“All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war,” said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. “You need human sense-making and to make moral, ethical, and intellectual decisions in an incredibly confusing, fraught, scary situation.”

AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, “Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War,” published in International Security.

Many policy makers assume human soldiers could be replaced with automated systems, ideally making militaries less dependent on human labor and more effective on the battlefield. This is called the substitution theory of AI, but Lindsay and Goldfarb state that AI should not be seen as a substitute, but rather a complement to existing human strategy.

“Machines are good at prediction, but they depend on data and judgment, and the most difficult problems in war are information and strategy,” he said. “The conditions that make AI work in commerce are the conditions that are hardest to meet in a military environment because of its unpredictability.”

An example Lindsay and Goldfarb highlight is the Rio Tinto mining company, which uses self-driving trucks to transport materials, reducing costs and risks to human drivers. There are abundant, predictable, and unbiased data traffic patterns and maps that require little human intervention unless there are road closures or obstacles.

War, however, usually lacks abundant unbiased data, and judgments about objectives and values are inherently controversial, but that doesn’t mean it’s impossible. The researchers argue AI would be best employed in bureaucratically stabilized environments on a task-by-task basis.

“All the excitement and the fear are about killer robots and lethal vehicles, but the worst case for military AI in practice is going to be the classically militaristic problems where you’re really dependent on creativity and interpretation,” Lindsay said. “But what we should be looking at is personnel systems, administration, logistics, and repairs.”

There are also consequences to using AI for both the military and its adversaries, according to the researchers. If humans are the central element to deciding when to use AI in warfare, then military leadership structure and hierarchies could change based on the person in charge of designing and cleaning data systems and making policy decisions. This also means adversaries will aim to compromise both data and judgment since they would largely affect the trajectory of the war. Competing against AI may push adversaries to manipulate or disrupt data to make sound judgment even harder. In effect, human intervention will be even more necessary.

Yet this is just the start of the argument and innovations.

“If AI is automating prediction, that’s making judgment and data really important,” Lindsay said. “We’ve already automated a lot of military action with mechanized forces and precision weapons, then we automated data collection with intelligence satellites and sensors, and now we’re automating prediction with AI. So, when are we going to automate judgment, or are there components of judgment cannot be automated?”

Until then, though, tactical and strategic decision making by humans continues to be the most important aspect of warfare.

“Writing quantum algorithms is radically different from writing classical computing programs and requires some understanding of quantum principles and the mathematics behind them,” said Andrey Y. Lokhov, a scientist at Los Alamos National Laboratory and lead author of the recently published guide in ACM Transactions on Quantum Computing. “Our guide helps quantum programmers get started in the field, which is bound to grow as more and more quantum computers with more and more qubits become commonplace.”

In succinct, stand-alone sections, the guide surveys 20 quantum algorithms — including famous, foundational quantum algorithms, such as Grover’s Algorithm for database searching and much more, and Shor’s Algorithm for factoring integers. Making the real-world connection, the guide then walks programmers through implementing the algorithms on IBM’s publicly available 5-qubit IBMQX4 quantum computer and others. In each case, the authors discuss the results of the implementation and explain differences between the simulator and the actual hardware runs.

“This article was the result of a rapid-response effort by the Information Science and Technology Institute at Los Alamos, where about 20 Lab staff members self-selected to learn about and implement a standard quantum algorithm on the IBM Q quantum system,” said Stephan Eidenbenz, a senior quantum computing scientist at Los Alamos, a coauthor of the article and director of ISTI when work on it began.

The goal was to prepare the Los Alamos workforce for the quantum era by guiding those staff members with little or no quantum computing experience all the way through implementation of a quantum algorithm on a real-life quantum computer, Eidenbenz said.

These staff members, in addition to a few students and well-established quantum experts, make up the long author list of this “crowd-sourced” overview article that has already been heavily cited, Eidenbenz said.

The first section of the guide covers the basics of quantum computer programming, explaining qubits and qubit systems, fundamental quantum concepts of superposition and entanglement and quantum measurements before tackling the deeper material of unitary transformations and gates, quantum circuits and quantum algorithms.

The section on the IBM quantum computer covers the set of gates available for algorithms, the actual physical gates implemented, how the qubits are connected and the sources of noise, or errors.

Another section looks at the various types of quantum algorithms. From there, the guide dives into the 20 selected algorithms, with a problem definition, description and steps for implementing each one on the IBM or, in a few cases, other computers.

Extensive references at the end of the guide will help interested readers go deeper in their explorations of quantum algorithms.

Information Science and Technology Institute at Los Alamos National Laboratory through the Laboratory Directed Research and Development program.

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Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length.

Made up of swaths of sediment surrounding the river, channel belts, once hardened into rock, preserve the paths of rivers that once were. However, reconstructing details about an ancient river from channel belt deposits is a notoriously difficult task.

New research from scientists at The University of Texas at Austin is making progress on that front. Lead author Tian Dong, a postdoctoral researcher at the UT Jackson School of Geosciences, said that by analyzing modern rivers they have been able to come up with a rule that connects channel belts to river patterns, finding that, in general, the more channels a river has, the narrower its channel belt.

Since the physics shaping rivers is the same over time and place, the rule should hold for ancient rivers and rivers on other planets, too, according to co-author Timothy Goudge, an assistant professor at the Jackson School.

“We can look at a river deposit from 100 million years ago on Earth or from 3.5 billion years ago on Mars and we can say something about what the actual river looked like,” he said.

The results were published on June 13 in the journal Geology.

In addition to helping scientists envision ancient rivers, the rule can also help them interpret how these rivers influenced the broader landscape. Rivers with narrower channel belts are able to more readily access the surrounding floodplain — which shapes how landscapes are built up and material is deposited downstream.

“For multichannel systems, such as braided rivers, they actually sit in a very narrow channel belt so they’re very close to the floodplain,” said Dong. “So potentially there’s more interaction between the river and floodplain material.”

The rule does have some caveats. It does not hold for confined rivers that are kept from freely migrating by their surrounding landscape. But when rivers are free to move and meander across the land there is a direct connection between an increasing number of river channels and a narrowing channel belt. The scientists also found that as the belt narrows, it also becomes smoother with less sharp edges.

The researchers discovered the rule by analyzing 30 modern rivers and their channel belts, drawing on high-resolution images and elevation data captured by satellites. Dong said that he had a hunch about the connection, noticing a trend between river channel and channel belt width from scrolling around on Google Earth. But he wasn’t sure if his intuition would prove correct once the data was computed.

“No one had really looked at the relationship between river and channel-belt planform shapes systematically, so we didn’t really know what we’d expect,” Dong said.

In addition to having narrower channel belts, the research also found that multichannel rivers take up more space on the channel belt, taking up 50% or more of the channel belt area. In contrast, single-channel systems, like meandering rivers, take up as little as 1%. This further boosts the ability of multichannel rivers to pick up and move sediments, Dong said. Since organic matter from plants and animals is among those sediments, it means that multichannel rivers may not store organic carbon on their floodplains as long before transporting it into the ocean — where it can affect sea life.

Channel belts are a common feature on Mars, serving as a reminder of the Red Planet’s wetter past. They are also probably found on Saturn’s moon Titan, where rivers of liquid methane have been identified by space probes.

Both Goudge and Dong said they hope to apply their research on rivers to learn about the geology that shapes other worlds.

“For future work, we will be looking to apply these metrics to other planets in our solar system and see what we can see,” Goudge said.

The research was funded by a Postdoctoral Fellowship award to Dong from the National Science Foundation.

As the researchers peered closer into the cloud, they noticed that its magnetic field was curiously twisted. And then — as they examined a newborn star within the cloud — they spotted a hidden star, tucked behind it.

“It’s the star’s sibling, basically,” said Northwestern’s Erin Cox, who led the new study. “We think these stars formed far apart, and one moved closer to the other to form a binary. When the star traveled closer to its sibling, it shifted the dynamics of the cloud to twist its magnetic field.”

The new findings provide insight into binary star formation and how magnetic fields influence the earliest stages of developing stars.

Cox will present this research at the 240th meeting of the American Astronomical Society (AAS) in Pasadena, California. “The Twisted Magnetic Field of L483” will take place on Tuesday, June 14, as a part of a session on “Magnetic Fields and Galaxies.” The Astrophysical Journal also will publish the study next week.

Cox is a postdoctoral associate at Northwestern’s Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA).

Twisted mystery

Stellar nurseries are wild and wondrous places. As dense clouds of gas and dust collapse to form stars, they launch outflows of stellar material at hypersonic speeds. A magnetic field surrounding a star-forming cloud is typically parallel to these outflows. When Cox and her collaborators observed the large-scale L483 cloud, they discovered just that. The magnetic field matched this typical profile.

But then the astrophysicists decided to take a closer look with NASA’s Stratospheric Observatory for Infrared Astronomy (SOFIA), and this is when things became strange. The magnetic field was not, in fact, parallel to the newborn stars’ outflows. Instead, the field was twisted at a 45-degree angle, with respect to the outflows.

“At first, it matched what theory predicts,” Cox said. “If you have a magnetized collapse, then the magnetic field is controlling how the star is forming. We expect to see this parallelism. But theory can say one thing, and observations can say another.”

Unusual binary formation

Although more observations are needed, Cox believes a previously hidden sibling star may be responsible for the twisted field. Using SOFIA, the astrophysics team spotted one newborn star forming inside an envelope of material. But upon closer examination with radio telescopes at the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile, the researchers spotted the second star, sharing the same stellar envelope.

“These stars are still young and still forming,” Cox said. “The stellar envelope is what supplies the material to form the stars. It’s similar to rolling a snowball in snow to make it bigger and bigger. The young stars are ‘rolling’ in material to build up mass.”

About the same distance apart as our sun to Pluto, the two young stars form a binary system. Currently, astrophysicists agree that binaries can be formed when star-forming clouds are large enough to produce two stars or when the disc rotating around a young star partially collapses to make a second star.

But for the twin stars in L483, Cox suspects something unusual is at play.

“There is newer work that suggests it’s possible to have two stars form faraway from each other, and then one star moves in closer to form a binary,” Cox said. “We think that’s what is happening here. We don’t know why one star would move toward another one, but we think the moving star shifted the dynamics of the system to twist the magnetic field.”

Cox believes this new work ultimately could provide new insights into how binary stars — and the planets that orbit them — form. Most people are familiar with the iconic scene from “Star Wars,” in which Luke Skywalker wistfully gazes up at the binary stars that his home planet Tatooine orbits. Now, scientists know this scenario is not merely science fiction; planets orbiting binary stars potentially could be habitable worlds.

“Learning how binary stars form is exciting because planet and star formation take place at the same time, and binary stars dynamically interact with each other,” Cox said. “In our census of exoplanets, we know planets exist around these double stars, but we don’t know much about how these planets differ from the ones that live around isolated stars. With new instruments coming online to discover and probe new binary systems, we will be able to test these results with a statistical sample.”

The study, “The twisted magnetic field of the protobinary L483,” was supported by NASA and the National Science Foundation.

After it fills with cargo, the pit pinches off to form a clathrin-coated, membrane-bound vesicle inside the cell, which then proceeds to its proper destination. In cultured cells, hundreds of these clathrin-coated vesicles can form every minute.

However, conflicting models exist for how these vesicles assemble, and this has left a critical knowledge gap. One model is the constant-curvature model, where curvature is induced simultaneously with clathrin polymerization. Another, the flat-to-curved transition model, says that a flat lattice of clathrin molecules first assembles on the inside of the cell membrane, followed by a conformational change to form the clathrin-coated pit and vesicle.

Evidence for both models can be seen by electron microscopy — but those are static snapshots of fixed cells that do not reveal the actual nanometer-scale dynamics and possible pathways of clathrin-mediated endocytosis.

Now Alexa Mattheyses and colleagues at the University of Alabama at Birmingham and Emory University are filling that knowledge gap, using a sophisticated fluorescence microscopy imaging called STAR microscopy. This allowed them to follow clathrin-coated vesicle formation in living cells from initiation to completion, over periods up to 100 seconds.

Their study, reported in the journal Nature Communications, supports what has been called the flexible model of clathrin-coated vesicle formation, which includes both the constant-curvature and the flat-to-curved transition pathways.

“We show clathrin accumulation is preferentially simultaneous with curvature formation at shorter-lived clathrin-coated vesicles, but favors a flat-to-curved transition at longer-lived clathrin-coated vesicles,” said Mattheyses, an associate professor in the UAB Department of Cell, Developmental and Integrative Biology. “Together, our results provide experimental evidence in favor of the flexible model of endocytosis, where a single model of curvature formation cannot encompass the heterogeneity of cell-mediated endocytosis dynamics.”

Simultaneous Two-wavelength Axial Ratiometry, or STAR, microscopy is based on total internal reflection fluorescence, or TIRF, microscopy that allows visualization of fluorescently tagged proteins at or close to the plasma membrane, for a distance of about 100 to 200 nanometers. In STAR microscopy, the protein is labeled with two fluorophores that have different excitation wavelengths to take advantage of the wavelength-dependent penetration depth of the evanescent field. The result is an ability to simultaneously resolve both protein accumulation, as measured by fluorescence intensity, and distance from the plasma membrane, as measured by the fluorescence-intensity ratio of the two fluorophores. That distance, which represents the nanometer z-distribution, is a measure of curvature in the current study.

Using monkey kidney fibroblast cells, Mattheyses and colleagues labeled the clathrin CLCa light chain with fluorophores that have excitation wavelengths of 488 and 647 nanometers. They then stimulated the cells with epidermal growth factor to induce endocytosis. Quantitative analysis using the UAB Cheaha supercomputer resulted in 1,948 CLCa-STAR accumulations from 13 cells.

They found evidence of three models of curvature initiation: nucleation, where curvature formation started less than one second before clathrin appearance; constant curvature, where curvature formation started one to four seconds after clathrin arrival; and flat-to-curved transition, where curvature started more than four seconds after clathrin accumulation.

To analyze the data further, the researchers grouped endocytic events based on the clathrin lifetimes and the models of vesicle formation. This revealed interesting features.

“We found that short-lived endocytic events, less than 20 seconds, were formed predominantly through the constant-curvature model, while longer events — more than 20 seconds — favored the flat-to-curved transition,” Mattheyses said. “The proportion of nucleation events was smaller and favored short-lived events.”

To test whether this flexible model of clathrin-mediated endocytosis may be universal among different cell lines, the researchers also imaged human umbilical vein endothelial cells, stimulated by vascular endothelial growth factor to stimulate endocytosis. They found a similar range of endocytic events.

“These data show the previously unappreciated heterogeneity of clathrin-mediated endocytosis dynamics and plasticity of clathrin-coated vesicle formation, and they suggest different cell types or cargos can use this flexibility to impact the mode of curvature formation,” Mattheyses said. “Future research using STAR microscopy will define how the recruitment of accessory proteins and the role of biophysical parameters contribute to vesicle formation and how different clathrin dynamics impact signaling and cellular homeostasis.”

Co-authors of the study, “Imaging vesicle formation dynamics supports the flexible model of clathrin-mediated endocytosis,” are Tomasz J. Nawara, Yancey D. Williams II, Tejeshwar C. Rao and Elizabeth Sztul, UAB Department of Cell, Developmental and Integrative Biology; and Yuesong Hu and Khalid Salaita, Emory University Department of Chemistry, Atlanta, Georgia.

Mattheyses and Salaita developed STAR microscopy together at Emory in 2015. Mattheyses has been working with TIRF microscopy since her Ph.D. work at the University of Michigan with Daniel Axelrod, Ph.D., considered by many a pioneer of the technique.

Support came from National Institutes of Health grant GM3099, National Science Foundation CAREER grant 83200 and American Heart Association grant 906086.

Cell, Developmental and Integrative Biology is a department in the Marnix E. Heersink School of Medicine at UAB.