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Spintronics research shows material's magnetic properties can predict how a spin current changes with temperature

by Tohoku University

Spintronics is a field garnering immense attention for its range of potential advantages for conventional electronics. These include reducing power consumption, high-speed operation, non-volatility, and the potential for new functionalities.

Spintronics exploits the intrinsic spin of electrons, and fundamental to the field is controlling the flows of the spin degree of freedom, i.e., spin currents. Scientists are focused on ways to create, remove, and control them for future applications.

Detecting spin currents is no easy feat. It requires the use of macroscopic voltage measurement, which looks at the overall voltage changes across a material. However, a common stumbling block has been a lack of understanding as to how this spin current actually moves or propagates within the material itself.

A team of researchers now report a method to predict how spin current changes with temperature. The study is published in Applied Physics Letters .

"Using neutron scattering and voltage measurements, we demonstrated that the magnetic properties of the material can predict how a spin current changes with temperature," says Yusuke Nambu, co-author of the paper and an associate professor at Tohoku University's Institute for Materials Research (IMR).

Nambu and his colleagues discovered that the spin current signal changes direction at a specific magnetic temperature and decreases at low temperatures. Additionally, they found that the spin direction, or magnon polarization, flips both above and below this critical magnetic temperature. This change in magnon polarization correlates with the spin current's reversal, shedding light on its propagation direction.

Furthermore, the material studied displayed magnetic behaviors with distinct gap energies. This suggests that below the temperature linked to this gap energy, spin current carriers are absent, leading to the observed decrease in the spin current signal at lower temperatures. Remarkably, the spin current's temperature dependence follows an exponential decay, mirroring the neutron scattering results.

Nambu emphasizes that their findings underscore the significance of understanding microscopic details in spintronics research. "By clarifying the magnetic behaviors and their temperature variations , we can gain a comprehensive understanding of spin currents in insulating magnets, paving the way for predicting spin currents more accurately and potentially developing advanced materials with enhanced performance."

Journal information: Applied Physics Letters

Provided by Tohoku University

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Journal of Hydraulic Research | Vol. 59. Issue 1, 2021

The Journal of Hydraulic Research (JHR) publishes research papers in theoretical, experimental and computational hydraulics and fluid mechanics, relating to rivers, lakes, estuaries, coasts, constructed waterways, and some internal flows such as pipe flows. To reflect current trends in water research, articles that explore outcomes of interdisciplinary hydro-environment studies with a strong fluid mechanical component are especially welcome. Although preference is given to fundamental issues, papers focusing on important unconventional or emerging applications of broad interest are also appreciated.

IAHR members can access the latest issue online . 

State-of-the-art paper

Free-surface behaviour of shallow turbulent flows    

Fabio Muraro, Giulio Dolcetti, Andrew Nichols, Simon J. Tait and Kirill V. Horoshenkov 

Pages: 1-20 | DOI: 10.1080/00221686.2020.1870007

Vision paper

Challenging hydraulic structures of the twenty-first century – from bubbles, transient turbulence to fish passage

Hubert Chanson, Xinqian Leng and Hang Wang

Pages: 21-35 | DOI: 10.1080/00221686.2020.1871429

Forum paper

Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers R eτ = 150, 400 and 1020    

Umair Ahmed, David Apsley, Timothy Stallard, Peter Stansby and Imran Afgan

Pages: 36-50 | DOI: 10.1080/00221686.2020.1729265

Research papers

Detachment of dilatant soil due to high hydraulic shear stresses explained   

Myron van Damme

Pages: 51-60 | DOI: 10.1080/00221686.2020.1714758

Hydrodynamic loads on buildings in floods

Clemente Cantelmo and Giovanni Cuomo

Pages: 61-74 | DOI: 10.1080/00221686.2020.1714759

Apparent cohesion effects on overtopping-induced fluvial dike breaching

Ismail Rifai, Kamal El Kadi Abderrezzak, Willi H. Hager, Sebastien Erpicum, Pierre Archambeau, Damien Violeau, Michel Pirotton and Benjamin Dewals

Pages: 75-87 | DOI: 10.1080/00221686.2020.1714760

Hydrodynamics and secondary currents in an asymmetrical rectangular canal with streamwise beam

Pedro X. Sanchez, Xinqian Leng and Hubert Chanson

Pages: 88-99 | DOI: 10.1080/00221686.2020.1714761

Localized roughness effects in non-uniform hydraulic waterways    

L. Robin Andersson, I. A. Sofia Larsson, J. Gunnar I. Hellström, Anton J. Burman and Patrik Andreasson

Pages: 100-108 | DOI: 10.1080/00221686.2020.1744744

Buckling analogy for 2D deformation of placed ripraps exposed to overtopping

Ganesh H. R. Ravindra, Fjola G. Sigtryggsdottir and Leif Lia 

Pages: 109-119 | DOI: 10.1080/00221686.2020.1744745

Effect of viscosity changes on the motion of debris flow by considering entrainment

Wei Liu, Siming He, Zheng Chen, Shuaixing Yan and Yu Deng

Pages: 120-135 | DOI: 10.1080/00221686.2020.1744746

Critical submergence for side circular intake in an open channel flow

Muhammed Hashid, Ajmal Hussain and Zulfequar Ahmad

Pages: 136-147 | DOI: 10.1080/00221686.2020.1744749

A gradient method for fuzzy analysis of water distribution networks

Naser Moosavian

Pages: 148-157 | DOI: 10.1080/00221686.2020.1744748

Technical notes

Numerical investigation of air demand by the free surface tunnel flows

Wangru Wei, Jun Deng and Weilin Xu

Pages: 158-165 | DOI: 10.1080/00221686.2020.1744747

Lattice Boltzmann method for shallow water flow with wave radiation stress

Haifei Liu, Liming Xing and Xin Chen

Pages: 166-175 | DOI: 10.1080/00221686.2020.1729264

A Lagrangian drifter for surveys of water surface roughness in streams by Christian Noss, Kaan Koca, Peggy Zinke, Pierre-Yves Henry, Christy Ushanth Navaratnam, Jochen Aberle and Andreas Lorke, J. Hydraulic Res. 58(3), 471–488.  

Giulio Dolcetti and Simon J. Tait

Pages: 176-178 | DOI: 10.1080/00221686.2020.1780497

Closure to “A Lagrangian drifter for surveys of water surface roughness in streams” by Christian Noss, Kaan Koca, Peggy Zinke, Pierre-Yves Henry, Christy Ushanth Navaratnam, Jochen Aberle and Andreas Lorke, J. Hydraulic Res.

Christian Noss, Kaan Koca, Peggy Zinke, Pierre-Yves Henry, Christy Ushanth Navaratnam, Jochen Aberle and Andreas Lorke

Pages: 178-179 | DOI: 10.1080/00221686.2020.1729267

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

• Education & Politics
• Partisanship & Issues
• Politics & Policy

Jenn Hatfield is a writer/editor at Pew Research Center

Most Americans think U.S. K-12 STEM education isn’t above average, but test results paint a mixed picture

About 1 in 4 u.s. teachers say their school went into a gun-related lockdown in the last school year, about half of americans say public k-12 education is going in the wrong direction, what public k-12 teachers want americans to know about teaching, what’s it like to be a teacher in america today, most popular.

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To build a better AI helper, start by modeling the irrational behavior of humans

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To build AI systems that can collaborate effectively with humans, it helps to have a good model of human behavior to start with. But humans tend to behave suboptimally when making decisions.

This irrationality, which is especially difficult to model, often boils down to computational constraints. A human can’t spend decades thinking about the ideal solution to a single problem.

Researchers at MIT and the University of Washington developed a way to model the behavior of an agent, whether human or machine, that accounts for the unknown computational constraints that may hamper the agent’s problem-solving abilities.

Their model can automatically infer an agent’s computational constraints by seeing just a few traces of their previous actions. The result, an agent’s so-called “inference budget,” can be used to predict that agent’s future behavior.

In a new paper, the researchers demonstrate how their method can be used to infer someone’s navigation goals from prior routes and to predict players’ subsequent moves in chess matches. Their technique matches or outperforms another popular method for modeling this type of decision-making.

Ultimately, this work could help scientists teach AI systems how humans behave, which could enable these systems to respond better to their human collaborators. Being able to understand a human’s behavior, and then to infer their goals from that behavior, could make an AI assistant much more useful, says Athul Paul Jacob, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique .

“If we know that a human is about to make a mistake, having seen how they have behaved before, the AI agent could step in and offer a better way to do it. Or the agent could adapt to the weaknesses that its human collaborators have. Being able to model human behavior is an important step toward building an AI agent that can actually help that human,” he says.

Jacob wrote the paper with Abhishek Gupta, assistant professor at the University of Washington, and senior author Jacob Andreas, associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Learning Representations.

Modeling behavior

Researchers have been building computational models of human behavior for decades. Many prior approaches try to account for suboptimal decision-making by adding noise to the model. Instead of the agent always choosing the correct option, the model might have that agent make the correct choice 95 percent of the time.

However, these methods can fail to capture the fact that humans do not always behave suboptimally in the same way.

Others at MIT have also studied more effective ways to plan and infer goals in the face of suboptimal decision-making.

To build their model, Jacob and his collaborators drew inspiration from prior studies of chess players. They noticed that players took less time to think before acting when making simple moves and that stronger players tended to spend more time planning than weaker ones in challenging matches.

“At the end of the day, we saw that the depth of the planning, or how long someone thinks about the problem, is a really good proxy of how humans behave,” Jacob says.

They built a framework that could infer an agent’s depth of planning from prior actions and use that information to model the agent’s decision-making process.

The first step in their method involves running an algorithm for a set amount of time to solve the problem being studied. For instance, if they are studying a chess match, they might let the chess-playing algorithm run for a certain number of steps. At the end, the researchers can see the decisions the algorithm made at each step.

Their model compares these decisions to the behaviors of an agent solving the same problem. It will align the agent’s decisions with the algorithm’s decisions and identify the step where the agent stopped planning.

From this, the model can determine the agent’s inference budget, or how long that agent will plan for this problem. It can use the inference budget to predict how that agent would react when solving a similar problem.

An interpretable solution

This method can be very efficient because the researchers can access the full set of decisions made by the problem-solving algorithm without doing any extra work. This framework could also be applied to any problem that can be solved with a particular class of algorithms.

“For me, the most striking thing was the fact that this inference budget is very interpretable. It is saying tougher problems require more planning or being a strong player means planning for longer. When we first set out to do this, we didn’t think that our algorithm would be able to pick up on those behaviors naturally,” Jacob says.

The researchers tested their approach in three different modeling tasks: inferring navigation goals from previous routes, guessing someone’s communicative intent from their verbal cues, and predicting subsequent moves in human-human chess matches.

Their method either matched or outperformed a popular alternative in each experiment. Moreover, the researchers saw that their model of human behavior matched up well with measures of player skill (in chess matches) and task difficulty.

Moving forward, the researchers want to use this approach to model the planning process in other domains, such as reinforcement learning (a trial-and-error method commonly used in robotics). In the long run, they intend to keep building on this work toward the larger goal of developing more effective AI collaborators.

This work was supported, in part, by the MIT Schwarzman College of Computing Artificial Intelligence for Augmentation and Productivity program and the National Science Foundation.

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