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The EdResearch for Action "Design Principles Series" focuses on a single program or practice that has been proven to have positive effects on student outcomes. Authors -- leading experts from across the field of education research -- look across many high-quality studies of similar programs to identify the components and conditions that are key to its effective implementation. The "Design Principles Series" helps practitioners adapt and successfully implement an evidence-based program to meet the needs of their target population. This brief is an update of the previous version published in 2021. It incorporates new research on effective design and implementation components in high-impact tutoring programs. [This brief was prepared in collaboration with the University of Virginia, School of Education and Human Development and National Student Support Accelerator.]

Budget constraints and limited supplies of local tutors have caused many K-12 school districts to pivot from individual tutoring in-person toward small-group tutoring online to expand access to personalized instruction. We conduct a field experiment to explore the effect of increasing student-tutor ratios on middle school students' math achievement and growth during an online tutoring program. We leverage a novel feature of the program where tutors often taught individual and small-group tutoring sessions, allowing them to directly compare their experiences across these settings. Both experimental estimates and tutor survey responses suggest 1:1 tutoring is more effective than 3:1 tutoring online. Tutoring small groups in an online format presents additional challenges for personalizing instruction, developing relationships, fostering participation, and managing student behavior.

The purpose of this study was to examine the relationship between students' engagement with Legends of Learning's Math Basecamp (MBC) and students' math achievement at the elementary school level in Rialto Unified School District (RUSD) in California. Researchers examined the association between MBC usage and student achievement scores on the Smarter Balanced Mathematics assessment for 3,273 students in grades 3-5 using ordinary least squares (OLS) regression. Additionally, researchers used propensity score matching to determine a causal relationship between MBC usage and math scores for 1,608 students. OLS regression results reveal that as the number of sessions of Legends of Learning's Math Basecamp increases, achievement on California's Smarter Balanced Mathematics assessment increases significantly. Further, propensity score matching results indicate that completing at least 24 sessions of MBC significantly increases student scores on the Smarter Balanced Mathematics assessment. Further research is needed to confirm a causal relationship between participation in Math Basecamp and math achievement, which could be done in a Randomized Controlled Trial (RCT) or a robust quasi-experimental design (QED). Expanding future studies to include additional populations and settings, as well as additional analytic approaches can confirm the robustness of these findings and the generalizability of Math Basecamp's impact. [This report was prepared for Legends of Learning.]

Most Americans today need some education beyond high school to secure a well-paying job, and higher education is a key factor in social and economic mobility. But higher education is not accessible to all students. To further equalize opportunity, states must understand options for improving the way they fund higher education. America's public K-12 system has been grappling with similar funding disparities for decades -- challenges that offer important lessons for postsecondary funding. Bellwether's analysis, "Strengthening State Higher Education Funding: Lessons Learned From K-12," identifies insights and lessons from K-12 finance policy that may be instructive as states rework how they fund higher education. This report explores four topics and associated questions that are also relevant across K-12 education: (1) Equity: How can states distribute funding to students and public IHEs with the greatest needs?; (2) Adequacy: How much funding is enough to achieve the desired outcomes?; (3) Cost Sharing: How can local governments and states share the cost of public higher education?; and (4) Stability: How can public IHEs secure consistent and predictable funding?

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, directly applying them to surgery often yields suboptimal results. Fully fine-tuning on limited surgical data can cause overfitting and catastrophic forgetting, compromising model robustness and generalization. Although Low-Rank Adaptation (LoRA) addresses some adaptation issues, its uniform parameter distribution neglects the inherent feature hierarchy, where earlier layers, learning more general features, require more parameters than later ones. To tackle this issue, we introduce Depth Anything in Robotic Endoscopic Surgery (DARES), a novel approach that employs a new adaptation technique, Vector Low-Rank Adaptation (Vector-LoRA) on the DAM V2 to perform self-supervised monocular depth estimation in RAS scenes. To enhance learning efficiency, we introduce Vector-LoRA by integrating more parameters in earlier layers and gradually decreasing parameters in later layers. We also design a reprojection loss based on the multi-scale SSIM error to enhance depth perception by better tailoring the foundation model to the specific requirements of the surgical environment. The proposed method is validated on the SCARED dataset and demonstrates superior performance over recent state-of-the-art self-supervised monocular depth estimation techniques, achieving an improvement of 13.3% in the absolute relative error metric. The code and pre-trained weights are available at https://github.com/mobarakol/DARES., Comment: 11 pages

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

We present a deep X-Shooter rest-frame UV to optical spectral analysis of the heavily reddened quasar, ULASJ2315+043 at z=2.566, known to reside in a major-merger host galaxy. The rest-frame optical is best-fit by a dust-reddened quasar E(B-V)_QSO = 1.55 with black-hole mass log10(Hbeta, MBH [M_sol]) = 10.26 +\- 0.05, bolometric luminosity L_Bol = 10^48.16 erg s^-1 and Eddington-scaled accretion rate log10(\lambda_Edd) = -0.19. We find remarkable similarities between ULASJ2315+043 and the high-redshift Little Red Dots (LRDs). The rest-frame UV cannot be explained by a dusty quasar component alone and requires an additional blue component consistent with either a star-forming host galaxy or scattered AGN light. We detect broad high-ionisation emission lines in the rest-UV, supporting the scattered light interpretation for the UV excess. The scattering fraction represents just 0.05% of the total luminosity of ULASJ2315+043. Analysis of the mid infra-red SED suggests an absence of hot dust on torus-scales similar to what is observed for LRDs. The obscuring medium is therefore likely on galaxy scales. We detect narrow, blueshifted associated absorption line systems in CIV, NV, SiIV and SiIII. There is evidence for significant high-velocity (>1000 km s^-1) outflows in both the broad and narrow line regions as traced by CIV and [OIII] emission. The kinetic power of the [OIII] wind is e_ion = 10^44.61 erg s^-1 ~ 0.001 L_Bol. ULASJ2315+043 is likely in an important transition phase where star formation, black-hole accretion and multi-phase gas flows are simultaneously occurring., Comment: Accepted to MNRAS: 13th Aug 2024. 18 pages, 12 figures

The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median $\text{T}_1$ by $\sim22\%$ ($p=0.002$), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch prior to niobium deposition also show $\sim33\%$ lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials' differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS.

Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongly affected by the choice of the numerical representation for describing the composition, structure and chemical elements. Structure controls the properties, but often only the composition of a candidate material is available. Existing elemental descriptors lack direct access to structural insights such as the coordination geometry of an element. In this study, we introduce Local Environment-induced Atomic Features, LEAFs, which incorporate information about the statistically preferred local coordination geometry for atoms in crystal structure into descriptors for chemical elements, enabling the modelling of materials solely as compositions without requiring knowledge of their crystal structure. In the crystal structure, each atomic site can be described by similarity to common local structural motifs; by aggregating these features of similarity from the experimentally verified crystal structures of inorganic materials, LEAFs formulate a set of descriptors for chemical elements and compositions. The direct connection of LEAFs to the local coordination geometry enables the analysis of ML model property predictions, linking compositions to the underlying structure-property relationships. We demonstrate the versatility of LEAFs in structure-informed property predictions for compositions, mapping of chemical space in structural terms, and prioritising elemental substitutions. Based on the latter for predicting crystal structures of binary ionic compounds, LEAFs achieve the state-of-the-art accuracy of 86 per cent. These results suggest that the structurally informed description of chemical elements and compositions developed in this work can effectively guide synthetic efforts in discovering new materials., Comment: 10 pages, 4 figures, supplementary information

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

The Hartmann test is a method used to measure the wavefront error in a focal optical system, wherein a mask with a pattern of small holes is placed at the system's aperture stop. By taking an image at a defocused plane, the differences between the ideal and real positions of the reimaged holes (called the transverse ray aberrations) can be measured, which can then be used to estimate the wavefront error. However, the Hartmann test is usually used with an on-axis field. In this paper, we present a wavefront sensing method which generalizes the classical Hartmann test for off-axis field angles and arbitrary reference wavefronts. Our method involves taking images at two defocused planes, and then using the real reimaged hole positions on both planes to estimate the trajectories of rays from the system's exit pupil, at which the reference wavefront is situated. We then propagate the rays forward from the reference wavefront to one of the two defocused planes, in order to find the ideal reimaged hole positions, from which we can compute transverse ray aberrations. We derive and solve a pair of nonlinear partial differential equations relating transverse ray aberrations to wavefront error, using Zernike decomposition and nonlinear least squares. Our method has been verified on simulated data from the 7-lens f/2.25 red camera system of the GMT-Consortium Large Earth Finder (G-CLEF), a high resolution optical echelle spectrograph which will be a first light instrument for the Giant Magellan Telescope (GMT)., Comment: 18 pages, 23 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Future millimeter wavelength experiments aim to both increase aperture diameters and broaden bandwidths to increase the sensitivity of the receivers. These changes produce a challenging anti-reflection (AR) design problem for refracting and transmissive optics. The higher frequency plastic optics require consistently thin polymer coats across a wide area, while wider bandwidths require multilayer designs. We present multilayer AR coats for plastic optics of the high frequency BICEP Array receiver (200-300 GHz) utilizing an expanded polytetrafluoroethylene (ePTFE) membrane, layered and compressively heat-bonded to itself. This process allows for a range of densities (from 0.3g/cc to 1g/cc) and thicknesses (>0.05mm) over a wide radius (33cm), opening the parameter space of potential AR coats in interesting directions. The layered ePTFE membrane has been combined with other polymer layers to produce band average reflections between 0.2% and 0.6% on high density polyethylene and a thin high modulus polyethylene window, respectively.

This paper presents the CHiME-8 DASR challenge which carries on from the previous edition CHiME-7 DASR (C7DASR) and the past CHiME-6 challenge. It focuses on joint multi-channel distant speech recognition (DASR) and diarization with one or more, possibly heterogeneous, devices. The main goal is to spur research towards meeting transcription approaches that can generalize across arbitrary number of speakers, diverse settings (formal vs. informal conversations), meeting duration, wide-variety of acoustic scenarios and different recording configurations. Novelties with respect to C7DASR include: i) the addition of NOTSOFAR-1, an additional office/corporate meeting scenario, ii) a manually corrected Mixer 6 development set, iii) a new track in which we allow the use of large-language models (LLM) iv) a jury award mechanism to encourage participants to explore also more practical and innovative solutions. To lower the entry barrier for participants, we provide a standalone toolkit for downloading and preparing such datasets as well as performing text normalization and scoring their submissions. Furthermore, this year we also provide two baseline systems, one directly inherited from C7DASR and based on ESPnet and another one developed on NeMo and based on NeMo team submission in last year C7DASR. Baseline system results suggest that the addition of the NOTSOFAR-1 scenario significantly increases the task's difficulty due to its high number of speakers and very short duration.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources., Comment: 17 pages, 10 figures. Includes Supplemental Information

We construct three dimensional non-semisimple topological field theories from the unrolled quantum group of the Lie superalgebra $\mathfrak{osp}(1 \vert 2)$. More precisely, the quantum group depends on a root of unity $q=e^{\frac{2 \pi \sqrt{-1}}{r}}$, where $r$ is a positive integer greater than $2$, and the construction applies when $r$ is not congruent to $4$ modulo $8$. The algebraic result which underlies the construction is the existence of a relative modular structure on the non-finite, non-semisimple category of weight modules for the quantum group. We prove a Verlinde formula which allows for the computation of dimensions and Euler characteristics of topological field theory state spaces of unmarked surfaces. When $r$ is congruent to $\pm 1$ or $\pm 2$ modulo $8$, we relate the resulting $3$-manifold invariants with physicists' $\widehat{Z}$-invariants associated to $\mathfrak{osp}(1 \vert 2)$. Finally, we establish a relation between $\widehat{Z}$-invariants associated to $\mathfrak{sl}(2)$ and $\mathfrak{osp}(1 \vert 2)$ which was conjectured in the physics literature., Comment: 38 pages

Haystack and Owens Valley Radio Observatory (OVRO) observations recently revealed strong sinusoidal total flux density variations that maintained coherence between 1975 and 2021 in the blazar PKS 2131-021 ($z=1.283)$. This was interpreted as possible evidence of a supermassive black hole binary (SMBHB). Extended observations through 2023 show coherence over 47.9~years, with an observed period $P_\textrm{15 GHz}=(1739.3 \pm 1.2) \, {\rm days}$. We reject, with $p$-value = $5.3 \times 10^{-7}$, the hypothesis that the variations are due to random fluctuations in the red noise tail of the power spectral density. There is clearly a constant-period physical phenomenon in PKS 2131-021 producing coherent intermittent sinusoidal flux density variations. We find the coherent sinusoidal intensity variations extend from below 2.7 GHz to optical frequencies, from which we derive an observed period $P_\textrm{optical}=(1764 \pm 36)$ days. Across this broad frequency range there is a monotonic phase shift in the sinusoidal variations with frequency. The same coherent periodicity is possibly also observed at $\gamma$-ray energies. The importance of well-vetted SMBHB candidates to searches for gravitational waves is pointed out. We estimate the fraction of blazars that are SMBHB candidates to be $>1$ in 100. Thus monitoring programs covering tens of thousands of blazars could discover hundreds of SMBHB candidates., Comment: 23 pages, 17 figures, 5 tables, submitted to ApJ

Finite element simulations are an enticing tool to evaluate heart valve function in healthy and diseased patients; however, patient-specific simulations derived from 3D echocardiography are hampered by several technical challenges. In this work, we present an open-source method to enforce matching between finite element simulations and in vivo image-derived heart valve geometry in the absence of patient-specific material properties, leaflet thickness, and chordae tendineae structures. We evaluate FEBio Finite Element Simulations with Shape Enforcement (FINESSE) using three synthetic test cases covering a wide range of model complexity. Our results suggest that FINESSE can be used to not only enforce finite element simulations to match an image-derived surface, but to also estimate the first principal leaflet strains within +/- 0.03 strain. Key FINESSE considerations include: (i) appropriately defining the user-defined penalty, (ii) omitting the leaflet commissures to improve simulation convergence, and (iii) emulating the chordae tendineae behavior via prescribed leaflet free edge motion or a chordae emulating force. We then use FINESSE to estimate the in vivo valve behavior and leaflet strains for three pediatric patients. In all three cases, FINESSE successfully matched the target surface with median errors similar to or less than the smallest voxel dimension. Further analysis revealed valve-specific findings, such as the tricuspid valve leaflet strains of a 2-day old patient with HLHS being larger than those of two 13-year old patients. The development of this open source pipeline will enable future studies to begin linking in vivo leaflet mechanics with patient outcomes, Comment: 31 pages, 8 figures

The observation of neutron stars enables the otherwise impossible study of fundamental physical processes. The timing of binary radio pulsars is particularly powerful, as it enables precise characterization of their (three-dimensional) positions and orbits. PSR~J0437$-$4715 is an important millisecond pulsar for timing array experiments and is also a primary target for the Neutron Star Interior Composition Explorer (NICER). The main aim of the NICER mission is to constrain the neutron star equation of state by inferring the compactness ($M_p/R$) of the star. Direct measurements of the mass $M_p$ from pulsar timing therefore substantially improve constraints on the radius $R$ and the equation of state. Here we use observations spanning 26 years from Murriyang, the 64-m Parkes radio telescope, to improve the timing model for this pulsar. Among the new precise measurements are the pulsar mass $M_p=1.418\pm 0.044$ $M_{\odot}$, distance $D=156.96 \pm 0.11$ pc, and orbital inclination angle $i=137.506 \pm 0.016^\circ$, which can be used to inform the X-ray pulse profile models inferred from NICER observations. We demonstrate that these results are consistent between multiple data sets from the Parkes Pulsar Timing Array (PPTA), each modeled with different noise assumptions. Using the longest available PPTA data set, we measure an apparent second derivative of the pulsar spin frequency and discuss how this can be explained either by kinematic effects due to the proper motion and radial velocity of the pulsar or excess low-frequency noise such as a gravitational-wave background., Comment: 13 pages, 3 figures, accepted for publication in Astrophysical Journal Letters

The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-class classification. Building on Ilyas et al. (2019), we investigate the vulnerability of lightweight one-class classifiers to adversarial attacks and possible reasons for it. Our results show that lightweight one-class classifiers learn features that are not robust (e.g. texture) under stronger attacks. However, unlike in multi-class classification (Ilyas et al., 2019), these non-robust features are not always useful for the one-class task, suggesting that learning these unpredictive and non-robust features is an unwanted consequence of training., Comment: CVPR Visual and Anomaly Detection (VAND) Workshop 2024

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

We perform twenty experiments on an acoustically-forced laminar premixed Bunsen flame and assimilate high-speed footage of the natural emission into a physics-based model containing seven parameters. The experimental rig is a ducted Bunsen flame supplied by a mixture of methane and ethylene. A high-speed camera captures the natural emission of the flame, from which we extract the position of the flame front. We use Bayesian inference to combine this experimental data with our prior knowledge of this flame's behaviour. This prior knowledge is expressed through (i) a model of the kinematics of a flame front moving through a model of the perturbed velocity field, and (ii) a priori estimates of the parameters of the above model with quantified uncertainties. We find the most probable a posteriori model parameters using Bayesian parameter inference, and quantify their uncertainties using Laplace's method combined with first-order adjoint methods. This is substantially cheaper than other common Bayesian inference frameworks, such as Markov Chain Monte Carlo. This process results in a quantitatively-accurate physics-based reduced-order model of the acoustically forced Bunsen flame for injection velocities ranging from 1.75 m/s to 2.99 m/s and equivalence ratio values ranging from 1.26 to 1.47, using seven parameters. We use this model to evaluate the heat release rate between experimental snapshots, to extrapolate to different experimental conditions, and to calculate the flame transfer function and its uncertainty for all the flames. Since the proposed model relies on only seven parameters, it can be trained with little data and successfully extrapolates beyond the training dataset. Matlab code is provided so that the reader can apply it to assimilate further flame images into the model.

When running at scale, modern scientific workflows require middleware to handle allocated resources, distribute computing payloads and guarantee a resilient execution. While individual steps might not require sophisticated control methods, bringing them together as a whole workflow requires advanced management mechanisms. In this work, we used RADICAL-EnTK (Ensemble Toolkit) - one of the SDK components of the ECP ExaWorks project - to implement and execute the novel Exascale Additive Manufacturing (ExaAM) workflows on up to 8000 compute nodes of the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. EnTK allowed us to address challenges such as varying resource requirements (e.g., heterogeneity, size, and runtime), different execution environment per workflow, and fault tolerance. And a native portability feature of the developed EnTK applications allowed us to adjust these applications for Frontier runs promptly, while ensuring an expected level of resource utilization (up to 90%).

Upward mobility is core to the American dream yet remains unobtainable for many Americans. And although education is widely viewed as a key lever to support students' upward mobility, there is too little actionable information about how schools promote lifelong success. As a result, PK-12 education tends to focus on conventional indicators of success, such as standardized test scores, that--though important--are unlikely to capture the full set of skills that drive upward mobility. This report reviews the available evidence on the direct links between PK-12 education and economic mobility. We find that the current research offers little guidance about which skills and competencies in PK-12 education are most important for economic success. In part, this is because few studies connect students' PK-12 experiences to their economic success as adults. Additionally, the existing research defines success narrowly in terms of wages, ignoring other dimensions like finding dignity in one's work and a sense of autonomy and belonging in one's community. Furthermore, current research does not adequately address the relationship between individual and contextual factors that affect children's readiness to learn, how school systems function, and how success in school translates into longer-term success. Some research examines how students across different groups (e.g., by race or ethnicity and gender) experience the PK-12 education system, but few explore why patterns differ through a lens that considers historical and contemporary forms of oppression such as racism and sexism. We argue that for schools and education policymakers to best equip all students to attain lifelong success, a new generation of research at the intersection of PK-12 education and economic mobility is necessary. This research should seek to understand how the PK-12 environment can foster skill development that drives upward mobility, how measures of those skills function across different people and places, and how student-, school-, and system-level measures are all shaped by broader factors both in and beyond schools.

This report examines state portraits of a graduate and how state strategies for development and implementation can support future readiness. It also provides recommendations for state education agencies to consider to lift up the importance and visibility of these portraits. Several states have begun the process of developing a "portrait" or "profile of a graduate," which is a holistic look at the skills and competencies students need to master to thrive in work, postsecondary educational opportunities, community, and their personal lives. To better understand how states are utilizing the portrait of a graduate to prepare students for career and college, we conducted a scan of all 50 states to see which states had developed state-wide portraits of a graduate or visions of a high school graduate. The results inform the findings and recommendations of this report, including the skills states are highlighting as essential for students' future success and how states are making this vision actionable in service of cultivating future-ready graduates. As this review indicates, states are keenly aware that today's students will be called on to meet the growing challenges of the 21st century, as among the top skills are critical thinking and problem- solving, social awareness skills, and being an active citizen. Moreover, states deeply understand it is necessary to develop students' social and emotional skills and competencies. This report delves deeper into how states are implementing their portrait of a graduate to help students meet these expectations.

In the United States, K-12 school finance is largely controlled by the states. Every year, hundreds of billions of dollars in public funds are distributed based on 51 different configurations of formulas, rules, and regulations to over 13,000 districts that vary in terms of the students they serve, their ability to raise revenue locally, and many other characteristics. Good school finance systems compensate for factors states cannot control (e.g., student poverty, labor costs) using levers that they can control (e.g., driving funding to districts that need it most). The authors have devised a framework that evaluates states based largely on how well they accomplish this balance. Each state's funding is assessed while accounting directly for the students and communities served by its public schools. In this sixth edition of the annual report, the K-12 school finance systems of all 50 states and the District of Columbia is evaluated. The latest year of data presented is the 2020-21 school year. [This report was co-prepared by University of Miami School of Education and Human Development.]

DNA origami consists of a long scaffold strand and short staple strands that self-assemble into a target 2D or 3D shape. It is a widely used construct in nucleic acid nanotechnology, offering a cost-effective way to design and create diverse nanoscale shapes. With promising applications in areas such as nanofabrication, diagnostics, and therapeutics, DNA origami has become a key tool in the bionanotechnology field. Simulations of these structures can offer insight into their shape and function, thus speeding up and simplifying the design process. However, simulating these structures, often comprising thousands of base pairs, poses challenges due to their large size. OxDNA, a coarse-grained model specifically designed for DNA nanotechnology, offers powerful simulation capabilities. Its associated ecosystem of visualization and analysis tools can complement experimental work with in silico characterization. This tutorial provides a general approach to simulating DNA origami structures using the oxDNA ecosystem, tailored for experimentalists looking to integrate computational analysis into their design workflow.

We present a method for automatically producing human-like vocal imitations of sounds: the equivalent of "sketching," but for auditory rather than visual representation. Starting with a simulated model of the human vocal tract, we first try generating vocal imitations by tuning the model's control parameters to make the synthesized vocalization match the target sound in terms of perceptually-salient auditory features. Then, to better match human intuitions, we apply a cognitive theory of communication to take into account how human speakers reason strategically about their listeners. Finally, we show through several experiments and user studies that when we add this type of communicative reasoning to our method, it aligns with human intuitions better than matching auditory features alone does. This observation has broad implications for the study of depiction in computer graphics., Comment: SIGGRAPH Asia 2024

Improving the automatic and timely recognition of construction and demolition waste (C&DW) composition is crucial for enhancing business returns, economic outcomes, and sustainability. Technologies like computer vision, artificial intelligence (AI), robotics, and internet of things (IoT) are increasingly integrated into waste processing to achieve these goals. While deep learning (DL) models show promise in recognising homogeneous C&DW piles, few studies assess their performance with mixed, highly contaminated material in commercial settings. Drawing on extensive experience at a C&DW materials recovery facility (MRF) in Sydney, Australia, we explore the challenges and opportunities in developing an advanced automated mixed C&DW management system. We begin with an overview of the evolution of waste management in the construction industry, highlighting its environmental, economic, and societal impacts. We review various C&DW analysis techniques, concluding that DL-based visual methods are the optimal solution. Additionally, we examine the progression of sensor and camera technologies for C&DW analysis as well as the evolution of DL algorithms focused on object detection and material segmentation. We also discuss C&DW datasets, their curation, and innovative methods for their creation. Finally, we share insights on C&DW visual analysis, addressing technical and commercial challenges, research trends, and future directions for mixed C&DW analysis. This paper aims to improve the efficiency of C&DW management by providing valuable insights for ongoing and future research and development efforts in this critical sector.