Computers Math

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

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.
Today’s commercial charging infrastructure can be a jungle. The market is dynamic and complex with a variety of subscriptions and free competition between providers. At some fast charging stations, congestion and long queues may even occur. In a new study, researchers at Chalmers have created a mathematical model to investigate how fast charging stations controlled by artificial intelligence, AI, can help by offering electric car drivers personalised prices, which the drivers can choose to accept or refuse. The AI uses algorithms that can adjust prices based on individual factors, such as battery level and the car’s geographic location.
“The electric car drivers can choose to share information with the charging station providers and receive a personal price proposal from a smart charging station. In our study, we could show how rational and self-serving drivers react by only accepting offers that are beneficial to themselves. This leads to both price and waiting times being minimized,” says Balázs Kulcsár, professor at the department of electrical engineering at Chalmers.
In the study, the drivers always had the option to refuse the personal price, and choose a conventional charging station with a fixed price instead. The personal prices received by the drivers could differ significantly from each other, but were almost always lower than the market prices. For the providers of charging stations, the iterative AI algorithm can find out which individual prices are accepted by the buyer, and under which conditions. However, during the course of the study, the researchers noted that on some occasions the algorithm raised the price significantly when the electric car’s batteries were almost completely empty, and the driver consequently had no choice but to accept the offer.
“Smart charging stations can solve complex pricing in a competitive market, but our study shows that they need to be developed and introduced with privacy protection for consumers, well in line with responsible-ethical AI paradigms,” says Balázs Kulcsár.
More about the study
The researchers created a mathematical model of the interaction between profit-maximising fast charging stations and electric car users. The “charging stations” could offer public market prices or AI-driven profit-maximising personal prices, which the “electric car users” could then accept or reject based on their own conditions and needs. In most cases, the results were promising, as the AI-generated prices were lower than the market prices. More

Researchers in George Mason University’s College of Public Health have leveraged the power of artificial intelligence (AI) analytical models to match a patient’s medical history to the most effective antidepressant, allowing patients to find symptom relief sooner. The free website, MeAgainMeds.com, provides evidence-based recommendations, allowing clinicians and patients to find the optimal antidepressant the first time.
“Many people with depression must try multiple antidepressants before finding the right one that alleviates their symptoms. Our website reduces the number of medications that patients are asked to try. The system recommends to the patient what has worked for at least 100 other patients with the same exact relevant medical history,” said Farrokh Alemi, principal investigator and professor of health informatics at George Mason University’s College of Public Health.
AI helped to simplify the very complex task of making thousands of guidelines easily accessible to patients and clinicians. The guidelines that researchers created are complicated because of the amount of clinical information that is relevant in prescribing an antidepressant; AI seamlessly simplifies the task.
With AI at its core, MeAgainMeds.com analyzes clinician or patient responses to a few anonymous medical history questions to determine which oral antidepressant would best meet the specific needs. The website does not ask for any personal identifiable information and it does not prescribe medication changes. Patients are advised to visit their primary health care provider for any changes in medication.
In 2018, the Centers for Disease Control reported that more than 13% of adults use antidepressants, and the number has only increased since the pandemic and other epidemics since 2020. This website could help millions of people find relief more quickly.
Alemi and his team analyzed 3,678,082 patients who took 10,221,145 antidepressants. The oral antidepressants analyzed were amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. From the data, they created 16,770 subgroups of at least 100 cases, using reactions to prior antidepressants, current medication, history of physical illness, history of mental illness, key procedures, and other information. The subgroups and remission rates drive the AI to produce an evidence-based medication recommendation.
“By matching patients to the subgroups, clinicians can prescribe the medication that works best for people with similar medical history,” said Alemi. The researchers and website recommend that patients who use the site take the information to their clinicians, who will ultimately decide whether to prescribe the recommended medicine.
Alemi and his team tested a protype version of the site in 2023, which they advertised on social media. At that time, 1,500 patients used the website. Their goal is to improve the website and expand its user base. The initial research was funded by the Commonwealth of Virginia and by the Robert Wood Johnson Foundation.
The researchers’ most recent paper in a series of papers on response to antidepressants analyzed 2,467 subgroups of patients who had received psychotherapy. “Effectiveness of Antidepressants in Combination with Psychotherapy” was published online in The Journal of Mental Health Policy and Economics in March 2024. Additional authors include Tulay G Soylu from Temple University, and Mary Cannon and Conor McCandless from Royal College of Surgeons in Dublin, Ireland. More

A team of researchers at the University of Waterloo have developed a new machine-learning method that detects hate speech on social media platforms with 88 per cent accuracy, saving employees from hundreds of hours of emotionally damaging work.
The method, dubbed the Multi-Modal Discussion Transformer (mDT), can understand the relationship between text and images as well as put comments in greater context, unlike previous hate speech detection methods. This is particularly helpful in reducing false positives, which are often incorrectly flagged as hate speech due to culturally sensitive language.
“We really hope this technology can help reduce the emotional cost of having humans sift through hate speech manually,” said Liam Hebert, a Waterloo computer science PhD student and the first author of the study. “We believe that by taking a community-centred approach in our applications of AI, we can help create safer online spaces for all.”
Researchers have been building models to analyze the meaning of human conversations for many years, but these models have historically struggled to understand nuanced conversations or contextual statements. Previous models have only been able to identify hate speech with as much as 74 per cent accuracy, below what the Waterloo research was able to accomplish.
“Context is very important when understanding hate speech,” Hebert said. “For example, the comment ‘That’s gross!’ might be innocuous by itself, but its meaning changes dramatically if it’s in response to a photo of pizza with pineapple versus a person from a marginalized group.
“Understanding that distinction is easy for humans, but training a model to understand the contextual connections in a discussion, including considering the images and other multimedia elements within them, is actually a very hard problem.”
Unlike previous efforts, the Waterloo team built and trained their model on a dataset consisting not only of isolated hateful comments but also the context for those comments. The model was trained on 8,266 Reddit discussions with 18,359 labelled comments from 850 communities.
“More than three billion people use social media every day,” Hebert said. “The impact of these social media platforms has reached unprecedented levels. There’s a huge need to detect hate speech on a large scale to build spaces where everyone is respected and safe.”
The research, Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media, was recently published in the proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence. More

A new study to be presented at the SLEEP 2024 annual meeting found a distinct relationship between sleep duration, social media usage, and brain activation across brain regions that are key for executive control and reward processing.
Results show a correlation between shorter sleep duration and greater social media usage in teens. The analysis points to involvement of areas within the frontolimbic brain regions, such as the inferior and middle frontal gyri, in these relationships. The inferior frontal gyrus, key in inhibitory control, may play a crucial role in how adolescents regulate their engagement with rewarding stimuli such as social media. The middle frontal gyrus, involved in executive functions and critical in assessing and responding to rewards, is essential in managing decisions related to the balancing of immediate rewards from social media with other priorities like sleep. These results suggest a nuanced interaction between specific brain regions during adolescence and their influence on behavior and sleep in the context of digital media usage.
“As these young brains undergo significant changes, our findings suggest that poor sleep and high social media engagement could potentially alter neural reward sensitivity,” said Orsolya Kiss, who has a doctorate in cognitive psychology and is a research scientist at SRI International in Menlo Park, California. “This intricate interplay shows that both digital engagement and sleep quality significantly influence brain activity, with clear implications for adolescent brain development.”
This study involved data from 6,516 adolescents, ages 10-14 years, from the Adolescent Brain Cognitive Development Study. Sleep duration was assessed from the Munich Chronotype questionnaire, and recreational social media use through the Youth Screen Time Survey. Brain activities were analyzed from functional MRI scans during the monetary incentive delay task, targeting regions associated with reward processing. The study used three different sets of models and switched predictors and outcomes each time. Results were adjusted for age, COVID-19 pandemic timing, and socio-demographic characteristics.
Kiss noted that these results provide new insights into how two significant aspects of modern adolescent life — social media usage and sleep duration — interact and impact brain development.
“Understanding the specific brain regions involved in these interactions helps us identify potential risks and benefits associated with digital engagement and sleep habits,” Kiss said. “This knowledge is especially important as it could guide the development of more precise, evidence-based interventions aimed at promoting healthier habits.”
The American Academy of Sleep Medicine recommends that teenagers 13 to 18 years of age should sleep 8 to 10 hours on a regular basis. The AASM also encourages adolescents to disconnect from all electronic devices at least 30 minutes to an hour before bedtime.
This study was supported by grants from the National Institutes of Health. The research abstract was published recently in an online supplement of the journal Sleep and will be presented Sunday, June 2, and Wednesday, June 5, during SLEEP 2024 in Houston. SLEEP is the annual meeting of the Associated Professional Sleep Societies, a joint venture of the American Academy of Sleep Medicine and the Sleep Research Society. More

Cultivating creativity in schools is vital for a future driven by artificial intelligence (AI). But while teachers embrace creativity as an essential 21st century skill, a lack of valid and reliable creativity tests means schools struggle to assess student achievement.
Now, a new machine-learning model developed by the University of South Australia is providing teachers with access to high-quality, fit-for-purpose creativity tests, that can score assessments in a fraction of the time and a fraction of the cost.
Applied to the current empirical creativity test — Test of Creative Thinking — Drawing Production (TCT-DP) — the new algorithm marks a test in a single millisecond, as opposed to the standard 15-minute human-marked test.
The development could save teachers thousands of hours in an already overloaded schedule.
Lead researcher, UniSA’s Prof David Cropley says the algorithm presents a game changing innovation for schools.
“Creativity is an essential skill for the next generation, particularly because it is a skill that cannot be automated,” Prof Cropley says.
“But because there is a lack of affordable and efficient tools to measure creativity in schools, students are either not being tested, or are being graded subjectively, which is inconsistent and unreliable.

“The TCT-DP test has long been acknowledged as the premier tool to assess creativity in school aged children, but as it is expensive, slow, and labour-intensive, it’s out of reach for most schools.
“Our algorithm changes this. Not only is the cost of running the algorithm reduced by a factor of more than 20, but the results are fast and incredibly accurate.
“For example, a manually scored test for a school with 1000 students would cost approximately $25,000 and require about 10-weeks to receive test results; with UniSA’s algorithm, the same testing could be conducted for approximately $1000 with results delivered in 1-2 days.
“This puts the test within direct reach of schools and teachers, giving them the means to assess creativity accurately and cheaply.”
Co-researcher, UniSA’s Dr Rebecca Marrone says the capacity to test and measure creativity has additional benefits for students who are sometimes overlooked.
“Testing for creativity opens up an avenue beyond more traditional intelligence testing,” Dr Marrone says.
“Testing for creativity helps identify students who may have abilities that do not show up on traditional approaches to testing in school. For example, a child who does poorly on traditional IQ tests, but is highly creative, could easily slip through the cracks.
“Developing creativity also protects children on the lower end of the achievement spectrum by training them in a skill that is not vulnerable to automation, which can help buffer them against the effects of digital transformation.”
The algorithm is currently being developed as a desktop app for teachers to use in the classroom. Ahead of this, classroom teachers interested in using the TCT-DP are invited to contact the UniSA team to discuss their needs. More