Computers Math

From small committees to national elections, group decision-making can be complicated — and it may not always settle on the best choice. That’s partly because some members of the group do research on their own, and others take their cues from the people around them.
That distinction is readily observed around election time. “Many voters couldn’t tell you the policy platforms for the candidates they’re voting for,” says applied mathematician Vicky Chuqiao Yang at the Santa Fe Institute. “Many individuals are uninformed, and they’re most likely to rely on information they get from others.”
Social scientists have long sought ways to study the phenomenon of group decision-making, but that’s a tricky undertaking. Researchers in a range of disciplines have tried to tackle the problem, with parallel efforts often leading to conflicting conclusions. Most existing models examine the effect of a single variable, which means they don’t capture the whole picture.
“The outcome of collective decision making is the result of complex interactions of many variables,” says Yang, “and those interactions are rarely taken into account” in previous work.
To overcome that challenge, Yang recently led the development of a mathematical framework that captures the influence of multiple interactions among members of a group. “You can plug in multiple effects and see their behavior and how they manifest in the group at the same time,” she explains.
Those effects include the influence of social learners. The model predicted, for example, that decision-making groups have a critical threshold of people who get their information from others. Below that threshold, the group chooses the high-quality outcome. Above it, the group can end up choosing the better or worse option.
The model also predicted a significant role for “committed minorities,” or people who refuse to change their minds, no matter the evidence. These committed minorities can be bolstered, Yang says, by social learners, though every group is different.
The mathematical model is both simple and general, and can accurately reflect the multitude of moving parts within a system. Yang’s collaborators include psychologist and SFI Professor Mirta Galesic, economist Ani Harutyunyan at the Sunwater Institute, and Harvey McGuinness, an undergraduate at Johns Hopkins University and former student researcher at SFI. (The whole project began, said Yang, with a question from McGuinness.) The group reported on the framework in a paper published in Proceedings of the National Academy of Sciences.
Yang says she hopes the model will help bring together parallel work from different disciplines. These disciplines have found separate effects at work in collective decision-making, “but we don’t yet have a holistic understanding that gives a recipe for good collective decision making,” she said. “Our work brings us one step closer to it.”
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Ultrafast electron microscope opens up new avenues for the development of sensors and quantum devices.
Everyone who has ever been to the Grand Canyon can relate to having strong feelings from being close to one of nature’s edges. Similarly, scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have discovered that nanoparticles of gold act unusually when close to the edge of a one-atom thick sheet of carbon, called graphene. This could have big implications for the development of new sensors and quantum devices.
This discovery was made possible with a newly established ultrafast electron microscope (UEM) at Argonne’s Center for Nanoscale Materials (CNM), a DOE Office of Science User Facility. The UEM enables the visualization and investigation of phenomena at the nanoscale and on time frames of less than a trillionth of a second. This discovery could make a splash in the growing field of plasmonics, which involves light striking a material surface and triggering waves of electrons, known as plasmonic fields.
“With ultrafast capabilities, there’s no telling what we might see as we tweak different materials and their properties.” — Haihua Liu, Argonne nanoscientist
For years, scientists have been pursuing development of plasmonic devices with a wide range of applications — from quantum information processing to optoelectronics (which combine light-based and electronic components) to sensors for biological and medical purposes. To do so, they couple two-dimensional materials with atomic-level thickness, such as graphene, with nanosized metal particles. Understanding the combined plasmonic behavior of these two different types of materials requires understanding exactly how they are coupled.
In a recent study from Argonne, researchers used ultrafast electron microscopy to look directly at the coupling between gold nanoparticles and graphene. More

“Skin electronics” are thin flexible electronics that could be mounted onto the skin. While it may sound like something out of science fiction, it is anticipated that soon such devices can serve as next-generation devices with a wide range of applications such as health monitoring, health diagnosis, virtual reality, and human-machine interface.
As it is expected, creating such devices requires components that are soft and stretchable to be mechanically compatible with the human skin. One of the vital components of skin electronics is an intrinsically stretchable conductor that transmits electrical signals between devices. For reliable operation and high-quality performance, a stretchable conductor which features ultrathin thickness, metal-like conductivity, high stretchability, and ease of patternability is required. Despite extensive research, it was not yet possible to develop a material that possesses all of these properties simultaneously, due to the fact that they often have trade-offs between one another.
Led by professor HYEON Taeghwan and KIM Dae-Hyeong, researchers at the Center for Nanoparticle Research within the Institute for Basic Science (IBS) in Seoul, South Korea unveiled a new method to fabricate a composite material in a form of nanomembrane, which comes with all of the above-mentioned properties. The new composite material consists of metal nanowires that are tightly packed in a monolayer within ultrathin rubber film.
This novel material was made using a process that the team developed called a “float assembly method.” The float assembly takes advantage of the Marangoni effect, which occurs in two liquid phases with different surface tensions. When there is a gradient in surface tension, a Marangoni flow is generated away from the region with lower surface tension towards the region with higher surface tension. This means that dropping a liquid with lower surface tension on the water surface lowers the surface tension locally, and the resulting Marangoni flow causes the dropped liquid to spread thinly across the surface of the water.
The nanomembrane is created using a float assembly method which consists of a three-step process. The first step involves dropping a composite solution, which is a mixture of metal nanowires, rubber dissolved in toluene, and ethanol, on the surface of the water. The toluene-rubber phase remains above the water due to its hydrophobic property, while the nanowires end up on the interface between the water and toluene phases. The ethanol within the solution mixes with the water to lower the local surface tension, which generates Marangoni flow that propagates outward and prevents the aggregation of the nanowires. This assembles the nanomaterials into a monolayer at the interface between water and a very thin rubber/solvent film. In the second step, the surfactant is dropped to generate a second wave of Marangoni flow which tightly compacts the nanowires. Finally, in the third step, the toluene is evaporated and a nanomembrane with a unique structure in which a highly compacted monolayer of nanowires is partially embedded in an ultrathin rubber film is obtained.
Its unique structure allows efficient strain distribution in ultrathin rubber film, leading to excellent physical properties, such as a stretchability of over 1,000%, and a thickness of only 250 nm. The structure also allows cold welding and bi-layer stacking of the nanomembrane onto each other, which leads to a metal-like conductivity over 100,000 S/cm. Furthermore, the researchers demonstrated that the nanomembrane can be patterned using photolithography, which is a key technology that is widely used for manufacturing commercial semiconductor devices and advanced electronics. Therefore, it is expected that the nanomembrane can serve as a new platform material for skin electronics.
The implications of this study may go well beyond the development of skin electronics. While this study showcased a composite material consisting of silver nanowires within styrene-ethylene-butylene-styrene (SEBS) rubber, it is also possible to use the float assembly method on various nanomaterials such as magnetic nanomaterials and semiconducting nanomaterials, as well as other types of elastomers such as TPU and SIS. Therefore, it is expected that the float assembly can open new research fields involving various types of nanomembranes with different functions.
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Scientists from the Division of Sustainable Energy and Environmental Engineering at Osaka University used an established machine learning technique called generative adversarial networks to digitally remove clouds from aerial images. By using the resulting data as textures for 3D models, more accurate datasets of building image masks can be automatically generated. When setting two artificial intelligence networks against each other, the team was able to improve the data quality without the need for previously labeled images. This work may help automate computer vision jobs critical to civil engineering.
Machine learning is a powerful method for accomplishing artificial intelligence tasks, such as filling in missing information. One popular application is repairing images that are obscured, for example, when aerial images of buildings are blocked by clouds. While this can be done by hand, it is very time consuming, and even the machine learning algorithms that are currently available require many training images in order to work. Thus, improving the representation of buildings in virtual 3D models using aerial photographs requires additional steps.
Now, researchers at Osaka University have improved the accuracy of automatically generated datasets by applying the existing machine learning method called generative adversarial networks (GANs). The idea of GANs is to pit two different algorithms against each other. One is the “generative network,” that proposes reconstructed images without clouds. Competing against it is the “discriminative network,” that uses a convolutional neural network to attempt to tell the difference between the digitally repaired pictures and actual images without clouds. Over time, both networks get increasingly better at their respective jobs, leading to highly realistic images with the clouds digitally erased. “By training the generative network to ‘fool’ the discriminative network into thinking an image is real, we obtain reconstructed images that are more self-consistent,” first author Kazunosuke Ikeno explains.
The team used 3D virtual models with photographs from an open-source dataset as input. This allowed for the automatic generation of digital “masks” that overlaid reconstructed buildings over the clouds. “This method makes it possible to detect buildings in areas without labeled training data,” senior author Tomohiro Fukuda says. The trained model could detect buildings with an “intersection over union” value of 0.651, which measures how accurately the reconstructed area corresponds to the actual area. This method can be extended to improving the quality of other datasets in which some areas are obscured, such as medical images.
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Researchers from the University of Technology Sydney (UTS) and TMOS, an Australian Research Council Centre of Excellence, have taken the fight to online hackers with a giant leap towards realizing affordable, accessible quantum communications, a technology that would effectively prevent the decryption of online activity. Everything from private social media messaging to banking could become more secure due to new technology created with a humble piece of adhesive tape.
Quantum communication is still in its early development and is currently feasible only in very limited fields due to the costs associated with fabricating the required devices. The TMOS researches have developed new technology that integrates quantum sources and waveguides on chip in a manner that is both affordable and scalable, paving the way for future everyday use.
The development of fully functional quantum communication technologies has previously been hampered by the lack of reliable quantum light sources that can encode and transmit the information.
In a paper published today in ACS Photonics, the team describes a new platform to generate these quantum emitters based on hexagonal boron nitride, also known as white graphene. Where current quantum emitters are created using complex methods in expensive clean rooms, these new quantum emitters can be created using $20 worth of white graphene pressed on to a piece of adhesive tape.
These 2D materials can be pressed onto a sticky surface such as the adhesive tape and exfoliated, which is essentially peeling off the top layer to create a flex. Multiple layers of this flex can then be assembled in a Lego-like style, offering a new bottom up approach as a substitute for 3D systems.
TMOS Chief Investigator Igor Aharonovich said: “2D materials, like hexagonal boron nitride, are emerging materials for integrated quantum photonics, and are poised to impact the way we design and engineer future optical components for secured communication.”
In addition to this evolution in photon sources, the team has developed a high efficiency on-chip waveguide, a vital component for on-chip optical processing.
Lead author Chi Li said: “Low signal levels have been a significant barrier preventing quantum communications from evolving into practical, workable models. We hope that with this new development, quantum comms will become an everyday technology that improves people’s lives in new and exciting ways.”
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A new paper from the University of Surrey and the University of Cambridge has detailed how two relatively unexplored semiconducting materials can satisfy the telecommunication industry’s hunger for enormous amounts of data at ever-greater speeds.
Light-emitting diode (LED)-based communications techniques allow computing devices, including mobile phones, to communicate with one another by using infrared light. However, LED techniques are underused because in its current state LED transmits data at far slower speeds than other wireless technologies such as light-fidelity (Li-Fi).
In a paper published by Nature Electronics, the researchers from Surrey and Cambridge, along with partners from the University of Electronic Science and Technology of China, examine how organic semiconductors, colloidal quantum dots (CQDs) and metal halide perovskites (perovskites), can be used in LED-based optical communications systems.
The research team explored efforts to improve the performance and efficiency of these LEDs, and they considered their potential applications in on-chip interconnects and Li-Fi.
Dr Aobo Ren, the co-first author and visiting postdoctoral researcher at the University of Surrey, said:
“There’s excitement surrounding CQDs and perovskites because they offer great promise for low-power, cost-effective and scalable communications modules. More

Researchers are working with community leaders to develop a mobile alert app to help locate missing people with dementia.
Noelannah Neubauer, a postdoctoral fellow at the University of Waterloo’s School of Public Health Sciences, said “Community ASAP” is aimed at addressing a gap in available tools when it comes to alerts for missing older adults and people living with dementia in Canada.
“We have Amber Alerts for missing children, but nothing for this population other than police and civilians circulating information via social media such as Twitter and Facebook,” said Neubauer, who is the first author of a study that tested the efficacy and useability of Community ASAP.
The U.S. already uses a system called Silver Alert, and there have been efforts in some provinces, such as British Columbia, to create a citizen-led alert system. The issue with piggybacking onto the Amber Alert system is that too many people go missing every day, according to Neubauer. Almost 750,000 Canadians live with dementia, and 60 per cent of them wander at least once, and some repeatedly.
“The sheer number of missing people from this population would mean that alerts would go off multiple times a day in certain jurisdictions, running the risk of significant alert fatigue,” Neubauer said. “Community ASAP gets around this by having people sign up to receive the alert on Android and iOS operating systems and choosing the radius from where the missing person was last seen to their current location. Most missing cases take place one kilometre from the place they were last seen.”
“A key concern is that if someone gets lost and is not found within 24 hours, they have a 50 per cent chance of experiencing serious injury or death,” said Lili Liu, principal investigator, and dean of the Faculty of Health at Waterloo. “We proposed recommendations for community alert systems specific to Canada, such as Community ASAP, at an online national forum on community alert systems for missing older adults last fall.”
For the study, researchers engaged people living with dementia, their care partners, police services, search and rescue organizations and health and social service providers in Ontario, Alberta and British Columbia to develop the alert system that engages community citizens, as volunteers, to look out for people with dementia reported missing.
They went through three iterations of the app and consulted with these stakeholder groups along the way to test its accuracy and useability, walking through scenarios to simulate the events that transpire during a missing person event. In these scenarios, participants assumed the key roles in the Community ASAP system, including the missing person with dementia, care partner, coordinator, and volunteers.
The idea for this app came from Ron Beleno, an entrepreneur with experience caring for his father, who lived with dementia. Beleno is turning Community ASAP into a start-up company, and Liu’s research team continues to work with governments and organizations to coordinate a system that works across the country.
The study, “Mobile alert app to engage community volunteers to help locate missing persons with dementia,” co-authored by Noelannah Neubauer, Christine Daum, Antonio Miguel-Cruz and Lili Liu, all affiliated with the University of Waterloo, was recently published in Plos One. More

A new AI (artificial intelligence) tool is set to enable scientists to more accurately forecast Arctic sea ice conditions months into the future. The improved predictions could underpin new early-warning systems that protect Arctic wildlife and coastal communities from the impacts of sea ice loss.
Published this week (Thursday 26 August) in the journal Nature Communications, an international team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute describe how the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead — something that has eluded scientists for decades.
Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain. These accelerating changes have dramatic consequences for our climate, for Arctic ecosystems, and Indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle.
IceNet, the AI predictive tool, is almost 95% accurate in predicting whether sea ice will be present two months ahead — better than the leading physics-based model.
Lead author Tom Andersson, Data Scientist at the BAS AI Lab and funded by The Alan Turing Institute, explains: “The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years. IceNet has the potential to fill an urgent gap in forecasting sea ice for Arctic sustainability efforts and runs thousands of times faster than traditional methods.”
Dr Scott Hosking, Principal Investigator, Co-leader of the BAS AI Lab and Senior Research Fellow at The Alan Turing Institute, says: “I’m excited to see how AI is making us rethink how we undertake environmental research. Our new sea ice forecasting framework fuses data from satellite sensors with the output of climate models in ways traditional systems simply couldn’t achieve.”
Unlike conventional forecasting systems that attempt to model the laws of physics directly, the authors designed IceNet based on a concept called deep learning. Through this approach, the model ‘learns’ how sea ice changes from thousands of years of climate simulation data, along with decades of observational data to predict the extent of Arctic sea ice months into the future.
Tom Andersson concludes: “Now we’ve demonstrated that AI can accurately forecast sea ice, our next goal is to develop a daily version of the model and have it running publicly in real-time, just like weather forecasts. This could operate as an early warning system for risks associated with rapid sea ice loss.”
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