Computers Math

Biological processes depend on puzzle pieces coming together and interacting. Under specific conditions, these interactions can create something new without external input. This is called self-organization, as seen in a school of fish or a flock of birds. Interestingly, the mammalian embryo develops similarly. In PNAS, David Brückner and Gašper Tkačik from the Institute of Science and Technology Austria (ISTA) introduce a mathematical framework that analyzes self-organization from a single cell to a multicellular organism.
When an embryo develops, many types of cells with different functions need to be generated. For example, some cells will become part of the eye and record visual stimuli, while others will be part of the gut and help digest food. To determine their roles, cells are constantly communicating with each other using chemical signals.
Thanks to this communication, during development, everything is well synchronized and coordinated, and yet there is no central control responsible for this. The cell collective is self-organized and orchestrated by the interactions between the individuals. Each cell reacts to signals of its neighbors. Based on such self-organization, the mammalian embryo develops from a single fertilized egg cell into a multicellular organism.
David Brückner and Gašper Tkačik from the Institute of Science and Technology Austria (ISTA) have now established a mathematical framework that helps analyze this process and predict its optimal parameters. Published in PNAS, this approach represents a unifying mathematical language to describe biological self-organization in embryonic development and beyond.
The self-assembling embryo
In nature, self-organization is all around us: we can observe it in fish schools, bird flocks, or insect collectives, and even in microscopic processes regulated by cells. NOMIS fellow and ISTA postdoc David Brückner is interested in getting a better understanding of these processes from a theoretical standpoint. His focus lies on embryonic development — a complex process governed by genetics and cells communicating with each other.
During embryonic development, a single fertilized cell turns into a multicellular embryo containing organs with lots of different features. “For many steps in this developmental process, the system has no extrinsic signal that directs it what to do. There is an intrinsic property of the system that allows it to establish patterns and structures,” says Brückner. “The intrinsic property is what is known as self-organization.” Even with unpredictable factors — which physicists call “noise” — the embryonic patterns are formed reliably and consistently. In recent years, scientists have gained a deeper understanding of the molecular details that drive this complex process. A mathematical framework to analyze and quantify its performance, however, was lacking. The language of information theory provides answers.

Bridging expertise
“Information theory is a universal language to quantify structure and regularity in statistical ensembles, which are a collection of replicates of the same process. Embryonic development can be seen as such a process that reproducibly generates functional organisms that are very similar but not identical,” says Gašper Tkačik, professor at ISTA and expert in this field. For a long time, Tkačik has been studying how information gets processed in biological systems, for instance in the fly embryo. “In the early fly embryo, patterns are not self-organized,” he continues. “The mother fly puts chemicals into the egg that instruct the cells on what actions to take.” As the Tkačik group had already developed a framework for this system, Brückner reached out to develop one for the mammalian embryo as well. “With Gašper’s expertise in information theory, we were able to put it together,” Brückner adds excitedly.
Beyond embryo development?
During embryonic development, cells exchange signals and are constantly subject to random, unpredictable fluctuations (noise). Therefore, cellular interactions must be robust. The new framework measures how these interactions are possibly optimized to withstand noise. Using computer simulations of interacting cells, the scientists explored the conditions under which a system can still have a stable final result despite introducing fluctuations.
Although the framework has proven to be successful on three different developmental models that all rely on chemical and mechanical signaling, additional work will be required to apply it to experimental recordings of developmental systems. “In the future, we want to study more complex models with more parameters and dimensions,” Tkačik says. “By quantifying more complex models, we could also apply our framework to experimentally measured patterns of chemical signals in developing embryos,” adds Brückner. For this purpose, the two theoretical scientists will team up with experimentalists. More

An international research team around Marcus Sperling, a researcher at the Faculty of Physics, University of Vienna, has sparked interest in the scientific community with pioneering results in quantum physics: In their current study, the researchers reinterpret the Higgs mechanism, which gives elementary particles mass and triggers phase transitions, using the concept of “magnetic quivers.” The work has now been published in the journal “Physical Review Letters.”
The foundation of Marcus Sperling’s research, which lies at the intersection of physics and mathematics, is Quantum Field Theory (QFT) — a physical-mathematical concept within quantum physics focused on describing particles and their interactions at the subatomic level. Since 2018, he has developed the so-called “magnetic quivers” along with colleagues — a graphical tool that summarizes all the information needed to define a QFT, thus displaying complex interactions between particle fields or other physical quantities clearly and intuitively.
Metaphorical Magnetic Quivers
A quiver consists of directed arrows and nodes. The arrows represent the quantum fields (matter fields), while the nodes represent the interactions — e.g., strong, weak, or electromagnetic — between the fields. The direction of the arrows indicates how the fields are charged under the interactions, e.g., what electric charge the particles carry. Marcus Sperling explains, “The term ‘magnetic’ is also used metaphorically here to point to the unexpected quantum properties that are made visible by these representations. Similar to the spin of an electron, which can be detected through a magnetic field, magnetic quivers reveal certain properties or structures in the QFTs that may not be obvious at first glance.” Thus, they offer a practical way to visualize and analyze complex quantum phenomena, facilitating new insights into the underlying mechanisms of the quantum world.
Supersymmetric QFTs
For the current study, the stable ground states (vacua) — the lowest energy configuration in which no particles or excitations are present — in a variety of “supersymmetric QFTs” were explored. These QFTs, with their simplified space-time symmetry, serve as a laboratory environment, as they resemble real physical systems of subatomic particles but have certain mathematical properties that facilitate calculations. FWF START award winner Sperling said, “Our research deals with the fundamentals of our understanding of physics. Only after we have understood the QFTs in our laboratory environment can we apply these insights to more realistic QFT models.” The concept of magnetic quivers — one of the main research topics of Sperling’s START project at the University of Vienna — was used as a tool to provide a precise geometric description of the new quantum vacua.
Decay & Fission: Higgs Mechanism Reinterpreted
With calculations based on linear algebra, the research team demonstrated that — analogous to radioactivity in atomic nuclei — a magnetic quiver can decay into a more stable state or fission into two separate quivers. These transformations offer a new understanding of the Higgs mechanism in QFTs, which either decay into simpler QFTs or fission into separate, independent QFTs. Physicist Sperling stated, “The Higgs mechanism explains how elementary particles acquire their mass by interacting with the Higgs field, which permeates the entire universe. Particles interact with this field as they move through space — similar to a swimmer moving through water.” A particle that has no mass usually moves at the speed of light. However, when it interacts with the Higgs field, it “sticks” to this field and becomes sluggish, leading to the manifestation of its mass. The Higgs mechanism is thus a crucial concept for understanding the fundamental building blocks and forces of the universe. Mathematically, the “decay and fission” algorithm is based on the principles of linear algebra and a clear definition of stability. It operates autonomously and requires no external inputs. The results achieved through physics-inspired methods are not only relevant in physics but also in mathematical research: They offer a fundamental and universally valid description of the complex, intertwined structures of the quantum vacua, representing a significant advance in mathematics. More

A team at Pohang University of Science and Technology (POSTECH), spearheaded by Professor Su Seok Choi and Ph.D. candidate Seungmin Nam from the Department of Electrical Engineering, has developed a novel stretchable photonic device that can control light wavelengths in all directions. This pioneering study was published in Light: Science & Applications on May 22.
Structural colors are produced through the interaction of light with microscopic nanostructures, creating vibrant hues without relying on traditional color mixing methods. Conventional displays and image sensors blend the three primary colors (red, green, and blue), while structural color technology leverages the inherent wavelengths of light, resulting in more vivid and diverse color displays. This innovative approach is gaining recognition as a promising technology in the nano-optics and photonics industries.
Traditional color mixing techniques, which use dyes or luminescent materials, are limited to passive and fixed color representation. In contrast, tunable color technology dynamically controls nanostructures corresponding to specific light wavelengths, allowing for the free adjustment of pure colors. Previous research has primarily been limited to unidirectional color tuning, typically shifting colors from red to blue. Reversing this shift — from blue to red, which has a longer wavelength — has been a significant challenge. Current technology only allows adjustments towards shorter wavelengths, making it difficult to achieve diverse color representation in the ideal free wavelength direction. Therefore, a new optical device capable of bidirectional and omnidirectional wavelength adjustment is needed to maximize the utilization of wavelength control technology.
Professor Choi’s team addressed these challenges by integrating chiral liquid crystal elastomers (CLCEs) with dielectric elastomer actuators (DEAs). CLCEs are flexible materials capable of structural color changes, while DEAs induce flexible deformation of dielectrics in response to electrical stimuli. The team optimized the actuator structure to allow both expansion and contraction, combining it with CLCEs, and developed a highly adaptable stretchable device. This device can freely adjust the wavelength position across the visible spectrum, from shorter to longer wavelengths and vice versa.
In their experiments, the researchers demonstrated that their CLCE-based photonic device could control structural colors over a broad range of visible wavelengths (from blue at 450nm to red at 650nm) using electrical stimuli. This represents a significant advancement over previous technologies, which were limited to unidirectional wavelength tuning.
This research not only establishes a foundational technology for advanced photonic devices but also highlights its potential for various industrial applications.
Professor Choi remarked, “This technology can be applied in displays, optical sensors, optical camouflage, direct optical analogue encryption, biomimetic sensors, and smart wearable devices, among many other applications involving light, color, and further broadband electromagnetic waves beyond visible band. We aim to expand its application scope through ongoing research.” More

Scientists at the Terasaki Institute for Biomedical Innovation (TIBI), have employed artificial intelligence techniques to improve the design and production of nanofibers used in wearable nanofiber acoustic energy harvesters (NAEH). These acoustic devices capture sound energy from the environment and convert it into electrical energy, which can then be applied in useful devices, such as hearing aids.
Many efforts have been made to capture naturally occurring and abundant energy sources from our surrounding environment. Relatively recent advances such as solar panels and wind turbines allow us to efficiently harvest energy from the sun and wind, convert it into electrical energy, and store it for various applications. Similarly, conversions of acoustic energy can be seen in amplifying devices such as microphones, as well as in wearable, flexible electronic devices for personalized healthcare.
Currently, there has been much interest in using piezoelectric nanogenerators — devices that convert mechanical vibrations, stress, or strain into electrical power — as acoustic energy harvesters. These nanogenerators can convert mechanical energy from sound waves to generate electricity; however, this conversion of sound waves is inefficient, as it occurs mainly in the high frequency sound range, and most environmental sound waves are in the low frequency range. Additionally, choosing optimal materials, structural design, and fabrication parameters make the production of piezoelectric nanogenerators challenging.
As described in their paper in Nano Research, the TIBI scientists’ approach to these challenges was two-fold: first, they chose their materials strategically and elected to fabricate nanofibers using polyvinylfluoride (PVDF), which are known for their ability to capture acoustic energy efficiently. When making the nanofiber mixture, polyurethane (PU) was added to the PVDF solution to impart flexibility, and electrospinning (a technique for generating ultrathin fibers) was used to produce the composite PVDF/PU nanofibers.
Secondly, the team applied artificial intelligence (AI) techniques to determine the best fabrication parameters involved in electrospinning the PVDF/polyurethane nanofibers; these parameters included the applied voltage, electrospinning time, and drum rotation speed. Employing these techniques allowed the team to tune the parameter values to obtain maximum power generation from their PVDF/PU nanofibers.
To make their nanoacoustic energy harvester, the TIBI scientists fashioned their PVDF/PU nanofibers into a nanofibrous mat and sandwiched it between aluminum mesh layers that functioned as electrodes. The entire assembly was then encased by two flexible frames.
In tests against conventionally fabricated NAEHs, the resultant AI-generated PVDF/PU NAEHs were found to have better overall performance, yielding a power density level more than 2.5 times higher and a significantly higher energy conversion efficiency (66% vs 42%). Furthermore, the AI-generated PVDF/PU NAEHs were able to obtain these results when tested with a wide range of low-frequency sound — well within the levels found in ambient background noise. This allows for excellent sound recognition and the ability to distinguish words with high resolution.
“Models using artificial intelligence optimization, such as the one described here, minimize time spent on trial and error and maximize the effectiveness of the finished product,” said Ali Khademhosseini, Ph.D., TIBI’s director and CEO. “This can have far-reaching effects on the fabrication of medical devices with significant practicability.” More

For survivors of strokes, which afflict nearly 800,000 Americans each year, regaining fine motor skills like writing and using utensils is critical for recovering independence and quality of life. But getting intensive, frequent rehabilitation therapy can be challenging and expensive.
Now, researchers at NYU Tandon School of Engineering are developing a new technology that could allow stroke patients to undergo rehabilitation exercises at home by tracking their wrist movements through a simple setup: a smartphone strapped to the forearm and a low-cost gaming controller called the Novint Falcon.
The Novint Falcon, a desktop robot typically used for video games, can guide users through specific arm motions and track the trajectory of its controller. But it cannot directly measure the angle of the user’s wrist, which is essential data for therapists providing remote rehabilitation.
In a paper presented at SPIE Smart Structures + Nondestructive Evaluation 2024, the researchers proposed using the Falcon in tandem with a smartphone’s built-in motion sensors to precisely monitor wrist angles during rehab exercises.
“Patients would strap their phone to their forearm and manipulate this robot,” said Maurizio Porfiri, NYU Tandon Institute Professor and director of its Center for Urban Science + Progress (CUSP), who is the paper’s senior author. “Data from the phone’s inertial sensors can then be combined with the robot’s measurements through machine learning to infer the patient’s wrist angle.”
The researchers collected data from a healthy subject performing tasks with the Falcon while wearing motion sensors on the forearm and hand to capture the true wrist angle. They then trained an algorithm to predict the wrist angles based on the sensor data and Falcon controller movements.
The resulting algorithm could predict wrist angles with over 90% accuracy, a promising initial step toward enabling remote therapy with real-time feedback in the absence of an in-person therapist.

“This technology could allow patients to undergo rehabilitation exercises at home while providing detailed data to therapists remotely assessing their progress,” Roni Barak Ventura, the paper’s lead author who was an NYU Tandon postdoctoral fellow at the time of the study. “It’s a low-cost, user-friendly approach to increasing access to crucial post-stroke care.”
The researchers plan to further refine the algorithm using data from more subjects. Ultimately, they hope the system could help stroke survivors stick to intensive rehab regimens from the comfort of their homes.
“The ability to do rehabilitation exercises at home with automatic tracking could dramatically improve quality of life for stroke patients,” said Barak Ventura. “This portable, affordable technology has great potential for making a difficult recovery process much more accessible.”
This study adds to NYU Tandon’s body of work that aims to improve stroke recovery. In 2022, Researchers from NYU Tandon began collaborating with the FDA to design a regulatory science tool based on biomarkers to objectively assess the efficacy of rehabilitation devices for post-stroke motor recovery and guide their optimal usage. A study from earlier this year unveiled advances in technology that uses implanted brain electrodes to recreate the speaking voice of someone who has lost speech ability, which can be an outcome from stroke. More

There’s a hot new BEC in town that has nothing to do with bacon, egg, and cheese. You won’t find it at your local bodega, but in the coldest place in New York: the lab of Columbia physicist Sebastian Will, whose experimental group specializes in pushing atoms and molecules to temperatures just fractions of a degree above absolute zero.
Writing in Nature, the Will lab, supported by theoretical collaborator Tijs Karman at Radboud University in the Netherlands, has successfully created a unique quantum state of matter called a Bose-Einstein Condensate (BEC) out of molecules.
Their BEC, cooled to just five nanoKelvin, or about -459.66 °F, and stable for a strikingly long two seconds, is made from sodium-cesium molecules. Like water molecules, these molecules are polar, meaning they carry both a positive and a negative charge. The imbalanced distribution of electric charge facilitates the long-range interactions that make for the most interesting physics, noted Will.
Research the Will lab is excited to pursue with their molecular BECs includes exploring a number of different quantum phenomena, including new types of superfluidity, a state of matter that flows without experiencing any friction. They also hope to turn their BECs into simulators that can recreate the enigmatic quantum properties of more complex materials, like solid crystals.
“Molecular Bose-Einstein condensates open up whole new areas of research, from understanding truly fundamental physics to advancing powerful quantum simulations,” he said. “This is an exciting achievement, but it’s really just the beginning.”
It’s a dream come true for the Will lab and one that’s been decades in the making for the larger ultracold research community.
To Go Colder, Add Microwaves
Microwaves are a form of electromagnetic radiation with a long history at Columbia. In the 1930s, physicist Isidor Isaac Rabi, who would go on to the Nobel Prize in Physics, did pioneering work on microwaves that led to the development of airborne radar systems. “Rabi was one of the first to control the quantum states of molecules and was a pioneer of microwave research,” said Will. “Our work follows in that 90-year-long tradition.”

While you may be familiar with the role of microwaves in heating up your food, it turns out they can also facilitate cooling. Individual molecules have a tendency to bump into each other and will, as a result, form bigger complexes that disappear from the samples. Microwaves can create small shields around each molecule that prevent them from colliding, an idea proposed by Karman, their collaborator in the Netherlands. With the molecules shielded against lossy collisions, only the hottest ones can be preferentially removed from the sample — the same physics principle that cools your cup of coffee when you blow along the top of it, explained author Niccolò Bigagli. Those molecules that remain will be cooler, and the overall temperature of the sample will drop.
The team came close to creating molecular BEC last fall in work published in Nature Physics that introduced the microwave shielding method. But another experimental twist was necessary.When they added a second microwave field, cooling became even more efficient and sodium-cesium finally crossed the BEC threshold — a goal the Will lab had harbored since it opened at Columbia in 2018.
“This was fantastic closure for me,” said Bigagli, who graduated with his PhD in physics this spring and was a founding lab member. “We went from not having a lab set up yet to these fantastic results.”
In addition to reducing collisions, the second microwave field can also manipulate the molecules’ orientation. That in turn is a means to control how they interact, which the lab is currently exploring. “By controlling these dipolar interactions, we hope to create new quantum states and phases of matter,” said co-author and Columbia postdoc Ian Stevenson.
A New World for Quantum Physics Opens
Ye, a pioneer of ultracold science based in Boulder, considers the results a beautiful piece of science. “The work will have important impacts on a number of scientific fields, including the study of quantum chemistry and exploration of strongly correlated quantum materials,” he commented. “Will’s experiment features precise control of molecular interactions to steer the system toward a desired outcome — a marvelous achievement in quantum control technology.”
The Columbia team, meanwhile, is excited to have a theoretical description of interactions between molecules that have been validated experimentally. “We really have a good idea of the interactions in this system, which is also critical for the next steps, like exploring dipolar many-body physics,” said Karman. “We’ve come up with schemes to control interactions, tested these in theory, and implemented them in the experiment. It’s been really an amazing experience to see these ideas for microwave ‘shielding’ being realized in the lab.”

There are dozens of theoretical predictions that can now be tested experimentally with the molecular BECs, which co-first author and PhD student Siwei Zhang noted, are quite stable. Most ultracold experiments take place within a second — some as short as a few milliseconds — but the lab’s molecular BECs last upwards of two seconds. “That will really let us investigate open questions in quantum physics,” he said.
One idea is to create artificial crystals with the BECs trapped in an optical lattice made from lasers. This would enable powerful quantum simulations that mimic the interactions in natural crystals, noted Will, which is a focus area of condensed matter physics. Quantum simulators are routinely made with atoms, but atoms have short-range interactions — they practically have to be on top of one another — which limits how well they can model more complicated materials. “The molecular BEC will introduce more flavor,” said Will.
That includes dimensionality, said co-first author and PhD student Weijun Yuan. “We would like to use the BECs in a 2D system. When you go from three dimensions to two, you can always expect new physics to emerge,” he said. 2D materials are a major area of research at Columbia; having a model system made of molecular BECs could help Will and his condensed matter colleagues explore quantum phenomena including superconductivity, superfluidity, and more.
“It seems like a whole new world of possibilities is opening up,” Will said. More

A novel, human-inspired approach to training artificial intelligence (AI) systems to identify objects and navigate their surroundings could set the stage for the development of more advanced AI systems to explore extreme environments or distant worlds, according to research from an interdisciplinary team at Penn State.
In the first two years of life, children experience a somewhat narrow set of objects and faces, but with many different viewpoints and under varying lighting conditions. Inspired by this developmental insight, the researchers introduced a new machine learning approach that uses information about spatial position to train AI visual systems more efficiently. They found that AI models trained on the new method outperformed base models by up to 14.99%. They reported their findings in the May issue of the journal Patterns.
“Current approaches in AI use massive sets of randomly shuffled photographs from the internet for training. In contrast, our strategy is informed by developmental psychology, which studies how children perceive the world,” said Lizhen Zhu, the lead author and doctoral candidate in the College of Information Sciences and Technology at Penn State.
The researchers developed a new contrastive learning algorithm, which is a type of self-supervised learning method in which an AI system learns to detect visual patterns to identify when two images are derivations of the same base image, resulting in a positive pair. These algorithms, however, often treat images of the same object taken from different perspectives as separate entities rather than as positive pairs. Taking into account environmental data, including location, allows the AI system to overcome these challenges and detect positive pairs regardless of changes in camera position or rotation, lighting angle or condition and focal length, or zoom, according to the researchers.
“We hypothesize that infants’ visual learning depends on location perception. In order to generate an egocentric dataset with spatiotemporal information, we set up virtual environments in the ThreeDWorld platform, which is a high-fidelity, interactive, 3D physical simulation environment. This allowed us to manipulate and measure the location of viewing cameras as if a child was walking through a house,” Zhu added.
The scientists created three simulation environments — House14K, House100K and Apartment14K, with ’14K’ and ‘100K’ referring to the approximate number of sample images taken in each environment. Then they ran base contrastive learning models and models with the new algorithm through the simulations three times to see how well each classified images. The team found that models trained on their algorithm outperformed the base models on a variety of tasks. For example, on a task of recognizing the room in the virtual apartment, the augmented model performed on average at 99.35%, a 14.99% improvement over the base model. These new datasets are available for other scientists to use in training through www.child-view.com.
“It’s always hard for models to learn in a new environment with a small amount of data. Our work represents one of the first attempts at more energy-efficient and flexible AI training using visual content,” said James Wang, distinguished professor of information sciences and technology and advisor of Zhu.
The research has implications for the future development of advanced AI systems meant to navigate and learn from new environments, according to the scientists.
“This approach would be particularly beneficial in situations where a team of autonomous robots with limited resources needs to learn how to navigate in a completely unfamiliar environment,” Wang said. “To pave the way for future applications, we plan to refine our model to better leverage spatial information and incorporate more diverse environments.”
Collaborators from Penn State’s Department of Psychology and Department of Computer Science and Engineering also contributed to this study. This work was supported by the U.S. National Science Foundation, as well as the Institute for Computational and Data Sciences at Penn State. More

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device described in ACS Sensors could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, improving the process for patients and technicians.
During an MRI scan, a patient must stay entirely still for several minutes at a time, otherwise “motion artifacts” could appear and blur the final image. To ensure a clear picture, patient movement needs to be identified as soon as it happens, allowing the scan to stop and for the technician to take a new one. Motion tracking could be achieved using sensors embedded into the MRI table; however, magnetic materials can’t be used because metals interfere with the MRI technology itself. One technology that’s well-suited for this unique situation, and avoids the need for metal or magnetic components, is the triboelectric nanogenerator (TENG), which powers itself using static electricity generated by friction between polymers. So, Li Tao, Zhiyi Wu and colleagues wanted to design a TENG-based sensor that could be incorporated into an MRI machine to help prevent motion artifacts.
The team created the TENG by sandwiching two layers of plastic film painted with graphite-based conductive ink around a central layer of silicone. These materials were specifically chosen as they would not interfere with an MRI scan. When pressed together, electrostatic charges from the plastic film moved to the conductive ink, creating a current that could then flow out through a wire.
This sensor was incorporated into an MRI table designed to lay under a patient’s head. In tests, when a person turned their head from side to side or raised it off the table, the sensor detected these movements and transmitted a signal to a computer. Then, an audible alert played, a pop-up window on the technician’s computer appeared and the MRI scan ceased. The researchers say that this work could help make MRI scans more efficient and less frustrating for patients and technicians alike by producing better images during a single procedure. More