Computers Math

In order to earn certification in general surgery, residents in the United States need to demonstrate proficiency in the Fundamentals of Laparoscopic program (FLS), a test that requires manipulation of laparoscopic tools within a physical training unit. Central to that assessment is a quantitative score, known as the FLS score, which is manually calculated using a formula that is time-consuming and labor-intensive.
By combining brain optical imaging, and a deep learning framework they call “Brain-NET,” a multidisciplinary team of engineers at Rensselaer Polytechnic Institute, in close collaboration with the Department of Surgery at the Jacobs School of Medicine & Biomedical Sciences at the University at Buffalo, has developed a new methodology that has the potential to transform training and the certification process for surgeons.
In a new article in IEEE Transactions on Biomedical Engineering, the researchers demonstrated how Brain-NET can accurately predict a person’s level of expertise in terms of their surgical motor skills, based solely on neuroimaging data. These results support the future adoption of a new, more efficient method of surgeon certification that the team has developed.
“This is an area of expertise that is really unique to RPI,” said Xavier Intes, a professor of biomedical engineering at Rensselaer, who led this research.
According to Intes, Brain-NET not only performed more quickly than the traditional prediction model, but also more accurately, especially as it analyzed larger datasets.
Brain-NET builds upon the research team’s earlier work in this area. Researchers led by Suvranu De, the head of the Rensselaer Department of Mechanical, Aerospace, and Nuclear Engineering, previously showed that they could accurately assess a doctor’s surgical motor skills by analyzing brain activation signals using optical imaging.
In addition to its potential to streamline the surgeon certification process, the development of Brain-NET, combined with that optical imaging analysis, also enables real-time score feedback for surgeons who are training.
“If you can get the measurement of the predicted score, you can give feedback right away,” Intes said. “What this opens the door to is to engage in remediation or training.”

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

Of ever-increasing concern for operating a tactical communications network is the possibility that a sophisticated adversary may detect friendly transmissions. Army researchers developed an analysis framework that enables the rigorous study of the detectability of ultraviolet communication systems, providing the insights needed to deliver the requirements of future, more secure Army networks.
In particular, ultraviolet communication has unique propagation characteristics that not only allow for a novel non-line-of-sight optical link, but also imply that the transmissions may be harder for an adversary to detect.
Building off of experimentally validated channel modeling, channel simulations, and detection and estimation theory, the developed framework enables the evaluation of tradeoffs associated with different design choices and the manner of operation of ultraviolet communication systems, said Dr. Robert Drost of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.
“While many techniques have been proposed to decrease the detectability of conventional radio-frequency, or RF, communications, the increased atmospheric absorption of deep-ultraviolet wavelengths implies that ultraviolet communication, or UVC, has a natural low-probability-of-detection, or LPD, characteristic,” Drost said.
“In order to fully take advantage of this characteristic, a rigorous understanding of the LPD properties of UVC is needed.”
In particular, Drost said, such understanding is essential for optimizing the design and operation of UVC systems and networks and for predicting the quality of the LPD property in a given scenario, such as using UVC to securely network a command post that has an estimate of the direction and distance to the adversary.

advertisement

Without such a predictive capability, he said, users would lack the guidance needed to know the extent and limit of their detectability, and this lack of awareness would substantially limit the usefulness of the LPD capability.
The researchers, including Drs. Mike Weisman, Fikadu Dagefu, Terrence Moore and Drost from CCDC ARL and Dr. Hakan Arlsan, Oak Ridge Associated Universities postdoctoral fellow at the lab, demonstrated this by applying their framework to produce a number of key insights regarding the LPD characteristics of UVC, including:
LPD capability is relatively insensitive to a number of system and channel properties, which is important for the robustness of the LPD property Adversarial line-of-sight detection of a non-line-of-sight communication link is not as significant of a concern as one might fear Perhaps counter to intuition, steering of a UVC transmitter does not appear to be an effective detection-mitigation strategy in many cases Line-of-sight UVC link provides non-line-of-sight standoff distances that are commensurate with the communication range
Prior modeling and experimental research has demonstrated that UVC signals attenuate dramatically at long distance, leading to the hypothesis that UVC has a fundamental LPD property, Drost said. However, there has been little effort on rigorously and precisely quantifying this property in terms of the detectability of a communication signal.
“Our work provides a framework enabling the study of the fundamental limits of detectability for an ultraviolet communication system meeting desired communication performance requirements,” Drost said.

advertisement

Although this research is focused on longer-term applications, he said, it is addressing the Army Modernization Priority on Networks by developing the fundamental understanding of a novel communications capability, with a goal of providing the Soldier with network connectivity despite challenging environments that include adversarial activity.
“The future communications and networking challenges that the Army faces are immense, and it is essential that we explore all possible means to overcoming those challenges,” Drost said. “Our research is ensuring that the community has the fundamental understanding of the potential for and limitations of using ultraviolet wavelengths for communications, and I am confident that this understanding will inform the development of future Army networking capabilities. Conducting fundamental research that impacts decision making and Army technologies is why we work for the Army, and it is very satisfying to know that our work will ultimately support the warfighter in his or her mission.”
The researchers are currently continuing to develop refined understanding of how best to design and operate ultraviolet communications, and an important next step is the application of this framework to understand the detectability of a network of ultraviolet communications systems.
Another key effort involves the experimental characterization, exploration and demonstration of this technology in a practical network using ARL’s Common Sensor Radio, a sophisticated mesh-networking radio designed to provide robust and energy-efficient networking.
This research supports the laboratory’s FREEDOM (Foundational Research for Electronic Warfare in Multi-Domain Operations) Essential Research Program goal of studying the integration of low-signature communications technologies with advanced camouflage and decoy techniques.
According to Drost, the work is also an on-ramp to studying how ultraviolet communications and other communications modalities, including conventional radio-frequency communications, can operate together in a seamless and autonomous extremely heterogeneous network, which the researchers believe is needed in order to fully realize the benefits of individual novel communication technologies.
As they make continued progress on these fundamental research questions, the researchers will continue to work closely with their transition partner at the CCDC C5ISR (Command, Control, Computers, Communications, Cyber, Intelligence, Surveillance and Reconnaissance) Center to push ultraviolet communications toward nearer term transition to the warfighter. More

As we use resources, such as coal, oil, natural gas, copper, silicon and aluminum, to power massive computer farms and process digital information, our technological progress is redistributing Earth’s matter from physical atoms to digital information — the fifth state of matter, alongside liquid, solid, gas and plasma.
Eventually, we will reach a point of full saturation, a period in our evolution in which digital bits will outnumber atoms on Earth, a world “mostly computer simulated and dominated by digital bits and computer code,” according to an article published in AIP Advances, by AIP Publishing.
It is just a matter of time.
“We are literally changing the planet bit by bit, and it is an invisible crisis,” author Melvin Vopson said.
Vopson examines the factors driving this digital evolution. He said the impending limit on the number of bits, the energy to produce them, and the distribution of physical and digital mass will overwhelm the planet soon.
For example, using current data storage densities, the number of bits produced per year and the size of a bit compared to the size of an atom, at a rate of 50% annual growth, the number of bits would equal the number of atoms on Earth in approximately 150 years.
It would be approximately 130 years until the power needed to sustain digital information creation would equal all the power currently produced on planet Earth, and by 2245, half of Earth’s mass would be converted to digital information mass.
“The growth of digital information seems truly unstoppable,” Vopson said. “According to IBM and other big data research sources, 90% of the world’s data today has been created in the last 10 years alone. In some ways, the current COVID-19 pandemic has accelerated this process as more digital content is used and produced than ever before.”
Vopson draws on the mass-energy equivalence in Einstein’s theory of general relativity; the work of Rolf Landauer, who applied the laws of thermodynamics to information; and the work of Claude Shannon, the inventor of the digital bit.
In 2019, Vopson formulated a principle that postulates that information moves between states of mass and energy just like other matter.
“The mass-energy-information equivalence principle builds on these concepts and opens up a huge range of new physics, especially in cosmology,” he said. “When one brings information content into existing physical theories, it is almost like an extra dimension to everything in physics.”

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

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.

Story Source:
Materials provided by Cell Press. Note: Content may be edited for style and length. More

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.

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

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.”

Story Source:
Materials provided by University of Sheffield. Note: Content may be edited for style and length. More

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.”

Story Source:
Materials provided by National Institutes of Natural Sciences. Note: Content may be edited for style and length. More

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.”

Story Source:
Materials provided by University of Illinois at Urbana-Champaign, News Bureau. Original written by Lois Yoksoulian. Note: Content may be edited for style and length. More