Aurelia Butler

Triplons are tricky little things. Experimentally, they’re exceedingly difficult to observe. And even then, researchers usually conduct the tests on macroscopic materials, in which measurements are expressed as an average across the whole sample.
That’s where designer quantum materials offer a unique advantage, says Academy Research Fellow Robert Drost, the first author of a paper published in Physical Review Letters on August 22. These designer quantum materials let researchers create phenomena not found in natural compounds, ultimately enabling the realization of exotic quantum excitations.
‘These materials are very complex. They give you very exciting physics, but the most exotic ones are also challenging to find and study. So, we are trying a different approach here by building an artificial material using individual components,’ says Professor Peter Liljeroth, head of the Atomic Scale physics research group at Aalto University.
Quantum materials are governed by the interactions between electrons at the microscopic level. These electronic correlations lead to unusual phenomena like high-temperature superconductivity or complex magnetic states, and quantum correlations give rise to new electronic states.
In the case of two electrons, there are two entangled states known as singlet and triplet states. Supplying energy to the electron system can excite it from the singlet to the triplet state. In some cases, this excitation can propagate through a material in an entanglement wave known as a triplon. These excitations are not present in conventional magnetic materials, and measuring them has remained an open challenge in quantum materials.
The team’s triplon experiments
In the new study, the team used small organic molecules to create an artificial quantum material with unusual magnetic properties. Each of the cobalt-phthalocyanine molecules used in the experiment contains two frontier electrons. More

With the rise in global demand for staple crop products projected to substantially increase by 2050 due to population growth, rising per capita income, and the growing use of biofuels, it is necessary to adopt sustainable agricultural intensification practices in existing croplands to meet this demand. However, estimation processes currently employed in the global South remain inadequate. Traditional methods like self-reporting and crop cutting have their limitations, and remote sensing technologies are not fully utilized in this context.
However, recent advancements in artificial intelligence and machine learning, particularly deep learning with convolutional neural networks (CNNs), offer promising solutions here. To explore the scope of this new technology, researchers from Japan conducted a study focusing on rice. They used ground-based digital images taken at harvesting stage of the crop, combined with CNNs, to estimate rice yield. Their study appeared online on 29 June 2023 and was published on 28 July 2023 in Volume 5 of Plant Phenomics.
“We started by conducting an extensive field campaign. We gathered rice canopy images and rough grain yield data from 20 locations in seven countries in order to create a comprehensive multinational database,” says Dr. Yu Tanaka, Associate Professor at the Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, who led the study.
The images were captured using digital cameras which could gather the required data from a distance of 0.8-0.9 meters, vertically downwards from the rice canopy. With Dr. Kazuki Saito of the International Rice Research Institute (formerly Africa Rice Center) and other collaborators, the team successfully created a database of 4,820 yield data of harvesting plots and 22,067 images, encompassing various rice cultivars, production systems, and crop management practices.
Next, a CNN model was developed to estimate the grain yield for each of the collected images. The team used a visual-occlusion method to visualize the additive effect of different regions in the rice canopy images. It involved masking specific parts of the images and observing how the model’s yield estimation changed in response to the masked regions. The insights gained from this method allowed the researchers to understand how the CNN model interpreted various features in the rice canopy images, influencing its accuracy and its ability to distinguish between yield-contributing components and non-contributing elements in the canopy.
The model performed well, explaining around 68%-69% of yield variation in the validation and test datasets. Study results highlighted the importance of panicles — loose-branching clusters of flowers — in yield estimation through occlusion-based visualization. The model could predict yield accurately during the ripening stage, recognizing mature panicles, and also detect cultivar and water management differences in yield in the prediction dataset. Its accuracy, however, decreased as image resolution decreased.
Nevertheless, the model proved robust, showing good accuracy at different shooting angles and times of day. “Overall, the developed CNN model demonstrated promising capabilities in estimating rough grain yield from rice canopy images across diverse environments and cultivars. Another appealing aspect is that it is highly cost effective and does not require labor-intensive crop cuts or complex remote-sensing technologies,” says Dr. Tanaka enthusiastically.
The study emphasizes the potential of CNN-based models for monitoring rice productivity at regional scales. However, the model’s accuracy may vary under different conditions, and further research should focus on adapting the model to low-yielding and rainy environments. The AI-based method has also been made available to farmers and researchers through a simple smartphone application, thus greatly improving accessibility of the technology and its real-life applications. The name of this application is ‘HOJO’, and it is already available on iOS and Android. The researchers hope that their work will lead to better management of rice fields and assist accelerated breeding programs, contributing positively to global food production and sustainability initiatives. More

In physics, a “disordered system” refers to a physical system whose components — e.g. its atoms — are not organized in any discernible way. Like a drawer full of random socks, a disordered system lacks a well-defined, ordered pattern due to various factors like impurities, defects, or interactions between components.
This randomness makes it difficult to predict the system’s behavior accurately. And given that disordered systems are found in anything from materials science to climate or social networks and beyond, this limitation can be a serious, real-life problem.
Now, a team of scientists led by Lenka Zdeborová at EPFL have developed a novel approach to understanding how things change and evolve in disordered systems, even when they are undergoing rapid changes, like a temperature change. The study was carried out by Freya Behrens at Zdeborová’s lab, and Barbora Hudcová visiting EPFL from the Charles University in Prague.
The approach is called the Backtracking Dynamical Cavity Method (BDCM) and it works by looking first at the end state of the system rather than the beginning; instead of studying the system’s trajectory forward from the start, it traces the steps backward from stable points.
But why “cavity”? The term comes from the “Cavity Method” in statistical physics and refers to isolating a particular component of a complex system to make it easier to study — putting it in a conceptual “hole” or “cavity” while ignoring all the other components.
In a similar way, the BDCM isolates a specific component of the disordered system, but working instead backwards to understand its evolution throughout time. This innovative twist provides valuable insights about the system’s dynamic properties, even when it is far from equilibrium, like how materials cool down or how opinions on a social network evolve, or even how our brains work.
“From our early results, we saw that it can be quite deceiving to only look at the number of attractors of the system,” says Freya Behrens, referring to stable states that a system settles into over time. “Just because there are many attractors of a given type, it does not mean your dynamics end up there. But we really did not expect that taking just a few steps back from the attractor into its basin would reveal so many details about the complete dynamics. It was quite surprising.”
Applying the BDCM to a random arrangement of magnets, the scientists found out what happens to their energy of when they rapidly cool down or what type of patterns they form when they start with different arrangements.
“What I like a lot about this work is that we obtained theoretical answers to basic yet open questions about the dynamics of the Ising model, among the most studied models in statistical physics,” says Lenka Zdeborová. “The method we developed is very versatile, indicating that it will find many applications in studies of the dynamics of complex interacting systems, for which the Ising model is one of the simplest examples. Some application areas I can foresee include social dynamics, learning in neural networks or, for instance, gene regulation. I am looking forward to seeing the follow-up work!” More

Parents should be taught how to better understand the increasingly volatile social media landscape that is deploying sophisticated algorithms, according to a new study from the University of Surrey.
The study investigated how parents interpret and navigate social media algorithms that are central to their children’s digital experiences.
The research found that a child’s age shaped their parent’s perspective on a platform’s algorithm. For example, the study revealed that parents of toddlers who watched YouTube, while concerned, often regarded their fears to be an issue for the future.
Researchers also found that parents’ own views on social media platforms often influence how they manage their children’s online activities. Even though parents think their own online data is different from their offspring’s, the study found a lot of overlap because of shared family information and data.
Professor Ranjana Das, lead investigator and Professor of Media and Communication at the University of Surrey, said:
“Parents engage with so many platforms in the course of their day-to-day parenting. We wanted to see how they make sense of and interact with the algorithms responsible for serving themselves and their children with the content on those platforms.”
Professor Das interviewed 30 parents who are raising children aged 0 to 18 across England. More

Researchers from Austria and USA have designed a new type of quantum computer that uses fermionic atoms to simulate complex physical systems. The processor uses programmable neutral atom arrays and is capable of simulating fermionic models in a hardware-efficient manner using fermionic gates. The team led by Peter Zoller demonstrated how the new quantum processor can efficiently simulate fermionic models from quantum chemistry and particle physics.
Fermionic atoms are atoms that obey the Pauli exclusion principle, which means that no two of them can occupy the same quantum state simultaneously. This makes them ideal for simulating systems where fermionic statistics play a crucial role, such as molecules, superconductors and quark-gluon plasmas. “In qubit-based quantum computers extra resources need to be dedicated to simulate these properties, usually in the form of additional qubits or longer quantum circuits,” explains Daniel Gonzalez Cuadra from the research group led by Peter Zoller at the Institute for Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences (ÖAW) and the Department of Theoretical Physics at the University of Innsbruck, Austria.
Quantum information in fermionic particles
A fermionic quantum processor is composed of a fermionic register and a set of fermionic quantum gates. “The register consists on a set of fermionic modes, which can be either empty or occupied by a single fermion, and these two states form the local unit of quantum information,” says Daniel Gonzalez Cuadra. “The state of the system we want to simulate, such as a molecule composed of many electrons, will be in general a superposition of many occupation patterns, which can be directly encoded into this register.” This information is then processed using a fermionic quantum circuit, designed to simulate for example the time evolution of a molecule. Any such circuit can be decomposed into a sequence of just two types of fermionic gates, a tunneling and an interaction gate.
The researchers propose to trap fermionic atoms in an array of optical tweezers, which are highly focused laser beams that can hold and move atoms with high precision. “The required set of fermionic quantum gates can be natively implemented in this platform: tunneling gates can be obtained by controlling the tunneling of an atom between two optical tweezers, while interaction gates are implemented by first exciting the atoms to Rydberg states, carrying a strong dipole moment,” says Gonzalez Cuadra.
Quantum chemistry to particle physics
Fermionic quantum processing is particularly useful to simulate the properties of systems composed of many interacting fermions, such as electrons in a molecule or in a material, or quarks inside a proton, and has therefore applications in many fields, ranging from quantum chemistry to particle physics. The researchers demonstrate how their fermionic quantum processor can efficiently simulate fermionic models from quantum chemistry and lattice gauge theory, which are two important fields of physics that are hard to solve with classical computers. “By using fermions to encode and process quantum information, some properties of the simulated system are intrinsically guaranteed at the hardware level, which would require additional resources in a standard qubit-based quantum computer,” says Daniel Gonzalez Cuadra. “I am very excited about the future of the field, and I would like to keep contributing to it by identifying the most promising applications for fermionic quantum processing, and by designing tailored algorithms that can run in near-term devices.”
The current results were published in the Proceedings of the National Academy of Sciences (PNAS). The research was financially supported by the Austrian Science Fund FWF, European Union and Simons Foundation, among others. More

Researchers at UC San Francisco and UC Berkeley have developed a brain-computer interface (BCI) that has enabled a woman with severe paralysis from a brainstem stroke to speak through a digital avatar.
It is the first time that either speech or facial expressions have been synthesized from brain signals. The system can also decode these signals into text at nearly 80 words per minute, a vast improvement over commercially available technology.
Edward Chang, MD, chair of neurological surgery at UCSF, who has worked on the technology, known as a brain computer interface, or BCI, for more than a decade, hopes this latest research breakthrough, appearing Aug. 23, 2023, in Nature, will lead to an FDA-approved system that enables speech from brain signals in the near future.
“Our goal is to restore a full, embodied way of communicating, which is really the most natural way for us to talk with others,” said Chang, who is a member of the UCSF Weill Institute for Neuroscience and the Jeanne Robertson Distinguished Professor in Psychiatry. “These advancements bring us much closer to making this a real solution for patients.”
Chang’s team previously demonstrated it was possible to decode brain signals into text in a man who had also experienced a brainstem stroke many years earlier. The current study demonstrates something more ambitious: decoding brain signals into the richness of speech, along with the movements that animate a person’s face during conversation.
Chang implanted a paper-thin rectangle of 253 electrodes onto the surface of the woman’s brain over areas his team has discovered are critical for speech. The electrodes intercepted the brain signals that, if not for the stroke, would have gone to muscles in her, tongue, jaw and larynx, as well as her face. A cable, plugged into a port fixed to her head, connected the electrodes to a bank of computers.
For weeks, the participant worked with the team to train the system’s artificial intelligence algorithms to recognize her unique brain signals for speech. This involved repeating different phrases from a 1,024-word conversational vocabulary over and over again, until the computer recognized the brain activity patterns associated with the sounds. More

A growing repertoire of cell and molecule-based immunotherapies is offering patients with indomitable cancers new hope by mobilizing their immune systems against tumor cells. An emerging class of such immunotherapeutics, known as T cell bispecific antibodies (TCBs), are of growing importance with several TCBs that the U.S. Food and Drug Administration (FDA) approved for the treatment of leukemias, lymphomas, and myelomas. These antibody drugs label tumor cells with one of their ends, and attract immune cells with another end to coerce them into tumor cell killing.
One major challenge in the development of TCBs and other immunotherapy drugs is that the antigens targeted by TCBs can be present not only on tumor cells, but also healthy cells in the body. This can lead to “on-target, off-tumor” cell killing and unwanted injury of vital organs, such as the kidney, liver, and others, that can put patients participating in clinical trials at risk. Currently, there are no human in vitro models of the kidney that sufficiently recapitulate the 3D architecture, cell diversity, and functionality of organs needed to assess on-target, off-tumor effects at a preclinical stage.
Now, a new cross-disciplinary, cross-organizational study created an immune-infiltrated kidney tissue model for investigating on-target, off-tumor effects of TCBs and potentially other immunotherapy drugs. The team of bioengineers and immune-oncologists who performed the study at the Wyss Institute for Biologically Inspired Engineering at Harvard University, Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard Medical School (HMS), and the Roche Innovation Centers in Switzerland and Germany, developed an immune-infiltrated human kidney organoid-on-chip model composed of tiny kidney tissue segments that contain vasculatureand forming nephrons, which can be infiltrated by circulating immune cells. They used this model to understand the specific toxicity of a pre-clinical TCB tool compound that targets the well-characterized tumor antigen Wilms’ Tumor 1 (WT-1) in certain tumors. Importantly, WT-1 is also expressed at much lower levels in the kidney, making it an important organ to study its potential on-target, off-tumor effects in. Their findings are published in PNAS.
“Together with our collaborators at Roche, we extended our vascularized kidney organoid-on-chip model to include an immune cell population that contains cytotoxic T cells with the potential to kill not only tumor cells, but also other cells that present target antigens,” said Wyss Core Faculty member Jennifer Lewis, Sc.D., the study’s senior author. “Our pre-clinical human in vitro model provides important insights regarding which cells are targeted by a given TCB and what, if any, off-target damage arises.” Lewis is also the Hansjörg Wyss Professor of Biologically Inspired Engineering at SEAS and co-leader of the Wyss Institute’s 3D Organ Engineering Initiative.
Incorporating immunity into a kidney organoid-on-chip
In 2019, Lewis’ group, together with that of Joseph Bonventre, M.D., Ph.D. at Brigham and Women’s hospital along with co-author Ryuji Morizane, M.D., Ph.D., found that exposing kidney organoids created from human pluripotent stem cells to the constant flow of fluids during their differentiation enhanced their on-chip vascularization and maturation of glomeruli and tubular compartments, relative to static controls. The researchers’ observations were enabled by a 3D printed millifluidic chip, in which kidney organoids are subjected to nutrient and differentiation factor-laden media flowed at controlled rates during their differentiation. The chip device allows researchers to directly observe the tissue using confocal microscopy through a transparent window in real-time.
“Given that this in vitro model represents most of the cell types in the kidney and incorporates the immune system, itcould support the assessment of on and off-target effects from TCBs as well as complex cellular interactions,” said Kimberly Homan, Ph.D., a former postdoctoral researcher in Lewis’ lab, first author of the initial work, and a co-corresponding author of this new study. Homan has since left Lewis’ lab to join Genentech as Director of the Complex in vitro Systems lab where she continued to provide expertise to the project collaborators. More

A tailsitter is a fixed-wing aircraft that takes off and lands vertically (it sits on its tail on the landing pad), and then tilts horizontally for forward flight. Faster and more efficient than quadcopter drones, these versatile aircraft can fly over a large area like an airplane but also hover like a helicopter, making them well-suited for tasks like search-and-rescue or parcel delivery.
MIT researchers have developed new algorithms for trajectory planning and control of a tailsitter that take advantage of the maneuverability and versatility of this type of aircraft. Their algorithms can execute challenging maneuvers, like sideways or upside-down flight, and are so computationally efficient that they can plan complex trajectories in real-time.
Typically, other methods either simplify the system dynamics in their trajectory planning algorithm or use two different models, one for helicopter mode and one for airplane mode. Neither approach can plan and execute trajectories that are as aggressive as those demonstrated by the MIT team.
“We wanted to really exploit all the power the system has. These aircraft, even if they are very small, are quite powerful and capable of exciting acrobatic maneuvers. With our approach, using one model, we can cover the entire flight envelope — all the conditions in which the vehicle can fly,” says Ezra Tal, a research scientist in the Laboratory for Information and Decision Systems (LIDS) and lead author of a new paper describing the work.
Tal and his collaborators used their trajectory generation and control algorithms to demonstrate tailsitters that perform complex maneuvers like loops, rolls, and climbing turns, and they even showcased a drone race where three tailsitters sped through aerial gates and performed several synchronized, acrobatic maneuvers.
These algorithms could potentially enable tailsitters to autonomously perform complex moves in dynamic environments, such as flying into a collapsed building and avoiding obstacles while on a rapid search for survivors.
Joining Tal on the paper are Gilhyun Ryou, a graduate student in the Department of Electrical Engineering and Computer Science (EECS); and senior author Sertac Karaman, associate professor of aeronautics and astronautics and director of LIDS.The research appears in IEEE Transactions on Robotics.
Tackling tailsitter trajectories More