Computers Math

Among the most active fields of research in modern physics, both at an academic level and beyond, are quantum computation and communication, which apply quantum phenomena such as superposition and entanglement to perform calculations, or to exchange information. A number of research groups around the world have built quantum devices that are able to perform calculations faster than any classical computer. Yet, there is still a long way to go before these devices can be converted into marketable quantum computers. One reason for this is that both quantum computation and quantum communication are strongly deteriorated by the ease with which a quantum superposition state can be destroyed, or entanglement between two or more quantum particles can be lost.
The primary approach to overcome these limitations is the application of so-called quantum error-correcting codes. This, however, requires an amount of resources exceeding that which can be currently achieved in a controlled way. While, in the long run, error correction is likely to become an integral part of future quantum devices, a complementary approach is to mitigate the noise — that is, the cumulative effect of uncorrected errors — without relying on so many additional resources. These are referred to as noise reduction schemes.
Noise mitigation without additional resources through simple quantum schemes
A new approach along this research line was recently proposed to reduce noise in a communication scheme between two parties. Imagine two parties who want to communicate by exchanging a quantum particle, yet the particle has to be sent over some faulty transmission lines.
Recently, a team of researchers at Hong-Kong University proposed that an overall reduction in noise could be achieved by directing the particle along a quantum superposition of paths through regions of noise in opposite order. In particular, while classically a particle can only travel along one path, in quantum mechanics it can move along multiple paths at once. If one uses this property to send the particle along two quantum paths, one can, for instance, lead the particle across the noisy regions in opposite order simultaneously. This effect had been demonstrated experimentally by two independent research investigations.
These results suggested that, to achieve this noise reduction, it is necessary to place the noisy transmission lines in a quantum superposition of opposite orders. Shortly after this, research groups in Vienna and in Grenoble realised that this effect can also be achieved via simpler configurations, which can even completely eliminate the noise between the two parties.
All of these schemes have now been implemented experimentally and compared with each other by a research team led by Philip Walther at the University of Vienna. In this work, different ways of passing through two noisy regions in quantum superposition are compared for a variety of noise types. The experimental results are also supported with numerical simulations to extend the study to more generic types of noise. Surprisingly, it is found that the simplest schemes for quantum superposition of noisy channels also offer the best reduction of the noise affecting communication.
“Error correction in modern quantum technologies is among the most pressing needs of current quantum computation and communication schemes. Our work shows that, at least in the case of quantum communication, already with the technologies currently in use it may be possible to mitigate this issue with no need for additional resources,” says Giulia Rubino, first author of the publication in Physical Review Research. The ease of the demonstrated technique allows immediate use in current long-distance communications, and promises potential further applications in quantum computation and quantum thermodynamics.

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Researchers from the University of Basel have developed a virtual reality app for smartphones to reduce fear of heights. Now, they have conducted a clinical trial to study its efficacy. Trial participants who spent a total of four hours training with the app at home showed an improvement in their ability to handle real height situations.
Fear of heights is a widespread phenomenon. Approximately 5% of the general population experiences a debilitating level of discomfort in height situations. However, the people affected rarely take advantage of the available treatment options, such as exposure therapy, which involves putting the person in the anxiety-causing situation under the guidance of a professional. On the one hand, people are reluctant to confront their fear of heights. On the other hand, it can be difficult to reproduce the right kinds of height situations in a therapy setting.
This motivated the interdisciplinary research team led by Professor Dominique de Quervain to develop a smartphone-based virtual reality exposure therapy app called Easyheights. The app uses 360° images of real locations, which the researchers captured using a drone. People can use the app on their own smartphones together with a special virtual reality headset.
Gradually increasing the height
During the virtual experience, the user stands on a platform that is initially one meter above the ground. After allowing acclimatization to the situation for a certain interval, the platform automatically rises. In this way, the perceived distance above the ground increases slowly but steadily without an increase in the person’s level of fear.
The research team studied the efficacy of this approach in a randomized, controlled trial and published the results in the journal NPJ Digital Medicine. Fifty trial participants with a fear of heights either completed a four-hour height training program (one 60-minute session and six 30-minute sessions over the course of two weeks) using virtual reality, or were assigned to the control group, which did not complete these training sessions.
Before and after the training phase — or the same period of time without training — the trial participants ascended the Uetliberg lookout tower near Zurich as far as their fear of heights allowed them. The researchers recorded the height level reached by the participants along with their subjective fear level at each level of the tower. At the end of the trial, the researchers evaluated the results from 22 subjects who completed the Easyheights training and 25 from the control group.
The group that completed the training with the app exhibited less fear on the tower and was able to ascend further towards the top than they could before completing the training. The control group exhibited no positive changes. The efficacy of the Easyheights training proved comparable to that of conventional exposure therapy.
Therapy in your own living room
Researchers have already been studying the use of virtual reality for treating fear of heights for more than two decades. “What is new, however, is that smartphones can be used to produce the virtual scenarios that previously required a technically complicated type of treatment, and this makes it much more accessible,” explains Dr. Dorothée Bentz, lead author of the study.
The results from the study suggest that the repeated use of a smartphone-based virtual reality exposure therapy can greatly improve the behavior and subjective state of well-being in height situations. People who suffer from a mild fear of heights will soon be able to download the free app from major app stores and complete training sessions on their own. However, the researchers recommend that people who suffer from a serious fear of heights only use the app with the supervision of a professional.
The current study is one of several projects in progress at the Transfaculty Research Platform for Molecular and Cognitive Neurosciences, led by Professor Andreas Papassotiropoulos and Professor Dominique de Quervain. Their goal is to improve the treatment of mental disorders through the use of new technologies and to make these treatments widely available.

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Emerging robotics technology may soon help construction companies and contractors create buildings in less time at higher quality and at lower costs.
Purdue University innovators developed and are testing a novel construction robotic system that uses an innovative mechanical design with advances in computer vision sensing technology to work in a construction setting.
The technology was developed with support from the National Science Foundation.
“Our work helps to address workforce shortages in the construction industry by automating key construction operations,” said Jiansong Zhang, an assistant professor of construction management technology in the Purdue Polytechnic Institute. “On a construction site, there are many unknown factors that a construction robot must be able to account for effectively. This requires much more advanced sensing and reasoning technologies than those commonly used in a manufacturing environment.”
The Purdue team’s custom end effector design allows for material to be both placed and fastened in the same operation using the same arm, limiting the amount of equipment that is required to complete a given task.
Computer vision algorithms developed for the project allow the robotic system to sense building elements and match them to building information modeling (BIM) data in a variety of environments, and keep track of obstacles or safety hazards in the system’s operational context.
“By basing the sensing for our robotic arm around computer vision technology, rather than more limited-scope and expensive sensing systems, we have the capability to complete many sensing tasks with a single affordable sensor,” Zhang said. “This allows us to implement a more robust and versatile system at a lower cost.”
Undergraduate researchers in Zhang’s Automation and Intelligent Construction (AutoIC) Lab helped create this robotic technology.
The innovators worked with the Purdue Research Foundation Office of Technology Commercialization to patent the technology.
This work will be featured at OTC’s 2021 Technology Showcase: The State of Innovation. The annual showcase, being held virtually this year Feb. 10-11, will feature novel innovations from inventors at Purdue and across the state of Indiana.

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Researchers at Geisinger have found that a computer algorithm developed using echocardiogram videos of the heart can predict mortality within a year.
The algorithm — an example of what is known as machine learning, or artificial intelligence (AI) — outperformed other clinically used predictors, including pooled cohort equations and the Seattle Heart Failure score. The results of the study were published in Nature Biomedical Engineering.
“We were excited to find that machine learning can leverage unstructured datasets such as medical images and videos to improve on a wide range of clinical prediction models,” said Chris Haggerty, Ph.D., co-senior author and assistant professor in the Department of Translational Data Science and Informatics at Geisinger.
Imaging is critical to treatment decisions in most medical specialties and has become one of the most data-rich components of the electronic health record (EHR). For example, a single ultrasound of the heart yields approximately 3,000 images, and cardiologists have limited time to interpret these images within the context of numerous other diagnostic data. This creates a substantial opportunity to leverage technology, such as machine learning, to manage and analyze this data and ultimately provide intelligent computer assistance to physicians.
For their study, the research team used specialized computational hardware to train the machine learning model on 812,278 echocardiogram videos collected from 34,362 Geisinger patients over the last ten years. The study compared the results of the model to cardiologists’ predictions based on multiple surveys. A subsequent survey showed that when assisted by the model, cardiologists’ prediction accuracy improved by 13 percent. Leveraging nearly 50 million images, this study represents one of the largest medical image datasets ever published.
“Our goal is to develop computer algorithms to improve patient care,” said Alvaro Ulloa Cerna, Ph.D., author and senior data scientist in the Department of Translational Data Science and Informatics at Geisinger. “In this case, we’re excited that our algorithm was able to help cardiologists improve their predictions about patients, since decisions about treatment and interventions are based on these types of clinical predictions.”

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School closures, the loss of public spaces, and having to work remotely due to the coronavirus pandemic have caused major disruptions in people’s social lives all over the world.
Researchers from City University of Hong Kong, the Chinese Academy of Sciences, and Rensselaer Polytechnic Institute suggest a reduction in fatal coronavirus cases can be achieved without the need for so much social disruption. They discuss the impacts of the closures of various types of facilities in the journal Chaos, from AIP Publishing.
After running thousands of simulations of the pandemic response in New York City with variations in social distancing behavior at home, in schools, at public facilities, and in the workplace while considering differences in interactions between different age groups, the results were stunning. The researchers found school closures are not largely beneficial in preventing serious cases of COVID-19. Less surprisingly, social distancing in public places, particularly among elderly populations, is the most important.
“School only represents a small proportion of social contact. … It is more likely that people get exposure to viruses in public facilities, like restaurants and shopping malls,” said Qingpeng Zhang, one of the authors. “Since we focus here on the severe infections and deceased cases, closing schools contributes little if the elderly citizens are not protected in public facilities and other places.”
Because New York City is so densely populated, the effects of schools are significantly smaller than general day-to-day interactions in public, because students are generally the least vulnerable to severe infections. But keeping public spaces open allows for spread to occur from less-vulnerable young people to the more-vulnerable older population.
“Students may bridge the connection between vulnerable people, but these people are already highly exposed in public facilities,” Zhang said. “In other cities where people are much more distanced, the results may change.”
Though the present findings are specific to New York, replacing the age and location parameters in the model can extend its results to any city. This will help determine the ideal local control measures to contain the pandemic with minimal social disruptions.
“These patterns are unique for different cities, and good practice in one city may not translate to another city,” said Zhang.
The authors emphasized that while these findings have promising implications, the model is still just a model, and it cannot capture the intricacies and subtle details of real-life interactions to a perfect extent. The inclusion of mobile phone, census, transportation, or other big data in the future can help inform a more realistic decision.
“Given the age and location mixing patterns, there are so many variables to be considered, so the optimization is challenging,” said Zhang. “Our model is an attempt.”

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Wearable devices can identify COVID-19 cases earlier than traditional diagnostic methods and can help track and improve management of the disease, Mount Sinai researchers report in one of the first studies on the topic. The findings were published in the Journal of Medical Internet Research on January 29.
The Warrior Watch Study found that subtle changes in a participant’s heart rate variability (HRV) measured by an Apple Watch were able to signal the onset of COVID-19 up to seven days before the individual was diagnosed with the infection via nasal swab, and also to identify those who have symptoms.
“This study highlights the future of digital health,” says the study’s corresponding author Robert P. Hirten, MD, Assistant Professor of Medicine (Gastroenterology) at the Icahn School of Medicine at Mount Sinai, and member of the Hasso Plattner Institute for Digital Health at Mount Sinai and the Mount Sinai Clinical Intelligence Center (MSCIC). “It shows that we can use these technologies to better address evolving health needs, which will hopefully help us improve the management of disease. Our goal is to operationalize these platforms to improve the health of our patients and this study is a significant step in that direction. Developing a way to identify people who might be sick even before they know they are infected would be a breakthrough in the management of COVID-19.”
The researchers enrolled several hundred health care workers throughout the Mount Sinai Health System in an ongoing digital study between April and September 2020. The participants wore Apple Watches and answered daily questions through a customized app. Changes in their HRV — a measure of nervous system function detected by the wearable device — were used to identify and predict whether the workers were infected with COVID-19 or had symptoms. Other daily symptoms that were collected included fever or chills, tiredness or weakness, body aches, dry cough, sneezing, runny nose, diarrhea, sore throat, headache, shortness of breath, loss of smell or taste, and itchy eyes.
Additionally, the researchers found that 7 to 14 days after diagnosis with COVID-19, the HRV pattern began to normalize and was no longer statistically different from the patterns of those who were not infected.
“This technology allows us not only to track and predict health outcomes, but also to intervene in a timely and remote manner, which is essential during a pandemic that requires people to stay apart,” says the study’s co-author Zahi Fayad, PhD, Director of the BioMedical Engineering and Imaging Institute, Co-Founder of the MSCIC, and the Lucy G. Moses Professor of Medical Imaging and Bioengineering at the Icahn School of Medicine at Mount Sinai.
The Warrior Watch Study draws on the collaborative effort of the Hasso Plattner Institute for Digital Health and the MSCIC, which represents a diverse group of data scientists, engineers, clinical physicians, and researchers across the Mount Sinai Health System who joined together in the spring of 2020 to combat COVID-19. The study will next take a closer look at biometrics including HRV, sleep disruption, and physical activity to better understand which health care workers are at risk of the psychological effects of the pandemic. More

Soft robots may not be in touch with human feelings, but they are getting better at feeling human touch.
Cornell University researchers have created a low-cost method for soft, deformable robots to detect a range of physical interactions, from pats to punches to hugs, without relying on touch at all. Instead, a USB camera located inside the robot captures the shadow movements of hand gestures on the robot’s skin and classifies them with machine-learning software.
The group’s paper, “ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification,” published in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies. The paper’s lead author is doctoral student, Yuhan Hu.
The new ShadowSense technology is the latest project from the Human-Robot Collaboration and Companionship Lab, led by the paper’s senior author, Guy Hoffman, associate professor in the Sibley School of Mechanical and Aerospace Engineering.
The technology originated as part of an effort to develop inflatable robots that could guide people to safety during emergency evacuations. Such a robot would need to be able to communicate with humans in extreme conditions and environments. Imagine a robot physically leading someone down a noisy, smoke-filled corridor by detecting the pressure of the person’s hand.
Rather than installing a large number of contact sensors — which would add weight and complex wiring to the robot, and would be difficult to embed in a deforming skin — the team took a counterintuitive approach. In order to gauge touch, they looked to sight.

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“By placing a camera inside the robot, we can infer how the person is touching it and what the person’s intent is just by looking at the shadow images,” Hu said. “We think there is interesting potential there, because there are lots of social robots that are not able to detect touch gestures.”
The prototype robot consists of a soft inflatable bladder of nylon skin stretched around a cylindrical skeleton, roughly four feet in height, that is mounted on a mobile base. Under the robot’s skin is a USB camera, which connects to a laptop. The researchers developed a neural-network-based algorithm that uses previously recorded training data to distinguish between six touch gestures — touching with a palm, punching, touching with two hands, hugging, pointing and not touching at all — with an accuracy of 87.5 to 96%, depending on the lighting.
The robot can be programmed to respond to certain touches and gestures, such as rolling away or issuing a message through a loudspeaker. And the robot’s skin has the potential to be turned into an interactive screen.
By collecting enough data, a robot could be trained to recognize an even wider vocabulary of interactions, custom-tailored to fit the robot’s task, Hu said.
The robot doesn’t even have to be a robot. ShadowSense technology can be incorporated into other materials, such as balloons, turning them into touch-sensitive devices.
In addition to providing a simple solution to a complicated technical challenge, and making robots more user-friendly to boot, ShadowSense offers a comfort that is increasingly rare in these high-tech times: privacy.
“If the robot can only see you in the form of your shadow, it can detect what you’re doing without taking high fidelity images of your appearance,” Hu said. “That gives you a physical filter and protection, and provides psychological comfort.”
The research was supported by the National Science Foundation’s National Robotic Initiative.

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Systems designed to detect deepfakes — videos that manipulate real-life footage via artificial intelligence — can be deceived, computer scientists showed for the first time at the WACV 2021 conference which took place online Jan. 5 to 9, 2021.
Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed.
“Our work shows that attacks on deepfake detectors could be a real-world threat,” said Shehzeen Hussain, a UC San Diego computer engineering Ph.D. student and first co-author on the WACV paper. “More alarmingly, we demonstrate that it’s possible to craft robust adversarial deepfakes in even when an adversary may not be aware of the inner workings of the machine learning model used by the detector.”
In deepfakes, a subject’s face is modified in order to create convincingly realistic footage of events that never actually happened. As a result, typical deepfake detectors focus on the face in videos: first tracking it and then passing on the cropped face data to a neural network that determines whether it is real or fake. For example, eye blinking is not reproduced well in deepfakes, so detectors focus on eye movements as one way to make that determination. State-of-the-art Deepfake detectors rely on machine learning models for identifying fake videos.
The extensive spread of fake videos through social media platforms has raised significant concerns worldwide, particularly hampering the credibility of digital media, the researchers point out. “”If the attackers have some knowledge of the detection system, they can design inputs to target the blind spots of the detector and bypass it,” ” said Paarth Neekhara, the paper’s other first coauthor and a UC San Diego computer science student.
Researchers created an adversarial example for every face in a video frame. But while standard operations such as compressing and resizing video usually remove adversarial examples from an image, these examples are built to withstand these processes. The attack algorithm does this by estimating over a set of input transformations how the model ranks images as real or fake. From there, it uses this estimation to transform images in such a way that the adversarial image remains effective even after compression and decompression.??

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The modified version of the face is then inserted in all the video frames. The process is then repeated for all frames in the video to create a deepfake video. The attack can also be applied on detectors that operate on entire video frames as opposed to just face crops.
The team declined to release their code so it wouldn’t be used by hostile parties.
High success rate
Researchers tested their attacks in two scenarios: one where the attackers have complete access to the detector model, including the face extraction pipeline and the architecture and parameters of the classification model; and one where attackers can only query the machine 
 learning model to figure out the probabilities of a frame being classified as real or fake. In the first scenario, the attack’s success rate is above 99 percent for uncompressed videos. For compressed videos, it was 84.96 percent. In the second scenario, the success rate was 86.43 percent for uncompressed and 78.33 percent for compressed videos. This is the first work which demonstrates successful attacks on state-of-the-art deepfake detectors.
“To use these deepfake detectors in practice, we argue that it is essential to evaluate them against an adaptive adversary who is aware of these defenses and is intentionally trying to foil these defenses,”? the researchers write. “We show that the current state of the art methods for deepfake detection can be easily bypassed if the adversary has complete or even partial knowledge of the detector.”
To improve detectors, researchers recommend an approach similar to what is known as adversarial training: during training, an adaptive adversary continues to generate new deepfakes that can bypass the current state of the art detector; and the detector continues improving in order to detect the new deepfakes.
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
*Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar, Department of Electrical and Computer Engineering, UC San Diego Paarth Neekhara, Julian McAuley, Department of Computer Science and Engineering, UC San Diego More