Computers Math

Obsessive-compulsive disorder (OCD) in children and adolescents is associated with impaired education and worse general health later in life. Access to specialist treatment is often limited. According to a study from Centre for Psychiatry Research at Karolinska Institutet in Sweden and Region Stockholm, internet-delivered cognitive behavioural therapy (CBT) can be as effective as conventional CBT. The study, published in the journal JAMA, can help make treatment for OCD more widely accessible.
Obsessive-compulsive disorder (OCD) is a potentially serious mental disorder that normally debuts in childhood.
Symptoms include intrusive thoughts that trigger anxiety (obsessions), and associated repetitive behaviours (compulsions), which are distressing and time consuming.
Early diagnosis and treatment are essential to minimise the long-term medical and socioeconomic consequences of the disorder, including suicide risk.
The psychological treatment of OCD requires highly trained therapists and access to this kind of competence is currently limited to a handful of specialist centres across Sweden.
Earlier research has shown that while CBT helps a majority of young people who receive it, several years can pass between the onset of symptoms and receipt of treatment. More

As the use of artificial intelligence (AI) in health applications grows, health providers are looking for ways to improve patients’ experience with their machine doctors.
Researchers from Penn State and University of California, Santa Barbara (UCSB) found that people may be less likely to take health advice from an AI doctor when the robot knows their name and medical history. On the other hand, patients want to be on a first-name basis with their human doctors.
When the AI doctor used the first name of the patients and referred to their medical history in the conversation, study participants were more likely to consider an AI health chatbot intrusive and also less likely to heed the AI’s medical advice, the researchers added. However, they expected human doctors to differentiate them from other patients and were less likely to comply when a human doctor failed to remember their information.
The findings offer further evidence that machines walk a fine line in serving as doctors, said S. Shyam Sundar, James P. Jimirro Professor of Media Effects in the Donald P. Bellisario College of Communications and co-director of the Media Effects Research Laboratory at Penn State.
“Machines don’t have the ability to feel and experience, so when they ask patients how they are feeling, it’s really just data to them,” said Sundar, who is also an affiliate of Penn State’s Institute for Computational and Data Sciences (ICDS). “It’s possibly a reason why people in the past have been resistant to medical AI.”
Machines do have advantages as medical providers, said Joseph B. Walther, distinguished professor in communication and the Mark and Susan Bertelsen Presidential Chair in Technology and Society at UCSB. He said that, like a family doctor who has treated a patient for a long time, computer systems could — hypothetically — know a patient’s complete medical history. In comparison, seeing a new doctor or a specialist who knows only your latest lab tests might be a more common experience, said Walther, who is also director of the Center for Information Technology and Society at UCSB. More

People are more persuaded by the actual messages contained in social media posts than they are by how many others viewed the posts, a new study suggests.
Researchers found that when people watched YouTube videos either for or against e-cigarette use, their level of persuasion wasn’t directly affected by whether the video said it was viewed by more than a million people versus by fewer than 20.
What mattered for persuasion was viewers’ perception of the message as truthful and believable.
“There wasn’t a bandwagon effect in which people were persuaded by a video just because a lot of other people watched it,” said Hyunyi Cho, lead author of the study and professor of communication at The Ohio State University.
“The message itself was most important for persuasion.”
The study will appear in the June 2021 issue of the journal Media Psychology. More

A novel technique for studying vortices in quantum fluids has been developed by Lancaster physicists.
Andrew Guthrie, Sergey Kafanov, Theo Noble, Yuri Pashkin, George Pickett and Viktor Tsepelin, in collaboration with scientists from Moscow State University, used tiny mechanical resonators to detect individual quantum vortices in superfluid helium.
Their work is published in the current volume of Nature Communications.
This research into quantum turbulence is simpler than turbulence in the real world, which is observed in everyday phenomena such as surf, fast flowing rivers, billowing storm clouds, or chimney smoke. Despite the fact it is so commonplace and is found at every level, from the galaxies to the subatomic, it is still not fully understood.
Physicists know the fundamental Navier-Stokes Equations which govern the flow of fluids such as air and water, but despite centuries of trying, the mathematical equations still cannot be solved.
Quantum turbulence may provide the clues to an answer.
Turbulence in quantum fluids is much simpler than its “messy” classical counterpart, and being made up of identical singly-quantised vortices, can be thought of as providing an “atomic theory” of the phenomenon.
Unhelpfully, turbulence in quantum systems, for example in superfluid helium 4, takes place on microscopic scales, and so far scientists have not had tools with sufficient precision to probe eddies this small.
But now the Lancaster team, working at temperature of a few thousandths of a degree above absolute zero, has harnessed nanoscience to allow the detection of single quantum vortices (with core sizes on a par with atomic diameters) by using a nanoscale “guitar string “in the superfluid.
How the team does it is to trap a single vortex along the length of the “string” (a bar of around 100 nanometres across). The resonant frequency of the bar changes when a vortex is trapped, and thus the capture and release rate of vortices can be followed, opening a window into the turbulent structure.
Dr Sergey Kafanov who initiated this research said: “The devices developed have many other uses, one of which is to ping the end of a partially trapped vortex to study the nanoscale oscillations of the vortex core. Hopefully the studies will add to our insight into turbulence and may provide clues on how to solve these stubborn equations.”
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Materials provided by Lancaster University. Note: Content may be edited for style and length. More

As more private data is stored and shared digitally, researchers are exploring new ways to protect data against attacks from bad actors. Current silicon technology exploits microscopic differences between computing components to create secure keys, but artificial intelligence (AI) techniques can be used to predict these keys and gain access to data. Now, Penn State researchers have designed a way to make the encrypted keys harder to crack.
Led by Saptarshi Das, assistant professor of engineering science and mechanics, the researchers used graphene — a layer of carbon one atom thick — to develop a novel low-power, scalable, reconfigurable hardware security device with significant resilience to AI attacks. They published their findings in Nature Electronics today (May 10).
“There has been more and more breaching of private data recently,” Das said. “We developed a new hardware security device that could eventually be implemented to protect these data across industries and sectors.”
The device, called a physically unclonable function (PUF), is the first demonstration of a graphene-based PUF, according to the researchers. The physical and electrical properties of graphene, as well as the fabrication process, make the novel PUF more energy-efficient, scalable, and secure against AI attacks that pose a threat to silicon PUFs.
The team first fabricated nearly 2,000 identical graphene transistors, which switch current on and off in a circuit. Despite their structural similarity, the transistors’ electrical conductivity varied due to the inherent randomness arising from the production process. While such variation is typically a drawback for electronic devices, it’s a desirable quality for a PUF not shared by silicon-based devices.
After the graphene transistors were implemented into PUFs, the researchers modeled their characteristics to create a simulation of 64 million graphene-based PUFs. To test the PUFs’ security, Das and his team used machine learning, a method that allows AI to study a system and find new patterns. The researchers trained the AI with the graphene PUF simulation data, testing to see if the AI could use this training to make predictions about the encrypted data and reveal system insecurities.
“Neural networks are very good at developing a model from a huge amount of data, even if humans are unable to,” Das said. “We found that AI could not develop a model, and it was not possible for the encryption process to be learned.”
This resistance to machine learning attacks makes the PUF more secure because potential hackers could not use breached data to reverse engineer a device for future exploitation, Das said. Even if the key could be predicted, the graphene PUF could generate a new key through a reconfiguration process requiring no additional hardware or replacement of components.
“Normally, once a system’s security has been compromised, it is permanently compromised,” said Akhil Dodda, an engineering science and mechanics graduate student conducting research under Das’s mentorship. “We developed a scheme where such a compromised system could be reconfigured and used again, adding tamper resistance as another security feature.”
With these features, as well as the capacity to operate across a wide range of temperatures, the graphene-based PUF could be used in a variety of applications. Further research can open pathways for its use in flexible and printable electronics, household devices and more.
Paper co-authors include Dodda, Shiva Subbulakshmi Radhakrishnan, Thomas Schranghamer and Drew Buzzell from Penn State; and Parijat Sengupta from Purdue University. Das is also affiliated with the Penn State Department of Materials Science and Engineering and the Materials Research Institute.
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Materials provided by Penn State. Original written by Gabrielle Stewart. Note: Content may be edited for style and length. More

Log on to any app store, and parents will find hundreds of options for children that claim to be educational. But new research suggests these apps might not be as beneficial to children as they seem.
A new study analyzed some of the most downloaded educational apps for kids using a set of four criteria designed to evaluate whether an app provides a high-quality educational experience for children. The researchers found that most of the apps scored low, with free apps scoring even lower than their paid counterparts on some criteria.
Jennifer Zosh, associate professor of human development and family studies at Penn State Brandywine, said the study — recently published in the Journal of Children and Media — suggests apps shouldn’t replace human interaction nor do they guarantee learning.
“Parents shouldn’t automatically trust that something marked ‘educational’ in an app store is actually educational,” Zosh said. “By co-playing apps with their children, talking to them about what is happening as they play, pointing out what is happening in the real world that relates to something shown in an app, and selecting apps that minimize distraction, they are able to leverage the pillars of learning and can successfully navigate this new digital childhood.”
According to previous research, about 98 percent of kids ages eight and under live in a home with some type of mobile device, like a smartphone or tablet. While watching videos and playing games are popular ways children spend their time on these devices, the researchers said there are also many apps that are not only popular but claim to be educational.
Marisa Meyer, a research assistant at the University of Michigan, said the idea for the study came about when reviewing the top-downloaded apps on the Google Play marketplace for different research. More

The promise of a quantum internet depends on the complexities of harnessing light to transmit quantum information over fiber optic networks. A potential step forward was reported today by researchers in Sweden who developed integrated chips that can generate light particles on demand and without the need for extreme refrigeration.
Quantum computing today relies on states of matter, that is, electrons which carry qubits of information to perform multiple calculations simultaneously, in a fraction of the time it takes with classical computing.
The co-author of the research, Val Zwiller, Professor at KTH Royal Institute of Technology, says that in order to integrate quantum computing seamlessly with fiber-optic networks — which are used by the internet today — a more promising approach would be to harness optical photons.
“The photonic approach offers a natural link between communication and computation,” he says. “That’s important, since the end goal is to transmit the processed quantum information using light.”
But in order for photons to deliver qubits on-demand in quantum systems, they need to be emitted in a deterministic, rather than probabilistic, fashion. This can be accomplished at extremely low temperatures in artificial atoms, but today the research group at KTH reported a way to make it work in optical integrated circuits — at room temperature.
The new method enables photon emitters to be precisely positioned in integrated optical circuits that resemble copper wires for electricity, except that they carry light instead, says co-author of the research, Ali Elshaari, Associate Professor at KTH Royal Institute of Technology.
The researchers harnessed the single-photon-emitting properties of hexagonal boron nitride (hBN), a layered material. hBN is a compound commonly used is used ceramics, alloys, resins, plastics and rubbers to give them self-lubricating properties. They integrated the material with silicon nitride waveguides to direct the emitted photons.
Quantum circuits with light are either operated at cryogenic temperatures — plus 4 Kelvin above absolute zero — using atom-like single photon sources, or at room temperature using random single photon sources, Elshaari says. By contrast, the technique developed at KTH enables optical circuits with on-demand emission of light particles at room temperature.
“In existing optical circuits operating at room temperature, you never know when the single photon is generated unless you do a heralding measurement,” Elshaari says. “We realized a deterministic process that precisely positions light-particles emitters operating at room temperature in an integrated photonic circuit.”
The researchers reported coupling of hBN single photon emitter to silicon nitride waveguides, and they developed a method to image the quantum emitters. Then in a hybrid approach, the team built the photonic circuits with respect to the quantum sources locations using a series of steps involving electron beam lithography and etching, while still preserving the high quality nature of the quantum light.
The achievement opens a path to hybrid integration, that is, incorporating atom-like single-photon emitters into photonic platforms that cannot emit light efficiently on demand.
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Materials provided by KTH, Royal Institute of Technology. Note: Content may be edited for style and length. More

Computer scientists at the University of California San Diego have developed a more accurate navigation system that will allow robots to better negotiate busy clinical environments in general and emergency departments more specifically. The researchers have also developed a dataset of open source videos to help train robotic navigation systems in the future.
The team, led by Professor Laurel Riek and Ph.D. student Angelique Taylor, detail their findings in a paper for the International Conference on Robotics and Automation taking place May 30 to June 5 in Xi’an, China.
The project stemmed from conversations with clinicians over several years. The consensus was that robots would best help physicians, nurses and staff in the emergency department by delivering supplies and materials. But this means robots have to know how to avoid situations where clinicians are busy tending to a patient in critical or serious condition.
“To perform these tasks, robots must understand the context of complex hospital environments and the people working around them,” said Riek, who holds appointments both in computer science and emergency medicine at UC San Diego.
Taylor and colleagues built the navigation system, the Safety Critical Deep Q-Network (SafeDQN), around an algorithm that takes into account how many people are clustered together in a space and how quickly and abruptly these people are moving. This is based on observations of clinicians’ behavior in the emergency department. When a patient’s condition worsens, a team immediately gathers around them to render aid. Clinicians’ movements are quick, alert and precise. The navigation system directs the robots to move around these clustered groups of people, staying out of the way.
“Our system was designed to deal with the worst case scenarios that can happen in the ED,” said Taylor, who is part of Riek’s Healthcare Robotics lab at the UC San Diego Department of Computer Science and Engineering.
The team trained the algorithm on videos from YouTube, mostly coming from documentaries and reality shows, such as “Trauma: Life in the ER” and “Boston EMS.” The set of more than 700 videos is available for other research teams to train other algorithms and robots.
Researchers tested their algorithm in a simulation environment, and compared its performance to other state-of-the-art robotic navigation systems. The SafeDQN system generated the most efficient and safest paths in all cases.
Next steps include testing the system on a physical robot in a realistic environment. Riek and colleagues plan to partner with UC San Diego Health researchers who operate the campus’ healthcare training and simulation center.
The algorithms could also be used outside of the emergency department, for example during search and rescue missions.
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Materials provided by University of California – San Diego. Note: Content may be edited for style and length. More