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We study $p$-adic manifolds associated with twisted points of quotient stacks $\mathcal{X} = [U/G]$ and their quotient spaces $\pi:\mathcal{X} \to X$. We prove several structural results about the fibres of $\pi$ and derive in particular a formula expressing $p$-adic integrals on $X$ in terms of the cyclotomic inertia stack of $\mathcal{X}$, generalizing the orbifold formula for Deligne-Mumford stacks. We then apply our formalism to moduli stacks of $1$-dimensional sheaves on del Pezzo surfaces and show that their refined BPS-invariants are computed locally on the coarse moduli space by a $p$-adic integral. As a consequence we recover the $\chi$-independence of these invariants previously proven by Maulik-Shen. Along the way we derive a new formula for the plethystic logarithm on the $\lambda$-ring of functions on $k$-linear stacks, which might be of independent interest., Comment: 34 pages. Comments welcome!

In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice., Comment: 11 pages, 11 figures, to be published in IEEE VIS 2024 / IEEE Transactions on Visualization and Computer Graphics

To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to facilitate a cross-national, multiteam, design-based research and professional support initiative called Research on Scaling the Impact of Innovations in Education (ROSIE). Since 2021, ROSIE has brought together 15 researcher and practitioner teams working in 30 low- and middle-income countries to study the process of scaling education initiatives for impact. This brief reflects on the 15 KIX teams' experiences to shed light on the role of research in the scaling process. The brief distinguishes between research for scaling and research about the scaling process itself, discusses different ways KIX-ROSIE teams are conducting and using research in their scaling work, and identifies some of the research challenges and solutions that emerged.

To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to facilitate a cross-national, multiteam, design-based research and professional support initiative called Research on Scaling the Impact of Innovations in Education (ROSIE). Since 2020, ROSIE has brought together 15 teams of researchers and practitioners working in 30 low-and middle-income countries to study the process of scaling education initiatives for impact. Equity-based scaling requires significant time, sensitivity, and resources and is not necessarily incentivized by wider forces and project parameters. Looking at how KIX-ROSIE teams pursued equity is the focus of this brief. Special attention is paid to four distinct ways equity was prioritized in their work. Additionally, this brief raises some conceptual complexities around equity in global education and offers reflections on equity challenges and opportunities.

To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to facilitate a cross-national, multiteam, design-based research and professional support initiative called Research on Scaling the Impact of Innovations in Education (ROSIE). Since 2021, ROSIE has brought together 15 researcher and practitioner teams working in 30 low- and middle-income countries to study the process of scaling education initiatives for impact. This brief reflects on the 15 KIX teams' experiences to shed light on the role of champions in the scaling process. The brief introduces a typology of champions and ways of working with them as it also shares examples of how scaling teams are identifying champions and managing common challenges to the engagement of champions (such as time constraints, personnel turnover, and cultural dynamics).

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

In 2018 the Center for Universal Education (CUE) at Brookings launched a series of Real-time Scaling Labs (RTSL) to generate more evidence and provide practical recommendations on how to expand, deepen, and sustain the impact of education initiatives leading to transformative change in education systems, especially for the most disadvantaged children and youth. The purpose of this report is to look across all six of the RTSL cases to analyze common themes, insights, and lessons learned about the process of scaling as well as interesting divergences, and to offer considerations for others looking to learn from or build on this work. This report is intended for governments, education implementers, donors, and researchers who are interested in collaborative approaches to scaling impact in education. Section 1 examines key scaling lessons that emerged across all of the RTSL cases and analyzes scaling drivers (key levers, forces, or factors critical to making progress toward the scaling goal) at three different units of analysis to explore the way scaling happens at the system, institution, and individual levels. The second section analyzes how the RTSL model worked in practice, its strengths and challenges, and how it contributed to scaling. The report concludes with a set of core recommendations for governments, implementing organizations, donors, and researchers developed in consultation with the RTSL learning community. [This report was written with assistance from Brad Olsen and Katie Portnoy.]

Millions Learning, a project of the Center for Universal Education (CUE) at the Brookings Institution, explores scaling and sustaining effective innovations leading to improved system-wide approaches. CUE is implementing a series of Real-time Scaling Labs (RTSLs), in partnership with local institutions in several countries, to generate evidence and provide practical recommendations around the process of scaling in global education. CUE, in partnership with Youth Impact, launched an RTSL in Botswana focused on the process of implementing, adapting, scaling, and sustaining the Teaching at the Right Level (TaRL) approach in primary schools across the country. The case of TaRL in Botswana offers a rich example of how an innovation developed and refined in one context can be adapted to and strategically expanded in a new location by a local organization in partnership with the government. While TaRL's scaling journey in Botswana is still unfolding, the case provides an opportunity to investigate key facilitating factors, decisions, and barriers that have contributed to and constrained the scaling process to date. This summary, and the report it draws from, explores four key lessons that have emerged from the work of scaling TaRL in Botswana.

Many bacteria share the fascinating ability to sense Earth's magnetic field -- a process known as magnetotaxis. These bacteria synthesize magnetic nanoparticles, called magnetosomes, within their own cell body and arrange them to form a linear magnetic chain. The chain, which behaves like a compass needle, aligns the microorganisms with the geomagnetic field. Here, we measure the magnetic hysteresis of an individual bacterium of the species Magnetospirillum gryphiswaldense via ultrasensitive torque magnetometry. These measurements, in combination with transmission electron microscopy and micromagnetic simulations, reveal the magnetic configurations of the magnetosomes, their progression as a function of applied field, as well as the total remanent magnetic moment and effective magnetic anisotropy of a chain within a single bacterium. Knowledge of magnetic properties is crucial both for understanding the mechanisms behind magnetotaxis and for the design of systems exploiting magnetotactic bacteria in biomedical applications., Comment: 8 pages, 4 figures, 5-page supplemental material

Fast neutron beams are of relevance for many scientific and industrial applications. This paper explores fast neutron production using a TANDEM accelerator at the Legnaro National Laboratories, via an energetic ion beam (90 MeV $^{14}N$) onto a lithium target. The high energy models for nuclear collision of FLUKA foresee large neutron yields for reactions of this kind. The experiment aimed at validating the expected neutron yields from FLUKA simulations, using two separate and independent set-ups: one based on the multi-foil activation technique, and the other on the time of flight technique, by using liquid scintillator detectors. The results of the experiment show clear agreement of the measured spectra with the FLUKA simulations, both in the shape and the magnitude of the neutron flux at the measured positions. The neutron spectrum is centered around the 8 MeV range with mild tails, and a maximum neutron energy spanning up to 50 MeV. These advantageous results provide a starting point in the development of fast neutron beams based on high energy ion beams from medium-sized accelerator facilities.

The Center for Universal Education (CUE) at Brookings has been investigating efforts to scale and sustain evidence-based initiatives leading to large-scale improvements in children's learning. CUE has been implementing a series of collaborative action research initiatives called Real-time Scaling Labs (RTSL), in partnership with local institutions in several countries. The goal of these RTSLs is to generate evidence and provide practical recommendations around the process of scaling in global education--encouraging a stronger link between research and practice. These summary findings and the accompanying full report (ED626286) focus on one of the scaling labs in Jordan -- launched in 2019 in collaboration with the Central Bank of Jordan (CBJ), the Ministry of Education, and Jordanian nonprofit organization INJAZ. The lab focused on this partnership-led process of implementing, adapting, and scaling the National Financial Education Program (FEP) in grades 7-12 across the country. While the lab has focused on the experience of FEP to date, it serves as a case study into larger questions of how an evidence-based initiative can achieve progress toward national sustainable scale, with lessons that are transferable beyond FEP and Jordan. [This summary was written with Jenny Perlman Robinson, Maya Elliott, and Omar Qargha.]

With the rapid evolution and diversification of Internet applications, their communication-quality criteria are continuously evolving. To globally optimize communication quality, the Internet's control plane thus needs to optimize inter-domain paths on diverse criteria, and should provide flexibility for adding new criteria or modifying existing ones. However, existing inter-domain routing protocols and proposals satisfy these requirements at best to a limited degree. We propose IREC, an inter-domain routing architecture that enables multi-criteria path optimization with extensible criteria through parallel execution and real-time addition of independent routing algorithms, together with the possibility for end domains to express their desired criteria to the control plane. We show IREC's viability by implementing it on a global testbed, and use simulations on a realistic Internet topology to demonstrate IREC's potential for path optimization in real-world deployments.

In this paper we prove motivic versions of the Langlands-Shelstad Fundamental Lemma and Ng\^o's Geometric Stabilization. To achieve this, we follow the strategy from the recent proof by Groechenig, Wyss and Ziegler which avoided the use of perverse sheaves using instead $p$-adic integration and Tate duality. We make a key use of a construction of Denef and Loeser which assigns a virtual motive to any definable set in the theory of pseudo-finite fields., Comment: 56 pages

The current Internet lacks quality-of-service guarantees for real-time applications like video calls and gaming, cloud-based systems, financial transactions, telesurgery, and other remote applications that benefit from reliable communication. To address this problem, this paper introduces Hummingbird: a novel, lightweight bandwidth-reservation system that provides fine-grained inter-domain reservations for end hosts and introduces several improvements over previous designs. Hummingbird enables flexible and composable reservations with end-to-end guarantees, and addresses an often overlooked, but crucial, aspect of bandwidth reservation systems: incentivization of network providers. Hummingbird represents bandwidth reservations as tradeable assets which allows markets to emerge that ensure fair and efficient resource allocation and encourage deployment by remunerating providers. This incentivization is facilitated by decoupling reservations from network identities, which enables novel control-plane mechanisms and allows us to design a control plane based on smart contracts. Hummingbird also provides an efficient reservation data plane which streamlines the processing on routers and thus simplifies the implementation, deployment, and traffic policing while maintaining robust security properties., Comment: 20 pages, 15 figures

A spin-photon interface should operate with both coherent photons and a coherent spin to enable cluster-state generation and entanglement distribution. In high-quality devices, self-assembled GaAs quantum dots are near-perfect emitters of on-demand coherent photons. However, the spin rapidly decoheres via the magnetic noise arising from the host nuclei. Here, we address this drawback by implementing an all-optical nuclear-spin cooling scheme on a GaAs quantum dot. The electron-spin coherence time increases 156-fold from $T_2^*$ = 3.9 ns to 0.608 $\mu$s. The cooling scheme depends on a non-collinear term in the hyperfine interaction. The results show that such a term is present even though the strain is low and no external stress is applied. Our work highlights the potential of optically-active GaAs quantum dots as fast, highly coherent spin-photon interfaces.

Focused-electron-beam-induced deposition is a promising technique for patterning nanomagnets for spin qubit control in a single step. We fabricate cobalt nanomagnets in such a process, obtaining cobalt contents and saturation magnetizations comparable to or higher than those typically obtained using electron-beam lithography. We characterize the nanomagnets using transmission electron microscopy and image their stray magnetic field using scanning NV magnetometry, finding good agreement with micromagnetic simulations. The magnetometry reveals the presence of magnetic domains and halo side-deposits, which are common for this fabrication technique. Finally, we estimate dephasing times for electron spin qubits in the presence of disordered stray fields due to these side-deposits.

A novel algorithm for the computation of the quadratic numerical range is presented and exemplified yielding much better results in less time compared to the random vector sampling method. Furthermore, a bound on the probability for the random vector sampling method to produce a point exceeding a neighborhood of the expectation value in dependence on norm and size of the matrix is given.

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to ensure that analyses meet their intended goals. The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative -- a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis., Comment: 26 pages, 4 figures

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs; it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases., Comment: 51 pages, 2 figures, 4 tables

In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents end-user applications requiring specific router capabilities from reaching their full potential. Moreover, the inability to influence the traffic's forwarding path results in applications communicating over undesired routes, while alternative paths with more desirable properties remain unusable. In this work, we present FABRID, a system that enables applications to forward traffic flexibly, potentially on multiple paths selected to comply with user-defined preferences, where information about forwarding devices is exposed and transparently attested by autonomous systems (ASes). The granularity of this information is chosen by each AS individually, protecting them from leaking sensitive network details, while the secrecy and authenticity of preferences embedded within the users' packets are protected through efficient cryptographic operations. We show the viability of FABRID by deploying it on a global SCION network test bed, and we demonstrate high throughput on commodity hardware.

Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction mechanism have been limited due to complexity involved in the FFE process. Data-driven machine learning (ML) models effectively account for this complexity, but the model training requires considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 +/- 0.05 and an average RMSE of 12.1% +/- 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 +/- 0.05 and an RMSE of 14.3% +/- 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes.

Raman spectroscopy is a powerful technique to characterize materials. It probes non-destructively chemical composition, crystallinity, defects, strain and coupling phenomena. However, the Raman response of surfaces or thin films is often weak and obscured by dominant bulk signals. Here we overcome this limitation by placing a transferable porous gold membrane (PAuM) on top of the surface of interest. Slot-like nanopores in the membrane act as plasmonic slot antennas and enhance the Raman response of the surface or thin film underneath. Simultaneously, the PAuM suppresses the penetration of the excitation laser into the bulk, efficiently blocking the bulk Raman signal. Using graphene as a model surface, we show that these two simultaneous effects lead to an increase in the surface-to-bulk Raman signal ratio by three orders of magnitude. We find that 90% of the Raman enhancement occurs within the top 2.5 of the material, demonstrating truly surface-sensitive Raman scattering. To validate our approach, we analyze the surface of a LaNiO3 thin film. We observe a Raman mode splitting for the LaNiO3 surface-layer, which is spectroscopic evidence that the surface structure differs from the bulk. This result underpins that PAuM give direct access to Raman signatures of surfaces and their structural properties.

We consider differential operators $A$ that can be represented by means of a so-called closure relation in terms of a simpler operator $A_{\operatorname{ext}}$ defined on a larger space. We analyze how the spectral properties of $A$ and $A_{\operatorname{ext}}$ are related and give sufficient conditions for exponential stability of the semigroup generated by $A$ in terms of the semigroup generated by $A_{\operatorname{ext}}$. As applications we study the long-term behaviour of a coupled wave-heat system on an interval, parabolic equations on bounded domains that are coupled by matrix valued potentials, and of linear infinite-dimensional port-Hamiltonian systems with dissipation on an interval., Comment: 31 pages