Aurelia Butler

In their pursuit of maximum reward, people suffering from gambling disorder rely less on exploring new but potentially better strategies, and more on proven courses of action that have already led to success in the past. The neurotransmitter dopamine in the brain may play an important role in this, a study in biological psychology conducted at the University of Cologne’s Faculty of Human Sciences by Professor Dr Jan Peters und Dr Antonius Wiehler suspects. The article ‘Attenuated directed exploration during reinforcement learning in gambling disorder’ has appeared in the latest edition of the Journal of Neuroscience, published by the Society for Neuroscience.
Gambling disorder affects slightly less than one percent of the population — often men — and is in some ways similar to substance abuse disorders. Scientists suspect that this disorder, like other addiction disorders, is associated with changes in the dopamine system. The brain’s reward system releases the neurotransmitter dopamine during gambling. Since dopamine is important for the planning and control of actions, among other things, it could also affect strategic learning processes.
‘Gambling disorder is of scientific interest among other things because it is an addiction disorder that is not tied to a specific substance’, Professor Dr Jan Peters, one of the authors, remarked. The psychologists examined how gamblers plan their actions to maximize rewards — how their so called reinforcement learning works. In the study, participants had to decide between already proven options or new ones in order to win as much as possible. At the same time, the scientists used functional magnetic resonance imaging to measure activity in regions of the brain that are important for processing reward stimuli and planning actions.
Twenty-three habitual gamblers and twenty-three control subjects (all male) performed what is known as a ‘four-armed bandit task’. The name of this type of decision-making task refers to slot machines, known colloquially as ‘one-armed bandits’. In each run, the participants had to choose between four options (‘four-armed bandit’, in this case four coloured squares), whose winnings slowly changed. Different strategies can be employed here. For example, one can choose the option that yielded the highest profit last time. However, it is also possible to choose the option where the chance of winning is most uncertain — the option promising maximum information gain. The latter is also called directed (or uncertainty-based) exploration.
Both groups won about the same amount of money and exhibited directed exploration. However, this was significantly less pronounced in the group of gamblers than in the control group. These results indicate that gamblers are less adaptive to changing environments during reinforcement learning. At the neural level, gamblers showed changes in a network of brain regions that has been associated with directed exploration in previous studies. In one previous study by the two biological psychologists, pharmacologically raising the dopamine level in healthy participants had shown a very similar effect on behaviour. ‘Although this indicates that dopamine might also play an important role in the reduction of directed exploration in gamblers, more research would have to be conducted to prove such a correlation,’ said Dr Antonius Wiehler.
Further research also needs to clarify whether the observed changes in decision-making behaviour in gamblers are a risk factor for, or a consequence of, regular gambling.
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Materials provided by University of Cologne. Note: Content may be edited for style and length. More

Using your face to unlock your phone is a pretty genius security protocol. But like any advanced technology, hackers and thieves are always up to the challenge, whether that’s unlocking your phone with your face while you sleep or using a photo from social media to do the same.
Like every other human biometric identification system before it (fingerprints, retina scans) there are still significant security flaws in some of the most advanced identity verification technology. Brigham Young University electrical and computer engineering professor D.J. Lee has decided there is a better and more secure way to use your face for restricted access.
It’s called Concurrent Two-Factor Identity Verification (C2FIV) and it requires both one’s facial identity and a specific facial motion to gain access. To set it up, a user faces a camera and records a short 1-2 second video of either a unique facial motion or a lip movement from reading a secret phrase. The video is then input into the device, which extracts facial features and the features of the facial motion, storing them for later ID verification.
“The biggest problem we are trying to solve is to make sure the identity verification process is intentional,” said Lee, a professor of electrical and computer engineering at BYU. “If someone is unconscious, you can still use their finger to unlock a phone and get access to their device or you can scan their retina. You see this a lot in the movies — think of Ethan Hunt in Mission Impossible even using masks to replicate someone else’s face.”
To get technical, C2FIV relies on an integrated neural network framework to learn facial features and actions concurrently. This framework models dynamic, sequential data like facial motions, where all the frames in a recording have to be considered (unlike a static photo with a figure that can be outlined).
Using this integrated neural network framework, the user’s facial features and movements are embedded and stored on a server or in an embedded device and when they later attempt to gain access, the computer compares the newly generated embedding to the stored one. That user’s ID is verified if the new and stored embeddings match at a certain threshold.
“We’re pretty excited with the technology because it’s pretty unique to add another level of protection that doesn’t cause more trouble for the user,” Lee said.
In their preliminary study, Lee and his Ph.D. student Zheng Sun recorded 8,000 video clips from 50 subjects making facial movements such as blinking, dropping their jaw, smiling or raising their eyebrows as well as many random facial motions to train the neural network. They then created a dataset of positive and negative pairs of facial motions and inputted higher scores for the positive pairs (those that matched). Currently, with the small dataset, the trained neural network verifies identities with over 90% accuracy. They are confident the accuracy can be much higher with a larger dataset and improvements on the network.
Lee, who has filed a patent on the tech already, said the idea is not to compete with Apple or have the application be all about smartphone access. In his opinion, C2FIV has broader application, including accessing restricted areas at a workplace, online banking, ATM use, safe deposit box access or even hotel room entry or keyless entry/access to your vehicle.
“We could build this very tiny device with a camera on it and this device could be deployed easily at so many different locations,” Lee said. “How great would it be to know that even if you lost your car key, no one can steal your vehicle because they don’t know your secret facial action?”
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Materials provided by Brigham Young University. Original written by Todd Hollingshead. Note: Content may be edited for style and length. More

If you want to build a fully functional nanosized robot, you need to incorporate a host of capabilities, from complicated electronic circuits and photovoltaics to sensors and antennas.
But just as importantly, if you want your robot to move, you need it to be able to bend.
Cornell researchers have created micron-sized shape memory actuators that enable atomically thin two-dimensional materials to fold themselves into 3D configurations. All they require is a quick jolt of voltage. And once the material is bent, it holds its shape — even after the voltage is removed.
As a demonstration, the team created what is potentially the world’s smallest self-folding origami bird. And it’s not a lark.
The group’s paper, “Micrometer-sized electrically programmable shape memory actuators for low-power microrobotics,” published in Science Robotics and was featured on the cover. The paper’s lead author is postdoctoral researcher Qingkun Liu.
The project is led by Itai Cohen, professor of physics, and Paul McEuen, the John A. Newman Professor of Physical Science. More

In researching the causes and potential treatments for degenerative conditions such as Alzheimer’s or Parkinson’s disease, neuroscientists frequently struggle to accurately identify cells needed to understand brain activity that gives rise to behavior changes such as declining memory or impaired balance and tremors.
A multidisciplinary team of Georgia Institute of Technology neuroscience researchers, borrowing from existing tools such as graphical models, have uncovered a better way to identify cells and understand the mechanisms of the diseases, potentially leading to better understanding, diagnosis, and treatment.
Their research findings were reported Feb. 24 in the journal eLife. The research was supported by the National Institutes of Health and the National Science Foundation.
The field of neuroscience studies how the nervous system functions, and how genes and environment influence behavior. By using new technologies to understand natural and dysfunctional states of biological systems, neuroscientists hope to ultimately bring cures to diseases. Before that can happen, neuroscientists first must understand which cells in the brain are driving behavior but mapping the brain activity cell by cell isn’t as simple as it appears.
No Two Brain Cells Are Alike
Traditionally, scientists established a coordinate system to map each cell location by comparing images to an atlas, but the notion in literature that “all brains look the same is absolutely not true,” said Hang Lu, the Love Family Professor of Chemical and Biomolecular Engineering in Georgia Tech’s School of Chemical and Biomolecular Engineering. More

A sustainable, powerful micro-supercapacitor may be on the horizon, thanks to an international collaboration of researchers from Penn State and the University of Electronic Science and Technology of China. Until now, the high-capacity, fast-charging energy storage devices have been limited by the composition of their electrodes — the connections responsible for managing the flow of electrons during charging and dispensing energy. Now, researchers have developed a better material to improve connectivity while maintaining recyclability and low cost.
They published their results on Feb. 8 in the Journal of Materials Chemistry A.
“The supercapacitor is a very powerful, energy-dense device with a fast-charging rate, in contrast to the typical battery — but can we make it more powerful, faster and with a really high retention cycle?” asked Jia Zhu, corresponding author and doctoral student conducting research in the laboratory of Huanyu “Larry” Cheng, Dorothy Quiggle Career Development Professor in Penn State’s Department of Engineering Science and Mechanics.
Zhu worked under Cheng’s mentorship to explore the connections in a micro-supercapacitor, which they use in their research on small, wearable sensors to monitor vital signs and more. Cobalt oxide, an abundant, inexpensive material that has a theoretically high capacity to quickly transfer energy charges, typically makes up the electrodes. However, the materials that mix with cobalt oxide to make an electrode can react poorly, resulting in a much lower energy capacity than theoretically possible.
The researchers ran simulations of materials from an atomic library to see if adding another material — also called doping — could amplify the desired characteristics of cobalt oxide as an electrode by providing extra electrons while minimizing, or entirely removing, the negative effects. They modeled various material species and levels to see how they would interact with cobalt oxide.
“We screened possible materials but found many that might work were too expensive or toxic, so we selected tin,” Zhu said. “Tin is widely available at a low cost, and it’s not harmful to the environment.”
In the simulations, the researchers found that by partially substituting some of the cobalt for tin and binding the material to a commercially available graphene film — a single-atom thick material that supports electronic materials without changing their properties — they could fabricate what they called a low-cost, easy-to-develop electrode.
Once the simulations were completed, the team in China conducted experiments to see if the simulation could be actualized.
“The experimental results verified a significantly increased conductivity of the cobalt oxide structure after partial substitution by tin,” Zhu said. “The developed device is expected to have promising practical applications as the next-generation energy storage device.”
Next, Zhu and Cheng plan to use their own version of graphene film — a porous foam created by partially cutting and then breaking the material with lasers — to fabricate a flexible capacitor to allow for easy and fast conductivity.
“The supercapacitor is one key component, but we’re also interested in combining with other mechanisms to serve as both an energy harvester and a sensor,” Cheng said. “Our goal is to put a lot of functions into a simple, self-powered device.”
The National Natural Science Foundation of China supported this work.
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Materials provided by Penn State. Original written by Ashley WennersHerron. Note: Content may be edited for style and length. More

Numeric anchoring is a long-established technique of marketing communication. Once a price is mentioned, that number serves as the basis for — or “anchors” — all future discussions and decisions. But new research shows that this phenomenon is not limited to decisions that involve numbers, the use and understanding of which require high-level cognitive thinking. Anchoring also biases judgments at relatively low levels of cognition when no numbers are involved.
In research recently published in the Journal of Behavioral Decision Making, Gaurav Jain, an assistant professor in the Lally School of Management at Rensselaer Polytechnic Institute, demonstrated that anchoring even occurs in perceptual domains, like sight, sound, and touch.
To test his novel theory that anchoring could happen without numbers as the starting point, Jain conducted several studies involving different senses. For example, to test decision-making relating to haptics — or touch — he asked subjects to close their eyes and touch sandpaper of a certain grit. When the subjects opened their eyes, he offered them 16 sandpaper choices and asked them to find the grit that matched the first one.
Jain anchored the range of options by making participants start with either a relatively finer or coarser grit than the initial one. Those subjects that were anchored with the finer grit chose sandpaper that was finer than the one they originally touched — and the converse was true for those anchored with the coarser grit.
“My findings offer marketing professionals another fundamental tool to guide consumer behavior by anchoring a product or message through their senses,” Jain said. Additionally, Jain’s research offers critical insight into the underpinnings of the phenomenon of anchoring.
Even in academic circles, questions remain about how decisions are made and the role anchors play. Do people go from the anchor point to their final decision in one move? Or do they take incremental steps away from the anchor?
Jain’s experiments gave him the opportunity to watch the decision-making process in action, leading to a conclusion that reconciles these two models. He found that his subjects reached their final decision by taking small jumps away from the anchor point, but each of those jumps were influenced by the anchor’s placement.
“Discovering exactly how we humans make decisions has been nearly impossible,” Jain said. “With this research, I found an opening into the black box of the human brain. I’ve shown how decision-making works in the perceptual domains, and it signals directly how it may work in numerical domains.”
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Materials provided by Rensselaer Polytechnic Institute. Original written by Jeanne Hedden Gallagher. Note: Content may be edited for style and length. More

Researchers improve their newly established quantum algorithm, bringing it to one-tenth the computational cost of Quantum Phase Estimation, and use it to directly calculate the vertical ionization energies of light atoms and molecules such as CO, O2, CN, F2, H2O, NH3 within 0.1 electron volts of precision.
Quantum computers have seen a lot attention recently as they are expected to solve certain problems that are outside the capabilities of normal computers. Primary to these problems is determining the electronic states of atoms and molecules so they can be used more effectively in a variety of industries — from lithium-ion battery designs to in silico technologies in drug development. A common way scientists have approached this problem is by calculating the total energies of the individual states of a molecule or atom and then determine the difference in energy between these states. In nature, many molecules grow in size and complexity, and the cost to calculate this constant flux is beyond the capability of any traditional computer or currently establish quantum algorithms. Therefore, theoretical predictions of the total energies have only been possible if molecules are not sizable and isolated from their natural environment.
“For quantum computers to be a reality, its algorithms must be robust enough to accurately predict the electronic states of atoms and molecules, as they exist in nature, ” state Kenji Sugisaki and Takeji Takui from the Graduate School of Science, Osaka City University.
In December 2020, Sugisaki and Takui, together with their colleagues, led a team of researchers to develop a quantum algorithm they call Bayesian eXchange coupling parameter calculator with Broken-symmetry wave functions (BxB), that predicts the electronic states of atoms and molecules by directly calculating the energy differences. They noted that energy differences in atoms and molecules remain constant, regardless to how complex and large they get despite their total energies grow as the system size. “With BxB, we avoided the common practice of calculating the total energies and targeted the energy differences directly, keeping computing costs within polynomial time,” they state. “Since then, our goal has been to improve the efficiency of our BxB software so it can predict the electronic sates of atoms and molecules with chemical precision.”
Using the computing costs of a well-known algorithm called Quantum Phase Estimation (QPE) as a benchmark, “we calculated the vertical ionization energies of small molecules such as CO, O2, CN, F2, H2O, NH3 within 0.1 electron volts (eV) of precision,” states the team, using half the number of qubits, bringing the calculation cost on par with QPE.
Their findings will be published online in the March edition of The Journal of Physical Chemistry Letters.
Ionization energy is one of the most fundamental physical properties of atoms and molecules and an important indicator for understanding the strength and properties of chemical bonds and reactions. In short, accurately predicting the ionization energy allows us to use chemicals beyond the current norm. In the past, it was necessary to calculate the energies of the neutral and ionized states, but with the BxB quantum algorithm, the ionization energy can be obtained in a single calculation without inspecting the individual total energies of the neutral and ionized states. “From numerical simulations of the quantum logic circuit in BxB, we found that the computational cost for reading out the ionization energy is constant regardless of the atomic number or the size of the molecule,” the team states, “and that the ionization energy can be obtained with a high accuracy of 0.1 eV after modifying the length of the quantum logic circuit to be less than one tenth of QPE.” (See image for modification details)
With the development of quantum computer hardware, Sugisaki and Takui, along with their team, are expecting the BxB quantum algorithm to perform high-precision energy calculations for large molecules that cannot be treated in real time with conventional computers.
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Materials provided by Osaka City University. Note: Content may be edited for style and length. More

The first known study to explore optimal outpatient exam scheduling given the flexibility of inpatient exams has resulted in shorter wait times for magnetic resonance imaging (MRI) patients at Lahey Hospital & Medical Center in Burlington, Mass. A team of researchers from Dartmouth Engineering and Philips worked to identify sources of delays for MRI procedures at Lahey Hospital in order to optimize scheduling and reduce overall costs for the hospital by 23 percent.
The Dartmouth-led study, “Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation,” was sponsored by Philips and recently published by Health Care Management Science in collaboration with Lahey Hospital.
“Excellence in service and positive patient experiences are a primary focus for the hospital. We continuously monitor various aspects of patient experiences and one key indicator is patient wait times,” said Christoph Wald, chair of the department of radiology at Lahey Hospital and professor of radiology at Tufts University Medical School. “With a goal of wanting to improve patient wait times, we worked with data science researchers at Philips and Dartmouth to help identify levers for improvement that might be achieved without impeding access.”
Prior to working with the researchers, on an average weekday, outpatients at Lahey Hospital waited about 54 minutes from their arrival until the beginning of their exam. Researchers determined that one of the reasons for the routine delays was a complex scheduling system, which must cater to emergency room patients, inpatients, and outpatients; while exams for inpatients are usually flexible and can be delayed if necessary, other appointments cannot.
“Mathematical models and algorithms are crucial to improve the efficiency of healthcare systems, especially in the current crisis we are going through. By analyzing the patient data, we found that delays were prominent because the schedule was not optimal,” said first author Yifei Sun, a Dartmouth Engineering PhD candidate. “This research uses optimization and simulation tools to help the MRI centers of Lahey Hospital better plan their schedule to reduce overall cost, which includes patient waiting time.”
First, the researchers reviewed data to analyze and identify sources of delays. They then worked on developing a mathematical model to optimize the length of each exam slot and the placement of inpatient exams within the overall schedule. Finally, the researchers developed an algorithm to minimize the wait time and cost associated with exam delays for outpatients, the idle time of equipment, employee overtime, and cancelled inpatient exams.
“This iterative improvement process did result in measurable improvements of patient wait times,” said Wald. “The construction and use of a simulation model have been instrumental in educating the Lahey team about the benefits of dissecting workflow components to arrive at an optimized process outcome. We have extended this approach to identify bottlenecks in our interventional radiology workflow and to add additional capacity under the constraints of staffing schedules.”
The researchers believe their solutions are broadly applicable, as the issue is common to many mid-sized hospitals throughout the country.
“We also provided suggestions for hospitals that don’t have optimization tools or have different priorities, such as patient waiting times or idle machine times,” said Sun, who worked on the paper with her advisor Vikrant Vaze, the Stata Family Career Development Associate Professor of Engineering at Dartmouth.
The other co-authors of the paper are: Usha Nandini Raghavan and Christopher S. Hall, both from Philips, and Patricia Doyle and Stacey Sullivan Richard of Lahey Hospital.
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Materials provided by Thayer School of Engineering at Dartmouth. Original written by Julie Bonette. Note: Content may be edited for style and length. More