Aurelia Butler

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

A collaboration of nuclear theorists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Argonne National Laboratory, Temple University, Adam Mickiewicz University of Poland, and the University of Bonn, Germany, has used supercomputers to predict the spatial distributions of charges, momentum, and other properties of “up” and “down” quarks within protons. The results, just published in Physical Review D, revealed key differences in the characteristics of the up and down quarks.
“This work is the first to leverage a new theoretical approach to obtain a high-resolution map of quarks within a proton,” said Swagato Mukherjee of Brookhaven Lab’s nuclear theory group and a coauthor on the paper. “Our calculations show that the up quark is more symmetrically distributed and spread over a smaller distance than the down quark. These differences imply that up and down quarks may make different contributions to the fundamental properties and structure of the proton, including its internal energy and spin.”
Coauthor Martha Constantinou of Temple University noted, “Our calculations provide input for interpreting data from nuclear physics experiments exploring how quarks and the gluons that hold them together are distributed within the proton, giving rise to the proton’s overall properties.”
Such experiments are already taking place at the Continuous Electron Beam Accelerator Facility (CEBAF), a DOE Office of Science user facility at Thomas Jefferson National Accelerator Facility. Higher resolution versions are planned for the future Electron-Ion Collider (EIC) at Brookhaven Lab. In these experiments, high-energy electrons emit virtual particles of light that scatter off and change the overall momentum of a proton without breaking it apart. The way the momentum of the proton changes in response to these scatterings reveals details about the quarks and gluons — the inner components of the proton — sort of like an x-ray imaging technique for the building blocks of bulk matter.
New theoretical approach to GPD
Specifically, the scatterings give scientists access to the Generalized Parton Distribution (GPD) of the proton — parton being the collective name for quarks and gluons. If you picture the proton as a bag filled with marbles representing quarks and gluons, the GPD provides a description of how the energy-momentum and other characteristics of these marbles are distributed within the bag — for example, when the bag is shaken and the marbles move around. It can be compared to a map that indicates the likelihood of finding a marble with a specific energy-momentum at a particular position inside the bag. Knowing the distribution of these quark and gluon characteristics allows scientists to understand the inner workings of the proton, which may lead to new ways to apply that knowledge.
“To obtain a detailed map, we need to analyze many scattering interactions, involving various values of momentum change of the proton,” said Shohini Bhattacharya, a research associate in Brookhaven’s nuclear theory group and the RIKEN BNL Research Center (RBRC). More

A common metal paper clip will stick to a magnet. Scientists classify such iron-containing materials as ferromagnets. A little over a century ago, physicists Albert Einstein and Wander de Haas reported a surprising effect with a ferromagnet. If you suspend an iron cylinder from a wire and expose it to a magnetic field, it will start rotating if you simply reverse the direction of the magnetic field.
“Einstein and de Haas’s experiment is almost like a magic show,” said Haidan Wen, a physicist in the Materials Science and X-ray Science divisions of the U.S. Department of Energy’s (DOE) Argonne National Laboratory. ​”You can cause a cylinder to rotate without ever touching it.”
In Nature magazine, a team of researchers from Argonne and other U.S. national laboratories and universities now report an analogous yet different effect in an ​”anti”-ferromagnet. This could have important applications in devices requiring ultra-precise and ultrafast motion control. One example is high-speed nanomotors for biomedical applications, such as use in nanorobots for minimally invasive diagnosis and surgery.
The difference between a ferromagnet and antiferromagnet has to do with a property called electron spin. This spin has a direction. Scientists represent the direction with an arrow, which can point up or down or any direction in between. In the magnetized ferromagnet mentioned above, the arrows associated with all the electrons in the iron atoms can point in the same direction, say, up. Reversing the magnetic field reverses the direction of the electron spins. So, all arrows are pointing down. This reversal leads to the cylinder’s rotation.
“In this experiment, a microscopic property, electron spin, is exploited to elicit a mechanical response in a cylinder, a macroscopic object,” said Alfred Zong, a Miller Research Fellow at the University of California, Berkeley.
In antiferromagnets, instead of the electron spins all pointing up, for example, they alternate from up to down between adjacent electrons. These opposite spins cancel each other out, and antiferromagnets thus do not respond to changes in a magnetic field as ferromagnets do.
“The question we asked ourselves is, can electron spin elicit a response in an antiferromagnet that is different but similar in spirit to that from the cylinder rotation in the Einstein-de Hass experiment?” Wen said. More

A new statistical tool can help researchers get meaningful results when a randomized experiment, considered the gold standard, is not possible.
Randomized experiments split participants into groups by chance, with one undergoing an intervention and the other not. But in real-world situations, they can’t always be done. Companies might not want to use the method, or such experiments might be against the law.
Developed by a researcher at The University of Texas at Austin, the new tool called two-step synthetic control adapts an existing research workaround, known as the synthetic control method.
The traditional synthetic control method creates synthetic control groups from the data, in place of real ones. The groups are weighted statistically and compared with a group undergoing an intervention.
But the synthetic control method does not perfectly apply to all situations, especially ones in which the intervention group is different from control groups, according to Kathleen Li, an assistant professor of marketing at the McCombs School of Business. In these scenarios, the method’s lack of flexibility could lead to less accurate results.
“Our framework allows managers and policymakers to estimate effects they previously weren’t able to estimate accurately,” said Li, who developed the tool along with Venkatesh Shankar of Texas A&M University. “They get a more precise estimate that can help them make more informed decisions.”
The study, published in advance online in the journal Management Science, offers a two-step synthetic control approach: First, it determines whether the traditional synthetic control method applies to a given case. If it does not, the second step uses a more flexible framework that allows weighted controls to differ from 100% or to shift the control group up and down.The researchers tested the new method on a real-world situation by looking at sales of tampons: how they responded in 2016, when New York repealed a sales tax on them. More