Computers Math

It’s commonly known that gun sales go up after a mass shooting, but two competing hypotheses have been put forth to explain why that’s the case: is it because people fear more violence and want to protect themselves, or is it because mass shootings trigger discussions about tighter gun regulations, which sends people out to stock up? In a new study appearing August 11 in the journal Patterns, investigators used data science to study this phenomenon. By working with spatio-temporal data from all the states in the US, they determined that the increase in firearm purchases after mass shootings is driven by a concern about regulations rather than a perceived need for protection.
“It’s been well documented that mass shootings are linked to increases in firearm purchases, but the motivation behind this connection has been understudied,” says first author Maurizio Porfiri, Institute Professor at the New York University Tandon School of Engineering, who is currently on research sabbatical at the Technical University of Cartagena in Spain. “Previous research on this topic has been done mostly from the perspective of social science. We instead used a data-science approach.”
Porfiri and his colleagues employed a statistical method called transfer entropy analysis, which is used to study large, complex systems like financial markets and climate-change models. With this approach, two variables are defined, and then computational techniques are used to determine if the future of one of them can be predicted by the past of the other. “This is a step above studying correlation,” Porfiri explains. “It’s actually looking at causation. Unique to this study is the analysis of spatio-temporal data, by examining the behavior of all the US states”
The data that were put into consideration came from several sources: FBI background checks, which enabled the approximation of monthly gun sales by state; a Washington Post database on mass shootings; and news coverage about mass shooting from five major newspapers around the country. The news stories were put in two categories: those that mentioned gun regulations and those that didn’t. In all, the study used data related to 87 mass shootings that occurred in the United States between 1999 and 2017.
The researchers also rated individual states by how restrictive their gun laws are. “We expected to find that gun sales increased in states that have more permissive gun laws, but it was less expected in states with restrictive laws. We saw it in both,” Porfiri says. “Also, when we looked at particular geographic areas, we didn’t find any evidence that gun sales increased when mass shootings happened nearby.”
He adds that one limitation of the data is that news coverage may not fully capture public sentiment at a given time. In addition, although the study was successful in determining causal links among states, more work is needed to study the nature of these relationships, especially when one has laws that are much more restrictive than another
Porfiri usually uses computational systems to study topics related to engineering, including ionic polymer metal composites and underwater robots. His reason for studying mass shootings is personal: he received his PhD in 2006 from Virginia Tech, which, the following year, was the site where — at that time — the deadliest mass shooting in the country took place. One member of his PhD committee was killed in the shooting, and he knew many others who were deeply affected.
For him, this project is part of a larger effort to study gun violence. “Mass shootings are a small part of death from guns,” Porfiri says. “Suicide and homicide are much more common. But mass shootings are an important catalyst for a larger discussion. I plan to look at the wider role of guns in the future.”
This study is part of the collaborative activities carried out under the programs of the region of Murcia (Spain): “Groups of Excellence of the region of Murcia, the Fundación Séneca, Science and Technology Agency” project 19884/GERM/15 and “Call for Fellowships for Guest Researcher Stays at Universities and OPIS” project 21144/IV/19. The researcher was also supported by the New York University Research Challenge Fund Program and Mitsui-USA foundation.

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In severe cases of COVID-19, damage can spread beyond the lungs and into other organs, such as the heart, liver, kidney and parts of the neurological system. Beyond these specific sets of organs, however, the virus seems to lack impact.
Ernesto Estrada, from the University of Zaragoza and Agencia Aragonesa para la Investigación Foundation in Spain, aimed to uncover an explanation as to how it is possible for these damages to propagate selectively rather than affecting the entire body. He discusses his findings in the journal Chaos, from AIP Publishing.
In order to enter human cells, the coronavirus relies on interactions with an abundant protein called angiotensin-converting enzyme 2.
“This receptor is ubiquitous in most human organs, such that if the virus is circulating in the body, it can also enter into other organs and affect them,” Estrada said. “However, the virus affects some organs selectively and not all, as expected from these potential mechanisms.”
Once inside a human cell, the virus’s proteins interact with those in the body, allowing for its effects to cultivate. COVID-19 damages only a subset of organs, signaling to Estrada that there must be a different pathway for its transmission. To uncover a plausible route, he considered the displacements of proteins prevalent in the lungs and how they interact with proteins in other organs.
“For two proteins to find each other and form an interaction complex, they need to move inside the cell in a subdiffusive way,” Estrada said.
He described this subdiffusive motion as resembling a drunkard walking on a crowded street. The crowd presents obstacles to the drunkard, stunting displacement and making it difficult to reach the destination.
Similarly, proteins in a cell face several crowded obstacles they must overcome in order to interact. Adding to the complexity of the process, some proteins exist within the same cell or organ, but others do not.
Taking these into account, Estrada developed a mathematical model that allowed him to find a group of 59 proteins within the lungs that act as the primary activators affecting other human organs. A chain of interactions, beginning with this set, triggers changes in proteins down the line, ultimately impacting their health.
“Targeting some of these proteins in the lungs with existing drugs will prevent the perturbation of the proteins expressed in organs other than the lungs, avoiding multiorgan failure, which, in many cases, conduces the death of the patient,” Estrada said.
How the affected proteins travel between organs remains an open question that Estrada is dedicating for future studies.
The article, “Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS CoV-2,” is authored by Ernesto Estrada. The article will appear in Chaos on Aug. 11, 2020.

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Scientists from the University of Sheffield have used mathematical modelling to understand why flocks of long-tailed tits segregate themselves into different parts of the landscape.
The team tracked the birds around Sheffield’s Rivelin Valley which eventually produced a pattern across the landscape, using maths helped the team to reveal the behaviours causing these patterns.
The findings, published in the Journal of Animal Ecology, show that flocks of long-tailed tits are less likely to avoid places where they have interacted with relatives and more likely to avoid larger flocks, whilst preferring the centre of woodland.
It was previously unknown why flocks of long-tailed tits live in separate parts of the same area, despite there being plenty of food to sustain multiple flocks and the birds not showing territorial behaviour.
The equations used to understand the birds are similar to those developed by Alan Turing to describe how animals get their spotted and striped patterns. Turing’s famous mathematics indicates if patterns will appear as an animal grows in the womb, here it’s used to find out which behaviours lead to the patterns across the landscape.
Territorial animals often live in segregated areas that they aggressively defend and stay close to their den. Before this study, these mathematical ideas had been used to understand the patterns made by territorial animals such as coyotes, meerkats and even human gangs. However, this study was the first to use the ideas on non-territorial animals with no den pinning them in place.
Natasha Ellison, PhD student at the University of Sheffield who led the study, said: “Mathematical models help us understand nature in an extraordinary amount of ways and our study is a fantastic example of this.”
“Long-tailed tits are too small to be fitted with GPS trackers like larger animals, so researchers follow these tiny birds on foot, listening for bird calls and identifying birds with binoculars. The field work is extremely time consuming and without the help of these mathematical models these behaviours wouldn’t have been discovered.”

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Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future.
A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification. The AI enabled the team to perform the processing without human intervention.
Automated processing techniques for extraction and judgment of features with deep-learning algorithms have been rapidly developed since 2012. Now they usually surpass humans in terms of accuracy and are used for autonomous vehicles, security cameras, and many other applications. Dr. Ken-ichi Tadaki, a Project Assistant Professor at NAOJ, came up with the idea that if AI can classify images of cats and dogs, it should be able to distinguish “galaxies with spiral patterns” from “galaxies without spiral patterns.” Indeed, using training data prepared by humans, the AI successfully classified the galaxy morphologies with an accuracy of 97.5%. Then applying the trained AI to the full data set, it identified spirals in about 80,000 galaxies.
Now that this technique has been proven effective, it can be extended to classify galaxies into more detailed classes, by training the AI on the basis of a substantial number of galaxies classified by humans. NAOJ is now running a citizen-science project “GALAXY CRUISE,” where citizens examine galaxy images taken with the Subaru Telescope to search for features suggesting that the galaxy is colliding or merging with another galaxy. The advisor of “GALAXY CRUISE,” Associate Professor Masayuki Tanaka has high hopes for the study of galaxies using artificial intelligence and says, “The Subaru Strategic Program is serious Big Data containing an almost countless number of galaxies. Scientifically, it is very interesting to tackle such big data with a collaboration of citizen astronomers and machines. By employing deep-learning on top of the classifications made by citizen scientists in GALAXY CRUISE, chances are, we can find a great number of colliding and merging galaxies.”

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Electronic filters are essential to the inner workings of our phones and other wireless devices. They eliminate or enhance specific input signals to achieve the desired output signals. They are essential, but take up space on the chips that researchers are on a constant quest to make smaller. A new study demonstrates the successful integration of the individual elements that make up electronic filters onto a single component, significantly reducing the amount of space taken up by the device.
Researchers at the University of Illinois, Urbana-Champaign have ditched the conventional 2D on-chip lumped or distributed filter network design — composed of separate inductors and capacitors — for a single, space-saving 3D rolled membrane that contains both independently designed elements.
The results of the study, led by electrical and computer engineering professor Xiuling Li, are published in the journal Advanced Functional Materials.
“With the success that our team has had on rolled inductors and capacitors, it makes sense to take advantage of the 2D to 3D self-assembly nature of this fabrication process to integrate these different components onto a single self-rolling and space-saving device,” Li said.
In the lab, the team uses a specialized etching and lithography process to pattern 2D circuitry onto very thin membranes. In the circuit, they join the capacitors and inductors together and with ground or signal lines, all in a single plane. The multilayer membrane can then be rolled into a thin tube and placed onto a chip, the researchers said.
“The patterns, or masks, we use to form the circuitry on the 2D membrane layers can be tuned to achieve whatever kind of electrical interactions we need for a particular device,” said graduate student and co-author Mark Kraman. “Experimenting with different filter designs is relatively simple using this technique because we only need to modify that mask structure when we want to make changes.”
The team tested the performance of the rolled components and found that under the current design, the filters were suitable for applications in the 1-10 gigahertz frequency range, the researchers said. While the designs are targeted for use in radio frequency communications systems, the team posits that other frequencies, including in the megahertz range, are also possible based on their ability to achieve high power inductors in past research.
“We worked with several simple filter designs, but theoretically we can make any filter network combination using the same process steps,” said graduate student and lead author Mike Yang. “We took what was already out there to provide a new, easier platform to lump these components together closer than ever.”
“Our way of integrating inductors and capacitors monolithically could bring passive electronic circuit integration to a whole new level,” Li said. “There is practically no limit to the complexity or configuration of circuits that can be made in this manner, all with one mask set.”

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“The idea had been going around my head for about 15 years, but I never had the time or the resources to do anything about it.” But now Luc Thévenaz, the head of the Fiber Optics Group in EPFL’s School of Engineering, has finally made it happen: his lab has developed a technology to amplify light inside the latest hollow-core optical fibers.
Squaring the circle
Today’s optical fibers usually have a solid glass core, so there’s no air inside. Light can travel along the fibers but loses half of its intensity after 15 kilometers. It keeps weakening until it can hardly be detected at 300 kilometers. So to keep the light moving, it has to be amplified at regular intervals.
Thévenaz’s approach is based on new hollow-core optical fibers that are filled with either air or gas. “The air means there’s less attenuation, so the light can travel over a longer distance. That’s a real advantage,” says the professor. But in a thin substance like air, the light is harder to amplify. “That’s the crux of the problem: light travels faster when there’s less resistance, but at the same time it’s harder to act on. Luckily, our discovery has squared that circle.”
From infrared to ultraviolet
So what did the researchers do? “We just added pressure to the air in the fiber to give us some controlled resistance,” explains Fan Yang, postdoctoral student. “It works in a similar way to optical tweezers — the air molecules are compressed and form into regularly spaced clusters. This creates a sound wave that increases in amplitude and effectively diffracts the light from a powerful source towards the weakened beam so that it is amplified up to 100,000 times.” Their technique therefore makes the light considerably more powerful. “Our technology can be applied to any type of light, from infrared to ultraviolet, and to any gas,” he explains. Their findings have just been published in Nature Photonics.
An extremely accurate thermometer
Going forward, the technology could serve other purposes in addition to light amplification. Hollow-core or compressed-gas optical fibers could, for instance, be used to make extremely accurate thermometers. “We’ll be able to measure temperature distribution at any point along the fiber. So if a fire starts along a tunnel, we’ll know exactly where it began based on the increased temperature at a given point,” says Flavien Gyger, PhD student. The technology could also be used to create a temporary optical memory by stopping the light in the fiber for a microsecond — that’s ten times longer than is currently possible.

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Engineers at the National Institute of Standards and Technology (NIST) have developed a flexible, portable measurement system to support design and repeatable laboratory testing of fifth-generation (5G) wireless communications devices with unprecedented accuracy across a wide range of signal frequencies and scenarios.
The system is called SAMURAI, short for Synthetic Aperture Measurements of Uncertainty in Angle of Incidence. The system is the first to offer 5G wireless measurements with accuracy that can be traced to fundamental physical standards — a key feature because even tiny errors can produce misleading results. SAMURAI is also small enough to be transported to field tests.
Mobile devices such as cellphones, consumer Wi-Fi devices and public-safety radios now mostly operate at electromagnetic frequencies below 3 gigahertz (GHz) with antennas that radiate equally in all directions. Experts predict 5G technologies could boost data rates a thousandfold by using higher, “millimeter-wave” frequencies above 24 GHz and highly directional, actively changing antenna patterns. Such active antenna arrays help to overcome losses of these higher-frequency signals during transmission. 5G systems also send signals over multiple paths simultaneously — so-called spatial channels — to increase speed and overcome interference.
Many instruments can measure some aspects of directional 5G device and channel performance. But most focus on collecting quick snapshots over a limited frequency range to provide a general overview of a channel, whereas SAMURAI provides a detailed portrait. In addition, many instruments are so physically large that they can distort millimeter-wave signal transmissions and reception.
Described at a conference on Aug. 7, SAMURAI is expected to help resolve many unanswered questions surrounding 5G’s use of active antennas, such as what happens when high data rates are transmitted across multiple channels at once. The system will help improve theory, hardware and analysis techniques to provide accurate channel models and efficient networks.
“SAMURAI provides a cost-effective way to study many millimeter-wave measurement issues, so the technique will be accessible to academic labs as well as instrumentation metrology labs,” NIST electronics engineer Kate Remley said. “Because of its traceability to standards, users can have confidence in the measurements. The technique will allow better antenna design and performance verification, and support network design.”
SAMURAI measures signals across a wide frequency range, currently up to 50 GHz, extending to 75 GHz in the coming year. The system got its name because it measures received signals at many points over a grid or virtual “synthetic aperture.” This allows reconstruction of incoming energy in three dimensions — including the angles of the arriving signals — which is affected by many factors, such as how the signal’s electric field reflects off of objects in the transmission path.
SAMURAI can be applied to a variety of tasks from verifying the performance of wireless devices with active antennas to measuring reflective channels in environments where metallic objects scatter signals. NIST researchers are currently using SAMURAI to develop methods for testing industrial Internet of Things devices at millimeter-wave frequencies.
The basic components are two antennas to transmit and receive signals, instrumentation with precise timing synchronization to generate radio transmissions and analyze reception, and a six-axis robotic arm that positions the receive antenna to the grid points that form the synthetic aperture. The robot ensures accurate and repeatable antenna positions and traces out a variety of reception patterns in 3D space, such as cylindrical and hemispherical shapes. A variety of small metallic objects such as flat plates and cylinders can be placed in the test setup to represent buildings and other real-world impediments to signal transmission. To improve positional accuracy, a system of 10 cameras is also used to track the antennas and measure the locations of objects in the channel that scatter signals.
The system is typically attached to an optical table measuring 5 feet by 14 feet (1.5 meters by 4.3 meters). But the equipment is portable enough to be used in mobile field tests and moved to other laboratory settings. Wireless communications research requires a mix of lab tests — which are well controlled to help isolate specific effects and verify system performance — and field tests, which capture the range of realistic conditions.
Measurements can require hours to complete, so all aspects of the (stationary) channel are recorded for later analysis. These values include environmental factors such as temperature and humidity, location of scattering objects, and drift in accuracy of the measurement system.
The NIST team developed SAMURAI with collaborators from the Colorado School of Mines in Golden, Colorado. Researchers have verified the basic operation and are now incorporating uncertainty due to unwanted reflections from the robotic arm, position error and antenna patterns into the measurements. More

Corals in the Ocean are made up of coral polyps, a small soft creature with a stem and tentacles, they are responsible for nourishing the corals, and aid the coral’s survival by generating self-made currents through motion of their soft bodies.
Scientists from WMG at the University of Warwick, led by Eindhoven University of Technology in the Netherlands, developed a 1cm by 1cm wireless artificial aquatic polyp, which can remove contaminants from water. Apart from cleaning, this soft robot could be also used in medical diagnostic devices by aiding in picking up and transporting specific cells for analysis.
In the paper, ‘An artificial aquatic polyp that wirelessly attracts, grasps, and releases objects’ researchers demonstrate how their artificial aquatic polyp moves under the influence of a magnetic field, while the tentacles are triggered by light. A rotating magnetic field under the device drives a rotating motion of the artificial polyp’s stem. This motion results in the generation of an attractive flow which can guide suspended targets, such as oil droplets, towards the artificial polyp.
Once the targets are within reach, UV light can be used to activate the polyp’s tentacles, composed of photo-active liquid crystal polymers, which then bend towards the light enclosing the passing target in the polyp’s grasp. Target release is then possible through illumination with blue light.
Dr Harkamaljot Kandail, from WMG, University of Warwick was responsible for creating state of the art 3D simulations of the artificial aquatic polyps. The simulations are important to help understand and elucidate the stem and tentacles generate the flow fields that can attract the particles in the water.
The simulations were then used to optimise the shape of the tentacles so that the floating particles could be grabbed quickly and efficiently.
Dr Harkamaljot Kandail, from WMG, University of Warwick comments:
“Corals are such a valuable ecosystem in our oceans, I hope that the artificial aquatic polyps can be further developed to collect contaminant particles in real applications. The next stage for us to overcome before being able to do this is to successfully scale up the technology from laboratory to pilot scale. To do so we need to design an array of polyps which work harmoniously together where one polyp can capture the particle and pass it along for removal.”
Marina Pilz Da Cunha, from the Eindhoven University of Technology, Netherlands adds:
“The artificial aquatic polyp serves as a proof of concept to demonstrate the potential of actuator assemblies and serves as an inspiration for future devices. It exemplifies how motion of different stimuli-responsive polymers can be harnessed to perform wirelessly controlled tasks in an aquatic environment.”

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