American Physical Society

Abstract The topological properties of a material depend on its symmetries, parameters, and spatial dimension. Changes in these properties due to parameter and symmetry variations can be understood by computing the corresponding topological invariant. Since topological invariants are typically defined for a fixed spatial dimension, there is no existing framework to understand the effects of changing spatial dimensions via invariants. Here, we introduce a framework to study topological phase transitions as a system’s dimensionality is altered using real-space topological markers. Specifically, we consider Shiba lattices, which are class D materials formed by magnetic atoms on the surface of a conventional superconductor, and characterize the evolution of their topology when an initial circular island is deformed into a chain. We also provide a measure of the corresponding protection against disorder. Our framework is generalizable to any symmetry class and spatial dimension, potentially guiding the design of materials by identifying, for example, the minimum thickness of a slab required to maintain three-dimensional topological properties.

We study the binary discrimination problem of identification of boosted H\to ggH→gg decays from massive QCD jets in a systematic expansion in the strong coupling. Though this decay mode of the Higgs is unlikely to be discovered at the LHC, we analytically demonstrate several features of the likelihood ratio for this problem through explicit analysis of signal and background matrix elements. Through leading-order, we prove that by imposing a constraint on the jet mass and measuring the energy fraction of the softer subjet an improvement of signal to background ratio that is independent of the kinematics of the jets at high boosts can be obtained, and is approximately equal to the inverse of the strong coupling evaluated at the Higgs mass. At next-to-leading order, we construct a powerful discrimination observable through a sort of anomaly detection approach by simply inverting the next-to-leading order H\to ggH→gg matrix element with soft gluon emission, which is naturally infrared and collinear safe. Our analytic conclusions are validated in simulated data from all-purpose event generators and subsequent parton showering and demonstrate that the signal-to-background ratio can be improved by a factor of several hundred at high, but accessible, jet energies at the LHC.

A potent framework for enduring process enhancement results from combining Lean Six Sigma (LSS) with Machine Learning (ML) systems by uniting Lean operational effectiveness with decision-making approaches based on data analytics. Through predictive analytics and real-time monitoring and adaptive process control Machine Learning enhances the waste reduction and process optimization capabilities of Lean Six Sigma. The combination offers exceptional benefits within production settings and healthcare and finance sectors because these industries put priority on quality and efficiency performance. The successful implementation of these technologies demands solutions for addressing data quality issues and system integration problems and organizational resistance. The evolution of accessible real-time processing through ML tools will shape Lean Six Sigma through increasingly autonomous systems which make predictive and proactive decisions on their own. The union between Machine Learning and Lean Six Sigma will transform process optimization to deliver businesses better efficiency alongside superior quality standards along with stronger market positions.

The dilemma of attribution is not just limited to paradigm-shifting results [1]: researchers are always challenged by the question of authorship.Sometimes, the right people are not included in the author list, or people are added against their wishes or without their knowledge, or, in fields where author order is not systematized, the order does not reflect each author's contributions.These practices can lead to systematic inequity, especially for early career investigators.APS recently aligned its seventeen journals under a common set of Policies and Practices that includes a new section on authorship.Even as guidelines are being expanded and clarified they cannot address all contingencies and thus their application will always be situationally dependent and open to interpretation.Unlike in the literary world, a coauthor on a scientific paper does not need to have actually done the writing.For instance, does the use of equipment in a researcher's laboratory or time on their supercluster give them grounds for coauthorship?These are not new questions.As the Committee on Publication Ethics (COPE) writes, "What is clear is that authorship is a fluid, evolving concept and as it evolves so will the ethical challenges associated with it" [2].Indeed, when discussing this Editorial-both of us from a condensed matter background-it emerged that we had different views on which contributions merit authorship.Given this ambiguity, we recommend using communication, consistency, objectivity, and good judgment as guiding principles.When you start a research project, discuss the criteria for authorship with your collaborators.As you move from project to project, be consistent: if sample preparation is usually a reason for authorship on your papers, make it so for all papers.The Contributor Role Taxonomy (CRediT) scheme is an excellent guide to follow when making these deliberations, even if you do not choose to include an Author Contribution Statement.Finally, we have two seemingly contradictory pieces of advice: Be both disciplined and generous.Pressure to publish from administrators, funding bodies, and colleagues can push researchers down a slippery slope of including authors in an effort to improve the chances of journal acceptance or funding opportunities.Indeed, because of the ever growing reliance on metrics, the question of "who contributed" becomes the prisoner's dilemma [3]: we will always be tempted to "defect" and improve our chances by gaming the author list even when we know that if everybody did that it would seriously dilute the meaning of authorship.On the opposite side of the fulcrum, omission of coauthors is likewise problematic.While the deliberate omission of coauthors is an unequivocal ethical violation, omission by ignorance happens all the time.Indeed, even the omitted may not think they are entitled to coauthorship!Again, this self-doubt is notably problematic for junior scientists.Here the onus rests on senior authors: do not merely acknowledge "help with figures" or "help with the equipment."Instead, ask about the contribution.Mentors must bring everyone into the discussion and not rely solely on group hierarchy to make the decisions.Be as generous with people who contributed to the results as possible, but explain to would-be coauthors that being on the paper makes them accountable for the workthat is the discipline.Are they invested in the result?Are they willing to defend the contentseven if only restricted to their particular section, measurement, or calculation?These issues are often given little discussion: at every level, the community must be vigilant while also relying upon the good intentions of all who participate.

The dilemma of attribution is not just limited to paradigm-shifting results [1]: researchers are always challenged by the question of authorship.Sometimes, the right people are not included in the author list, or people are added against their wishes or without their knowledge, or, in fields where author order is not systematized, the order does not reflect each author's contributions.These practices can lead to systematic inequity, especially for early career investigators.APS recently aligned its seventeen journals under a common set of Policies and Practices that includes a new section on authorship.Even as guidelines are being expanded and clarified they cannot address all contingencies and thus their application will always be situationally dependent and open to interpretation.Unlike in the literary world, a coauthor on a scientific paper does not need to have actually done the writing.For instance, does the use of equipment in a researcher's laboratory or time on their supercluster give them grounds for coauthorship?These are not new questions.As the Committee on Publication Ethics (COPE) writes, "What is clear is that authorship is a fluid, evolving concept and as it evolves so will the ethical challenges associated with it" [2].Indeed, when discussing this Editorial-both of us from a condensed matter background-it emerged that we had different views on which contributions merit authorship.Given this ambiguity, we recommend using communication, consistency, objectivity, and good judgment as guiding principles.When you start a research project, discuss the criteria for authorship with your collaborators.As you move from project to project, be consistent: if sample preparation is usually a reason for authorship on your papers, make it so for all papers.The Contributor Role Taxonomy (CRediT) scheme is an excellent guide to follow when making these deliberations, even if you do not choose to include an Author Contribution Statement.Finally, we have two seemingly contradictory pieces of advice: Be both disciplined and generous.Pressure to publish from administrators, funding bodies, and colleagues can push researchers down a slippery slope of including authors in an effort to improve the chances of journal acceptance or funding opportunities.Indeed, because of the ever growing reliance on metrics, the question of "who contributed" becomes the prisoner's dilemma: [3] we will always be tempted to "defect" and improve our chances by gaming the author list even when we know that if everybody did that it would seriously dilute the meaning of authorship.On the opposite side of the fulcrum, omission of coauthors is likewise problematic.While the deliberate omission of coauthors is an unequivocal ethical violation, omission by ignorance happens all the time.Indeed, even the omitted may not think they are entitled to coauthorship!Again, this self-doubt is notably problematic for junior scientists.Here the onus rests on senior authors: do not merely acknowledge "help with figures" or "help with the equipment."Instead, ask about the contribution.Mentors must bring everyone into the discussion and not rely solely on group hierarchy to make the decisions.Be as generous with people who contributed to the results as possible, but explain to would-be coauthors that being on the paper makes them accountable for the work-that is the discipline.Are they invested in the result?Are they willing to defend the contents-even if only restricted to their particular section, measurement, or calculation?These issues are often given little discussion: at every level, the community must be vigilant while also relying upon the good intentions of all who participate.R.D.K. instigated this missive.R.

The dilemma of attribution is not just limited to paradigm-shifting results [1]: researchers are always challenged by the question of authorship.Sometimes, the right people are not included in the author list, or people are added against their wishes or without their knowledge, or, in fields where author order is not systematized, the order does not reflect each author's contributions.These practices can lead to systematic inequity, especially for early career investigators.APS recently aligned its seventeen journals under a common set of Policies and Practices that includes a new section on authorship.Even as guidelines are being expanded and clarified they cannot address all contingencies and thus their application will always be situationally dependent and open to interpretation.Unlike in the literary world, a coauthor on a scientific paper does not need to have actually done the writing.For instance, does the use of equipment in a researcher's laboratory or time on their supercluster give them grounds for coauthorship?These are not new questions.As the Committee on Publication Ethics (COPE) writes, "What is clear is that authorship is a fluid, evolving concept and as it evolves so will the ethical challenges associated with it" [2].Indeed, when discussing this Editorial-both of us from a condensed matter background-it emerged that we had different views on which contributions merit authorship.Given this ambiguity, we recommend using communication, consistency, objectivity, and good judgment as guiding principles.When you start a research project, discuss the criteria for authorship with your collaborators.As you move from project to project, be consistent: if sample preparation is usually a reason for authorship on your papers, make it so for all papers.The Contributor Role Taxonomy (CRediT) scheme is an excellent guide to follow when making these deliberations, even if you do not choose to include an Author Contribution Statement.Finally, we have two seemingly contradictory pieces of advice: Be both disciplined and generous.Pressure to publish from administrators, funding bodies, and colleagues can push researchers down a slippery slope of including authors in an effort to improve the chances of journal acceptance or funding opportunities.Indeed, because of the ever growing reliance on metrics, the question of "who contributed" becomes the prisoner's dilemma [3]: we will always be tempted to "defect" and improve our chances by gaming the author list even when we know that if everybody did that it would seriously dilute the meaning of authorship.On the opposite side of the fulcrum, omission of coauthors is likewise problematic.While the deliberate omission of coauthors is an unequivocal ethical violation, omission by ignorance happens all the time.Indeed, even the omitted may not think they are entitled to coauthorship!Again, this self-doubt is notably problematic for junior scientists.Here the onus rests on senior authors: do not merely acknowledge "help with figures" or "help with the equipment."Instead, ask about the contribution.Mentors must bring everyone into the discussion and not rely solely on group hierarchy to make the decisions.Be as generous with people who contributed to the results as possible, but explain to would-be coauthors that being on the paper makes them accountable for the workthat is the discipline.Are they invested in the result?Are they willing to defend the contentseven if only restricted to their particular section, measurement, or calculation?These issues are often given little discussion: at every level, the community must be vigilant while also relying upon the good intentions of all who participate.

BackgroundIt is well established in the literature that active learning instruction in introductory STEM courses results in many desired student outcomes. Yet, regular use of high-quality active learning is not the norm in many STEM departments. Using results of a national survey, we identified 16 departments where multiple instructors reported using high levels of active learning in their introductory chemistry, mathematics, or physics courses. We conducted interviews with 27 instructors in these 16 departments to better understand the characteristics of such departments.ResultsUsing grounded theory methodology, we developed a model that highlights relevant characteristics of departments with high use of active learning instruction in their introductory courses. According to this model, there are four main, interconnected characteristics of such departments: motivated people, knowledge about active learning, opportunities, and cultures and structures that support active learning. These departments have one or more people who are motivated to promote the use of active learning. These motivated people have knowledge about active learning as well as access to opportunities to promote the use of active learning. Finally, these departments have cultures and structures that support the use of active learning. In these departments, there is a positive feedback loop that works iteratively over time, where motivated people shape cultures/structures and these cultures/structures in turn increase the number and level of commitment of the motivated people. A second positive feedback loop was found between the positive outcome of using active learning instruction and the strengthening of cultures/structures supportive of active learning.ConclusionsAccording to the model, there are two main take-away messages for those interested in promoting the use of active learning. The first is that all four components of the model are important. A weak or missing component may limit the desired outcome. The second is that desired outcomes are obtained and strengthened over time through two positive feedback loops. Thus, there is a temporal aspect to change. In all of the departments that were part of our study, the changes took at minimum several years to enact. While our model was developed using only high-use of active learning departments and future work is needed to develop the model into a full change theory, our results do suggest that change efforts may be made more effective by increasing the robustness of the four components and the connections between them.

It is known that manipulating the polarization of light on the microscale or nanoscale is essential for integrated photonics and quantum optics. In this talk, we present our recent studies on polarization manipulation and multiplexing in optical metasurfaces. One is to realize multichannel distribution and transformation of entangled photons with dielectric metasurface, and the other is to break the limitation of polarization multiplexing in optical metasurfaces with engineered noise. The approaches achieve potential applications in optical display, data storage, information encryption, and quantum information networks.