Aurelia Butler

With a brain the size of a pinhead, insects perform fantastic navigational feats. They avoid obstacles and move through small openings. How do they do this, with their limited brain power? Understanding the inner workings of an insect’s brain can help us in our search towards energy-efficient computing, physicist Elisabetta Chicca of the University of Groningen demonstrates with her most recent result: a robot that acts like an insect.
It’s not easy to make use of the images that come in through your eyes, when deciding what your feet or wings should do. A key aspect here is the apparent motion of things as you move. ‘Like when you’re on a train’, Chicca explains. ‘The trees nearby appear to move faster than the houses far away. Insects use this information to infer how far away things are. This works well when moving in a straight line, but reality is not that simple.
Moving in curves makes the problem too complex for insects. To keep things manageable for their limited brainpower, they adjust their behaviour: they fly in a straight line, make a turn, then make another straight line. Chicca explains: ‘What we learn from this is: if you don’t have enough resources, you can simplify the problem with your behaviour.’
Brains on wheels
In search of the neural mechanism that drives insect behaviour, PhD student Thorben Schoepe developed a model of its neuronal activity and a small robot that uses this model to navigate. All this was done under Chicca’s supervision, and in close collaboration with neurobiologist Martin Egelhaaf of Bielefeld University, who helped to identify the insects’ computational principles.
Schoepe’s model is based on one main principle: always steer towards the area with the least apparent motion. He had his robot drive through a long ‘corridor’ — consisting of two walls with a random print on it — and the robot centred in the middle of the corridor, as insects tend to do.
In other (virtual) environments, such as a space with obstacles or small openings, Schoepe’s model also showed similar behaviour to insects. ‘The model is so good’, Chicca concludes, ‘that once you set it up, it will perform in all kinds of environments. That’s the beauty of this result.’

Hardwired instead of learned
The fact that a robot can navigate in a realistic environment is not new. Rather, the model gives insight into how insects do the job, and how they manage to do things so efficiently. Chicca explains: ‘Much of Robotics is not concerned with efficiency. We humans tend to learn new tasks as we grow up and within Robotics, this is reflected in the current trend of machine learning. But insects are able to fly immediately from birth. An efficient way of doing that is hardwired in their brains.’
In a similar way, you could make computers more efficient. Chicca shows a chip that her research group has previously developed: a strip with a surface area that is smaller than a key on your keyboard. In the future, she hopes to incorporate this specific insect behaviour in a chip as well. She comments: ‘Instead of using a general-purpose computer with all its possibilities, you can build specific hardware; a tiny chip that does the job, keeping things much smaller and energy-efficient.’
Elisabetta Chicca is part of the Groningen Cognitive Systems and Materials Center (CogniGron). Its mission is to develop materials-centred systems paradigms for cognitive computing based on modelling and learning at all levels: from materials that can learn to devices, circuits, and algorithms. More

Children’s interest in, and competence perceptions of, mathematics are generally quite positive as they begin school, but turn less positive during the first three years. Changes in interest and self-concept are also associated with each other. In other words, if a child’s interest fades, so does their competence perception, and vice versa.
This is shown by a recent study from Finland exploring the development of children’s motivation for mathematics during the early school years, and how that development is associated with their mathematics competence. The researchers followed nearly three hundred children for three years.
“A significant observation was that both school beginners’ higher initial motivation, and less decline in motivation during the follow-up, predicted better competence in the third grade, after accounting for initial differences in competence,” says Professor Markku Niemivirta of the University of Eastern Finland.
There were no gender differences in school beginners’ motivation and competence, but at the end of the follow-up, girls’ motivation had, on average, declined more than that of boys.
Gendered development is starting to show
The study shows that children are able to assess their motivation for mathematics rather accurately already when beginning school. In addition, children’s assessments of their interest and competence are already differentiated, despite being closely related.
“It is only natural that children are more interested in things they feel good at. And vice versa, they may do better in something they’re interested in.”
On average however, school beginners’ positive motivation starts to decline during the early school years, and the scale of this decline is associated with later differences in competence. Although there are no gender differences in competence, girls’ more negative change in motivation on average reflects an unfortunate gendered development, the traces of which remain visible until much later.

Practices for maintaining interest and having experiences of success
Although the negative change observed in the study may partly reflect children’s more realistic self-assessment over time, the researchers suspect that a role is also played by mathematics gradually getting more difficult, and an emphasis being placed on performance.
“The observed association between a change in motivation and competence shows, however, the added value of positive interest and self-concept. It would be important to develop and apply teaching practices that support and maintain children’s interest in mathematics and strengthen their experiences of success,” Niemivirta points out.
In the three-year study conducted by the Motivation, Learning and Well-being research collective, MoLeWe, children assessed their interest in, and competence perceptions of, mathematics annually. Mathematics competence was assessed by tests and teacher evaluations. More

In several applications of computer vision, such as augmented reality and self-driving cars, estimating the distance between objects and the camera is an essential task. Depth from focus/defocus is one of the techniques that achieves such a process using the blur in the images as a clue. Depth from focus/defocus usually requires a stack of images of the same scene taken with different focus distances, a technique known as focal stack.
Over the past decade or so, scientists have proposed many different methods for depth from focus/defocus, most of which can be divided into two categories. The first category includes model-based methods, which use mathematical and optics models to estimate scene depth based on sharpness or blur. The main problem with such methods, however, is that they fail for texture-less surfaces which look virtually the same across the entire focal stack.
The second category includes learning-based methods, which can be trained to perform depth from focus/defocus efficiently, even for texture-less surfaces. However, these approaches fail if the camera settings used for an input focal stack are different from those used in the training dataset.
Overcoming these limitations now, a team of researchers from Japan has come up with an innovative method for depth from focus/defocus that simultaneously addresses the abovementioned issues. Their study, published in the International Journal of Computer Vision, was led by Yasuhiro Mukaigawa and Yuki Fujimura from Nara Institute of Science and Technology (NAIST), Japan.
The proposed technique, dubbed deep depth from focal stack (DDFS), combines model-based depth estimation with a learning framework to get the best of both the worlds. Inspired by a strategy used in stereo vision, DDFS involves establishing a ‘cost volume’ based on the input focal stack, the camera settings, and a lens defocus model. Simply put, the cost volume represents a set of depth hypotheses — potential depth values for each pixel — and an associated cost value calculated on the basis of consistency between images in the focal stack. “The cost volume imposes a constraint between the defocus images and scene depth, serving as an intermediate representation that enables depth estimation with different camera settings at training and test times,” explains Mukaigawa.
The DDFS method also employs an encoder-decoder network, a commonly used machine learning architecture. This network estimates the scene depth progressively in a coarse-to-fine fashion, using ‘cost aggregation’ at each stage for learning localized structures in the images adaptively.
The researchers compared the performance of DDFS with that of other state-of-the-art depth from focus/defocus methods. Notably, the proposed approach outperformed most methods in various metrics for several image datasets. Additional experiments on focal stacks captured with the research team’s camera further proved the potential of DDFS, making it useful even with only a few input images in the input stacks, unlike other techniques.
Overall, DDFS could serve as a promising approach for applications where depth estimation is required, including robotics, autonomous vehicles, 3D image reconstruction, virtual and augmented reality, and surveillance. “Our method with camera-setting invariance can help extend the applicability of learning-based depth estimation techniques,” concludes Mukaigawa.
Here’s hoping that this study paves the way to more capable computer vision systems. More

The properties of normal materials, such as stiffness and flexibility, are determined by the molecular composition of the material, but the properties of metamaterials are determined by the geometry of the structure from which they are built. Researchers design these structures digitally and then have it 3D-printed. The resulting metamaterials can exhibit unnatural and extreme properties. Researchers have, for instance, designed metamaterials that, despite being solid, behave like a fluid.
“Traditionally, designers use the materials available to them to design a new device or a machine. The problem with that is that the range of available material properties is limited. Some properties that we would like to have, just don’t exist in nature. Our approach is: tell us what you want to have as properties and we engineer an appropriate material with those properties. What you will then get, is not really a material but something in-between a structure and a material, a metamaterial,” says professor Amir Zadpoor of the Department of Biomechanical Engineering.
Inverse design
Such a material discovery process requires solving a so-called inverse problem: the problem of finding the geometry that gives rise to the properties you desire. Inverse problems are notoriously difficult to solve, which is where AI comes into the picture. TU Delft researchers have developed deep learning models that solve these inverse problems.
“Even when inverse problems were solved in the past, they have been limited by the simplifying assumption that the small-scale geometry can be made from an infinite number of building blocks. The problem with that assumption is that metamaterials are usually made by 3D-printing and real 3D-printers have a limited resolution, which limits the number of building blocks that fit within a given device,” says first author Dr. Helda Pahlavani.
The AI models developed by TU Delft researchers break new ground by bypassing any such simplifying assumptions. “So we can now simply ask: how many building blocks does your manufacturing technique allow you to accommodate in your device? The model then finds the geometry that gives you your desired properties for the number of building blocks that you can actually manufacture.”
Unlocking full potential
A major practical problem neglected in previous research, has been the durability of metamaterials. Most existing designs break once they are used a few times. That is because existing metamaterials design approaches do not take durability into account. “So far, it has been only about what properties can be achieved. Our study considers durability and selects the most durable designs from a large pool of design candidates. This makes our designs really practical and not just theoretical adventures,” says Zadpoor.
The possibilities of metamaterials seem endless, but the full potential is far from being realised, says assistant professor Mohammad J. Mirzaali, corresponding author of the publication. This is because finding the optimal design of a metamaterial is currently still largely based on intuition, involves trial and error and is therefore labour-intensive. Using an inverse design process, where the desired properties are the starting point of the design, is still very rare within the metamaterials field. “But we think the step we have taken, is revolutionary in the field of metamaterials. It could lead to all kinds of new applications.” There are possible applications in orthopaedic implants, surgical instruments, soft robots, adaptive mirrors, and exo-suits. More

Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.
Many see quantum computing’s advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits (0s and 1s), while quantum computers deploy quantum bits (qubits) to store quantum information in values between 0 and 1. Under certain conditions this ability to process and store information in qubits can be used to design quantum algorithms that drastically outperform their classical counterparts. Notably, quantum’s ability to store information in values between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum ones.
However, quantum computers are finicky and have a tendency to lose information. Moreover, even if information loss can be avoided, it is difficult to translate it into classical information — which is necessary to yield a useful computation.
Classical computers suffer from neither of those two problems. Moreover, cleverly devised classical algorithms can further exploit the twin challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought — as recently reported in a research paper in the journal PRX Quantum.
The scientists’ results show that classical computing can be reconfigured to perform faster and more accurate calculations than state-of-the-art quantum computers.
This breakthrough was achieved with an algorithm that keeps only part of the information stored in the quantum state — and just enough to be able to accurately compute the final outcome.
“This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches,” explains Dries Sels, an assistant professor in New York University’s Department of Physics and one of the paper’s authors. “Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer.”
In seeking ways to optimize classical computing, Sels and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents the interactions between the qubits. Those types of networks have been notoriously hard to deal with, but recent advances in the field now allow these networks to be optimized with tools borrowed from statistical inference.

The authors compare the work of the algorithm to the compression of an image into a JPEG file, which allows large images to be stored using less space by eliminating information with barely perceivable loss in the quality of the image.
“Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image,” says the Flatiron Institute’s Joseph Tindall, who led the project. “We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further.”
The work was supported by the Flatiron Institute and a grant from the Air Force Office of Scientific Research (FA9550-21-1-0236). More

The past year has seen major advances in Large Language Models (LLMs) such as ChatGPT. The ability of these models to interpret and produce human text sources (and other sequence data) has implications for people in many areas of human activity. A new perspective paper in the journal Neuron argues that like many professionals, neuroscientists can either benefit from partnering with these powerful tools or risk being left behind.
In their previous studies, the authors showed that important preconditions are met to develop LLMs that can interpret and analyze neuroscientific data like ChatGPT interprets language. These AI models can be built for many different types of data, including neuroimaging, genetics, single-cell genomics, and even hand-written clinical reports.
In the traditional model of research, a scientist studies previous data on a topic, develops new hypotheses and tests them using experiments. Because of the massive amounts of data available, scientists often focus on a narrow field of research, such as neuroimaging or genetics. LLMs, however, can absorb more neuroscientific research than a single human ever could. In their Neuron paper, the authors argue that one day LLMs specialized in diverse areas of neuroscience could be used to communicate with one another to bridge siloed areas of neuroscience research, uncovering truths that would be impossible to find by humans alone. In the case of drug development, for example, an LLM specialized in genetics could be used along with a neuroimaging LLM to discover promising candidate molecules to stop neurodegeneration. The neuroscientist would direct these LLMs and verify their outputs.
Lead author Danilo Bzdok mentions the possibility that the scientist will, in certain cases, not always be able to fully understand the mechanism behind the biological processes discovered by these LLMs.
“We have to be open to the fact that certain things about the brain may be unknowable, or at least take a long time to understand,” he says. “Yet we might still generate insights from state-of-the-art LLMs and make clinical progress, even if we don’t fully grasp the way they reach conclusions.”
To realize the full potential of LLMs in neuroscience, Bzdok says scientists would need more infrastructure for data processing and storage than is available today at many research organizations. More importantly, it would take a cultural shift to a much more data-driven scientific approach, where studies that rely heavily on artificial intelligence and LLMs are published by leading journals and funded by public agencies. While the traditional model of strongly hypothesis-driven research remains key and is not going away, Bzdok says capitalizing on emerging LLM technologies might be important to spur the next generation of neurological treatments in cases where the old model has been less fruitful.
“To quote John Naisbitt, neuroscientists today are ‘drowning in information but starving for knowledge,'” he says. “Our ability to generate biomolecular data is eclipsing our ability to glean understanding from these systems. LLMs offer an answer to this problem. They may be able to extract, synergize and synthesize knowledge from and across neuroscience domains, a task that may or may not exceed human comprehension.” More

In quantum sensing, atomic-scale quantum systems are used to measure electromagnetic fields, as well as properties like rotation, acceleration, and distance, far more precisely than classical sensors can. The technology could enable devices that image the brain with unprecedented detail, for example, or air traffic control systems with precise positioning accuracy.
As many real-world quantum sensing devices are emerging, one promising direction is the use of microscopic defects inside diamonds to create “qubits” that can be used for quantum sensing. Qubits are the building blocks of quantum devices.
Researchers at MIT and elsewhere have developed a technique that enables them to identify and control a greater number of these microscopic defects. This could help them build a larger system of qubits that can perform quantum sensing with greater sensitivity.
Their method builds off a central defect inside a diamond, known as a nitrogen-vacancy (NV) center, which scientists can detect and excite using laser light and then control with microwave pulses. This new approach uses a specific protocol of microwave pulses to identify and extend that control to additional defects that can’t be seen with a laser, which are called dark spins.
The researchers seek to control larger numbers of dark spins by locating them through a network of connected spins. Starting from this central NV spin, the researchers build this chain by coupling the NV spin to a nearby dark spin, and then use this dark spin as a probe to find and control a more distant spin which can’t be sensed by the NV directly. The process can be repeated on these more distant spins to control longer chains.
“One lesson I learned from this work is that searching in the dark may be quite discouraging when you don’t see results, but we were able to take this risk. It is possible, with some courage, to search in places that people haven’t looked before and find potentially more advantageous qubits,” says Alex Ungar, a PhD student in electrical engineering and computer science and a member of the Quantum Engineering Group at MIT, who is lead author of a paper on this technique, which is published today in PRX Quantum.
His co-authors include his advisor and corresponding author, Paola Cappellaro, the Ford Professor of Engineering in the Department of Nuclear Science and Engineering and professor of physics; as well as Alexandre Cooper, a senior research scientist at the University of Waterloo’s Institute for Quantum Computing; and Won Kyu Calvin Sun, a former researcher in Cappellaro’s group who is now a postdoc at the University of Illinois at Urbana-Champaign.

Diamond defects
To create NV centers, scientists implant nitrogen into a sample of diamond.
But introducing nitrogen into the diamond creates other types of atomic defects in the surrounding environment. Some of these defects, including the NV center, can host what are known as electronic spins, which originate from the valence electrons around the site of the defect. Valence electrons are those in the outermost shell of an atom. A defect’s interaction with an external magnetic field can be used to form a qubit.
Researchers can harness these electronic spins from neighboring defects to create more qubits around a single NV center. This larger collection of qubits is known as a quantum register. Having a larger quantum register boosts the performance of a quantum sensor.
Some of these electronic spin defects are connected to the NV center through magnetic interaction. In past work, researchers used this interaction to identify and control nearby spins. However, this approach is limited because the NV center is only stable for a short amount of time, a principle called coherence. It can only be used to control the few spins that can be reached within this coherence limit.
In this new paper, the researchers use an electronic spin defect that is near the NV center as a probe to find and control an additional spin, creating a chain of three qubits.

They use a technique known as spin echo double resonance (SEDOR), which involves a series of microwave pulses that decouple an NV center from all electronic spins that are interacting with it. Then, they selectively apply another microwave pulse to pair the NV center with one nearby spin.
Unlike the NV, these neighboring dark spins can’t be excited, or polarized, with laser light. This polarization is a required step to control them with microwaves.
Once the researchers find and characterize a first-layer spin, they can transfer the NV’s polarization to this first-layer spin through the magnetic interaction by applying microwaves to both spins simultaneously. Then once the first-layer spin is polarized, they repeat the SEDOR process on the first-layer spin, using it as a probe to identify a second-layer spin that is interacting with it.
Controlling a chain of dark spins
This repeated SEDOR process allows the researchers to detect and characterize a new, distinct defect located outside the coherence limit of the NV center. To control this more distant spin, they carefully apply a specific series of microwave pulses that enable them to transfer the polarization from the NV center along the chain to this second-layer spin.
“This is setting the stage for building larger quantum registers to higher-layer spins or longer spin chains, and also showing that we can find these new defects that weren’t discovered before by scaling up this technique,” Ungar says.
To control a spin, the microwave pulses must be very close to the resonance frequency of that spin. Tiny drifts in the experimental setup, due to temperature or vibrations, can throw off the microwave pulses.
The researchers were able to optimize their protocol for sending precise microwave pulses, which enabled them to effectively identify and control second-layer spins, Ungar says.
“We are searching for something in the unknown, but at the same time, the environment might not be stable, so you don’t know if what you are finding is just noise. Once you start seeing promising things, you can put all your best effort in that one direction. But before you arrive there, it is a leap of faith,” Cappellaro says.
While they were able to effectively demonstrate a three-spin chain, the researchers estimate they could scale their method to a fifth layer using their current protocol, which could provide access to hundreds of potential qubits. With further optimization, they may be able to scale up to more than 10 layers.
In the future, they plan to continue enhancing their technique to efficiently characterize and probe other electronic spins in the environment and explore different types of defects that could be used to form qubits.
This research is supported, in part, by the U.S. National Science Foundation and the Canada First Research Excellence Fund. More