Aurelia Butler

Hyperspectral imaging (HSI) is a state-of-the-art technique that captures and processes information across a given electromagnetic spectrum. Unlike traditional imaging techniques that capture light intensity at specific wavelengths, HSI collects a full spectrum at each pixel in an image. This rich spectral data enables the distinction between different materials and substances based on their unique spectral signatures. Near-infrared hyperspectral imaging (NIR-HSI) has attracted significant attention in the food and industrial fields as a non-destructive technique for analyzing the composition of objects. A notable aspect of NIR-HSI is over-thousand-nanometer (OTN) spectroscopy, which can be used for the identification of organic substances, their concentration estimation, and 2D map creation. Additionally, NIR-HSI can be used to acquire information deep into the body, making it useful for the visualization of lesions hidden in normal tissues.
Various types of HSI devices have been developed to suit different imaging targets and situations, such as for imaging under a microscope or portable imaging and imaging in confined spaces. However, for OTN wavelengths, ordinary visible cameras lose sensitivity and only a few commercially available lenses exist that can correct chromatic aberration. Moreover, it is necessary to construct cameras, optical systems, and illumination systems for portable NRI-HSI devices, but no device that can acquire NIR-HSI with a rigid scope, crucial for portability, has been reported yet.
Now, in a new study, a team of researchers, led by Professor Hiroshi Takemura from Tokyo University of Science (TUS) and including Toshihiro Takamatsu, Ryodai Fukushima, Kounosuke Sato, Masakazu Umezawa, and Kohei Soga, all from TUS, Hideo Yokota from RIKEN, and Abian Hernandez Guedes and Gustavo M. Calico, both from the University of Las Palmas de Gran Canaria, has recently developed the world’s first rigid endoscope system capable of HSI from visible to OTN wavelengths. Their findings were published in Volume 32, Issue 9 of Optics Express on April 17, 2024.
At the core of this innovative system lies a supercontinuum (SC) light source and an acoustic-opto tunable filter (AOTF) that can emit specific wavelengths. Prof. Takemura explains, “An SC light source can output intense coherent white light, whereas an AOTF can extract light containing a specific wavelength. This combination offers easy light transmission to the light guide and the ability to electrically switch between a broad range of wavelengths within a millisecond.”
The team verified the optical performance and classification ability of the system, demonstrating its capability to perform HSI in the range of 490-1600 nm, enabling visible as well as NIR-HSI. Additionally, the results highlighted several advantages, such as the low light power of extracted wavelengths, enabling non-destructive imaging, and downsizing capability. Moreover, a more continuous NIR spectrum can be obtained compared to that of conventional rigid-scope-type devices.
To demonstrate their system’s capability, the researchers used it to acquire the spectra of six types of resins and employed a neural network to classify the spectra pixel-by-pixel in multiple wavelengths. The results revealed that when the OTN wavelength range was extracted from the HSI data for training, the neural network could classify seven different targets, including the six resins and a white reference, with an accuracy of 99.6%, reproducibility of 93.7%, and specificity of 99.1%. This means that the system can successfully extract molecular vibration information of each resin at each pixel.
Prof. Takemura and his team also identified several future research directions for improving this method, including enhancing image quality and recall in the visible region and refining the design of the rigid endoscope to correct chromatic aberrations over a wide area. With these further advancements, in the coming years, the proposed HSI technology is expected to facilitate new applications in industrial inspection and quality control, working as a “superhuman vision” tool that unlocks new ways of perceiving and understanding the world around us.
“This breakthrough, which combines expertise from different fields through a collaborative, cross-disciplinary approach, enables the identification of invaded cancer areas and the visualization of deep tissues such as blood vessels, nerves, and ureters during medical procedures, leading to improved surgical navigation. Additionally, it enables measurement using light previously unseen in industrial applications, potentially creating new areas of non-use and non-destructive testing,” remarks Prof. Takemura. “By visualizing the invisible, we aim to accelerate the development of medicine and improve the quality of life of physicians as well as patients.” More

An Australian-led study has found unusual insulating behaviour in a new atomically-thin material — and the ability to switch it on and off.
Materials that feature strong interactions between electrons can display unusual properties such as the ability to act as insulators even when they are expected to conduct electricity. These insulators, known as Mott insulators, occur when electrons become frozen because of strong repulsion they feel from other electrons nearby, preventing them from carrying a current.
Led by FLEET at Monash University, a new study (published in Nature Communications) has demonstrated a Mott insulating phase within an atomically-thin metal-organic framework (MOF), and the ability to controllably switch this material from an insulator to a conductor. This material’s ability to act as an efficient ‘switch’ makes it a promising candidate for application in new electronic devices such as transistors.
Electron interactions written in the stars
The atomically thin (or ‘2D’) material at the heart of the study is a type of MOF, a class of materials composed from organic molecules and metal atoms.
“Thanks to the versatility of supramolecular chemistry approaches — in particular applied on surfaces as substrates — we have an almost infinite number of combinations to construct materials from the bottom-up, with atomic scale precision,” explains corresponding author A/Prof Schiffrin. “In these approaches, organic molecules are used as building blocks, By carefully choosing the right ingredients, we can tune the properties of MOFs.”
The important tailor-made property of the MOF in this study is its star-shaped geometry, known as a kagome structure. This geometry enhances the influence of electron-electron interactions, directly leading to the realisation of a Mott insulator.

The on-off switch: electron population
The authors constructed the star-shaped kagome MOF from a combination of copper atoms and 9,10-dicyanoanthracene (DCA) molecules. They grew the material upon another atomically thin insulating material, hexagonal boron nitride (hBN), on an atomically flat copper surface, Cu(111).
“We measured the structural and electronic properties of the MOF at the atomic scale using scanning tunnelling microscopy and spectroscopy,” explains lead author Dr. Benjamin Lowe, who recently completed his PhD with FLEET. “This allowed us to measure an unexpected energy gap — the hallmark of an insulator.”
The authors’ suspicion that the experimentally measured energy gap was a signature of a Mott insulating phase was confirmed by comparing experimental results with dynamical mean-field theory calculations.
“The electronic signature in our calculations showed remarkable agreement with experimental measurements and provided conclusive evidence of a Mott insulating phase,” explains FLEET alum Dr. Bernard Field, who performed the theoretical calculations in collaboration with researchers from the University of Queensland and the Okinawa Institute of Science and Technology Graduate University in Japan.
The authors were also able to change the electron population in the MOF by using variations in the chemical environment of the hBN substrate and the electric field underneath the scanning tunnelling microscope tip.

When some electrons are removed from the MOF, the repulsion that the remaining electrons feel is reduced and they become unfrozen — allowing the material to behave like a metal. The authors were able to observe this metallic phase from a vanishing of the measured energy gap when they removed some electrons from the MOF. Electron population is the on-off switch for controllable Mott insulator to metal phase transitions.
What’s next?
The ability of this MOF to switch between Mott insulator and metal phases by modifying the electron population is a promising result that could be exploited in new types of electronic devices (for example, transistors). A promising next step towards such applications would be to reproduce these findings within a device structure in which an electric field is applied uniformly across the whole material.
The observation of a Mott insulator in a MOF which is easy to synthesise and contains abundant elements also makes these materials attractive candidates for further studies of strongly correlated phenomena — potentially including superconductivity, magnetism, or spin liquids. More

Researchers have found two novel types of attacks that target the conditional branch predictor found in high-end Intel processors, which could be exploited to compromise billions of processors currently in use.
The multi-university and industry research team led by computer scientists at University of California San Diego will present their work at the 2024 ACM ASPLOS Conference that begins tomorrow. The paper, “Pathfinder: High-Resolution Control-Flow Attacks Exploiting the Conditional Branch Predictor,” is based on findings from scientists from UC San Diego, Purdue University, Georgia Tech, the University of North Carolina Chapel Hill and Google.
They discover a unique attack that is the first to target a feature in the branch predictor called the Path History Register, which tracks both branch order and branch addresses. As a result, more information with more precision is exposed than with prior attacks that lacked insight into the exact structure of the branch predictor.
Their research has resulted in Intel and Advanced Micro Devices (AMD) addressing the concerns raised by the researchers and advising users about the security issues. Today, Intel is set to issue a Security Announcement, while AMD will release a Security Bulletin.
In software, frequent branching occurs as programs navigate different paths based on varying data values. The direction of these branches, whether “taken” or “not taken,” provides crucial insights into the executed program data. Given the significant impact of branches on modern processor performance, a crucial optimization known as the “branch predictor” is employed. This predictor anticipates future branch outcomes by referencing past histories stored within prediction tables. Previous attacks have exploited this mechanism by analyzing entries in these tables to discern recent branch tendencies at specific addresses.
In this new study, researchers leverage modern predictors’ utilization of a Path History Register (PHR) to index prediction tables. The PHR records the addresses and precise order of the last 194 taken branches in recent Intel architectures. With innovative techniques for capturing the PHR, the researchers demonstrate the ability to not only capture the most recent outcomes but also every branch outcome in sequential order. Remarkably, they uncover the global ordering of all branches. Despite the PHR typically retaining the most recent 194 branches, the researchers present an advanced technique to recover a significantly longer history.
“We successfully captured sequences of tens of thousands of branches in precise order, utilizing this method to leak secret images during processing by the widely used image library, libjpeg,” said Hosein Yavarzadeh, a UC San Diego Computer Science and Engineering Department PhD student and lead author of the paper.

The researchers also introduce an exceptionally precise Spectre-style poisoning attack, enabling attackers to induce intricate patterns of branch mispredictions within victim code. “This manipulation leads the victim to execute unintended code paths, inadvertently exposing its confidential data,” said UC San Diego computer science Professor Dean Tullsen.
“While prior attacks could misdirect a single branch or the first instance of a branch executed multiple times, we now have such precise control that we could misdirect the 732nd instance of a branch taken thousands of times,” said Tullsen.
The team presents a proof-of-concept where they force an encryption algorithm to transiently exit earlier, resulting in the exposure of reduced-round ciphertext. Through this demonstration, they illustrate the ability to extract the secret AES encryption key.
“Pathfinder can reveal the outcome of almost any branch in almost any victim program, making it the most precise and powerful microarchitectural control-flow extraction attack that we have seen so far,” said Kazem Taram, an assistant professor of computer science at Purdue University and a UC San Diego computer science PhD graduate.
In addition to Dean Tullsen and Hosein Yavarzadeh, other UC San Diego coauthors are. Archit Agarwal and Deian Stefan. Other coauthors include Christina Garman and Kazem Taram, Purdue University; Daniel Moghimi, Google; Daniel Genkin, Georgia Tech; Max Christman and Andrew Kwong, University of North Carolina Chapel Hill.
This work was partially supported by the Air Force Office of Scientific Research (FA9550- 20-1-0425); the Defense Advanced Research Projects Agency (W912CG-23-C-0022 and HR00112390029); the National Science Foundation (CNS-2155235, CNS-1954712, and CAREER CNS-2048262); the Alfred P. Sloan Research Fellowship; and gifts from Intel, Qualcomm, and Cisco. More

Quantum mechanical effects such as radioactive decay, or more generally: ‘tunneling’, display intriguing mathematical patterns. Two researchers at the University of Amsterdam now show that a 40-year-old mathematical discovery can be used to fully encode and understand this structure.
Quantum physics — easy and hard
In the quantum world, processes can be separated into two distinct classes. One class, that of the so-called ‘perturbative’ phenomena, is relatively easy to detect, both in an experiment and in a mathematical computation. Examples are plentiful: the light that atoms emit, the energy that solar cells produce, the states of qubits in a quantum computer. These quantum phenomena depend on Planck’s constant, the fundamental constant of nature that determines how the quantum world differs from our large-scale world, but in a simple way. Despite the ridiculous smallness of this constant — expressed in everyday units of kilograms, metres and seconds it takes a value that starts at the 34th decimal place after the comma — the fact that Planck’s constant is not exactly zero is enough to compute such quantum effects.
Then, there are the ‘nonperturbative’ phenomena. One of the best known is radioactive decay: a process where due to quantum effects, elementary particles can escape the attractive force that ties them to atomic nuclei. If the world were ‘classical’ — that is, if Planck’s constant were exactly zero — this attractive force would be impossible to overcome. In the quantum world, decay does occur, but still only occasionally; a single uranium atom, for example, would on average take over four billion years to decay. The collective name for such rare quantum events is ‘tunneling’: for the particle to escape, it has to ‘dig a tunnel’ through the energy barrier that keeps it tied to the nucleus. A tunnel that can take billions of years to dig, and makes The Shawshank Redemption look like child’s play.
Mathematics to the rescue
Mathematically, nonperturbative quantum effects are much more difficult to describe than their perturbative cousins. Still, over the century that quantum mechanics has existed, physicists have found many ways to deal with these effects, and to describe and predict them accurately. “Still, in this century-old problem, there was work left to be done,” says Alexander van Spaendonck, one of the authors of the new publication. “The descriptions of tunneling phenomena in quantum mechanics needed further unification — a framework in which all such phenomena could be described and investigated using a single mathematical structure.”
Surprisingly, such a structure was found in 40-year-old mathematics. In the 1980s, French mathematician Jean Écalle had set up a framework that he dubbed resurgence, and that had precisely this goal: giving structure to nonperturbative phenomena. So why did it take 40 years for the natural combination of Écalle’s formalism and the application to tunneling phenomena to be taken to their logical conclusion? Marcel Vonk, the other author of the publication, explains: “Écalle’s original papers were lengthy — over 1000 pages all combined — highly technical, and only published in French. As a result, it took until the mid-2000s before a significant number of physicists started getting familiar with this ‘toolbox’ of resurgence. Originally, it was mostly applied to simple ‘toy models’, but of course the tools were also tried on real-life quantum mechanics. Our work takes these developments to their logical conclusion.”
Beautiful structure

That conclusion is that one of the tools in Écalle’s toolbox, that of a ‘transseries’, is perfectly suited to describe tunneling phenomena in essentially any quantum mechanics problem, and does so always in the same way. By spelling out the mathematical details, the authors found that it became possible not only to unify all tunneling phenomena into a single mathematical object, but also to describe certain ‘jumps’ in how big the role of these phenomena is — an effect known as Stokes’ phenomenon.
Van Spaendonck: “Using our description Stokes’ phenomenon, we were able to show that certain ambiguities that had plagued the ‘classical’ methods of computing nonperturbative effects — infinitely many, in fact — all dropped out in our method. The underlying structure turned out to be even more beautiful than we originally expected. The transseries that describes quantum tunneling turns out to split — or ‘factorize’ — in a surprising way: into a ‘minimal’ transseries that describes the basic tunneling phenomena that essentially exist in any quantum mechanics problem, and an object that we called the ‘median transseries’ that describes the more problem-specific details, and that depends for example on how symmetric a certain quantum setting is.”
With this mathematical structure completely clarified, the next question is of course where the new lessons can be applied and what physicists can learn from them. In the case of radioactivity, for example, some atoms are stable whereas others decay. In other physical models, the lists of stable and unstable particles may vary as one slightly changes the setup — a phenomenon known as ‘wall-crossing’. What the researchers have in mind next is to clarify this notion of wall-crossing using the same techniques. This difficult problem has again been studied by many groups in many different ways, but now a similar unifying structure might be just around the corner. There is certainly light at the end of the tunnel. More

Whether you’re trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look for fluctuations.
Changes in data, from wildlife population to anxiety levels, can be an early warning signal that a system is reaching a critical threshold, known as a tipping point, in which those changes may accelerate or even become irreversible.
But which data points matter most? And which are simply just noise?
A new algorithm developed by University at Buffalo researchers can identify the most predictive data points that a tipping point is near. Detailed in Nature Communications, this theoretical framework uses the power of stochastic differential equations to observe the fluctuation of data points, or nodes, and then determine which should be used to calculate an early warning signal.
Simulations confirmed this method was more accurate at predicting theoretical tipping points than randomly selecting nodes.
“Every node is somewhat noisy — in other words, it changes over time — but some may change earlier and more drastically than others when a tipping point is near. Selecting the right set of nodes may improve the quality of the early warning signal, as well as help us avoid wasting resources observing uninformative nodes,” says the study’s lead author, Naoki Masuda, PhD, professor and director of graduate studies in the UB Department of Mathematics, within the College of Arts and Sciences.
The study was co-authored by Neil Maclaren, a postdoctoral research associate in the Department of Mathematics, and Kazuyuki Aihara, executive director of the International Research Center for Neurointelligence at the University of Tokyo.

The work was supported by the National Science Foundation and the Japan Science and Technology Agency.
Warning signals connected via networks
The algorithm is unique in that it fully incorporates network science into the process. While early warning signals have been applied to ecology and psychology for the last two decades, little research has focused on how those signals are connected within a network, Masuda says.
Consider depression. Recent research has considered it and other mental disorders as a network of symptoms influencing each other by creating feedback loops. A loss of appetite could mean the onset of five other symptoms in the near future, depending on how close those symptoms are on the network.
“As a network scientist, I felt network science could offer a unique or perhaps even improved approach to early warning signals,” Masuda says.
By thoroughly considering systems as networks, researchers found that simply selecting the nodes with highest fluctuations was not the best strategy. That’s because some selected nodes may be too closely related to other selected nodes.
“Even if we combine two nodes with nice early warning signals, we don’t necessarily get a more accurate signal. Sometimes combining a node with a good signal and another node with a mid-quality signal actually gives us a better signal,” Masuda says.
While the team validated the algorithm with numerical simulations, they say it can readily be applied to actual data because it does not require information about the network structure itself; it only requires two different states of the networked system to determine an optimal set of nodes.
“The next steps will be to collaborate with domain experts such as ecologists, climate scientists and medical doctors to further develop and test the algorithm with their empirical data and get insights into their problems,” Masuda says. More

Researchers Kazuaki Takasan and Kyogo Kawaguchi of the University of Tokyo with Kyosuke Adachi of RIKEN, Japan’s largest comprehensive research institution, have demonstrated that ferromagnetism, an ordered state of atoms, can be induced by increasing particle motility and that repulsive forces between atoms are sufficient to maintain it. The discovery not only extends the concept of active matter to quantum systems but also contributes to the development of novel technologies that rely on the magnetic properties of particles, such as magnetic memory and quantum computing. The findings were published in the journal Physical Review Research.
Flocking birds, swarming bacteria, cellular flows. These are all examples of active matter, a state in which individual agents, such as birds, bacteria, or cells, self-organize. The agents change from a disordered to an ordered state in what is called a “phase transition.” As a result, they move together in an organized fashion without an external controller.
“Previous studies have shown that the concept of active matter can apply to a wide range of scales, from nanometers (biomolecules) to meters (animals),” says Takasan, the first author. “However, it has not been known whether the physics of active matter can be applied usefully in the quantum regime. We wanted to fill in that gap.”
To fill the gap, the researchers needed to demonstrate a possible mechanism that could induce and maintain an ordered state in a quantum system. It was a collaborative work between physics and biophysics. The researchers took inspiration from the phenomena of flocking birds because, due to the activity of each agent, the ordered state is more easily achieved than in other types of active matter. They created a theoretical model in which atoms were essentially mimicking the behavior of birds. In this model, when they increased the motility of the atoms, the repulsive forces between atoms rearranged them into an ordered state called ferromagnetism. In the ferromagnetic state, spins, the angular momentum of subatomic particles and nuclei, align in one direction, just like how flocking birds face the same direction while flying.
“It was surprising at first to find that the ordering can appear without elaborate interactions between the agents in the quantum model,” Takasan reflects on the finding. “It was different from what was expected based on biophysical models.”
The researcher took a multi-faceted approach to ensure their finding was not a fluke. Thankfully, the results of computer simulations, mean-field theory, a statistical theory of particles, and mathematical proofs based on linear algebra were all consistent. This strengthened the reliability of their finding, the first step in a new line of research.
“The extension of active matter to the quantum world has only recently begun, and many aspects are still open,” says Takasan. “We would like to further develop the theory of quantum active matter and reveal its universal properties.” More

The same geometric quirk that lets visitors murmur messages around the circular dome of the whispering gallery at St. Paul’s Cathedral in London or across St. Louis Union Station’s whispering arch also enables the construction of high-resolution optical sensors. Whispering-gallery-mode (WGM) resonators have been used for decades to detect chemical signatures, DNA strands and even single molecules.
In the same way that the architecture of a whispering gallery bends and focuses sound waves, WGM microresonators confine and concentrate light in a tiny circular path. This enables WGM resonators to detect and quantify physical and biochemical characteristics, making them ideal for high-resolution sensing applications in fields such as biomedical diagnostics and environmental monitoring. However, the broad use of WGM resonators has been limited by their narrow dynamic range as well as their limited resolution and accuracy.
In a recent study, Lan Yang, the Edwin H. & Florence G. Skinner Professor, and Jie Liao, a postdoctoral research associate, both in the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering at Washington University in St. Louis, demonstrate a transformative approach to overcome these limitations: optical WGM barcodes for multimode sensing. Liao and Yang’s innovative technique allows simultaneous monitoring of multiple resonant modes within a single WGM resonator, considering distinctive responses from each mode, vastly expanding the range of measurements achievable.
WGM sensing uses a specific wavelelength of light that can circulate around the perimeter of the microresonator millions of times. When the sensor encounters a molecule, the resonant frequency of the circulating light shifts. Researchers can then measure that shift to detect and identify the presence of specific molecules.
“Multimode sensing allows us to pick up multiple resonance changes in wavelength, rather than just one,” Liao explained. “With multiple modes, we can expand optical WGM sensing to a greater range of wavelengths, achieve greater resolution and accuracy, and ultimately sense more particles.”
Liao and Yang found the theoretical limit of WGM detection and used it to estimate the sensing capabilities of a multimode system. They compared conventional single-mode with multimode sensing and determined that while single-mode sensing is limited to very narrow range — about 20 picometers (pm), constrained by the laser hardware — the range for multimode sensing is potentially limitless using the same setup.
“More resonance means more information,” Liao said. “We derived a theoretically infinite range, though we’re practically limited by the sensing apparatus. In this study, the experimental limit we found was about 350 times larger with the new method than the conventional method for WGM sensing.”
Commercial applications of multimode WGM sensing could include biomedical, chemical and environmental uses, Yang said. In biomedical applications, for instance, researchers could detect subtle changes in molecular interactions with unprecedented sensitivity to improve disease diagnosis and drug discovery. In environmental monitoring, with the capability to detect minute changes in environmental parameters such as temperature and pressure, multimode sensing could enable early warning systems for natural disasters or facilitate monitoring pollution levels in air and water.
This new technology also enables continuous monitoring of chemical reactions, as demonstrated in the recent experiments conducted by Yang’s group. This capability holds promise for real-time analysis and control of chemical processes, offering potential applications in fields such as pharmaceuticals, materials science, and the food industry.
“WGM resonators’ ultrahigh sensitivity lets us detect single particles and ions, but the potential of this powerful technology has not been fully utilized because we can’t use this ultrasensitive sensor directly to measure a complete unknown,” Liao added. “Multimode sensing enables that look into the unknown. By expanding our dynamic range to look at millions of particles, we can take on more ambitious projects and solve real-world problems.” More