Computers Math

When you bump into someone in the workplace or at your local coffee shop, you might call that an “encounter.” That’s the scientific term for it, too. As part of urgent efforts to fight COVID-19, a science is rapidly developing for measuring the number of encounters and the different levels of interaction in a group.
At the National Institute of Standards and Technology (NIST), researchers are applying that science to a concept they have created called “encounter metrics.” They have developed an encrypted method that can be applied to a device such as your phone to help with the ultimate goal of slowing down or preventing future pandemics. The method is also applicable to the COVID-19 pandemic.
Their research is explained in a pilot study published in the Journal of Research of NIST.
Encounter metrics measure the levels of interactions between members of a population. A level of interaction could be the number of people in a bathroom who are talking to each other or a group of people walking down a hallway. There are numerous levels of interactions because there are so many different ways people can interact with one another in different environments.
In order to mitigate the spread of an infectious disease there is the assumption that less communication and interaction with people in a community is essential. Fewer interactions among people means there is less of a chance of the disease spreading from one person to another. “We need to measure that. It’s important to develop technology to measure that and then see how we can use that technology to shape our working environment to slow future pandemics,” said NIST researcher René Peralta, an author of the NIST study.
Picture two people walking from opposite ends of a hallway who meet in the middle. To record this encounter, each person could carry their own phone or a Bluetooth device that broadcasts a signal as soon as the encounter occurs. One way of labeling this encounter is through the exchange of device IDs or pseudonyms. Each device sends its own pseudonym that belongs to the device itself. The pseudonyms could be changed every 10 minutes as a way to promote the privacy of the person’s identity. More

A Japanese research group combined motion analysis that uses smartphone application and machine learning that uses an anomaly detection method, thereby developing a technique to easily screen for carpal tunnel syndrome. Carpal tunnel syndrome is common amongst middle-aged women. The disease causes compressed nerves in the wrist, causing numbness and difficulty with finger movements. While an accurate diagnosis can be reached with nerve conduction study, this is not widely used because it requires expensive devices and specialized skills. Thus, a simple screen tool that does not require any specialized knowledge or techniques is desired.
The research group of Dr. Koji Fujita of Tokyo Medical and Dental University and associate professor Yuta Sugiura of Keio University focused on increasingly poor movements of the thumb with the advancement of the disease, and analyzed its characteristics. They developed a game application for smartphones that is played using the thumbs and prepared a program that acquires the trajectory of the thumb during a game play and estimates the possibility of the disease with machine learning. The application can screen for possible carpal tunnel syndrome using a simple game that can be played in 30 sec — 1 minute. Even without gathering patient data, they were able to effectively construct an estimate mode from the data of 12 asymptomatic participants using the anomaly detection method. When this program was applied to 15 new asymptomatic subjects and 36 patients with carpal tunnel syndrome to verify its accuracy, the result was promising with 93% sensitivity, 69% specificity, and 0.86 Area Under the Curve (AUC)(1). This is equivalent or better than the results of physical examinations by expert orthopedic surgeons.
The developed tool can be used to screen for possible carpal tunnel syndrome at sites where no expert is present, such as at home or at a health center. In the future, the research group aims to develop a system that is able to encourage an examination by an expert when the disease is suspected in order to prevent exacerbation. It would prevent inconvenience and social loss associated with exacerbation of a disease, which is more common among women, and contribute to creating a society where women play an active role.
The research was conducted as part of JST’s Strategic Basic Research Program, Precursory Research for Embryonic Science and Technology (PRESTO).
(1) Area Under the Curve (AUC)
This assessment item is used for each test method, and a higher value indicates a better test. Sensitivity is the ratio of correct positive results for subjects with a disease. Specificity is the ratio of correct negative results for subjects without a disease. AUC is the comprehensive assessment indicator of accuracy that combines sensitivity and specificity and takes a value between 0 and 1.
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The use of artificial intelligence (AI) is becoming more common in many branches of industry and online retailing. Traditional lines of work, such as transport logistics and driving, are developing in a similar direction although mainly out of public view. Scientists at the University of Göttingen have now investigated how efficient the use of AI can be in the commercial management of trucks. Their answer: the best option is an intelligent combination of human decision-making and AI applications. The study was published in the International Journal of Logistics Management.
“As has happened in the private sector, digital applications — as well as machine learning, a kind of AI — are increasingly permeating operations and processes in the transport and logistics sector,” explains Professor Matthias Klumpp from the Faculty of Economics. “The question in the commercial sector, however, is whether or not this contributes to achieving goals and efficiency in companies.”
To answer this question, the researchers compared the work efficiency of truck drivers in relation to their use of AI applications such as dynamic real-time navigation systems, cruise control and automated gear-shifting based on speed and topography and others. Looking at retail trade delivery by truck, they studied three comparison groups: the first drove exclusively following human decision-making patterns; the second used a combination of human and machine; and the third relied exclusively on fully automated decisions.
The researchers from the Production and Logistics Research Group concluded that an intelligent combination of human work and decision-making capabilities with AI applications promises the highest transport and driving efficiency: “On average, the second group achieved the most efficient transport trips, with the fewest interventions and deviations from the optimal path,” the authors said. “Clearly, neither a purely human decision-making structure nor a fully automated driving system can promise to meet current logistics requirements.”
The scientists therefore deduce that despite the progress of AI in the field of transportation by truck, human experience and decision-making capabilities will still be necessary in the longer term. “However, extensive training and qualification needs will occur by working with AI applications, especially for simple logistics activities,” the authors conclude. “Technology and AI innovations are therefore not a question for management alone. In particular, efficiency and competitive advantages can be achieved through their application in operational transport.”
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International genomics research led by the University of Leicester has used artificial intelligence (AI) to study an aggressive form of cancer, which could improve patient outcomes.
Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months.
New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.
Professor Dean Fennell, Chair of Thoracic Medical Oncology at the University of Leicester and Director of the Leicester Mesothelioma Research Programme, said:
“It has long been appreciated that asbestos causes mesothelioma, however how this occurs remains a mystery.
“Using AI to interrogate genomic ‘big data’, this initial work shows us that mesotheliomas follow ordered paths of mutations during development, and that these so-called trajectories predict not only how long a patient may survive, but also how to better treat the cancer — something Leicester aims to lead on internationally through clinical trial initiatives.”
While use of asbestos is now outlawed — and stringent regulations in place on its removal — each year around 25 people are diagnosed with mesothelioma in Leicestershire and 190 are diagnosed in the East Midlands. Cases of mesothelioma in the UK have increased by 61% since the early 1990s.
Until very recently, chemotherapy was the only licenced choice for patients with mesothelioma. However, treatment options start to become limited once people stop responding to their treatment.
Professor Fennell in collaboration with the University of Southampton recently made a major breakthrough in treating the disease by demonstrating that use of an immunotherapy drug called nivolumab increased survival and stabilised the disease for patients. This was the first-ever trial to demonstrate improved survival in patients with relapsed mesothelioma.
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Inspired by the mastery of artificial intelligence (AI) over games like Go and Super Mario, scientists at the National Synchrotron Light Source II (NSLS-II) trained an AI agent — an autonomous computational program that observes and acts — how to conduct research experiments at superhuman levels by using the same approach. The Brookhaven team published their findings in the journal Machine Learning: Science and Technology and implemented the AI agent as part of the research capabilities at NSLS-II.
As a U.S. Department of Energy (DOE) Office of Science User Facility located at DOE’s Brookhaven National Laboratory, NSLS-II enables scientific studies by more than 2000 researchers each year, offering access to the facility’s ultrabright x-rays. Scientists from all over the world come to the facility to advance their research in areas such as batteries, microelectronics, and drug development. However, time at NSLS-II’s experimental stations — called beamlines — is hard to get because nearly three times as many researchers would like to use them as any one station can handle in a day — despite the facility’s 24/7 operations.
“Since time at our facility is a precious resource, it is our responsibility to be good stewards of that; this means we need to find ways to use this resource more efficiently so that we can enable more science,” said Daniel Olds, beamline scientist at NSLS-II and corresponding author of the study. “One bottleneck is us, the humans who are measuring the samples. We come up with an initial strategy, but adjust it on the fly during the measurement to ensure everything is running smoothly. But we can’t watch the measurement all the time because we also need to eat, sleep and do more than just run the experiment.”
“This is why we taught an AI agent to conduct scientific experiments as if they were video games. This allows a robot to run the experiment, while we — humans — are not there. It enables round-the-clock, fully remote, hands-off experimentation with roughly twice the efficiency that humans can achieve,” added Phillip Maffettone, research associate at NSLS-II and first author on the study.
According to the researchers, they didn’t even have to give the AI agent the rules of the ‘game’ to run the experiment. Instead, the team used a method called “reinforcement learning” to train an AI agent on how to run a successful scientific experiment, and then tested their agent on simulated research data from the Pair Distribution Function beamline at NSLS-II.
Beamline Experiments: A Boss Level Challenge
Reinforcement learning is one strategy of training an AI agent to master an ability. The idea of reinforcement learning is that the AI agent perceives an environment — a world — and can influence it by performing actions. Depending on how the AI agent interacts with the world, it may receive a reward or a penalty, reflecting if this specific interaction is a good choice or a poor one. The trick is that the AI agent retains the memory of its interactions with the world, so that it can learn from the experience for when it tries again. In this way, the AI agent figures out how to master a task by collecting the most rewards. More

Engineers at Duke University have developed an electronics-free, entirely soft robot shaped like a dragonfly that can skim across water and react to environmental conditions such as pH, temperature or the presence of oil. The proof-of-principle demonstration could be the precursor to more advanced, autonomous, long-range environmental sentinels for monitoring a wide range of potential telltale signs of problems.
The soft robot is described online March 25 in the journal Advanced Intelligent Systems.
Soft robots are a growing trend in the industry due to their versatility. Soft parts can handle delicate objects such as biological tissues that metal or ceramic components would damage. Soft bodies can help robots float or squeeze into tight spaces where rigid frames would get stuck.
The expanding field was on the mind of Shyni Varghese, professor of biomedical engineering, mechanical engineering and materials science, and orthopaedic surgery at Duke, when inspiration struck.
“I got an email from Shyni from the airport saying she had an idea for a soft robot that uses a self-healing hydrogel that her group has invented in the past to react and move autonomously,” said Vardhman Kumar, a PhD student in Varghese’s laboratory and first author of the paper. “But that was the extent of the email, and I didn’t hear from her again for days. So the idea sort of sat in limbo for a little while until I had enough free time to pursue it, and Shyni said to go for it.”
In 2012, Varghese and her laboratory created a self-healing hydrogel that reacts to changes in pH in a matter of seconds. Whether it be a crack in the hydrogel or two adjoining pieces “painted” with it, a change in acidity causes the hydrogel to form new bonds, which are completely reversible when the pH returns to its original levels. More

The very first moments of the Universe can be reconstructed mathematically even though they cannot be observed directly. Physicists from the Universities of Göttingen and Auckland (New Zealand) have greatly improved the ability of complex computer simulations to describe this early epoch. They discovered that a complex network of structures can form in the first trillionth of a second after the Big Bang. The behaviour of these objects mimics the distribution of galaxies in today’s Universe. In contrast to today, however, these primordial structures are microscopically small. Typical clumps have masses of only a few grams and fit into volumes much smaller than present-day elementary particles. The results of the study have been published in the journal Physical Review D.
The researchers were able to observe the development of regions of higher density that are held together by their own gravity. “The physical space represented by our simulation would fit into a single proton a million times over,” says Professor Jens Niemeyer, head of the Astrophysical Cosmology Group at the University of Göttingen. “It is probably the largest simulation of the smallest area of the Universe that has been carried out so far.” These simulations make it possible to calculate more precise predictions for the properties of these vestiges from the very beginnings of the Universe.
Although the computer-simulated structures would be very short-lived and eventually “vaporise” into standard elementary particles, traces of this extreme early phase may be detectable in future experiments. “The formation of such structures, as well as their movements and interactions, must have generated a background noise of gravitational waves,” says Benedikt Eggemeier, a PhD student in Niemeyer’s group and first author of the study. “With the help of our simulations, we can calculate the strength of this gravitational wave signal, which might be measurable in the future.”
It is also conceivable that tiny black holes could form if these structures undergo runaway collapse. If this happens they could have observable consequences today, or form part of the mysterious dark matter in the Universe. “On the other hand,” says Professor Easther, “If the simulations predict black holes form, and we don’t see them, then we will have found a new way to test models of the infant Universe.”
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Microswimmers are artificial, self-propelled, microscopic particles. They are capable of directional motion in a solution. The Molecular Nanophotonics Group at Leipzig University has developed special particles that are smaller than one-thirtieth of the diameter of a hair. They can change their direction of motion by heating tiny gold particles on their surface and converting this energy into motion. “However, these miniaturised machines cannot take in and learn information like their living counterparts. To achieve this, we control the microswimmers externally so that they learn to navigate in a virtual environment through what is known as reinforcement learning,” said Cichos.
With the help of virtual rewards, the microswimmers find their way through the liquid while repeatedly being thrown off of their path, mainly by Brownian motion. “Our results show that the best swimmer is not the one that is fastest, but rather that there is an optimal speed,” said Viktor Holubec, who worked on the project as a fellow of the Alexander von Humboldt Foundation and has now returned to the university in Prague.
According to the scientists, linking artificial intelligence and active systems like in these microswimmers is a first small step towards new intelligent microscopic materials that can autonomously perform tasks while also adapting to their new environment. At the same time, they hope that the combination of artificial microswimmers and machine learning methods will provide new insights into the emergence of collective behaviour in biological systems. “Our goal is to develop artificial, smart building blocks that can perceive their environmental influences and actively react to them,” said the physicist. Once this method is fully developed and has been applied to other material systems, including biological ones, it could be used, for example, in the development of smart drugs or microscopic robot swarms.
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