Aurelia Butler

In the group of Prof. Karl Leo, physicists, material scientists and engineers are working jointly on the development of novel organic materials and devices for high performance, flexible and possibly even biocompatible electronics and optoelectronics of the future. Increasing the performance of organic devices is one of the key challenges in their research. It was only last year, when the team headed by Dr. Hans Kleemann announced an important breakthrough with the development of efficient, printable vertical organic transistors.
Now Dr. Zhongbin Wu, Dr. Yuan Liu, and PhD student Erjuan Guo present the first electronic device that combines a vertical organic permeable base transistor (OPBT) and an OLED. With this novel device concept of an organic permeable base light-emitting transistor (OPB-LET), the researchers succeeded in combining the function of a highly efficient switching transistor and an organic light-emitting diode as commonly employed in active matrix displays. Active matrix liquid crystal displays (AMLCD) usually contain a matrix of thin-film transistors to drive LCD pixels. Each individual pixel has a circuit with active components (mostly transistors). In this context, organic light-emitting transistors, three-terminal devices combining a thin-film transistor with a light-emitting diode, have generated increasing interest. However, increasing their efficiency while keeping the operating voltage low remains a key challenge.
“The key to construct the high performance OPB-LETs is the permeable base electrode located at the center of the device, forming a distinctive optical microcavity and regulating charge carrier injection and transport. The thus designed three-terminal vertical optoelectronic devices can simultaneously high efficiencies (up to 24.6%), high luminance (up to 12,513 cd m-2), and low driving voltages ( More

Nearly everything author Malcolm Gladwell said about how information spreads in his 2000 bestseller “The Tipping Point” is wrong, according to a recent study led by UCLA professor of sociology Gabriel Rossman.
“The main point of ‘The Tipping Point’ is if you want your idea to spread, you find the most popular person in the center of any given network and you sell them on your idea, and then they’ll sell the rest of the world on it,” Rossman said.
But Rossman’s latest study, recently published in Proceedings of the National Academy of Sciences, pokes holes in that widely accepted notion by showing how the presence of even just a bit of advertising or other mass communication — “top-down” information that comes from outside the network — effectively equalizes the influence of everyone across the network.
Rossman, together with co-author Jacob Fisher of Duke University and the University of Michigan, used a statistical programming language called R to build out network maps based on several different datasets. One set harnessed Twitter posts, along with retweets and mentions, over two weeks in 2011. Another used the Democratic National Committee email network from WikiLeaks’ 2016 data dump. Another used the emails of Enron executives subpoenaed in 2002. Six others were randomly generated.
These provided a network structure — a web of dots and lines showing how users in each network were connected to one another. Once those maps existed, Rossman and Fisher were able to see how quickly an idea might spread throughout the network if it started from the network’s single most important person or if it started from someone chosen at random.
They looked at that information spread in several ways, comparing via computer simulation how information moved throughout the networks when it came solely through word-of-mouth within a network (“bottom up”), when it came solely through external advertising or public information (“top down”) and when it came through varying bottom-up and top-down combinations.
What they discovered refutes Gladwell’s concept that network position is always paramount. They found that in instances where there is even a small amount of advertising — even when it is just a quarter of a percent as strong as word-of-mouth — there’s virtually no difference between the influence of the person at the center of a network and those further out on the string.
“It’s not that word-of-mouth doesn’t matter — it’s that nobody is particularly important for the word-of-mouth process,” Rossman said. “What we saw is that when advertising doesn’t exist, when advertising is exactly zero, it looks like whoever is Mr. Popular, whoever has the most central connections, really matters. And in that scenario, if you start with that person at the center of the network, like the leader of an organization or company, rather than the intern, then whatever you’re selling gets an uptick.”
But it takes only an incredibly weak amount of advertising to effectively neutralize the dominance of Mr. Popular, Rossman said. “Just a small amount changes the dynamic so that it practically doesn’t matter whether you start with Mr. Popular or the intern.”
Rossman is an expert on information spread in culture and mass media and is the author of “Climbing the Charts: What Radio Airplay Tells Us About the Diffusion of Innovation.”
The findings of his latest study, he notes, have wide-ranging implications, from selling products to a specific audience to understanding how to share information on vaccines with vulnerable communities.
“There’s a reasonably big body of literature that says you should find someone who appears to be structurally important to the network you’re trying to connect with,” he said. “We’re arguing that, if advertising exists, you can just pick somebody at random in the network and you’ll get just as good results as if you found the absolutely ideal person to start with.”

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Materials provided by University of California – Los Angeles. Original written by Jessica Wolf. Note: Content may be edited for style and length. More

A novel computer algorithm that could create a broadly reactive influenza vaccine for swine flu also offers a path toward a pan-influenza vaccine and possibly a pan-coronavirus vaccine as well, according to a new paper published in Nature Communications.
“This work takes us a step closer to a pan-swine flu virus vaccine,” said Bette Korber, a computational biologist at Los Alamos National Laboratory and a co-author on the paper. “The hope is to eventually be prepared with an effective and rapid response if another swine flu epidemic begins to spread in humans, but this swine flu vaccine could also be useful in a veterinary setting.” The immune responses to the vaccine showed very promising breadth against diverse viral variants. “The same basic principles may be applicable to developing a pan-coronavirus vaccine to enable a rapid vaccine response to future coronavirus cross-species jumps,” said Korber.
The algorithm, Epigraph, has already been used to predict therapeutic HIV vaccine candidates, and it has also shown promising potential as a pan-filovirus vaccine against highly diverse Ebola and Marburg viruses, protecting against disease when tested in an animal model.
Vaccination with the Epigraph-designed product led to the development of a strong cross-reactive antibody response in mice, the study showed. In swine, it induced strong cross-reactive antibody and T-cell responses. The research was conducted in close collaboration with researchers from the Nebraska Center for Virology at the University of Nebraska, St. Jude Children’s Research Hospital, and Los Alamos National Laboratory.
“We developed the Epigraph strategy for this kind of problem, and it can, in theory, be applied to many diverse pathogens,” said Korber, who created it in partnership with her husband, James Theiler, a Los Alamos Fellow. “The tool creates a cocktail of vaccine antigens designed to maximize efficacy across a highly diverse population.”
Since 2010, more than 460 swine-flu variant infections have been reported in humans in the United States. Pigs are susceptible to swine, avian, and human influenza viruses, making them the perfect “mixing vessel” for novel reassorted influenza viruses, the authors note. These novel reassorted viruses have significant pandemic potential if zoonosis (transfer from pigs to humans) occurs, as seen with 2009 H1N1 swine flu pandemic.

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Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length. More

The era of widespread remote learning brought about by the COVID-19 pandemic requires online testing methods that effectively prevent cheating, especially in the form of collusion among students. With concerns about cheating on the rise across the country, a solution that also maintains student privacy is particularly valuable.
In research published today in npj Science of Learning, engineers from Rensselaer Polytechnic Institute demonstrate how a testing strategy they call “distanced online testing” can effectively reduce students’ ability to receive help from one another in order to score higher on a test taken at individual homes during social distancing.
“Often in remote online exams, students can talk over the phone or internet to discuss answers,” said Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and the corresponding author on this paper. “The key idea of our method is to minimize this chance via discrete optimization aided by knowledge of a student’s competencies.”
When a distanced online test is performed, students receive the same questions, but at varying times depending on their skill level. For instance, students of highest mastery levels receive each question after other groups of students have already answered those questions. This approach, Wang said, reduces the incentive for students to receive help from those who have more mastery of the material. In order to determine the order of each student’s questions, their competence levels are estimated using their grade point averages, SAT scores, or midterm scores, depending on what is available at a specific point in the semester.
According to statistical tests and post-exam surveys, this method reduced the points gained through collusion by orders of magnitude when compared to conventional exam methods. As an added benefit, Wang said, when students knew collusion would not be possible, they were more motivated to study class material. Wang and his collaborators hope to share this pedagogical innovation beyond the Rensselaer campus.
“We plan to develop a good platform so that others can easily use this method,” said Wang, a member of the Center for Biotechnology and Interdisciplinary Studies at Rensselaer.

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Materials provided by Rensselaer Polytechnic Institute. Note: Content may be edited for style and length. More

A joint research team co-led by City University of Hong Kong (CityU) has developed a new soft tactile sensor with skin-comparable characteristics. A robotic gripper with the sensor mounted at the fingertip could accomplish challenging tasks such as stably grasping fragile objects and threading a needle. Their research provided new insight into tactile sensor design and could contribute to various applications in the robotics field, such as smart prosthetics and human-robot interaction.
Dr Shen Yajing, Associate Professor at CityU’s Department of Biomedical Engineering (BME) was one of the co-leaders of the study. The findings have been recently published in the scientific journal Science Robotics, titled “Soft magnetic skin for super-resolution tactile sensing with force self-decoupling.”
Mimicking human skin characteristics
A main characteristic of human skin is its ability to sense the shear force, meaning the force that makes two objects slip or slide over each other when coming into contact. By sensing the magnitude, direction and the subtle change of shear force, our skin can act as feedback and allow us to adjust how we should hold an object stably with our hands and fingers or how tight we should grasp it.
To mimick this important feature of human skin, Dr Shen and Dr Pan Jia, a collaborator from the University of Hong Kong (HKU), have developed a novel, soft tactile sensor. The sensor is in a multi-layered structure like human skin and includes a flexible and specially magnetised film of about 0.5mm thin as the top layer. When an external force is exerted on it, it can detect the change of the magnetic field due to the film’s deformation. More importantly, it can “decouple,” or decompose, the external force automatically into two components — normal force (the force applied perpendicularly to the object) and shear force, providing the accurate measurement of these two forces respectively.
“It is important to decouple the external force because each force component has its own influence on the object. And it is necessary to know the accurate value of each force component to analyse or control the stationary or moving state of the object,” explained Yan Youcan, PhD student at BME and the first author of the paper.

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Deep learning enhanced accuracy
Moreover, the senor possesses another human skin-like characteristic — the tactile “super-resolution” that allows it to locate the stimuli’s position as accurate as possible. “We have developed an efficient tactile super-resolution algorithm using deep learning and achieved a 60-fold improvement of the localisation accuracy for contact position, which is the best among super-resolution methods reported so far,” said Dr Shen. Such an efficient tactile super-resolution algorithm can help improve the physical resolution of a tactile sensor array with the least number of sensing units, thus reducing the number of wirings and the time required for signal transmitting.
“To the best of our knowledge, this is the first tactile sensor that achieved self-decoupling and super-resolution abilities simultaneously,” he added.
Robotic hand with the new sensor completes challenging tasks
By mounting the sensor at the fingertip of a robotic gripper, the team showed that robots can accomplish challenging tasks. For example, the robotic gripper stably grasped fragile objects like an egg while an external force trying to drag it away, or threaded a needle via teleoperation. “The super-resolution of our sensor helps the robotic hand to adjust the contact position when it grasps an object. And the robotic arm can adjust force magnitude based on the force decoupling ability of the tactile sensor,” explained Dr Shen.
He added that the sensor can be easily extended to the form of sensor arrays or even continuous electronic skin that covers the whole body of the robot in the future. The sensitivity and measurement range of the sensor can be adjusted by changing the magnetisation direction of the top layer (magnetic film) of the sensor without changing the sensor’s thickness. This enabled the e-skin to have different sensitivity and measurement range in different parts, just like human skin.
Also, the sensor has a much shorter fabrication and calibration processes compared with other tactile sensors, facilitating the actual applications.
“This proposed sensor could be beneficial to various applications in the robotics field, such as adaptive grasping, dextrous manipulation, texture recognition, smart prosthetics and human-robot interaction. The advancement of soft artificial tactile sensors with skin-comparable characteristics can make domestic robots become part of our daily life,” concluded Dr Shen. More

During World War II, British intelligence agents planted false documents on a corpse to fool Nazi Germany into preparing for an assault on Greece. “Operation Mincemeat” was a success, and covered the actual Allied invasion of Sicily.
The “canary trap” technique in espionage spreads multiple versions of false documents to conceal a secret. Canary traps can be used to sniff out information leaks, or as in WWII, to create distractions that hide valuable information.
WE-FORGE, a new data protection system designed at Dartmouth’s Department of Computer Science, uses artificial intelligence to build on the canary trap concept. The system automatically creates false documents to protect intellectual property such as drug design and military technology.
“The system produces documents that are sufficiently similar to the original to be plausible, but sufficiently different to be incorrect,” said V.S. Subrahmanian, the Distinguished Professor in Cybersecurity, Technology, and Society, and director of the Institute for Security, Technology, and Society.
Cybersecurity experts already use canary traps, “honey files,” and foreign language translators to create decoys that deceive would-be attackers. WE-FORGE improves on these techniques by using natural language processing to automatically generate multiple fake files that are both believable and incorrect. The system also inserts an element of randomness to keep adversaries from easily identifying the real document.
WE-FORGE can be used to create numerous fake versions of any technical design document. When adversaries hack a system, they are faced with the daunting task of figuring out which of the many similar documents is real.

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“Using this technique, we force an adversary to waste time and effort in identifying the correct document. Even if they do, they may not have confidence that they got it right,” said Subrahmanian.
Creating the false technical documents is no less daunting. According to the research team, a single patent can include over 1,000 concepts with up to 20 possible replacements. WE-FORGE can end up considering millions of possibilities for all of the concepts that might need to be replaced in a single technical document.
“Malicious actors are stealing intellectual property right now and getting away with it for free,” said Subrahmanian. “This system raises the cost that thieves incur when stealing government or industry secrets.”
The WE-FORGE algorithm works by computing similarities between concepts in a document and then analyzing how relevant each word is to the document. The system then sorts concepts into “bins” and computes the feasible candidate for each group.
“WE-FORGE can also take input from the author of the original document,” said Dongkai Chen, a graduate student at Dartmouth who worked on the project. “The combination of human and machine ingenuity can increase costs on intellectual-property thieves even more.”
As part of the research, the team falsified a series of computer science and chemistry patents and asked a panel of knowledgeable subjects to decide which of the documents were real.

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According to the research, published in ACM Transactions on Management Information Systems, the WE-FORGE system was able to “consistently generate highly believable fake documents for each task.”
Unlike other tools, WE-FORGE specializes in falsifying technical information rather than just concealing simple information, such as passwords.
WE-FORGE improves on an earlier version of the system — known as FORGE — by removing the time-consuming need to create guides of concepts associated with specific technologies. WE-FORGE also ensures that there is greater diversity among fakes, and follows an improved technique for selecting concepts to replace and their replacements.
Almas Abdibayev, Deepti Poluru Guarini and Haipeng Chen all contributed to this research while with Dartmouth’s Department of Computer Science.

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Materials provided by Dartmouth College. Original written by David Hirsch. Note: Content may be edited for style and length. More

Robots that could take on basic healthcare tasks to support the work of doctors and nurses may be the way of the future. Who knows, maybe a medical robot can prescribe your medicine someday? That’s the idea behind 3D structural-sensing robots being developed and tested at Simon Fraser University by Woo Soo Kim, associate professor in the School of Mechatronic Systems Engineering.
“The recent pandemic demonstrates the need to minimize human-to-human interaction between healthcare workers and patients,” says Kim, who authored two recent papers on the subject — a perspective on the technology and a demonstration of a robots’ usefulness in healthcare. “There’s an opportunity for sensing robots to measure essential healthcare information on behalf of care providers in the future.”
Kim’s research team programmed two robots, a humanoid figure and a robotic arm, to measure human physiological signals, working from Kim’s Additive Manufacturing Lab located in SFU Surrey’s new engineering building. The robotic arm, created using Kim’s 3D printed origami structures, contains biomedical electrodes on the tip of each finger. When the hand touches a person, it detects physiological signals, including those from an electrocardiogram (which monitors heartbeat), respiration rate, electromyogram (monitoring electrical signals from muscle movements) and temperature.
The humanoid robot can also monitor oxygen levels, which could be used to monitor the condition of those who develop severe COVID-19. The data can be viewed in real-time on the robot’s monitor or sent directly to the healthcare provider.
Kim plans further development and testing of the robot together with healthcare collaborators. At this stage, the robots are capable of passively gathering patient information. But within the next decade, he says it’s conceivable that healthcare robots fitted with artificial intelligence could take a more active role, interacting with the patient, processing the data they have collected and even prescribing medication.
Further study will also need to involve determining acceptance levels for this type of technology among various age groups, from youth to seniors, in a hospital setting.

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Materials provided by Simon Fraser University. Note: Content may be edited for style and length. More