Aurelia Butler

Researchers at the Complexity Science Hub have examined 1.2 million criminal incidents and developed an innovative method to identify patterns in criminal trajectories.
When it comes to preventing future crimes, it is essential to understand how past criminal behavior relates to future offenses. One key question is whether criminals tend to specialize in specific types of crimes or exhibit a generalist approach by engaging in a variety of illegal activities.
Despite the potential significance of systematically identifying patterns in criminal careers, especially in preventing recurrent offenses, there is a scarcity of comprehensive empirical studies on this subject.
“To address this gap, we conducted an exhaustive examination of over 1.2 million criminal incidents,” elaborates Stefan Thurner of the Complexity Science Hub. This comprehensive dataset encompassed all criminal reports filed against individuals over six years in a small Central European country.
Specialists With Certain Features
Criminal offenders who specialize in specific types of crimes typically are older and more frequently female than individuals involved in a broader range of offenses.
“These individuals, referred to as specialists, also tend to operate within a more confined geographic area, suggesting their dependence on local knowledge and potentially receiving support from individuals within that specific region, as opposed to offenders with a wider focus,” Thurner explains one of the study’s results. Furthermore, the researchers observed that these specialists tend to collaborate in tighter-knit local networks, increasing the likelihood of recurring partnerships. More

Researchers at the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) in Hamburg, Germany, have shown that a previously demonstrated ability to turn on superconductivity with a laser beam can be integrated on a chip, opening up a route toward opto-electronic applications.
Their work, now published in Nature Communications, also shows that the electrical response of photo-excited K3C60 is not linear, that is, the resistance of the sample depends on the applied current. This is a key feature of superconductivity, validates some of the previous observations and provides new information and perspectives on the physics of K3C60 thin films.
The optical manipulation of materials to produce superconductivity at high temperatures is a key research focus of the MPSD. So far, this strategy has proven successful in several quantum materials, including cuprates, k-(ET)2-X and K3C60. Enhanced electrical coherence and vanishing resistance have been observed in previous studies on the optically driven states in these materials.
In this study, researchers from the Cavalleri group deployed on-chip non-linear THz spectroscopy to open up the realm of picosecond transport measurements (a picosecond is a trillionth of a second). They connected thin films of K3C60 to photo-conductive switches with co-planar waveguides. Using a visible laser pulse to trigger the switch, they sent a strong electrical current pulse lasting just one picosecond through the material. After travelling through the solid at around half the speed of light, the current pulse reached another switch which served as a detector to reveal important information, such as the characteristic electrical signatures of superconductivity.
By simultaneously exposing the K3C60 films to mid-infrared light, the researchers were able to observe non-linear current changes in the optically excited material. This so-called critical current behavior and the Meissner effect are the two key features of superconductors. However, neither has been measured so far — making this demonstration of critical current behavior in the excited solid particularly significant. Moreover, the team discovered that the optically driven state of K3C60 resembled that of a so-called granular superconductor, consisting of weakly connected superconducting islands.
The MPSD is uniquely placed to carry out such measurements on the picosecond scale, with the on-chip set-up having been designed and built in-house. “We developed a technique platform which is perfect for probing non-linear transport phenomena away from equilibrium, like the non-linear and anomalous Hall effects, the Andreev reflection and others,” says lead author Eryin Wang, a staff scientist in the Cavalleri group. In addition, the integration of non-equilibrium superconductivity into opto-electronic platforms may lead to new devices based on this effect.
Andrea Cavalleri, who has founded and is currently leading the research group, adds: “This work underscores the scientific and technological developments within the MPSD in Hamburg, where new experimental methods are constantly being developed to achieve new scientific understanding. We have been working on ultrafast electrical transport methods for nearly a decade and are now in a position to study so many new phenomena in non-equilibrium materials, and potentially to introduce lasting changes in technology.”
The research underpinning these results was carried out in the laboratories of the MPSD at the Center for Free-Electron Laser Science (CFEL) in Hamburg, Germany. More

White faces generated by artificial intelligence (AI) now appear more real than human faces, according to new research led by experts at The Australian National University (ANU).
In the study, more people thought AI-generated white faces were human than the faces of real people. The same wasn’t true for images of people of colour.
The reason for the discrepancy is that AI algorithms are trained disproportionately on white faces, Dr Amy Dawel, the senior author of the paper, said.
“If white AI faces are consistently perceived as more realistic, this technology could have serious implications for people of colour by ultimately reinforcing racial biases online,” Dr Dawel said.
“This problem is already apparent in current AI technologies that are being used to create professional-looking headshots. When used for people of colour, the AI is altering their skin and eye colour to those of white people.”
One of the issues with AI ‘hyper-realism’ is that people often don’t realise they’re being fooled, the researchers found.
“Concerningly, people who thought that the AI faces were real most often were paradoxically the most confident their judgements were correct,” Elizabeth Miller, study co-author and PhD candidate at ANU, said.

“This means people who are mistaking AI imposters for real people don’t know they are being tricked.”
The researchers were also able to discover why AI faces are fooling people.
“It turns out that there are still physical differences between AI and human faces, but people tend to misinterpret them. For example, white AI faces tend to be more in-proportion and people mistake this as a sign of humanness,” Dr Dawel said.
“However, we can’t rely on these physical cues for long. AI technology is advancing so quickly that the differences between AI and human faces will probably disappear soon.”
The researchers argue this trend could have serious implications for the proliferation of misinformation and identity theft, and that action needs to be taken.
“AI technology can’t become sectioned off so only tech companies know what’s going on behind the scenes. There needs to be greater transparency around AI so researchers and civil society can identify issues before they become a major problem,” Dr Dawel said.
Raising public awareness can also play a significant role in reducing the risks posed by the technology, the researchers argue.
“Given that humans can no longer detect AI faces, society needs tools that can accurately identify AI imposters,” Dr Dawel said.
“Educating people about the perceived realism of AI faces could help make the public appropriately sceptical about the images they’re seeing online.” More

A form of brain-inspired computing that exploits the intrinsic physical properties of a material to dramatically reduce energy use is now a step closer to reality, thanks to a new study led by UCL and Imperial College London researchers.
In the new study, published in the journal Nature Materials, an international team of researchers used chiral (twisted) magnets as their computational medium and found that, by applying an external magnetic field and changing temperature, the physical properties of these materials could be adapted to suit different machine-learning tasks.
Such an approach, known as physical reservoir computing, has until now been limited due to its lack of reconfigurability. This is because a material’s physical properties may allow it to excel at a certain subset of computing tasks but not others.
In the new study, published in the journal Nature Materials, an international team of researchers used chiral (twisted) magnets as their computational medium and found that, by applying an external magnetic field and changing temperature, the physical properties of these materials could be adapted to suit different machine-learning tasks.
Dr Oscar Lee (London Centre for Nanotechnology at UCL and UCL Department of Electronic & Electrical Engineering), the lead author of the paper, said: “This work brings us a step closer to realising the full potential of physical reservoirs to create computers that not only require significantly less energy, but also adapt their computational properties to perform optimally across various tasks, just like our brains.
“The next step is to identify materials and device architectures that are commercially viable and scalable.”
Traditional computing consumes large amounts of electricity. This is partly because it has separate units for data storage and processing, meaning information has to be shuffled constantly between the two, wasting energy and producing heat. This is particularly a problem for machine learning, which requires vast datasets for processing. Training one large AI model can generate hundreds of tonnes of carbon dioxide. More

With Amazon aiming to make 10,000 deliveries with drones in Europe this year and Walmart planning to expand its drone delivery services to an additional 60,000 homes this year in the states, companies are investing more research and development funding into drone delivery, But are consumers ready to accept this change as the new normal?
Northwestern University’s Mobility and Behavior Lab, led by Amanda Stathopoulos, an associate professor of civil and environmental engineering, wanted to know if consumers were ready for robots to replace delivery drivers, in the form of automated vehicles, drones and robots. The team found that societally, there’s work to do to shift public perceptions of the near-future technology.
“We need to think really carefully about the effect of these new technologies on people and communities, and to tune in to what they think about these changes,” Stathopoulos, the study’s senior author, said.
The study, titled “Robots at your doorstep: Acceptance of near-future technologies for automated parcel delivery,” published last week in the journal Scientific Reports. Researchers noted a “complex and multifaceted” relationship between behavior and acceptance of near-future technologies for automated parcel delivery.
While people were generally more willing to accept an automated vehicle as a substitute for a delivery person — perhaps because there already is familiarity with self-driving cars — people disliked drones and robots as options. However, as delivery speed increased and price decreased, likelihood to accept the technology increased.
They also found that tech-savvy consumers were more accepting of the near-future technologies than populations less familiar with the technology.
Stathopoulos is the William Patterson Junior professor of civil and environmental engineering at Northwestern’s McCormick School of Engineering, where she studies the human aspects of new systems of mobility. She also is a faculty affiliate of Northwestern’s Transportation Center. She said especially after the pandemic, people have come to expect efficient delivery from e-commerce purchases as they increasingly work from home.

Maher Said, a graduate of Stathopoulos’s lab, is the study’s lead author.
“There’s a paradox: We’re having a hard time reconciling the convenience and the benefit of getting speedy, efficient delivery with its consequences, like poor labor conditions in warehouses, air pollution and congested streets,” Stathopoulos said. “We don’t really see that other role that we play as citizens or as users of the city. And one role is directly affecting the other role, and we are both. With automated delivery, we could reduce some of these issues.”
The team designed a survey to assess preferences of 692 U.S. respondents, asking questions about different delivery options and variables like delivery speed, package handling and general perceptions.
Stathopoulos said that while new modes of delivery present an exciting opportunity, societally, “we’re not there just yet.” As companies ramp up drone deliveries due in part to labor shortages and in part because existing systems cannot satisfy the sheer volume of e-commerce deliveries, the researchers caution that these innovations may fail because of a lack of public acceptance.
Stathopoulos said she thinks shipping and logistics centers should be placed at the “front and center” of city planning and design, as in some European cities, to recognize its importance and role in quality of life. Policy makers will also need to become part of the conversation as more drones enter the airspace and labor shifts. None of this will work, Stathopoulos argued, until companies begin to consolidate their unique systems.
“On the planning side, we need to make sure that we embrace the fact that the massive amount of deliveries is going to shape our cities,” Stathopoulos said. “Collaboration, coordination, and information sharing between companies has been a running challenge — but it’s not going to work if everyone has their own technology. It just destroys the purpose and builds redundant and overlapping systems.”
However, by listening to and conducting more frequent assessments of user acceptance of technologies, Stathopoulos argues that policy makers and companies can prepare for the future and work to overcome anxiety and reluctance to accept new technologies.
The study was supported by the National Science Foundation Career program. More

Deep within every piece of magnetic material, electrons dance to the invisible tune of quantum mechanics. Their spins, akin to tiny atomic tops, dictate the magnetic behavior of the material they inhabit. This microscopic ballet is the cornerstone of magnetic phenomena, and it’s these spins that a team of JILA researchers — headed by JILA Fellows and University of Colorado Boulder professors Margaret Murnane and Henry Kapteyn — has learned to control with remarkable precision, potentially redefining the future of electronics and data storage.
In a new Science Advances publication, the JILA team — along with collaborators from universities in Sweden, Greece, and Germany — probed the spin dynamics within a special material known as a Heusler compound: a mixture of metals that behaves like a single magnetic material. For this study, the researchers utilized a compound of cobalt, manganese, and gallium, which behaved as a conductor for electrons whose spins were aligned upwards and as an insulator for electrons whose spins were aligned downwards.
Using a form of light called extreme ultraviolet high-harmonic generation (EUV HHG) as a probe, the researchers could track the re-orientations of the spins inside the compound after exciting it with a femtosecond laser, which caused the sample to change its magnetic properties. The key to accurately interpreting the spin re-orientations was the ability to tune the color of the EUV HHG probe light.
“In the past, people haven’t done this color tuning of HHG,” explained co-first author and JILA graduate student Sinéad Ryan. “Usually, scientists only measured the signal at a few different colors, maybe one or two per magnetic element at most.” In a monumental first, the JILA team tuned their EUV HHG light probe across the magnetic resonances of each element within the compound to track the spin changes with a precision down to femtoseconds (a quadrillionth of a second).
“On top of that, we also changed the laser excitation fluence, so we were changing how much power we used to manipulate the spins,” Ryan elaborated, highlighting that that step was also an experimental first for this type of research.
Along with their novel approach, the researchers collaborated with theorist and co-first author Mohamed Elhanoty of Uppsala University, who visited JILA, to compare theoretical models of spin changes to their experimental data. Their results showed strong correspondence between data and theory. “We felt that we’d set a new standard with the agreement between the theory and the experiment,” added Ryan.
Fine Tuning Light Energy
To dive into the spin dynamics of their Heusler compound, the researchers brought an innovative tool to the table: extreme ultraviolet high-harmonic probes. To produce the probes, the researchers focused 800-nanometer laser light into a tube filled with neon gas, where the laser’s electric field pulled the electrons away from their atoms and then pushed them back. When the electrons snapped back, they acted like rubber bands released after being stretched, creating purple bursts of light at a higher frequency (and energy) than the laser that kicked them out. Ryan tuned these bursts to resonate with the energies of the cobalt and the manganese within the sample, measuring element-specific spin dynamics and magnetic behaviors within the material that the team could further manipulate. More

Physical properties (stability, solubility, etc.), critical to the performance of pharmaceutical and functional materials, are known to strongly depend on the solid-state form and environmental factors, such as temperature and relative humidity. Recognising that late appearing, more stable forms can lead to disappearing polymorphs and potentially market withdrawal of a life-saving medicine, the pharmaceutical industry has heavily invested in solid form screening platforms.
Quantitatively measuring the free energy differences between crystalline forms is no small challenge. Metastable crystal forms can be difficult to prepare in pure form and they are frequently susceptible to converting to more stable forms. Thus, having the ability to computationally model free energies means that the risks posed by physical instability can be understood and mitigated for all systems, including those that are experimentally intractable. The lack of reliable experimental benchmark data has been a major bottleneck in developing computational methods for accurately predicting solid-solid free energy differences. Reports in the literature are sparse and much of the experimental data on free energy determinations for molecules of pharmaceutical interest is simply not in the public domain.
To overcome this challenge, experts in academia and industry have compiled the first ever reliable experimental benchmark of solid-solid free energy differences for chemically diverse, industrially relevant systems. They then predicted these free energy differences using several methods pioneered by the group of Prof. Alexandre Tkatchenko within the Department of Physics and Materials Science at the University of Luxembourg, and further improved by Dr. Marcus Neumann and his team of researchers at Avant-garde Materials Simulation. Without using any empirical input, these calculations leveraging high performance computing (HPC) were able to predict and explain data from seven pharmaceutical companies with surprising accuracy. The potential future implications of this work are manifold, and this latest development is just one of many potential application of quantum mechanical calculations in the pharmaceutical industry.
“I am thrilled to see how computational methods developed in my academic group have been quickly adopted to reliably predict the energetics of drug crystal forms in the pharmaceutical industry in a matter of years, breaking the traditional barrier between research and industrial innovation,” remarks Prof. Tkatchenko.
“We owe a fair part of our success to the visionaries among our customers who have enabled us to create an industrial working environment with an academic touch that promotes creativity based on core values such as honesty, integrity, perseverance, team-spirit and genuine care for people and the environment,” points out Dr Marcus Neuman, founder and CEO of AMS.
“Building links between fundamental science, high performance computing, and major industry players in order to make a lasting impact for the future of health is no small feat,” said Prof. Jens Kreisel, Rector of the University of Luxembourg. “We take very seriously our mission of nurturing an ecosystem where researchers can drive societal change for good.” More

Over the past decade, AI has permeated nearly every corner of science: Machine learning models have been used to predict protein structures, estimate the fraction of the Amazon rainforest that has been lost to deforestation and even classify faraway galaxies that might be home to exoplanets.
But while AI can be used to speed scientific discovery — helping researchers make predictions about phenomena that may be difficult or costly to study in the real world — it can also lead scientists astray. In the same way that chatbots sometimes “hallucinate,” or make things up, machine learning models can sometimes present misleading or downright false results.
In a paper published online today (Thursday, Nov. 9) in Science, researchers at the University of California, Berkeley, present a new statistical technique for safely using the predictions obtained from machine learning models to test scientific hypotheses.
The technique, called prediction-powered inference (PPI), uses a small amount of real-world data to correct the output of large, general models — such as AlphaFold, which predicts protein structures — in the context of specific scientific questions.
“These models are meant to be general: They can answer many questions, but we don’t know which questions they answer well and which questions they answer badly — and if you use them naively, without knowing which case you’re in, you can get bad answers,” said study author Michael Jordan, the Pehong Chen Distinguished Professor of electrical engineering and computer science and of statistics at UC Berkeley. “With PPI, you’re able to use the model, but correct for possible errors, even when you don’t know the nature of those errors at the outset.”
The risk of hidden biases
When scientists conduct experiments, they’re not just looking for a single answer — they want to obtain a range of plausible answers. This is done by calculating a “confidence interval,” which, in the simplest case, can be found by repeating an experiment many times and seeing how the results vary. More