Computers Math

Math continues to be a powerful force against COVID-19.
Its latest contribution is a sophisticated algorithm, using municipal wastewater systems, for determining key locations in the detection and tracing of COVID-19 back to its human source, which may be a newly infected person or a hot spot of infected people. Timing is key, say the researchers who created the algorithm, especially when COVID-19 is getting better at transmitting itself, thanks to emerging variants.
“Being quick is what we want because in the meantime, a newly-infected person can infect others,” said Oded Berman, a professor of operations management and statistics at the University of Toronto’s Rotman School of Management.
This latest research builds on previous work Prof. Berman did with co-investigators Richard Larson of the Massachusetts Institute of Technology and Mehdi Nourinejad of York University. The trio initially developed two algorithms for identifying choice locations in a sewer system for manual COVID-19 testing and subsequent tracing back to the source. Sewers are a rich environment for detecting presence of the disease upstream because genetic remnants of its virus are shed in the stool of infected people up to a week before they may even know they are sick.
The investigators’ new research refines and optimizes that initial work by more accurately modelling a typical municipal sewer system’s treelike network of one-way pipes and manholes, and by speeding up the detection/tracing process through automatic sensors installed in specific manholes, chosen according to an easier-to-use algorithm.
Under this scenario, a sensor sends out an alert any time COVID-19 is detected. Manual testing is then done at a few manholes further upstream, also chosen according to the algorithm, until the final source is located, be that a small group of homes or a “hotspot” neighbourhood. Residents in that much smaller area can then be contacted for further testing and isolation as needed, limiting potential new outbreaks. More

Physicists in Israel have created a quantum interferometer on an atom chip. This device can be used to explore the fundamentals of quantum theory by studying the interference pattern between two beams of atoms. University of Groningen physicist, Anupam Mazumdar, describes how the device could be adapted to use mesoscopic particles instead of atoms. This modification would allow for expanded applications. A description of the device, and theoretical considerations concerning its application by Mazumdar, were published on 28 May in the journal Science Advances.
The device which scientists from the Ben-Gurion University of the Negev created is a so-called Stern Gerlach Interferometer, which was first proposed one hundred years ago by German physicists Otto Stern and Walter Gerlach. Their original aim of creating an interferometer with freely propagating atoms exposed to gradients from macroscopic magnets has not been practically realized until now. ‘Such experiments have been done using photons, but never with atoms’, explains Anupam Mazumdar, Professor of Theoretical Physics at the University of Groningen and one of the co-authors of the article in Science Advances.
Diamonds The Israeli scientists, led by Professor Ron Folman, created an interferometer on an atom chip, which can confine and/or manipulate atoms. A beam of rubidium atoms is levitated over the chip using magnets. Magnetic gradients are used to split the beam according to the spin values of the individual atoms. Spin is a magnetic moment that can have two values, either up or down. The spin-up and spin-down atoms are separated by a magnetic gradient. Subsequently, the two divergent beams are brought together again and recombined. The spin values are then measured, and an interference pattern is formed. Spin is a quantum phenomenon, and throughout this interferometer, the opposing spins are entangled. This makes the interferometer sensitive to other quantum phenomena.
Mazumdar was not involved in the construction of the chip, but he contributed theoretical insights to the paper. Together with a number of his colleagues, he previously proposed an experiment to determine whether gravity is in fact a quantum phenomenon using entangled mesoscopic objects, namely tiny diamonds that can be brought in a state of quantum superposition. ‘It would be possible to use these diamonds instead of the rubidium atoms on this interferometer’, he explains. However, this process would be highly complex as the device, which is currently operated at room temperature, would need to be cooled down to around 1 Kelvin for the mesoscopic experiment.
Free fall If this is realized, two of these atom chips could free fall together (to neutralize external gravity), so that any interaction occurring between them would depend on the gravitational pull between the two chips. Mazumdar and his colleagues aim to determine whether quantum entanglement of the pair occurs during free fall, which would mean that the force of gravity between the diamonds is indeed a quantum phenomenon. Another application of this experiment is the detection of gravity waves; their deformation of space-time should be visible in the interference pattern.
The actual implementation of this experiment is still a long way off, but Mazumdar is very excited now that the interferometer has been created. ‘It is already [a] quantum sensor, although we still have to work out exactly what it can detect. The experiment is like the first steps of a baby — now, we have to guide it to reach maturity.’
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Materials provided by University of Groningen. Note: Content may be edited for style and length. More

Graphene may be among the most exciting scientific discoveries of the last century. While it is strikingly familiar to us — graphene is considered an allotrope of carbon, meaning that it essentially the same substance as graphite but in a different atomic structure — graphene also opened up a new world of possibilities for designing and building new technologies.
The material is two-dimensional, meaning that each “sheet” of graphene is only 1 atom thick, but its bonds make it as strong as some of the world’s hardest metal alloys while remaining lightweight and flexible. This valuable, unique mix of properties have piqued the interest of scientists from a wide range of fields, leading to research in using graphene for next-generation electronics, new coatings on industrial instruments and tools, and new biomedical technologies.
It is perhaps graphene’s immense potential that has consequently caused one of its biggest challenges — graphene is difficult to produce in large volumes, and demand for the material is continually growing. Recent research indicates that using a liquid copper catalyst may be a fast, efficient way for producing graphene, but researchers only have a limited understanding of molecular interactions happening during these brief, chaotic moments that lead to graphene formation, meaning they cannot yet use the method to reliably produce flawless graphene sheets.
In order to address these challenges and help develop methods for quicker graphene production, a team of researchers at the Technical University of Munich (TUM) has been using the JUWELS and SuperMUC-NG high-performance computing (HPC) systems at the Jülich Supercomputing Centre (JSC) and Leibniz Supercomputing Centre (LRZ) to run high-resolution simulations of graphene formation on liquid copper.
A window into experiment
Graphene’s appeal primarily stems from the material’s perfectly uniform crystal structure, meaning that producing graphene with impurities is wasted effort. For laboratory settings or circumstances where only a small amount of graphene is needed, researchers can place a piece of scotch tape onto a graphite crystal and “peel” away atomic layers of the graphite using a technique that resembles how one would use tape or another adhesive to help remove pet hair from clothing. While this reliably produces flawless graphene layers, the process is slow and impractical for creating graphene for large-scale applications. More

You would not be surprised to see an elephant in the savanna or a plate in your kitchen. Based on your prior experiences and knowledge, you know that is where elephants and plates are often to be found. If you saw a mysterious object in your kitchen, how would you figure out what it was? You would rely on your expectations or prior knowledge. Should a computer approach the problem in the same way? The answer may surprise you. Cold Spring Harbor Laboratory Professor Partha Mitra described how he views problems like these in a “Perspective” in Nature Machine Intelligence. He hopes his insights will help researchers teach computers how to analyze complex systems more effectively.
Mitra thinks it helps to understand the nature of knowledge. Mathematically speaking, many data scientists try to create a model that can “fit an elephant,” or a set of complex data points. Mitra asks researchers to consider what philosophical framework would work best for a particular machine learning task:
“In philosophical terms, the idea is that there are these two extremes. One, you could say “rationalist,” and the other “empiricist” points of view. And really, it’s about the role of prior knowledge or prior assumptions.”
Rationalists versus empiricists
A rationalist views the world through the lens of prior knowledge. They expect a plate to be in a kitchen and an elephant in a savanna.
An empiricist analyzes the data exactly as it is presented. When they visit the savanna, they no more expect to see an elephant than they do a plate. More

When bringing technologies into the workplace, it pays to be realistic. Often, for instance, bringing new digital technology into an organization does not radically improve a firm’s operations. Despite high-level planning, a more frequent result is the messy process of frontline employees figuring out how they can get tech tools to help them to some degree.
That task can easily fall on overburdened workers who have to grapple with getting things done, but don’t always have much voice in an organization. So isn’t there a way to think systematically about implementing digital technology in the workplace?
MIT Professor Kate Kellogg thinks there is, and calls it “experimentalist governance of digital technology”: Let different parts of an organization experiment with the technology — and then centrally remove roadblocks to adopt the best practices that emerge, firm-wide.
“If you want to get value out of new digital technology, you need to allow local teams to adapt the technology to their setting,” says Kellogg, the David J. McGrath Jr. Professor of Management and Innovation at the MIT Sloan School of Management. “You also need to form a central group that’s tracking all these local experiments, and revising processes in response to problems and possibilities. If you just let everyone do everything locally, you’re going to see resistance to the technology, particularly among frontline employees.”
Kellogg’s perspective comes after she conducted an 18-month close ethnographic study of a teaching hospital, examining many facets of its daily workings — including things like the integration of technology into everyday medical practices.
Some of the insights from that organizational research now appear in a paper Kellogg has written, “Local Adaptation Without Work Intensification: Experimentalist Governance of Digital Technology for Mutually Beneficial Role Reconfiguration in Organizations,” recently published online in the journal Organization Science. More

First, it takes skill to fly the often expensive pieces of equipment smoothly and without crashing. And once you’ve mastered flying, there are camera angles, panning speeds, trajectories and flight paths to plan.
With all the sensors and processing power onboard a drone and embedded in its camera, there must be a better way to capture the perfect shot.
“Sometimes you just want to tell the drone to make an exciting video,” said Rogerio Bonatti, a Ph.D. candidate in Carnegie Mellon University’s Robotics Institute.
Bonatti was part of a team from CMU, the University of Sao Paulo and Facebook AI Research that developed a model that enables a drone to shoot a video based on a desired emotion or viewer reaction. The drone uses camera angles, speeds and flight paths to generate a video that could be exciting, calm, enjoyable or nerve-wracking — depending on what the filmmaker tells it.
The team presented their paper on the work at the 2021 International Conference on Robotics and Automation this month. More

North Carolina State University researchers have developed a computer simulation tool to predict when and where pests and diseases will attack crops or forests, and also test when to apply pesticides or other management strategies to contain them.
“It’s like having a bunch of different Earths to experiment on to test how something will work before spending the time, money and effort to do it,” said the study’s lead author Chris Jones, research scholar at North Carolina State University’s Center for Geospatial Analytics.
In the journal Frontiers in Ecology and the Environment, researchers reported on their efforts to develop and test the tool, which they called “PoPS,” for the Pest or Pathogen Spread Forecasting Platform. Working with the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service, they created the tool to forecast any type of disease or pathogen, no matter the location.
Their computer modeling system works by combining information on climate conditions suitable for spread of a certain disease or pest with data on where cases have been recorded, the reproductive rate of the pathogen or pest and how it moves in the environment. Over time, the model improves as natural resource managers add data they gather from the field. This repeated feedback with new data helps the forecasting system get better at predicting future spread, the researchers said.
“We have a tool that can be put into the hands of a non-technical user to learn about disease dynamics and management, and how management decisions will affect spread in the future,” Jones said.
The tool is needed as state and federal agencies charged with controlling pests and crop diseases face an increasing number of threats to crops, trees and other important natural resources. These pests threaten food supplies and biodiversity in forests and ecosystems.
“The biggest problem is the sheer number of new pests and pathogens that are coming in,” Jones said. “State and federal agencies charged with managing them have an ever-decreasing budget to spend on an ever-increasing number of pests. They have to figure out how to spend that money as wisely as possible.”
Already, researchers have been using PoPS to track the spread of eight different emerging pests and diseases. In the study, they described honing the model to track sudden oak death, a disease that has killed millions of trees in California since the 1990s. A new, more aggressive strain of the disease has been detected in Oregon.
They are also improving the model to track spotted lanternfly, an invasive pest in the United States that primarily infests a certain invasive type of tree known as “tree of heaven.” Spotted lanternfly has been infesting fruit crops in Pennsylvania and neighboring states since 2014. It can attack grape, apple and cherry crops, as well as almonds and walnuts.
The researchers said that just as meteorologists incorporate data into models to forecast weather, ecological scientists are using data to improve forecasting of environmental events — including pest or pathogen spread.
“There’s a movement in ecology to forecast environmental conditions,” said Megan Skrip, a study co-author and science communicator at the Center for Geospatial Analytics. “If we can forecast the weather, can we forecast where there will be an algal bloom, or what species will be in certain areas at certain times? This paper is one of the first demonstrations of doing this for the spread of pests and pathogens.”
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Materials provided by North Carolina State University. Original written by Laura Oleniacz. Note: Content may be edited for style and length. More

Stanford University computer science graduate student Mackenzie Leake has been quilting since age 10, but she never imagined the craft would be the focus of her doctoral dissertation. Included in that work is new prototype software that can facilitate pattern-making for a form of quilting called foundation paper piecing, which involves using a backing made of foundation paper to lay out and sew a quilted design.
Developing a foundation paper piece quilt pattern — which looks similar to a paint-by-numbers outline — is often non-intuitive. There are few formal guidelines for patterning and those that do exist are insufficient to assure a successful result.
“Quilting has this rich tradition and people make these very personal, cherished heirlooms but paper piece quilting often requires that people work from patterns that other people designed,” said Leake, who is a member of the lab of Maneesh Agrawala, the Forest Baskett Professor of Computer Science and director of the Brown Institute for Media Innovation at Stanford. “So, we wanted to produce a digital tool that lets people design the patterns that they want to design without having to think through all of the geometry, ordering and constraints.”
A paper describing this work is published and will be presented at the computer graphics conference SIGGRAPH 2021 in August.
Respecting the craft
In describing the allure of paper piece quilts, Leake cites the modern aesthetic and high level of control and precision. The seams of the quilt are sewn through the paper pattern and, as the seaming process proceeds, the individual pieces of fabric are flipped over to form the final design. All of this “sew and flip” action means the pattern must be produced in a careful order. More