Computers Math

A peace sign from Martin Luther King, Jr, becomes a rude gesture; President Donald Trump’s inauguration crowd scenes inflated; dolphins in Venice’s Grand Canal; and crocodiles on the streets of flooded Townsville — all manipulated images posted as truth.
Image editing software is so ubiquitous and easy to use, according to researchers from QUT’s Digital Media Research Centre, it has the power to re-imagine history.
And, they say, deadline-driven journalists lack the tools to tell the difference, especially when the images come through from social media.
Their study, Visual mis/disinformation in journalism and public communications, has been published in Journalism Practice. It was driven by the increased prevalence of fake news and how social media platforms and news organisations are struggling to identify and combat visual mis/disinformation presented to their audiences.
“When Donald Trump’s staff posted an image to his official Facebook page in 2019, journalists were able to spot the photoshopped edits to the president’s skin and physique because an unedited version exists on the White House’s official Flickr feed,” said lead author Dr T.J. Thomson.
“But what about when unedited versions aren’t available online and journalists can’t rely on simple reverse-image searches to verify whether an image is real or has been manipulated?

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“When it is possible to alter past and present images, by methods like cloning, splicing, cropping, re-touching or re-sampling, we face the danger of a re-written history — a very Orwellian scenario.”
Examples highlighted in the report include photos shared by news outlets last year of crocodiles on Townsville streets during a flood which were later shown to be images of alligators in Florida from 2014. It also quotes a Reuters employee on their discovery that a harrowing video shared during Cyclone Idai, which devastated parts of Africa in 2019, had been shot in Libya five years earlier.
An image of Dr Martin Luther King Jr’s reaction to the US Senate’s passing of the civil rights bill in 1964, was manipulated to make it appear that he was flipping the bird to the camera. This edited version was shared widely on Twitter, Reddit, and white supremacist website The Daily Stormer.
Dr Thomson, Associate Professor Daniel Angus, Dr Paula Dootson, Dr Edward Hurcombe, and Adam Smith have mapped journalists’ current social media verification techniques and suggest which tools are most effective for which circumstances.
“Detection of false images is made harder by the number of visuals created daily — in excess of 3.2 billion photos and 720,000 hours of video — along with the speed at which they are produced, published, and shared,” said Dr Thomson.

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“Other considerations include the digital and visual literacy of those who see them. Yet being able to detect fraudulent edits masquerading as reality is critically important.
“While journalists who create visual media are not immune to ethical breaches, the practice of incorporating more user-generated and crowd-sourced visual content into news reports is growing. Verification on social media will have to increase commensurately if we wish to improve trust in institutions and strengthen our democracy.”
Dr Thomson said a recent quantitative study performed by the International Centre for Journalists (ICFJ) found a very low usage of social media verification tools in newsrooms.
“The ICFJ surveyed over 2,700 journalists and newsroom managers in more than 130 countries and found only 11% of those surveyed used social media verification tools,” he said.
“The lack of user-friendly forensic tools available and low levels of digital media literacy, combined, are chief barriers to those seeking to stem the tide of visual mis/disinformation online.”
Associate Professor Angus said the study demonstrated an urgent need for better tools, developed with journalists, to provide greater clarity around the provenance and authenticity of images and other media.
“Despite knowing little about the provenance and veracity of the visual content they encounter, journalists have to quickly determine whether to re-publish or amplify this content,” he said.
“The many examples of misattributed, doctored, and faked imagery attest to the importance of accuracy, transparency, and trust in the arena of public discourse. People generally vote and make decisions based on information they receive via friends and family, politicians, organisations, and journalists.”
The researchers cite current manual detection strategies — using a reverse image search, examining image metadata, examining light and shadows; and using image editing software — but say more tools need to be developed, including more advanced machine learning methods, to verify visuals on social media.
Video: https://www.youtube.com/watch?v=S_flHHn1280&feature=emb_logo More

Australian scientists have developed a new type of sensor to measure and correct the distortion of starlight caused by viewing through the Earth’s atmosphere, which should make it easier to study the possibility of life on distant planets.
Using artificial intelligence and machine learning, University of Sydney optical scientists have developed a sensor that can neutralise a star’s ‘twinkle’ caused by heat variations in the Earth’s atmosphere. This will make the discovery and study of planets in distant solar systems easier from optical telescopes on Earth.
“The main way we identify planets orbiting distant stars is by measuring regular dips in starlight caused by planets blocking out bits of their sun,” said lead author Dr Barnaby Norris, who holds a joint position as a Research Fellow in the University of Sydney Astrophotonic Instrumentation Laboratory and in the University of Sydney node of Australian Astronomical Optics in the School of Physics.
“This is really difficult from the ground, so we needed to develop a new way of looking up at the stars. We also wanted to find a way to directly observe these planets from Earth,” he said.
The team’s invention will now be deployed in one of the largest optical telescopes in the world, the 8.2-metre Subaru telescope in Hawaii, operated by the National Astronomical Observatory of Japan.
“It is really hard to separate a star’s ‘twinkle’ from the light dips caused by planets when observing from Earth,” Dr Norris said. “Most observations of exoplanets have come from orbiting telescopes, such as NASA’s Kepler. With our invention, we hope to launch a renaissance in exoplanet observation from the ground.”
The research is published today in Nature Communications.

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NOVEL METHODS
Using the new ‘photonic wavefront sensor’ will help astronomers directly image exoplanets around distant stars from Earth.
Over the past two decades, thousands of planets beyond our solar system have been detected, but only a small handful have been directly imaged from Earth. This severely limits scientific exploration of these exoplanets.
Making an image of the planet gives far more information than indirect detection methods, like measuring starlight dips. Earth-like planets might appear a billion times fainter than their host star. And observing the planet separate from its star is like looking at a 10-cent coin held in Sydney, as viewed from Melbourne.
To solve this problem, the scientific team in the School of Physics developed a ‘photonic wavefront sensor’, a new way to allow the exact distortion caused by the atmosphere to be measured, so it can then be corrected by the telescope’s adaptive optics systems thousands of times a second.

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“This new sensor merges advanced photonic devices with deep learning and neural networks techniques to achieve an unprecedented type of wavefront sensor for large telescopes,’ Dr Norris said.
“Unlike conventional wavefront sensors, it can be placed at the same location in the optical instrument where the image is formed. This means it is sensitive to types of distortions invisible to other wavefront sensors currently used today in large observatories,” he said.
Professor Olivier Guyon from the Subaru Telescope and the University of Arizona is one of the world’s leading experts in adaptive optics. He said: “This is no doubt a very innovative approach and very different to all existing methods. It could potentially resolve several major limitations of the current technology. We are currently working in collaboration with the University of Sydney team towards testing this concept at Subaru in conjunction with SCExAO, which is one of the most advanced adaptive optics systems in the world.”
APPLICATION BEYOND ASTRONOMY
The scientists have achieved this remarkable result by building on a novel method to measure (and correct) the wavefront of light that passes through atmospheric turbulence directly at the focal plane of an imaging instrument. This is done using an advanced light converter, known as a photonic lantern, linked to a neural network inference process.
“This is a radically different approach to existing methods and resolves several major limitations of current approaches,” said co-author Jin (Fiona) Wei, a postgraduate student at the Sydney Astrophotonic Instrumentation Laboratory.
The Director of the Sydney Astrophotonic Instrumentation Laboratory in the School of Physics at the University of Sydney, Associate Professor Sergio Leon-Saval, said: “While we have come to this problem to solve a problem in astronomy, the proposed technique is extremely relevant to a wide range of fields. It could be applied in optical communications, remote sensing, in-vivo imaging and any other field that involves the reception or transmission of accurate wavefronts through a turbulent or turbid medium, such as water, blood or air.” More

Research from the University of Kent, the Research centre on Interactive Media, Smart systems and Emerging technologies — RISE Ltd and the University of Cyprus has revealed that Virtual Reality (VR) technology can have significant impact on the validity of remote health appointments for those with eating disorders, through a process called Virtual Reality Exposure Therapy (VRET).
This paper demonstrates the potential value of Multi-User Virtual Reality (MUVR) remote psychotherapy for those with body shape and weight concerns.
In the study, published in Human-Computer Interaction Journal, participants and therapists were fitted with VR Head-Mounted Displays and introduced to each other within the VR system. Participant would then customize their virtual avatar according to their look (body shape and size, skin tone and hair colour and shape). Participant and therapist were then “teleported” to two Virtual Environment interventions for several discussions, building up to the Mirror Exposure.
Mirror Exposure involves confrontation in a mirror with ones’ shape and body. In the MUVR, the participant faces the virtual avatar they customized to match their own physical body. Here, they were again able to adjust body shapes using virtual sliders, change clothing, skin tone, as well as hair style and colour. Clothing was then gradually reduced until the participant’s avatar was in their virtual underwear.
The participant was then asked to examine each part of their body and perform adjustments while describing their feelings, thoughts and concerns with the therapist, leading to virtual exposure therapy for the patient to their body shape and size through the customised avatar.
The study found that the avatar of the therapist was vital to the participant. The cartoonish avatar facilitated greater openness from participants, whilst therapist avatars in human-form represented the idea of negative judgement. In post-session interviews, participants noted the lack of fear of judgement as enabling them to commit to the session’s aims.
Dr Jim Ang, Senior Lecturer in Multimedia/Digital Systems and Supervisor of the study said: ‘The potential of Virtual Reality being used in addressing health issues with patients, remotely and without the issue of potential judgement, is for VR to be utilised throughout the health sector. Without the issue of judgement, which people can fear in advance of even seeking medical advice, VR can give people the confidence to engage with and embrace medical advice. In terms of the technical capabilities, the potential for VR to aid in remote non-contact medical appointments between patients and practitioners is huge, due particular consideration in times of pandemic.’
Dr Maria Matsangidou, Research Associate at RISE Ltd and Experimental Researcher of the study said: ‘Multi-User Virtual Reality is an innovative medium for psychotherapeutic interventions that allows for the physical separation of therapist and patient, providing thus more ‘comfortable’ openness by the patients. Exposure to patient worries about body shape and size may exhibit anxious reactions, but through the remote exposure therapy this can elicit new learning that helps the patient to shape new experiences.’

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Materials provided by University of Kent. Original written by Sam Wood. Note: Content may be edited for style and length. More

Researchers at Tokyo Institute of Technology (Tokyo Tech) working in collaboration with colleagues at Carnegie Mellon University, the University of St Andrews and the University of New South Wales have developed a wrist-worn device for 3D hand pose estimation. The system consists of a camera that captures images of the back of the hand, and is supported by a neural network called DorsalNet which can accurately recognize dynamic gestures.
Being able to track hand gestures is of crucial importance in advancing augmented reality (AR) and virtual reality (VR) devices that are already beginning to be much in demand in the medical, sports and entertainment sectors. To date, these devices have involved using bulky data gloves which tend to hinder natural movement or controllers with a limited range of sensing.
Now, a research team led by Hideki Koike at Tokyo Tech has devised a camera-based wrist-worn 3D hand pose recognition system which could in future be on par with a smartwatch. Their system can importantly allow capture of hand motions in mobile settings.
“This work is the first vision-based real-time 3D hand pose estimator using visual features from the dorsal hand region,” the researchers say. The system consists of a camera supported by a neural network named DorsalNet which can accurately estimate 3D hand poses by detecting changes in the back of the hand.
The researchers confirmed that their system outperforms previous work with an average of 20% higher accuracy in recognizing dynamic gestures, and achieves a 75% accuracy of detecting eleven different grasp types.
The work could advance the development of controllers that support bare-hand interaction. In preliminary tests, the researchers demonstrated that it would be possible to use their system for smart devices control, for example, changing the time on a smartwatch simply by changing finger angle. They also showed it would be possible to use the system as a virtual mouse or keyboard, for example by rotating the wrist to control the position of the pointer and using a simple 8-key keyboard for typing input.
They point out that further improvements to the system such as using a camera with a higher frame rate to capture fast wrist movement and being able to deal with more diverse lighting conditions will be needed for real world use.

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As the world grapples with the coronavirus (COVID-19) pandemic, another virus has been raging again in the Democratic Republic of the Congo in recent months: Ebola. Since the first terrifying outbreak in 2013, the Ebola virus has periodically emerged in Africa, causing horrific bleeding in its victims and, in many cases, death.
How can we battle these infectious agents that reproduce by hijacking cells and reprogramming them into virus-replicating machines? Science at the molecular level is critical to gaining the upper hand — research you’ll find underway in the laboratory of Professor Juan Perilla at the University of Delaware.
Perilla and his team of graduate and undergraduate students in UD’s Department of Chemistry and Biochemistry are using supercomputers to simulate the inner workings of Ebola, observing the way molecules move, atom by atom, to carry out their functions. In the team’s latest work, they reveal structural features of the virus’s coiled protein shell, or nucleocapsid, that may be promising therapeutic targets, more easily destabilized and knocked out by an antiviral treatment.
The research is highlighted in the Tuesday, Oct. 20 issue of the Journal of Chemical Physics, which is published by the American Institute of Physics, a federation of societies in the physical sciences representing more than 120,000 members.
“The Ebola nucleocapsid looks like a Slinky walking spring, whose neighboring rings are connected,” Perilla said. “We tried to find what factors control the stability of this spring in our computer simulations.”
The life cycle of Ebola is highly dependent on this coiled nucleocapsid, which surrounds the virus’s genetic material consisting of a single strand of ribonucleic acid (ssRNA). Nucleoproteins protect this RNA from being recognized by cellular defense mechanisms. Through interactions with different viral proteins, such as VP24 and VP30, these nucleoproteins form a minimal functional unit — a copy machine — for viral transcription and replication.

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While nucleoproteins are important to the nucleocapsid’s stability, the team’s most surprising finding, Perilla said, is that in the absence of single-stranded RNA, the nucleocapsid quickly becomes disordered. But RNA alone is not sufficient to stabilize it. The team also observed charged ions binding to the nucleocapsid, which may reveal where other important cellular factors bind and stabilize the structure during the virus’s life cycle.
Perilla compared the team’s work to a search for molecular “knobs” that control the nucleocapsid’s stability like volume control knobs that can be turned up to hinder virus replication.
The UD team built two molecular dynamics systems of the Ebola nucleocapsid for their study. One included single-stranded RNA; the other contained only the nucleoprotein. The systems were then simulated using the Texas Advanced Computing Center’s Frontera supercomputer — the largest academic supercomputer in the world. The simulations took about two months to complete.
Graduate research assistant Chaoyi Xu ran the molecular simulations, while the entire team was involved in developing the analytical framework and conducting the analysis. Writing the manuscript was a learning experience for Xu and undergraduate research assistant Tanya Nesterova, who had not been directly involved in this work before. She also received training as a next-generation computational scientist with support from UD’s Undergraduate Research Scholars program and NSF’s XSEDE-EMPOWER program. The latter has allowed her to perform the highest-level research using the nation’s top supercomputers. Postdoctoral researcher Nidhi Katyal’s expertise also was essential to bringing the project to completion, Perilla said.
While a vaccine exists for Ebola, it must be kept extremely cold, which is difficult in remote African regions where outbreaks have occurred. Will the team’s work help advance new treatments?
“As basic scientists we are excited to understand the fundamental principles of Ebola,” Perilla said. “The nucleocapsid is the most abundant protein in the virus and it’s highly immunogenic — able to produce an immune response. Thus, our new findings may facilitate the development of new antiviral treatments.”
Currently, Perilla and Jodi Hadden-Perilla are using supercomputer simulations to study the novel coronavirus that causes COVID-19. Although the structures of the nucleocapsid in Ebola and COVID-19 share some similarities — both are rod-like helical protofilaments and both are involved in the replication, transcription and packing of viral genomes — that is where the similarities end.
“We now are refining the methodology we used for Ebola to examine SARS-CoV-2,” Perilla said. More

Contagion processes, such as opinion formation or disease spread, can reach a tipping point, where the contagion either rapidly spreads or dies out. When modeling these processes, it is difficult to capture this complex transition, making the conditions that affect the tipping point a challenge to uncover.
In the journal Chaos, from AIP Publishing, Nicholas Landry and Juan G. Restrepo, from the University of Colorado Boulder, studied the parameters of these transitions by including three-person group interactions in a contagion model called the susceptible-infected-susceptible model.
In this model, an infected person who recovers from an infection can be reinfected. It is often used to understand the propagation of things like the flu but does not typically consider interactions between more than two people.
“With a traditional network SIS model, when you increase the infectivity of an idea or a disease, you don’t see the explosive transitions that you often see in the real world,” Landry said. “Including group interactions in addition to individual interactions has a profound effect on the system or population dynamics” and can lead to tipping point behavior.
Once the rate of infection or information transfer between individuals passes a critical point, the fraction of infected people explosively jumps to an epidemic for high enough group infectivity. More surprisingly, if the rate of infection decreases after this jump, the infected fraction does not immediately decrease. It remains an epidemic past that same critical point before moving back down to a healthy equilibrium.
This results in a loop region in which there may or may not be high levels of infection, depending on how many people are infected initially. How these group interactions are distributed affects the critical point at which an explosive transition occurs.
The authors also studied how variability in the group connections — for example, whether people with more friends also participate in more group interactions — changes the likelihood of tipping point behavior. They explain the emergence of this explosive behavior as the interplay between individual interactions and group interactions. Depending on which mechanism dominates, the system may exhibit an explosive transition.
Additional parameters can be added to the model to tune it for different processes and better understand how much of an individual’s social network must be infected for a virus or information to spread.
The work is currently theoretical, but the researchers have plans to apply the model to actual data from physical networks and consider other structural characteristics that real-world networks exhibit.

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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

The development of a new method to make non-volatile computer memory may have unlocked a problem that has been holding back machine learning and has the potential to revolutionize technologies like voice recognition, image processing and autonomous driving.
A team from Sandia National Laboratories, working with collaborators from the University of Michigan, published a paper in the peer-reviewed journal Advanced Materials that details a new method that will imbue computer chips that power machine-learning applications with more processing power by using a common material found in house paint in an analog memory device that enables highly energy-efficient machine inference operations.
“Titanium oxide is one of the most commonly made materials. Every paint you buy has titanium oxide in it. It’s cheap and nontoxic,” explains Sandia materials scientist Alec Talin. “It’s an oxide, there’s already oxygen there. But if you take a few out, you create what are called oxygen vacancies. It turns out that when you create oxygen vacancies, you make this material electrically conductive.”
Those oxygen vacancies can now store electrical data, giving almost any device more computing power. Talin and his team create the oxygen vacancies by heating a computer chip with a titanium oxide coating above 302 degrees Fahrenheit (150 degree Celsius), separate some of the oxygen molecules from the material using electrochemistry and create vacancies.
“When it cools off, it stores any information you program it with,” Talin said.
Energy efficiency a boost to machine learning
Right now, computers generally work by storing data in one place and processing that data in another place. That means computers have to constantly transfer data from one place to the next, wasting energy and computing power.

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The paper’s lead author, Yiyang Li, is a former Truman Fellow at Sandia and now an assistant professor of materials science at the University of Michigan. He explained how their process has the potential to completely change how computers work.
“What we’ve done is make the processing and the storage at the same place,” Li said. “What’s new is that we’ve been able to do it in a predictable and repeatable manner.”
Both he and Talin see the use of oxygen vacancies as a way to help machine learning overcome a big obstacle holding it back right now — power consumption.
“If we are trying to do machine learning, that takes a lot of energy because you are moving it back and forth and one of the barriers to realizing machine learning is power consumption,” Li said. “If you have autonomous vehicles, making decisions about driving consumes a large amount of energy to process all the inputs. If we can create an alternative material for computer chips, they will be able to process information more efficiently, saving energy and processing a lot more data.”
Research has everyday impact
Talin sees the potential in the performance of everyday devices.
“Think about your cell phone,” he said. “If you want to give it a voice command, you need to be connected to a network that transfers the command to a central hub of computers that listen to your voice and then send a signal back telling your phone what to do. Through this process, voice recognition and other functions happen right in your phone.”
Talin said the team is working on refining several processes and testing the method on a larger scale. The project is funded through Sandia’s Laboratory Directed Research and Development program.

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Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians’ “vision.”
In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT.
Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.
“We hope that this technique will help extract more information from a regular single-spectrum X-ray CT scan, make it more quantitative, and improve diagnosis,” said Wang, who is also the director of the Biomedical Imaging Center within the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.
Conventional CT scans produce images that show the shape of tissues within the body, but they don’t give doctors sufficient information about the composition of those tissues. Even with iodine and other contrast agents, which are used to help doctors differentiate between soft tissue and vasculature, it’s hard to distinguish between subtle structures.
A higher-level technology called dual-energy CT gathers two datasets in order to produce images that reveal both tissue shape and information about tissue composition. However, this imaging approach often requires a higher dose of radiation and is more expensive due to needed additional hardware.
“With traditional CT, you take a grayscale image, but with dual-energy CT you take an image with two colors,” Wang said. “With deep learning, we try to use the standard machine to do the job of dual-energy CT imaging.”
In this research, Wang and his team demonstrated how their neural network was able to produce those more complex images using single-spectrum CT data. The researchers used images produced by dual-energy CT to train their model and found that it was able to produce high-quality approximations with a relative error of less than 2%.
“Professor Wang and his team’s expertise in bioimaging is giving physicians and surgeons ‘new eyes’ in diagnosing and treating disease,” said Deepak Vashishth, director of CBIS. “This research effort is a prime example of the partnership needed to personalize and solve persistent human health challenges.”

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