Aurelia Butler

We interact with bits and bytes everyday — whether that’s through sending a text message or receiving an email.
There’s also quantum bits, or qubits, that have critical differences from common bits and bytes. These photons — particles of light — can carry quantum information and offer exceptional capabilities that can’t be achieved any other way. Unlike binary computing, where bits can only represent a 0 or 1, qubit behavior exists in the realm of quantum mechanics. Through “superpositioning,” a qubit can represent a 0, a 1, or any proportion between. This vastly increases a quantum computer’s processing speed compared to today’s computers.
“Learning about the capabilities of qubits has been a driving force for the emerging field of quantum technologies, opening up new and unexplored applications like quantum communication, computing and sensing,” said Hong Koo Kim, Professor of Electrical and Computer Engineering at the University of Pittsburgh Swanson School of Engineering.
Quantum technologies are important for a number of fields, like for banks protecting financial information or providing researchers with the speed needed to mimic all aspects of chemistry. And through quantum “entanglement,” qubits could “communicate” across vast distances as a single system. Kim and his graduate student, Yu Shi, made a discovery that may help quantum technology take a quantum leap.
It begins with a single photon
Photon-based quantum technologies rely on single photon sources that can emit individual photons.
These single photons can be generated from nanometer scale semiconductors, more commonly known as quantum dots. Similar to how microwave antennas broadcast mobile phone signals, a quantum dot acts as an antenna that radiates light.
“By performing rigorous analysis, we discovered that a quantum dot emitter — or a nanometer scale dipole antenna — traps a large amount of energy,” Kim explained. “The outer regime operation of a dipole emitter is well understood, but this is really the first time a dipole has been studied on the inside.”
Photons from those quantum dots come out with handedness, like us a right-handed or left-handed person, and quantum information is carried by this handedness of individual photons. As such, sorting them out to different pathways is an important task for quantum information processing. Kim’s team has developed a new way of separating differently-handed photons and efficiently harvesting them for further processing down the road.
“The findings of this work are expected to contribute to developing high-speed single photon sources, a critical component needed in quantum photonics,” Kim said. More

Workers feeling a specific form of stress are more likely than others to become the victims of a phishing attack, according to a study at the Department of Energy’s Pacific Northwest National Laboratory.
While most — if not all — of us feel stress in the workplace, scientists identified a specific form of stress that indicates who is more vulnerable to clicking on bogus content that could lead to malware and other cyber ills. The work could help workers and their employers increase their cybersecurity defenses by recognizing the warning signs when someone is about to make a risky click.
The team’s results from a study of 153 participants were published recently in the Journal of Information Warfare. The researchers noted that while the relatively small sample size limited their ability to tease out all of the relationships among more than two dozen variables they studied, the relationship between stress and response to the simulated phishing email was statistically significant.
The costs of phishing attacks are enormous. An analysis sponsored by Proofpoint and conducted by the Ponemon Institute estimates that large U.S. businesses lost, on average, $14.8 million apiece to fraudsters via phishing in 2021 alone.
Defenses include not just better technology but also improved awareness by would-be victims.
“The first step to defend ourselves is understanding the complex constellation of variables that make a person susceptible to phishing,” says PNNL psychologist Corey Fallon, a corresponding author of the study. “We need to tease out those factors that make people more or less likely to click on a dubious message.”
In their study, Fallon and colleagues found that people who reported a high level of work-related distress were significantly more likely to follow a phony phishing email’s link. Every one-point increase in self-reported distress increased the likelihood of responding to the simulated phishing email by 15 percent.

The scientists describe distress as a feeling of tension when someone on the job feels they’re in a difficult situation and unable to tackle the task at hand. Distress might stem from feeling their workload is too high, or they might be questioning whether they have adequate training or time to accomplish their work.
Fancy phish to explore phishing psychology
The 153 participants had agreed to take part in a study, but they were unaware that the phishing email sent a few weeks later was part of the planned study into human factors research.
As far as phishes go, this was a fancy phish. There was no mention of a large sum of money from an African prince, for example, and there were no outright spelling mistakes or gross grammatical errors.
“These were well-crafted emails deliberately designed to trick people and tailored to the organization,” said Jessica Baweja, a psychologist and an author of the study. “It was much harder to detect than the average phish.”
Each participant received one of four different versions of a message about an alleged new dress code to be implemented at their organization. The team tested three common phishing tactics separately and together. Here’s what they found: Urgency. 49 percent of recipients clicked on the links. Sample text: “This policy will go into effect 3 days from the receipt of this notice…acknowledge the changes immediately.” Threat. 47 percent clicked. “…comply with this change in dress code or you may be subject to disciplinary action.” Authority. 38 percent clicked. “Per the Office of General Counsel…” The three tactics together: 31 percent clicked.

While the team had expected that more tactics used together would result in more people clicking on the message, that wasn’t the case.
“It’s possible that the more tactics that were used, the more obvious it was a phishing message,” said author Dustin Arendt, a data scientist. “The tactics must be compelling, but there’s a middle ground. If too many tactics are used, it may be obvious that you’re being manipulated.”
In day-to-day operations, PNNL tests its staff with fake phishing emails periodically. Typically around just 1 percent of recipients will click. Far more employees spot the phish early on and provide crowd-sourced alerting to the Laboratory’s cybersecurity experts, said Joseph Higbee, PNNL’s chief information security officer. When a real phishing email is detected, the Laboratory purges the system of all instances of the email immediately. The information is frequently shared with other DOE laboratories.
Human-machine teaming to reduce cybersecurity risk
How can companies and employees use this data to reduce the risk?
“One option is to help people recognize when they are feeling distressed,” said Fallon, “so they can be extra aware and cautious when they’re especially vulnerable.”
In the future, one option might be human-machine teaming. If an algorithm notes a change in a work pattern that might indicate fatigue or inattention, a smart machine assistant could suggest a break from email. Automated alerts are becoming more common, for instance, when a driver drifts unexpectedly and the car issues a warning about fatigue. The researchers noted that the potential benefits of input from a machine assistant would need to be weighed against employee privacy concerns.
“It can be hard to see email as a threat,” said Baweja. “Our ancient brains aren’t wired to equate email with scary things. You’re working through emails all day and it’s routine; there’s little reason to think they could harm you or our organization.
“Organizations need to be thinking about how to encourage people to make good choices. People overestimate their ability to detect phishing emails,” she added.
PNNL researchers are continuing the work, but with a twist. Instead of asking what makes people more vulnerable to phishing, they will conduct a small study of people who resisted the bait, to learn more about their traits and state of mind as they monitor their email.
The work is part of a broader program in human-machine teaming and human factors research at PNNL, which recently hosted a Symposium on Human Factors.
The work was funded by the Cybersecurity and Infrastructure Security Agency, part of the Department of Homeland Security. In addition to Arendt, Baweja and Fallon, authors include Ji Young Yun and Nick Thompson of PNNL and Zhuanyi Shaw, formerly of PNNL. More

Computer scientists at the University of Massachusetts Amherst recently found that data-visualization experts have no agreed-upon understanding of who makes up one of their largest audiences — novice users. The work, which recently won a coveted Best Paper Award at the Association for Computing Machinery’s conference on Human Factors in Computing Systems (ACM CHI), is an important first step in ensuring more inclusive data visualizations, and thus data visualization that works for all users.
Data visualization is the representation of data in a visual and easily understandable way using common graphics such as charts, plots, infographics and animations. Using visual elements provides an accessible way to see and understand trends, outliers and patterns in data. One of the most familiar data visualizations — the pie chart — is legible to nearly everyone and has been a method used to quickly convey information since its invention in the early nineteenth century.
But, with the advent of the internet, the range, reach and complexity of such visualizations have grown exponentially. Think of the various online COVID trackers, graphics showing economic projections or the outcomes of national elections. “More and more, everyday people are relying on data visualizations to make decisions about their lives,” says Narges Mayhar, assistant professor in the Manning College of Information and Computer Science at UMass Amherst, and the paper’s senior author. “Even many of our collective decisions rest on data visualizations.”
Since a visualization’s use is dependent on its intelligibility, one would think that data visualization experts would have a clear and standard understanding of their audience, particularly their non-expert users. And yet, “despite many decades of data-visualization research, we had no clear notion of what makes someone a ‘novice,'” says Mayhar. This insight was important enough that the ACM CHI, the premier international conference for human-computer interaction, bestowed the Best Paper Award on the research, an honor reserved for the top 1% of submitted papers.
Mayhar, lead author Alyxander Burns, who completed the research as part of his graduate studies at UMass Amherst, and their co-authors combed through the past 30 years of visualization research and found 79 papers spread across seven academic journals that concerned themselves with identifying the audience for data visualizations. Within those 79 papers, they found that the definitions of a novice user ranged widely, from people who have difficulty “effectively utilizing GPU clusters” to those who lack knowledge of “ontological models.” Moreover, the team found that most researchers’ sample groups of users overwhelmingly skewed toward white, college-aged people living in the U.S.
“How do we know that the visualizations we create could work for older people, for those without college degrees, for people living in one of the world’s many other countries?” asks Mayhar. “We need to be clear, as a field, what we mean when we say ‘novice,’ and the goal of this paper is to change the way that visualization researchers think about novices, address their needs and design tools that work for everyone.” More

Researchers have developed a metallic gel that is highly electrically conductive and can be used to print three-dimensional (3D) solid objects at room temperature.
“3D printing has revolutionized manufacturing, but we’re not aware of previous technologies that allowed you to print 3D metal objects at room temperature in a single step,” says Michael Dickey, co-corresponding author of a paper on the work and the Camille & Henry Dreyfus Professor of Chemical and Biomolecular Engineering at North Carolina State University. “This opens the door to manufacturing a wide range of electronic components and devices.”
To create the metallic gel, the researchers start with a solution of micron-scale copper particles suspended in water. The researchers then add a small amount of an indium-gallium alloy that is liquid metal at room temperature. The resulting mixture is then stirred together.
As the mixture is stirred, the liquid metal and copper particles essentially stick to each other, forming a metallic gel “network” within the aqueous solution.
“This gel-like consistency is important, because it means you have a fairly uniform distribution of copper particles throughout the material,” Dickey says. “This does two things. First, it means the network of particles connect to form electrical pathways. And second, it means that the copper particles aren’t settling out of solution and clogging the printer.”
The resulting gel can be printed using a conventional 3D printing nozzle and retains its shape when printed. And, when allowed to dry at room temperature, the resulting 3D object becomes even more solid while retaining its shape.
However, if users decide to apply heat to the printed object while it is drying, some interesting things can happen.
The researchers found that the alignment of the particles influences how the material dries. For example, if you printed a cylindrical object, the sides would contract more than the top and bottom as it dries. If something is drying at room temperature, the process is sufficiently slow that it doesn’t create structural change in the object. However, if you apply heat — for example, put it under a heat lamp at 80 degrees Celsius — the rapid drying can cause structural deformation. Because this deformation is predictable, that means you can make a printed object change shape after it is printed by controlling the pattern of the printed object and the amount of heat the object is exposed to while drying.
“Ultimately, this sort of four-dimensional printing — the traditional three dimensions, plus time — is one more tool that can be used to create structures with the desired dimensions,” Dickey says. “But what we find most exciting about this material is its conductivity.
“Because the printed objects end up being as much as 97.5% metal, they are highly conductive. It’s obviously not as conductive as conventional copper wire, but it’s impossible to 3D print copper wire at room temperature. And what we’ve developed is far more conductive than anything else that can be printed. We’re pretty excited about the applications here.
“We’re open to working with industry partners to explore potential applications, and are always happy to talk with potential collaborators about future directions for research,” Dickey says.
The work was done with support from the National Natural Science Foundation of China, under grant number 52203101; and from the China Scholarship Council, under grant number 201906250075. More

“Listen, if there’s one thing the history of evolution has taught us is that life will not be contained. Life breaks free. It expands to new territories, and it crashes through barriers painfully, maybe even dangerously, but . . . life finds a way,” said Ian Malcolm, Jeff Goldblum’s character in Jurassic Park, the 1993 science fiction film about a park with living dinosaurs.
You won’t find any Velociraptors lurking around evolutionary biologist Jay T. Lennon’s lab; however, Lennon, a professor in the College of Arts and Sciences Department of Biology at Indiana University Bloomington, and his colleagues have found that life does indeed find a way. Lennon’s research team has been studying a synthetically constructed minimal cell that has been stripped of all but its essential genes. The team found that the streamlined cell can evolve just as fast as a normal cell — demonstrating the capacity for organisms to adapt, even with an unnatural genome that would seemingly provide little flexibility.
“It appears there’s something about life that’s really robust,” says Lennon. “We can simplify it down to just the bare essentials, but that doesn’t stop evolution from going to work.”
For their study, Lennon’s team used the synthetic organism, Mycoplasma mycoides JCVI-syn3B — a minimized version of the bacterium M. mycoides commonly found in the guts of goats and similar animals. Over millennia, the parasitic bacterium has naturally lost many of its genes as it evolved to depend on its host for nutrition. Researchers at the J. Craig Venter Institute in California took this one step further. In 2016, they eliminated 45 percent of the 901 genes from the natural M. mycoides genome — reducing it to the smallest set of genes required for autonomous cellular life. At 493 genes, the minimal genome of M. mycoides JCVI-syn3B is the smallest of any known free-living organism. In comparison, many animal and plant genomes contain more than 20,000 genes.
In principle, the simplest organism would have no functional redundancies and possess only the minimum number of genes essential for life. Any mutation in such an organism could lethally disrupt one or more cellular functions, placing constraints on evolution. Organisms with streamlined genomes have fewer targets upon which positive selection can act, thus limiting opportunities for adaptation.
Although M. mycoides JCVI-syn3B could grow and divide in laboratory conditions, Lennon and colleagues wanted to know how a minimal cell would respond to the forces of evolution over time, particularly given the limited raw materials upon which natural selection could operate as well as the uncharacterized input of new mutations.
“Every single gene in its genome is essential,” says Lennon in reference to M. mycoides JCVI-syn3B. “One could hypothesize that there is no wiggle room for mutations, which could constrain its potential to evolve.”
The researchers established that M. mycoides JCVI-syn3B, in fact, has an exceptionally high mutation rate. They then grew it in the lab where it was allowed to evolve freely for 300 days, equivalent to 2000 bacterial generations or about 40,000 years of human evolution.
The next step was to set up experiments to determine how the minimal cells that had evolved for 300 days performed in comparison to the original, non-minimal M. mycoides as well as to a strain of minimal cells that hadn’t evolved for 300 days. In the comparison tests, the researchers put equal amounts of the strains being assessed together in a test tube. The strain better suited to its environment became the more common strain.
They found that the non-minimal version of the bacterium easily outcompeted the unevolved minimal version. The minimal bacterium that had evolved for 300 days, however, did much better, effectively recovering all of the fitness that it had lost due to genome streamlining. The researchers identified the genes that changed the most during evolution. Some of these genes were involved in constructing the surface of the cell, while the functions of several others remain unknown.
Understanding how organisms with simplified genomes overcome evolutionary challenges has important implications for long-standing problems in biology — including the treatment of clinical pathogens, the persistence of host-associated endosymbionts, the refinement of engineered microorganisms, and the origin of life itself. The research done by Lennon and his team demonstrates the power of natural selection to rapidly optimize fitness in the simplest autonomous organism, with implications for the evolution of cellular complexity. In other words, it shows that life finds a way. More

New research by the University of Liverpool could signal a step change in the quest to design the new materials that are needed to meet the challenge of net zero and a sustainable future.
Publishing in the journal Nature, the Liverpool researchers have shown that a mathematical algorithm can guarantee to predict the structure of any material just based on knowledge of the atoms that make it up.
Developed by an interdisciplinary team of researchers from the University of Liverpool’s Departments of Chemistry and Computer Science, the algorithm systematically evaluates entire sets of possible structures at once, rather than considering them one at a time, to accelerate identification of the correct solution.
This breakthrough makes it possible to identify those materials that can be made and, in many cases, to predict their properties. The new method was demonstrated on quantum computers that have the potential to solve many problems faster than classical computers and can therefore speed up the calculations even further.
Our way of life depends on materials — “everything is made of something.” New materials are needed to meet the challenge of net zero, from batteries and solar absorbers for clean power to providing low-energy computing and the catalysts that will make the clean polymers and chemicals for our sustainable future.
This search is slow and difficult because there are so many ways that atoms could be combined to make materials, and in particular so many structures that could form. In addition, materials with transformative properties are likely to have structures that are different from those that are known today, and predicting a structure that nothing is known about is a tremendous scientific challenge.
Professor Matt Rosseinsky, from the University’s Department of Chemistry and Materials Innovation Factory, said: “Having certainty in the prediction of crystal structures now offers the opportunity to identify from the whole of the space of chemistry exactly which materials can be synthesised and the structures that they will adopt, giving us for the first time the ability to define the platform for future technologies.
“With this new tool, we will be able to define how to use those chemical elements that are widely available and begin to create materials to replace those based on scarce or toxic elements, as well as to find materials that outperform those we rely on today, meeting the future challenges of a sustainable society.”
Professor Paul Spirakis, from the University’s Department of Computer Science, said: “We managed to provide a general algorithm for crystal structure prediction that can be applied to a diversity of structures. Coupling local minimization to integer programming allowed us to explore the unknown atomic positions in the continuous space using strong optimization methods in a discrete space.
Our aim is to explore and use more algorithmic ideas in the nice adventure of discovering new and useful materials. Joining efforts of chemists and computer scientists was the key to this success.”
The research team includes researchers from the University of Liverpool’s Departments of Computer Science and Chemistry, the Materials Innovation Factory and the Leverhulme Research Centre for Functional Materials Design, which was established to develop new approaches to the design of functional materials at the atomic scale through interdisciplinary research.
This project has received funding from the Leverhulme Trust and the Royal Society. More

Life, from the perspective of thermodynamics, is a system out of equilibrium, resisting tendencies towards increasing their levels of disorder. In such a state, the dynamics are irreversible over time. This link between the tendency toward disorder and irreversibility is expressed as the arrow of time by the English physicist Arthur Eddington in 1927.
Now, an international team including researchers from Kyoto University, Hokkaido University, and the Basque Center for Applied Mathematics, has developed a solution for temporal asymmetry, furthering our understanding of the behavior of biological systems, machine learning, and AI tools.
“The study offers, for the first time, an exact mathematical solution of the temporal asymmetry — also known as entropy production — of nonequilibrium disordered Ising networks,” says co-author Miguel Aguilera of the Basque Center for Applied Mathematics.
The researchers focused on a prototype of large-scale complex networks called the Ising model, a tool used to study recurrently connected neurons. When connections between neurons are symmetric, the Ising model is in a state of equilibrium and presents complex disordered states called spin glasses. The mathematical solution of this state led to the award of the 2021 Nobel Prize in physics to Giorgio Parisi.
Unlike in living systems, however, spin crystals are in equilibrium and their dynamics are time-reversible. The researchers instead worked on the time-irreversible Ising dynamics caused by asymmetric connections between neurons.
The exact solutions obtained serve as benchmarks for developing approximate methods for learning artificial neural networks. The development of learning methods used in multiple phases may advance machine learning studies.
“The Ising model underpins recent advances in deep learning and generative artificial neural networks. So, understanding its behavior offers critical insights into both biological and artificial intelligence in general,” added Hideaki Shimazaki at KyotoU’s Graduate School of Informatics.
“Our findings are the result of an exciting collaboration involving insights from physics, neuroscience and mathematical modeling,” remarked Aguilera. “The multidisciplinary approach has opened the door to novel ways to understand the organization of large-scale complex networks and perhaps decipher the thermodynamic arrow of time.” More

A new method for connecting neurons in neuromorphic wetware has been developed by researchers from Osaka University and Hokkaido University. The wetware comprises conductive polymer wires grown in a three-dimensional configuration, done by applying square-wave voltage to electrodes submerged in a precursor solution. The voltage can modify wire conductance, allowing the network to be trained. This fabricated network is able to perform unsupervised Hebbian learning and spike-based learning.
The development of neural networks to create artificial intelligence in computers was originally inspired by how biological systems work. These ‘neuromorphic’ networks, however, run on hardware that looks nothing like a biological brain, which limits performance. Now, researchers from Osaka University and Hokkaido University plan to change this by creating neuromorphic ‘wetware’.
While neural-network models have achieved remarkable success in applications such as image generation and cancer diagnosis, they still lag far behind the general processing abilities of the human brain. In part, this is because they are implemented in software using traditional computer hardware that is not optimized for the millions of parameters and connections that these models typically require.
Neuromorphic wetware, based on memristive devices, could address this problem. A memristive device is a device whose resistance is set by its history of applied voltage and current. In this approach, electropolymerization is used to link electrodes immersed in a precursor solution using wires made of conductive polymer. The resistance of each wire is then tuned using small voltage pulses, resulting in a memristive device.
“The potential to create fast and energy-efficient networks has been shown using 1D or 2D structures,” says senior author Megumi Akai-Kasaya. “Our aim was to extend this approach to the construction of a 3D network.”
The researchers were able to grow polymer wires from a common polymer mixture called ‘PEDOT:PSS’, which is highly conductive, transparent, flexible, and stable. A 3D structure of top and bottom electrodes was first immersed in a precursor solution. The PEDOT:PSS wires were then grown between selected electrodes by applying a square-wave voltage on these electrodes, mimicking the formation of synaptic connections through axon guidance in an immature brain.
Once the wire was formed, the characteristics of the wire, especially the conductance, were controlled using small voltage pulses applied to one electrode, which changes the electrical properties of the film surrounding the wires.
“The process is continuous and reversible,” explains lead author Naruki Hagiwara, “and this characteristic is what enables the network to be trained, just like software-based neural networks.”
The fabricated network was used to demonstrate unsupervised Hebbian learning (i.e., when synapses that often fire together strengthen their shared connection over time). What’s more, the researchers were able to precisely control the conductance values of the wires so that the network could complete its tasks. Spike-based learning, another approach to neural networks that more closely mimics the processes of biological neural networks, was also demonstrated by controlling the diameter and conductivity of the wires.
Next, by fabricating a chip with a larger number of electrodes and using microfluidic channels to supply the precursor solution to each electrode, the researchers hope to build a larger and more powerful network. Overall, the approach determined in this study is a big step toward the realization of neuromorphic wetware and closing the gap between the cognitive abilities of humans and computers. More