually think these changes could theoretically still maintain engagement while also disallowing this overrepresentation of PRIME information,” says Brady. “User experience might actually improve by doing some of this.” More

Electronic devices typically use the charge of electrons, but spin — their other degree of freedom — is starting to be exploited. Spin defects make crystalline materials highly useful for quantum-based devices such as ultrasensitive quantum sensors, quantum memory devices, or systems for simulating the physics of quantum effects. Varying the spin density in semiconductors can lead to new properties in a material — something researchers have long wanted to explore — but this density is usually fleeting and elusive, thus hard to measure and control locally.
Now, a team of researchers at MIT and elsewhere has found a way to tune the spin density in diamond, changing it by a factor of two, by applying an external laser or microwave beam. The finding, reported this week in the journal PNAS, could open up many new possibilities for advanced quantum devices, the authors say. The paper is a collaboration between current and former students of professors Paola Cappellaro and Ju Li at MIT, and collaborators at Politecnico of Milano. The first author of the paper, Guoqing Wang PhD ’23, worked on his PhD thesis in Cappellaro’s lab and is now a postdoc at MIT.
A specific type of spin defect known as a nitrogen vacancy (NV) center in diamond is one of the most widely studied systems for its potential use in a wide variety of quantum applications. The spin of NV centers is sensitive to any physical, electrical, or optical disturbance, making them potentially highly sensitive detectors. “Solid-state spin defects are one of the most promising quantum platforms,” Wang says, partly because they can work under ambient, room-temperature conditions. Many other quantum systems require ultracold or other specialized environments.
“The nanoscale sensing capabilities of NV centers makes them promising for probing the dynamics in their spin environment, manifesting rich quantum many body physics yet to be understood,” Wang adds. “A major spin defect in the environment, called P1 center, can usually be 10 to 100 times more populous than the NV center and thus can have stronger interactions, making them ideal for studying many-body physics.”
But to tune their interactions, scientists need to be able to change the spin density, something that had previously seldom been achieved. With this new approach, Wang says, “We can tune the spin density so it provides a potential knob to actually tune such a system. That’s the key novelty of our work.”
Such a tunable system could provide more flexible ways of studying the quantum hydrodynamics, Wang says. More immediately, the new process can be applied to some existing nanoscale quantum-sensing devices as a way to improve their sensitivity.
Li, who holds a joint appointment in MIT’s departments of Nuclear Science and Engineering and Materials Science and Engineering, explains that today’s computers and information processing systems are all based on the control and detection of electrical charges, but some innovative devices are beginning to make use of the property called spin. The semiconductor company Intel, for example, has been experimenting with new kinds of transistors that couple spin and charge, potentially opening a path to devices based on spintronics. More

Researchers have found that robots can have a ‘U-shaped’ effect on profits: causing profit margins to fall at first, before eventually rising again.
The researchers, from the University of Cambridge, studied industry data from the UK and 24 other European countries between 1995 and 2017, and found that at low levels of adoption, robots have a negative effect on profit margins. But at higher levels of adoption, robots can help increase profits.
According to the researchers, this U-shaped phenomenon is due to the relationship between reducing costs, developing new processes and innovating new products. While many companies first adopt robotic technologies to decrease costs, this ‘process innovation’ can be easily copied by competitors, so at low levels of robot adoption, companies are focused on their competitors rather than on developing new products. However, as levels of adoption increase and robots are fully integrated into a company’s processes, the technologies can be used to increase revenue by innovating new products.
In other words, firms using robots are likely to focus initially on streamlining their processes before shifting their emphasis to product innovation, which gives them greater market power via the ability to differentiate from their competitors. The results are reported in the journal IEEE Transactions on Engineering Management.
Robots have been widely used in industry since the 1980s, especially in sectors where they can carry out physically demanding, repetitive tasks, such as automotive assembly. In the decades since, the rate of robot adoption has increased dramatically and consistently worldwide, and the development of precise, electrically controlled robots makes them particularly useful for high-value manufacturing applications requiring greater precision, such as electronics.
While robots have been shown to reliably raise labour productivity at an industry or country level, what has been less studied is how robots affect profit margins at a similar macro scale.
“If you look at how the introduction of computers affected productivity, you actually see a slowdown in productivity growth in the 1970s and early 1980s, before productivity starts to rise again, which it did until the financial crisis of 2008,” said co-author Professor Chander Velu from Cambridge’s Institute for Manufacturing. “It’s interesting that a tool meant to increase productivity had the opposite effect, at least at first. We wanted to know whether there is a similar pattern with robotics.”
“We wanted to know whether companies were using robots to improve processes within the firm, rather than improve the whole business model,” said co-author Dr Philip Chen. “Profit margin can be a useful way to analyse this.” More

Rice University physicists have shown that immutable topological states, which are highly sought for quantum computing, can be entangled with other, manipulable quantum states in some materials.
“The surprising thing we found is that in a particular kind of crystal lattice, where electrons become stuck, the strongly coupled behavior of electrons in d atomic orbitals actually act like the f orbital systems of some heavy fermions,” said Qimiao Si, co-author of a study about the research in Science Advances.
The unexpected find provides a bridge between subfields of condensed matter physics that have focused on dissimilar emergent properties of quantum materials. In topological materials, for example, patterns of quantum entanglement produce “protected,” immutable states that could be used for quantum computing and spintronics. In strongly correlated materials, the entanglement of billions upon billions of electrons gives rise to behaviors like unconventional superconductivity and the continual magnetic fluctuations in quantum spin liquids.
In the study, Si and co-author Haoyu Hu, a former graduate student in his research group, built and tested a quantum model to explore electron coupling in a “frustrated” lattice arrangement like those found in metals and semimetals that feature “flat bands,” states where electrons become stuck and strongly correlated effects are amplified.
The research is part of an ongoing effort by Si, who won a  Vannevar Bush Faculty Fellowship from the Defense Department in July to pursue the validation of a theoretical framework for controlling topological states of matter.
In the study, Si and Hu showed that electrons from d atomic orbitals could become part of larger, molecular orbitals that are shared by several atoms in the lattice. The research also showed that electrons in molecular orbitals could become entangled with other frustrated electrons, producing strongly correlated effects that were very familiar to Si, who has spent years studying heavy fermion materials.
“These are completely d-electron systems,” Si said. “In the d-electron world, it’s like you have a highway with multiple lanes. In the f-electron world, you can think of electrons moving in two tiers. One is like the d-electron highway, and the other is like a dirt road, where movement is very slow.”
Si said f-electron systems host very clean examples of strongly correlated physics, but they aren’t practical for everyday use. More

Researchers at North Carolina State University have developed a robotic gripping device that is gentle enough to pick up a drop of water, strong enough to pick up a 6.4 kilogram (14.1 pound) weight, dexterous enough to fold a cloth, and precise enough to pick up microfilms that are 20 times thinner than a human hair. In addition to possible manufacturing applications, the researchers also integrated the device with technology that allows the gripper to be controlled by the electrical signals produced by muscles in the forearm, demonstrating its potential for use with robotic prosthetics.
“It is difficult to develop a single, soft gripper that is capable of handling ultrasoft, ultrathin, and heavy objects, due to tradeoffs between strength, precision and gentleness,” says Jie Yin, corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at NC State. “Our design achieves an excellent balance of these characteristics.”
The design for the new grippers builds on an earlier generation of flexible, robotic grippers that drew on the art of kirigami, which involves both cutting and folding two-dimensional sheets of material to form three-dimensional shapes.
“Our new grippers also use kirigami, but are substantially different, as we learned a great deal from the previous design,” says Yaoye Hong, co-author of the paper and a recent Ph.D. graduate from NC State. “We’ve been able to improve the fundamental structure itself, as well as the trajectory of the grippers — meaning the path at which the grippers approach an object when grabbing it.”
The new design is able to achieve high degrees of strength and gentleness because of how it distributes force throughout the structure of the gripper.
“The strength of robotic grippers is generally measured in payload-to-weight ratio,” Yin says. “Our grippers weigh 0.4 grams and can lift up to 6.4 kilograms. That’s a payload-to-weight ratio of about 16,000. That is 2.5 times higher than the previous record for payload-to-weight ratio, which was 6,400. Combined with its characteristics of gentleness and precision, the strength of the grippers suggests a wide variety of applications.”
Another benefit of the new technology is that its attractive characteristics are driven primarily by its structural design, rather than by the materials used to fabricate the grippers. More

Anime, the Japanese art of animation, comprises hand-drawn sketches in an abstract form with unique characteristics and exaggerations of real-life subjects. While generative artificial intelligence (AI) has found use in the content creation such as anime portraits, its use to augment human creativity, and guide freehand drawings proves challenging. The primary challenge lies with the generation of suitable reference images corresponding with the incomplete and abstract strokes made during the freehand drawing process. This is particularly true when the strokes created during the drawing process are incomplete and offer insufficient information for generative AI to predict the final shape of the drawing.
To tackle this problem, a research team from Japan Advanced Institute of Science and Technology (JAIST) and Waseda University in Japan, sought to develop a novel generative AI tool that offers progressive drawing assistance and helps generate anime portraits from freehand sketches. The tool is based on a sketch-to-image (S2I) deep learning framework that matches raw sketches with latent vectors of the generative model. It employs a two-stage training strategy through the pre-trained Style Generative Adversarial Network (StyleGAN) — a state-of-the-art generative model that uses adversarial networks to generate new images.
The team, led by Dr. Zhengyu Huang from JAIST, including Associate Professor Haoran Xie and Professor Kazunori Miyata, and Lecturer Tsukasa Fukusato from Waseda University proposed a novel “stroke-level disentanglement,” a strategy that associates input strokes of a freehand sketch with edge-related attributes, in the latent structural code of StyleGAN. This approach allows users to manipulate the attribute parameters, thereby having greater autonomy over the properties of generated images. Dr. Huang says, “We introduced an unsupervised training strategy for stroke-level disentanglement in StyleGAN, which enables the automatic matching of rough sketches with sparse strokes to the corresponding local parts in anime portraits, all without the need for semantic labels.”
This study will be presented at ACM SIGGRAPH 2023, the premier conference for computer graphics and interactive techniques and the only CORE ranking A* conference in the research fields worldwide.
Regarding the development of the tool, Prof. Xie adds, “We first trained an image encoder using a pre-trained StyleGAN model as a teacher encoder. In the second stage, we simulated the drawing process of generated images without additional data to train the sketch encoder for incomplete progressive sketches. This helped us generate high-quality portrait images that align with the disentangled representations of teacher encoder.”
To further highlight the effectiveness and usability of AniFaceDrawing in aiding users with anime portrait creation, the team conducted a user study. They invited 15 graduate students to draw digital freehand anime-style portraits using the AniFaceDrawing tool, with the option to switch between rough and detailed guidance modes for line art. While the former provided prompts for specific facial parts, the latter provided prompts for the full-face portrait based on the user’s drawing progress. Participants could pin the generated guidance once it matched their expectations, and further refine their input sketch. This tool also allowed participants to select a reference image to generate a color portrait of their input sketch. Next, they evaluated the tool for user satisfaction and guidance matching through a survey.
The team noted that the system consistently provided high-quality facial guidance and effectively supported the creation of anime-style portraits, by not only enhancing user sketches, but also by generating desirable corresponding colored images. Prof. Fukusato remarks, “Our system could successfully transform the user’s rough sketches into high-quality anime portraits. The user study indicated that even novices could make reasonable sketches with the help of the system and end up with high-quality color art drawings.”
“Our generative AI framework enables users, regardless of their skill level and experience, to create professional anime portraits even from incomplete drawings. Our approach consistently produces high-quality image generation results throughout the creation process, regardless of the drawing order or how poor the initial sketches are,” summarizes Prof. Miyata.
In the long run, these findings can help democratize AI technology and assist users with creative tasks, thereby augmenting their creative capacity without technological barriers. More

Engineers are constantly searching for materials with novel, desirable property combinations. For example, an ultra-strong, lightweight material could be used to make airplanes and cars more fuel-efficient, or a material that is porous and biomechanically friendly could be useful for bone implants.
Cellular metamaterials — artificial structures composed of units, or cells, that repeat in various patterns — can help achieve these goals. But it is difficult to know which cellular structure will lead to the desired properties. Even if one focuses on structures made of smaller building blocks like interconnected beams or thin plates, there are an infinite number of possible arrangements to consider. So, engineers can manually explore only a small fraction of all the cellular metamaterials that are hypothetically possible.
Researchers from MIT and the Institute of Science and Technology Austria have developed a computational technique that makes it easier for a user to quickly design a metamaterial cell from any of those smaller building blocks, and then evaluate the resulting metamaterial’s properties.
Their approach, like a specialized CAD (computer-aided design) system for metamaterials, allows an engineer to quickly model even very complex metamaterials and experiment with designs that may have otherwise taken days to develop. The user-friendly interface also enables the user to explore the entire space of potential metamaterial shapes, since all building blocks are at their disposal.
“We came up with a representation that can cover all of the different shapes engineers have traditionally shown interest in. Because you can build them all the same way, that means you can switch between them more fluidly,” says MIT electrical engineering and computer science graduate student Liane Makatura, co-lead author of a paper on this technique.
Makatura wrote the paper with co-lead author Bohan Wang, an MIT postdoc; Yi-Lu Chen, a graduate student at the Institute of Science and Technology Austria (ISTA); Bolei Deng, an MIT postdoc; Chris Wojtan and Bernd Bickel, professors at ISTA; and senior author Wojciech Matusik, a professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group within the MIT Computer Science and Artificial Intelligence Laboratory. The research will be presented at SIGGRAPH.
A unified method
When a scientist develops a cellular metamaterial, she typically begins by choosing a representation that will be used to describe her potential designs. This choice determines the set of shapes that will be available for exploration. More