Computers Math

Researchers at the University of Surrey have successfully demonstrated proof-of-concept of using their multimodal transistor (MMT) in artificial neural networks, which mimic the human brain. This is an important step towards using thin-film transistors as artificial intelligence hardware and moves edge computing forward, with the prospect of reducing power needs and improving efficiency, rather than relying solely on computer chips.
The MMT, first reported by Surrey researchers in 2020, overcomes long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in the peer-reviewed journal Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems, which is a vital step towards manufacturing.
Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations. They used both measured and simulated MMT data to train an artificial neural network to identify handwritten numbers and compared the results with the built-in ReLU of the software. The results confirmed the potential of MMT devices for thin-film decision and classification circuits. The same approach could be used in more complex AI systems.
Unusually, the research was led by Surrey undergraduate Isin Pesch, who worked on the project during the final year research module of her BEng (Hons) in Electronic Engineering with Nanotechnology. Covid meant she had to study remotely from her home in Turkey, but she still managed to spearhead the development, complemented by an international research team, which also included collaborators in the University of Rennes, France and UCL, London.
Isin Pesch, lead author of the paper, which was written before she graduated in July 2021, said:
“There is a great need for technological improvements to support the growth of low cost, large area electronics which were shown to be used in artificial intelligence applications. Thin-film transistors have a role to play in enabling high processing power with low resource use. We can now see that MMTs, a unique type of thin-film transistor, invented at the University of Surrey, have the reliability and uniformity needed to fulfil this role.”
Dr Radu Sporea, Senior Lecturer at the University of Surrey’s Advanced Technology Institute, said:
“These findings are a reminder of how Surrey is a leader in AI research. Many of my colleagues focus on people-centred AI and how best to maximise the benefits for humans, including how to apply these new concepts ethically. Our research at the Advanced Technology Institute takes forward the physical implementation, as a stepping stone towards powerful yet affordable next-generation hardware. It’s fantastic that collaboration is resulting in such successes with researchers involved at all levels, from undergraduates like Isin when she led this research, to seasoned experts.”
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Decoding letters into sound is a key point in learning to read but is not enough to master it. “Reading calls upon several other essential mechanisms that we don’t necessarily think about, such as knowing how to move our eyes on the page or how to use our working memory to link words together in a coherent sentence,” points out Daphné Bavelier, a professor in the Psychology Section of the Faculty of Psychology and Educational Sciences (FPSE) at the UNIGE. “These other skills, such as vision, the deployment of attention, working memory, and cognitive flexibility, are known to be improved by action video games,” explains Angela Pasqualotto, first author of this study, which is based on her PhD thesis at the Department of Psychology and Cognitive Science of the University of Trento under the direction of Professors Venuti and De Angeli.
A child-friendly action video game to support learning
With this in mind, a video game was designed that combines action video games with mini games that train different executive functions, such as working memory, inhibition and cognitive flexibility, functions that are called upon during reading. “The universe of this game is an alternative world in which the child, accompanied by his Raku, a flying creature, must carry out different missions to save planets and progress in the game,” Angela Pasqualotto adds. The idea is to reproduce the components of an action game, without incorporating violence, so that it is suitable for young children. “For example, the Raku flies through a meteor shower, moving around to avoid those or aiming at them to weaken their impact, while collecting useful resources for the rest of the game, a bit like what you find in action video games.”
The scientists then worked with 150 Italian schoolchildren aged 8 to 12, divided into two groups: the first one played the video game developed by the team, and the second one played Scratch, a game that teaches children how to code. Both games require attentional control and executive functions, but in different manners. The action video game requires children to perform tasks within a time limit such as remembering a sequence of symbols or responding only when the Raku makes a specific sound while increasing the difficulty of these tasks according to the child’s performance. Scratch, the control game, requires planning, reasoning and problem solving. Children must manipulate objects and logical structures to establish the desired programming sequence.
“First, we tested the children’s ability to read words, non-words and paragraphs, and also we conducted an attention test that measures the child’s attentional control, a capacity we know is trained by action video games,” explains Daphne Bavelier. The children then followed the training with either the action video game or the control game, for six weeks, two hours a week under supervision at school. Children were tested at school by clinicians of the Laboratory of Observation Diagnosis and Education (UNITN).
Long-term improvement in reading skills
Shortly after the end of the training, the scientists repeated the tests on both groups of children. “We found a 7-fold improvement in attentional control in the children who played the action video game compared to the control group,” says Angela Pasqualotto. Even more remarkably, the research team observed a clear enhancement in reading, not only in terms of reading speed, but also in accuracy, whereas no improvement was noted for the control group. This improvement in literacy occurs even though the action video game does not require any reading activity.
“What is particularly interesting about this study is that we carried out three further assessment tests at 6 months, 12 months and 18 months after training. On each occasion, the trained children performed better than the control group, which proves that these improvements were sustained,” Angela Pasqualotto says. Moreover, the grades in Italian of the trained children became significantly better over time, showing a virtuous improvement in learning ability. “The effects are thus long-term, in line with the action video game strengthening the ability to learn how to learn,” says Daphne Bavelier.
Within the framework of the NCCR Evolving Language and in collaboration with Irene Altarelli (co-author of the article and researcher at LaPsyDE, University of Paris), the game will be adapted into German, French and English. “When reading, decoding is more or less difficult depending on the language. Italian, for example, is very transparent — each letter is pronounced — whereas French and English are quite opaque, resulting in rather different learning challenges. Reading in opaque languages requires the ability to learn exceptions, to learn how a variety of contexts impacts pronunciation and demands greater reliance on memorization,” comments Irene Altarelli. Will the benefits of action video games on reading acquisition extend to such complex learning environments as reading in French or English? This is the question that this study will help answer. In addition, the video game will be available entirely at home, remotely, as will the administration of reading and attention tests, in order to complement school lessons, rather than taking time out of school hours. More

Ecologists need to understand wild animal behaviours in order to conserve species, but following animals around can be expensive, dangerous, or sometimes impossible in the case of animals that move underwater or into areas we can’t reach easily.
Scientists turned to the next best thing: bio-logging devices that can be attached to animals and capture information about movement, breathing rate, heart rate, and more.
However, retrieving an accurate picture of what a tagged animal does as it journeys through its environment requires statistical analysis, especially when it comes to animal movement, and the methods statisticians use are always evolving to make full use of the large and complex data sets that are available.
A recent study by researchers at the Institute for the Oceans and Fisheries (IOF) and the UBC department of statistics has taken us a step closer to understanding the behaviours of northern resident killer whales by improving statistical tools useful for identifying animal behaviours that can’t be observed directly.
“The thing we really tackled with this paper was trying to get at some of those fine-scale behaviours that aren’t that easy to model,” said Evan Sidrow, a doctoral student in the department of statistics and the study’s lead author. “It’s a matter of finding behaviours on the order of seconds — maybe 10 to 15 seconds. Usually, it’s a matter of a whale looking around, and then actively swimming for a second to get over to a new location. We are trying to observe fleeting behaviours, like a whale catching a fish.”
The research team improved a statistical tool that is based on what is called a hidden Markov model, which is helpful for unlocking the mysteries hidden inside animal movement datasets. More

Inspired by the growth of bones in the skeleton, researchers at the universities of Linköping in Sweden and Okayama in Japan have developed a combination of materials that can morph into various shapes before hardening. The material is initially soft, but later hardens through a bone development process that uses the same materials found in the skeleton.
When we are born, we have gaps in our skulls that are covered by pieces of soft connective tissue called fontanelles. It is thanks to fontanelles that our skulls can be deformed during birth and pass successfully through the birth canal. Post-birth, the fontanelle tissue gradually changes to hard bone. Now, researchers have combined materials which together resemble this natural process.
“We want to use this for applications where materials need to have different properties at different points in time. Firstly, the material is soft and flexible, and it is then locked into place when it hardens. This material could be used in, for example, complicated bone fractures. It could also be used in microrobots — these soft microrobots could be injected into the body through a thin syringe, and then they would unfold and develop their own rigid bones,” says Edwin Jager, associate professor at the Department of Physics, Chemistry and Biology (IFM) at Linköping University.
The idea was hatched during a research visit in Japan when materials scientist Edwin Jager met Hiroshi Kamioka and Emilio Hara, who conduct research into bones. The Japanese researchers had discovered a kind of biomolecule that could stimulate bone growth under a short period of time. Would it be possible to combine this biomolecule with Jager’s materials research, to develop new materials with variable stiffness?
In the study that followed, published in Advanced Materials, the researchers constructed a kind of simple “microrobot,” one which can assume different shapes and change stiffness. The researchers began with a gel material called alginate. On one side of the gel, a polymer material is grown. This material is electroactive, and it changes its volume when a low voltage is applied, causing the microrobot to bend in a specified direction. On the other side of the gel, the researchers attached biomolecules that allow the soft gel material to harden. These biomolecules are extracted from the cell membrane of a kind of cell that is important for bone development. When the material is immersed in a cell culture medium — an environment that resembles the body and contains calcium and phosphor — the biomolecules make the gel mineralise and harden like bone.
One potential application of interest to the researchers is bone healing. The idea is that the soft material, powered by the electroactive polymer, will be able to manoeuvre itself into spaces in complicated bone fractures and expand. When the material has then hardened, it can form the foundation for the construction of new bone. In their study, the researchers demonstrate that the material can wrap itself around chicken bones, and the artificial bone that subsequently develops grows together with the chicken bone.
By making patterns in the gel, the researchers can determine how the simple microrobot will bend when voltage is applied. Perpendicular lines on the surface of the material make the robot bend in a semicircle, while diagonal lines make it bend like a corkscrew.
“By controlling how the material turns, we can make the microrobot move in different ways, and also affect how the material unfurls in broken bones. We can embed these movements into the material’s structure, making complex programmes for steering these robots unnecessary,” says Edwin Jager.
In order to learn more about the biocompatibility of this combination of materials, the researchers are now looking further into how its properties work together with living cells.
The research was carried out with financial support from organisations including the Japanese Society for the Promotion of Science (JSPS) Bridge Fellowship program and KAKENHI, the Swedish Research Council, Promobilia and STINT (Swedish Foundation for International Cooperation in Research and Higher Education).
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Materials provided by Linköping University. Original written by Karin Söderlund Leifler. Note: Content may be edited for style and length. More

The Internet of Things (IoT) is completely enmeshed in our daily lives, a network of connected laptops, phones, cars, fitness trackers — even smart toasters and refrigerators — that are increasingly able to make decisions on their own. But how to ensure that these devices benefit us, rather than exploit us or put us at risk? New work, led by Francine Berman at the University of Massachusetts Amherst, proposes a novel framework, the “impact universe,” that can help policymakers keep the public interest in focus amidst the rush to adopt ever-new digital technology.
“How,” asks Berman, Stuart Rice Honorary Chair and Research Professor in UMass Amherst’s Manning College of Information and Computer Sciences (CICS), “can we ensure that technology works for us, rather than the other way around?” Berman, lead author of a new paper recently published in the journal Patterns, and her co-authors sketch out what they call the “impact universe” — a way for policymakers and others to think “holistically about the potential impacts of societal controls for systems and devices in the IoT.”
One of the wonders of modern digital technology is that it increasingly makes decisions for us on its own. But, as Berman puts it, “technology needs adult supervision.”
The impact universe is a way of holistically sketching out all the competing implications of a given technology, taking into consideration environmental, social, economic and other impacts to develop effective policy, law and other societal controls. Instead of focusing on a single desirable outcome, sustainability, say, or profit, the impact universe allows us to see that some outcomes will come at the cost of others.
“The model reflects the messiness of real life and how we make decisions,” says Berman, but it brings clarity to that messiness so that decision makers can see and debate the tradeoffs and benefits of different social controls to regulate technology. The framework allows decisions makers to be more deliberate in their policy-making and to better focus on the common good.
Berman is at the forefront of an emerging field called public interest technology (PIT), and she’s building an initiative at UMass Amherst that unites campus students and scholars whose work is empowered by technology and focused on social responsibility. The ultimate goal of PIT is to develop the knowledge and critical thinking needed to create a society capable of effectively managing the digital ecosystem that powers our daily lives.
Berman’s co-authors, Emilia Cabrera, Ali Jebari and Wassim Marrakchi, were Harvard undergraduates and worked with Berman on the paper during her Radcliffe Fellowship at Harvard. The fellowship gave Berman a chance to work broadly with a multidisciplinary group of scholars and thinkers, and to appreciate the importance of designing, developing, and framing societal controls so that technology promotes the public benefit.
“The real world is complex and there are always competing priorities,” says Berman. “Tackling this complexity head-on by taking the universe of potential technology impacts into account is critical if we want digital technologies to serve society rather than overwhelm it.”
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Maps are essential for exploring trackless wilderness or vast expanses of ocean. The same is true for scientific studies that try to open up new fields and develop brand-new devices. A journey without maps and signposts tends to end in vain.
In the world of “neuromorphic devices,” an electronic device that mimics neural cells such as our brain, researchers have long been forced to travel without maps. Such devices will lead to a fresh field of brain-inspired computers with substantial benefits such as low-energy consumption. But its operation mechanism has remained unclear, particularly in regards to controlling the response speed control.
A research group from Tohoku University and the University of Cambridge brought clarity in a recent study published in the journal Advanced Electronic Materials on January 13, 2022.
They looked into organic electrochemical transistors (OECT), which are often applied in neuromorphic devices and control the movement of the ion in the active layer. The analysis revealed that response timescale depends on the size of ion in the electrolyte.
Based on these experimental results, the group modeled the neuromorphic response of the devices. Comparisons of the data showed that movements of the ions in the OECT controlled the response. This indicates tuning the timescale for ion movement can be an effective way to regulate the neuromorphic behavior of OECTs.
“We obtained a map that provides rational design guidelines for neuromorphic devices through changing ion size and material composition in the active layer,” said Shunsuke Yamamoto, paper corresponding author and assistant professor at Tohoku University’s Graduate School of Engineering. “Further studies will pave the way for application to artificial neural networks and lead to better and more precise designs of the conducting polymer materials used in this field.”
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Investigators who recently developed a mathematical model that indicated why treatment responses vary widely among individuals with COVID-19 have now used the model to identify biological markers related to these different responses. The team, which was led by scientists at Massachusetts General Hospital (MGH) and the University of Cyprus, notes that the model can be used to provide a better understanding of the complex interactions between illness and response and can help clinicians provide optimal care for diverse patients.
The work, which is published in EBioMedicine, was initiated because COVID-19 is extremely heterogeneous, meaning that illness following SARS-CoV-2 infection ranges from asymptomatic to life-threatening conditions such as respiratory failure or acute respiratory distress syndrome (ARDS), in which fluid collects in the lungs. “Even within the subset of critically ill COVID-19 patients who develop ARDS, there exists substantial heterogeneity. Significant efforts have been made to identify subtypes of ARDS defined by clinical features or biomarkers,” explains co-senior author Rakesh K. Jain, PhD, director of the E.L. Steele Laboratories for Tumor Biology at MGH and the Andrew Werk Cook Professor of Radiation Oncology at Harvard Medical School (HMS). “To predict disease progression and personalize treatment, it is necessary to determine the associations among clinical features, biomarkers and underlying biology. Although this can be achieved over the course of numerous clinical trials, this process is time-consuming and extremely expensive.”
As an alternative, Jain and his colleagues used their model to analyze the effects that different patient characteristics yield on outcomes following treatment with different therapies. This allowed the team to determine the optimal treatment for distinct categories of patients, reveal biologic pathways responsible for different clinical responses, and identify markers of these pathways.
The researchers simulated six patient types (defined by the presence or absence of different comorbidities) and three types of therapies that modulate the immune system. “Using a novel treatment efficacy scoring system, we found that older and hyperinflamed patients respond better to immunomodulation therapy than obese and diabetic patients,” says co-senior and corresponding author Lance Munn, PhD, who is the deputy director of the Steele Labs and an associate professor at HMS. “We also found that the optimal time to initiate immunomodulation therapy differs between patients and also depends on the drug itself.” Certain biological markers that differed based on patient characteristics determined optimal treatment initiation time, and these markers pointed to particular biologic programs or mechanisms that impacted a patient’s outcome. The markers also matched clinically identified markers of disease severity.
For COVID-19 as well as other conditions, the team’s approach could enable investigators to enrich a clinical trial with patients most likely to respond to a given drug. “Such enrichment based on prospectively predicted biomarkers is a potential strategy for increasing precision of clinical trials and accelerating therapy development,” says co-senior author Triantafyllos Stylianopoulos, PhD, an associate professor at the University of Cyprus.
Other co-authors include Sonu Subudhi, Chrysovalantis Voutouri, C. Corey Hardin, Mohammad Reza Nikmaneshi, Melin J. Khandekar and Sayon Dutta from MGH; and Ankit B. Patel and Ashish Verma from Brigham and Women’s Hospital.
Funding for the study was provided by the National Institutes of Health, Harvard Ludwig Cancer Center, Niles Albright Research Foundation and Jane’s Trust Foundation. Voutouri is a recipient of a Marie Sk?odowska Curie Actions Individual Fellowship.
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Computer engineers and radiologists at Duke University have developed an artificial intelligence platform to analyze potentially cancerous lesions in mammography scans to determine if a patient should receive an invasive biopsy. But unlike its many predecessors, this algorithm is interpretable, meaning it shows physicians exactly how it came to its conclusions.
The researchers trained the AI to locate and evaluate lesions just like an actual radiologist would be trained, rather than allowing it to freely develop its own procedures, giving it several advantages over its “black box” counterparts. It could make for a useful training platform to teach students how to read mammography images. It could also help physicians in sparsely populated regions around the world who do not regularly read mammography scans make better health care decisions.
The results appeared online December 15 in the journal Nature Machine Intelligence.
“If a computer is going to help make important medical decisions, physicians need to trust that the AI is basing its conclusions on something that makes sense,” said Joseph Lo, professor of radiology at Duke. “We need algorithms that not only work, but explain themselves and show examples of what they’re basing their conclusions on. That way, whether a physician agrees with the outcome or not, the AI is helping to make better decisions.”
Engineering AI that reads medical images is a huge industry. Thousands of independent algorithms already exist, and the FDA has approved more than 100 of them for clinical use. Whether reading MRI, CT or mammogram scans, however, very few of them use validation datasets with more than 1000 images or contain demographic information. This dearth of information, coupled with the recent failures of several notable examples, has led many physicians to question the use of AI in high-stakes medical decisions.
In one instance, an AI model failed even when researchers trained it with images taken from different facilities using different equipment. Rather than focusing exclusively on the lesions of interest, the AI learned to use subtle differences introduced by the equipment itself to recognize the images coming from the cancer ward and assigning those lesions a higher probability of being cancerous. As one would expect, the AI did not transfer well to other hospitals using different equipment. But because nobody knew what the algorithm was looking at when making decisions, nobody knew it was destined to fail in real-world applications. More