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State summative assessments play an important role in measuring student learning and guiding educational improvement efforts, despite their limitations. But there is growing momentum in individual states and nationally to rethink these assessments with an eye toward reducing time spent on testing and increasing the tests' instructional relevance. "Multiple Choices: Weighing Updates to State Summative Assessments" helps policymakers and advocates understand what they may gain and what they may lose when considering potential shifts in assessments. The report examines several of the most common proposed changes -- including reducing test length, matrix sampling, student sampling, grade-band testing, performance assessment, and through-year assessment -- and summarizes the potential gains, losses, and unknowns of each. The report also offers recommendations for state policymakers, federal policymakers, and advocates working to ensure that summative assessments better address the needs of educators, families, and students.

Many college students experience basic needs insecurity, which includes a lack of--or fear of the lack of--access to healthy food, stable housing, reliable transportation, affordable child care, physical and mental health care services, the internet and technology, and other necessities students need to survive and thrive in a postsecondary academic setting. This evaluation report focuses on measuring the extent to which students used basic needs services at select institutions supported by Basic Needs Initiative (BNI) grantees in the 2020-21 and 2021-22 academic years and the impact of using services on students' short-term academic outcomes, including enrollment intensity (credits attempted), credits earned, grade point average, and fall to winter/spring retention. Education Northwest partnered with four BNI grantees on this report--Arkansas Community Colleges, Auburn University's Hunger Solutions Institute, John Burton Advocates for Youth, and United Way of King County--to access student-level data on the use of basic needs services, student demographics, and academic outcomes from 20 postsecondary institutions in Arkansas, Alabama, California, and Washington. This report has three key findings with implications for practice: (1) Collecting data on students' use of basic needs services and linking it to student demographic and outcome data was a new activity for many of the institutions included in this report, and the basic needs services data had several limitations; (2) Very few students in our sample used basic needs services, suggesting a large gap between students' reported needs and their access to services; and (3) Access to basic needs services had a positive impact on the number of credits students attempted and earned was positively related with retention.

High-quality, stable child care and early education (CCEE) can have lasting, positive impacts on children. However, there are ongoing challenges in recruiting, supporting, and retaining a qualified, stable CCEE workforce that provides high-quality care. CCEE educators typically have low levels of compensation; limited opportunities for education, training, and professional development; inconsistent working conditions; and high levels of stress and burnout. There are also high rates of job turnover, which can diminish the quality of care by straining remaining educators. The COVID-19 pandemic has exacerbated these issues. While several states and localities are taking steps to build and stabilize their CCEE workforce, important questions remain about how to best strengthen the workforce to meet the needs of children and families, particularly those who experience poverty or who are racially, ethnically, and linguistically diverse. The "Building and Sustaining the Child Care and Early Education Workforce" (BASE) project team completed a literature review and an environmental scan that were designed to identify and document existing knowledge about the CCEE workforce and develop strategies to strengthen it. The literature review synthesized research on factors that shape CCEE workforce dynamics, defined here as how educators enter, advance in, and exit out of different roles, settings, and types of care. The literature review predominantly focused on studies conducted between 2017 and 2021 and examined the effectiveness of strategies that were implemented to build a sustained, qualified workforce. The environmental scan identified and reviewed the range of strategies currently being used across the country to build, advance, and sustain the CCEE workforce. The team also completed a data scan that assessed the strengths and weaknesses of several potential data sources that could be used to examine workforce dynamics and address key gaps in existing research.

Child care and early education (CCEE) are essential resources for families and children across the country. Yet qualified educators are choosing to leave their positions, and young professionals are choosing not to enter the field. This instability has been a long-lasting issue and is thought to be driven largely by structural and systemic forces that shape the everyday experiences of the CCEE workforce. These issues have been exacerbated by the COVID-19 pandemic. This report lays out a conceptual framework to serve as a guide for researchers, policymakers, and administrators who are conducting research on the CCEE workforce and who are designing and implementing strategies to improve entry, retention, turnover, and advancement of the workforce. This report is also intended to inform how the field can work to build and sustain this critical workforce. The conceptual framework consists of three main components that are intended to provide a clear understanding of factors that can disrupt the stability of the CCEE workforce pipeline. By articulating the main components, the conceptual framework encourages researchers, policymakers, and administrators to consider a range of multilevel influences on workforce dynamics when designing strategies and when evaluating whether strategies are working as intended. This framework also aims to underscore that the individuals who make up the workforce--many of whom have been marginalized due to race, ethnicity, social class, and gender--interact with broader social, political, and cultural contexts that shape economic and educational policies, structures, and opportunities.

This document provides technical detail and supporting data for the research findings discussed in 'The student journey in VET: the many faces of completion and non-completion'. The analysis in this technical paper explores: (1) an approach to identifying VET subject enrolment activity that serves a compliance or regulatory purpose; (2) variability in completion rates across VET qualifications, and associated differences in patterns of subject enrolments and outcomes; (3) different indicators of student outcomes in VET, including program completion, subject completion, and movement to subsequent VET; (4) student training pathways exploring the extent to which students undertook programs, stand-alone subjects, or a combination of the two, and how this training choice evolved over time; and (5) student training pathways exploring the extent to which students went on to enrol in a program at a higher, lower, or the same level of educations, and how these pathways compared for students who did and did not complete their initial program.

This research explores the many faces of course completion and non-completion in vocational education and training. By tracking student enrolments via the unique student identifier in the National VET Provider Collection, we gain a greater understanding of how individuals mix and match training types and their movement through VET, enabling identification of a broader range of training successes. One such measure of success is the completion of all enrolled subjects despite qualification non-completion. Using this measure, an additional 19.8% of 2016-commencing students had a successful outcome with VET, on top of the 49.5% who successfully completed their program. The analysis also showed that students mix and match various elements of training by enrolling in and completing subjects, either alone or in addition to program enrolments, and that compliance or regulatory-related subject enrolments impact training outcomes in complex ways.

Multilingual learners (MLs) are students whose primary language is not English and are eligible for English language development services through the Transitional Bilingual Instruction Program (TBIP). Eligible MLs receive TBIP services until they become proficient in English. During the 2022-23 school year, 149,357 students were identified as multilingual learners.

When a pulse of light traverses a material, it incurs a time delay referred to as the group delay. Should the group delay experienced by photons be attributed to the time they spend as atomic excitations? However reasonable this connection may seem, it appears problematic when the frequency of the light is close to the atomic resonance, as the group delay becomes negative in this regime. To address this question, we use the cross-Kerr effect to probe the degree of atomic excitation caused by a resonant transmitted photon, by measuring the phase shift on a separate beam that is weak and off-resonant. Our results, over a range of pulse durations and optical depths, are consistent with the recent theoretical prediction that the mean atomic excitation time caused by a transmitted photon (as measured via the time integral of the observed phase shift) equals the group delay experienced by the light. Specifically, we measure mean atomic excitation times ranging from $(-0.82\pm 0.31) \tau_0$ for the most narrowband pulse to $(0.54\pm 0.28) \tau_0$ for the most broadband pulse, where $\tau_0$ is the non-post-selected excitation time, given by the scattering (absorption) probability multiplied by the atomic lifetime $\tau_{\rm sp}$. These results suggest that negative values taken by times such as the group delay have more physical significance than has generally been appreciated.

Natural distribution shift causes a deterioration in the perception performance of convolutional neural networks (CNNs). This comprehensive analysis for real-world traffic data addresses: 1) investigating the effect of natural distribution shift and weather augmentations on both detection quality and confidence estimation, 2) evaluating model performance for both classification and object localization, and 3) benchmarking two common uncertainty quantification methods - Ensembles and different variants of Monte-Carlo (MC) Dropout - under natural and close-to-natural distribution shift. For this purpose, a novel dataset has been curated from publicly available autonomous driving datasets. The in-distribution (ID) data is based on cutouts of a single object, for which both class and bounding box annotations are available. The six distribution-shift datasets cover adverse weather scenarios, simulated rain and fog, corner cases, and out-of-distribution data. A granular analysis of CNNs under distribution shift allows to quantize the impact of different types of shifts on both, task performance and confidence estimation: ConvNeXt-Tiny is more robust than EfficientNet-B0; heavy rain degrades classification stronger than localization, contrary to heavy fog; integrating MC-Dropout into selected layers only has the potential to enhance task performance and confidence estimation, whereby the identification of these layers depends on the type of distribution shift and the considered task., Comment: This preprint has not undergone any post-submission improvements or corrections

The data that underlies automated methods in computer vision and machine learning, such as image retrieval and fine-grained recognition, often comes from crowdsourcing. In contexts that rely on the intrinsic motivation of users, we seek to understand how the application design affects a user's willingness to contribute and the quantity and quality of the data they capture. In this project, we designed three versions of a camera-based mobile crowdsourcing application, which varied in the amount of labeling effort requested of the user and conducted a user study to evaluate the trade-off between the level of user-contributed information requested and the quantity and quality of labeled images collected. The results suggest that higher levels of user labeling do not lead to reduced contribution. Users collected and annotated the most images using the application version with the highest requested level of labeling with no decrease in user satisfaction. In preliminary experiments, the additional labeled data supported increased performance on an image retrieval task.

Recently, the authors of Shen Yang Zhang (JMLR, 2022) developed a neural network with width $36d(2d + 1)$ and depth $11$, which utilizes a special activation function called the elementary universal activation function, to achieve the super approximation property for functions in $C([a,b]^d)$. That is, the constructed network only requires a fixed number of neurons to approximate a $d$-variate continuous function on a $d$-dimensional hypercube with arbitrary accuracy. Their network uses $\mathcal{O}(d^2)$ fixed neurons. One natural question to address is whether we can reduce the number of these neurons in such a network. By leveraging a variant of the Kolmogorov Superposition Theorem, our analysis shows that there is a neural network generated by the elementary universal activation function with only $366d +365$ fixed, intrinsic (non-repeated) neurons that attains this super approximation property. Furthermore, we present a family of continuous functions that requires at least width $d$, and therefore at least $d$ intrinsic neurons, to achieve arbitrary accuracy in its approximation. This shows that the requirement of $\mathcal{O}(d)$ intrinsic neurons is optimal in the sense that it grows linearly with the input dimension $d$, unlike some approximation methods where parameters may grow exponentially with $d$.

Abdominal diffusion-weighted magnetic resonance imaging (DW-MRI) is a powerful, non-invasive technique for characterizing lesions and facilitating early diagnosis. However, respiratory motion during a scan can degrade image quality. Binning image slices into respiratory phases may reduce motion artifacts, but when the standard binning algorithm is applied to DW-MRI, reconstructed volumes are often incomplete because they lack slices along the superior-inferior axis. Missing slices create black stripes within images, and prolonged scan times are required to generate complete volumes. In this study, we propose a new binning algorithm to minimize missing slices. We acquired free-breathing and shallow-breathing abdominal DW-MRI scans on seven volunteers and used our algorithm to correct for motion in free-breathing scans. First, we drew the optimal rigid bin partitions in the respiratory signal using a dynamic programming approach, assigning each slice to one bin. We then designed a probabilistic approach for selecting some slices to belong in two bins. Our proposed binning algorithm resulted in significantly fewer missing slices than standard binning (p<1.0e-16), yielding an average reduction of 82.98+/-6.07%. Our algorithm also improved lesion conspicuity and reduced motion artifacts in DW-MR images and Apparent Diffusion Coefficient (ADC) maps. ADC maps created from free-breathing images corrected for motion with our algorithm showed lower intra-subject variability compared to uncorrected free-breathing and shallow-breathing maps (p<0.001). Additionally, shallow-breathing ADC maps showed more consistency with corrected free-breathing maps rather than uncorrected free-breathing maps (p<0.01). Our proposed binning algorithm's efficacy in reducing missing slices increases anatomical accuracy and allows for shorter acquisition times compared to standard binning., Comment: 16 pages, 5 figures

Logistics and transportation networks require a large amount of resources to realize necessary connections between locations and minimizing these resources is a vital aspect of planning research. Since such networks have dynamic connections that are only available at specific times, intricate models are needed to portray them accurately. In this paper, we study the problem of minimizing the number of resources needed to realize a dynamic network, using the temporal graphs model. In a temporal graph, edges appear at specific points in time. Given a temporal graph and a natural number k, we ask whether we can cover every temporal edge exactly once using at most k temporal journeys; in a temporal journey consecutive edges have to adhere to the order of time. We conduct a thorough investigation of the complexity of the problem with respect to four dimensions: (a) whether the type of the temporal journey is a walk, a trail, or a path; (b) whether the chronological order of edges in the journey is strict or non-strict; (c) whether the temporal graph is directed or undirected; (d) whether the start and end points of each journey are given or not. We almost completely resolve the complexity of all these problems and provide dichotomies for each one of them with respect to k.

Optical cavities can provide fast and non-destructive readout of individual atomic qubits; however, scaling up to many qubits remains a challenge. Using locally addressed excited-state Stark shifts to tune atoms out of resonance, we realize site-selective hyperfine-state cavity readout across a 10-site array. The state discrimination fidelity is 0.994(1) for one atom and 0.989(2) averaged over the entire array at a survival probability of 0.975(1). To further speed up array readout, we demonstrate adaptive search strategies utilizing global/subset checks. Finally, we demonstrate repeated rounds of classical error correction, showing exponential suppression of logical error and extending logical memory fivefold beyond the single-bit idling lifetime., Comment: *These authors contributed equally to this work

This Birds-of-a-Feather (BOF) session on 6 November 2023 was organized by leaders and members of SatHub at the International Astronomical Union Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU CPS). SatHub is dedicated to observations, data analysis, software, and related activities. The session opened with a talk on the current state of affairs with regards to satellite constellation mitigation, with a focus on optical astronomy, and moved to focused discussion around the top-voted topics. These included tools and techniques for forecasting satellite positions and brightnesses as well as streak detection and masking., Comment: 4 pages, 1 figure, conference proceeding from ADASS 2023

The IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS), established in early 2022 and co-hosted by NSF's NOIRLab and the SKA Observatory, was created to unify efforts to work towards mitigating some of the effects of satellite constellations on astronomy. SatHub, one of the four sub-groups of CPS, focuses on software and related tools to aid observers and industry partners in addressing some of the issues caused by commercial satellite constellations., Comment: 3 pages, 2 figures, conference proceeding from ADASS 2023

Cooperative effects such as super- and subradiance can be observed in the fluorescence emitted by a system of N atoms in vacuum, after interaction with a laser beam. In the vicinity of a dielectric or metallic surface, Casimir-Polder effects can modify collective atomic frequency shifts and decay rates. In this work, we study cooperative fluorescent emission next to resonant surfaces using the coupled dipoles model. We show that cooperative effects, expected in free space, are absent when the atoms are close to a surface whose polariton resonances coincide with the dominant atomic dipole coupling. In this case, cooperative effects are overshadowed by the very fast decay of the atomic fluorescence into surface modes. We illustrate our formalism and our results by considering a line of cesium 6D3/2 atoms in front of a sapphire surface. Finally, we propose the study of Cesium 6P3/2 atoms in front of a resonant metasurface as the most promising scenario for experimentally demonstrating the results of our study., Comment: 9 pages, 5 figures

With diet and nutrition apps reaching 1.4 billion users in 2022 [1], it's no surprise that health apps like MyFitnessPal, Noom, and Calorie Counter, are surging in popularity. However, one major setback [2] of nearly all nutrition applications is that users must enter food data manually, which is time-consuming and tedious. Thus, there has been an increasing demand for applications that can accurately identify food items, analyze their nutritional content, and offer dietary recommendations in real-time. This paper introduces a comprehensive system that combines advanced computer vision techniques with nutrition analysis, implemented in a versatile mobile and web application. The system is divided into three key components: 1) food detection using the YOLOv8 model, 2) nutrient analysis via the Edamam Nutrition Analysis API, and 3) personalized meal recommendations using the Edamam Meal Planning and Recipe Search APIs. Designed for both mobile and web platforms, the application ensures fast processing times with an intuitive user interface, with features such as data visualizations using Chart.js, a login system, and personalized settings for dietary preferences, allergies, and cuisine choices. Preliminary results showcase the system's effectiveness, making it a valuable tool for users to make informed dietary decisions., Comment: 7 pages, 12 figures

This paper introduces an intelligent lecturing assistant (ILA) system that utilizes a knowledge graph to represent course content and optimal pedagogical strategies. The system is designed to support instructors in enhancing student learning through real-time analysis of voice, content, and teaching methods. As an initial investigation, we present a case study on lecture voice sentiment analysis, in which we developed a training set comprising over 3,000 one-minute lecture voice clips. Each clip was manually labeled as either engaging or non-engaging. Utilizing this dataset, we constructed and evaluated several classification models based on a variety of features extracted from the voice clips. The results demonstrate promising performance, achieving an F1-score of 90% for boring lectures on an independent set of over 800 test voice clips. This case study lays the groundwork for the development of a more sophisticated model that will integrate content analysis and pedagogical practices. Our ultimate goal is to aid instructors in teaching more engagingly and effectively by leveraging modern artificial intelligence techniques.

This research presents a comprehensive framework for analyzing liquidity in financial markets, particularly in the context of high-frequency trading. By leveraging advanced machine learning classification techniques, including Logistic Regression, Support Vector Machine, and Random Forest, the study aims to predict minute-level price movements using an extensive set of liquidity metrics derived from the Trade and Quote (TAQ) data. The findings reveal that employing a broad spectrum of liquidity measures yields higher predictive accuracy compared to models utilizing a reduced subset of features. Key liquidity metrics, such as Liquidity Ratio, Flow Ratio, and Turnover, consistently emerged as significant predictors across all models, with the Random Forest algorithm demonstrating superior accuracy. This study not only underscores the critical role of liquidity in market stability and transaction costs but also highlights the complexities involved in short-interval market predictions. The research suggests that a comprehensive set of liquidity measures is essential for accurate prediction, and proposes future work to validate these findings across different stock datasets to assess their generalizability.

Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation. All combinations were validated using synthetic motion-corrupted CMR slice segmentation generated from CT in 1699 cases, where the dense CT serves as the ground truth. In 199 testing cases, SSA-LTN achieved the best results for Dice score and Huasdorff distance (94.0% and 4.7 mm respectively, average over 5 labels) but gave topological errors in 8 cases. STN was effective as a plug-in tool for correcting all topological errors with minimal impact on overall performance (93.5% and 5.0 mm respectively). SSA also proves to be a valuable plug-in tool, enhancing performance over both STN-based and LTN-based models. The code for these different combinations is available at https://github.com/XESchong/STACOM2024., Comment: 13 pages, STACOM2024

Determining how close a winner of an election is to becoming a loser, or distinguishing between different possible winners of an election, are major problems in computational social choice. We tackle these problems for so-called weighted tournament solutions by generalizing the notion of margin of victory (MoV) for tournament solutions by Brill et. al to weighted tournament solutions. For these, the MoV of a winner (resp. loser) is the total weight that needs to be changed in the tournament to make them a loser (resp. winner). We study three weighted tournament solutions: Borda's rule, the weighted Uncovered Set, and Split Cycle. For all three rules, we determine whether the MoV for winners and non-winners is tractable and give upper and lower bounds on the possible values of the MoV. Further, we axiomatically study and generalize properties from the unweighted tournament setting to weighted tournaments.

Cultural differences in common ground may result in pragmatic failure and misunderstandings during communication. We develop our method Rational Speech Acts for Cross-Cultural Communication (RSA+C3) to resolve cross-cultural differences in common ground. To measure the success of our method, we study RSA+C3 in the collaborative referential game of Codenames Duet and show that our method successfully improves collaboration between simulated players of different cultures. Our contributions are threefold: (1) creating Codenames players using contrastive learning of an embedding space and LLM prompting that are aligned with human patterns of play, (2) studying culturally induced differences in common ground reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction. Our code is publicly available at github.com/icwhite/codenames.

NASA's return to the Moon presents unparalleled opportunities to advance high-impact scientific capabilities. At the cutting edge of these possibilities are extremely high-resolution interferometric observations at visible and ultraviolet wavelengths. Such technology can resolve the surfaces of stars, explore the inner accretion disks of nascent stars and black holes, and eventually enable us to observe surface features and weather patterns on nearby exoplanets. We have been awarded Phase 1 support from NASA's Innovative Advanced Concepts (NIAC) program to explore the feasibility of constructing a high-resolution, long-baseline UV/optical imaging interferometer on the lunar surface, in conjunction with the Artemis Program. A 1996 study comparing interferometers on the Moon versus free-flyers in space concluded that, without pre-existing lunar infrastructure, free-flyers were preferable. However, with the advent of the Artemis Program, it is now crucial to revisit the potential of building lunar interferometers. Our objective is to conduct a study with the same level of rigor applied to large baseline, free-flying interferometers during the 2003-2005 NASA Vision Missions Studies. This preparation is essential for timely and effective utilization of the forthcoming lunar infrastructure. In this paper, we highlight the groundbreaking potential of a lunar surface-based interferometer. This concept study will be a huge step forward to larger arrays on both the moon and free-flying in space, over a wide variety of wavelengths and science topics. Our Phase 1 study began in April 2024, and here we present a concise overview of our vision and the progress made so far., Comment: 6 pages, 3 figures, SPIE Astronomical Telescopes + Instrumentation 2024 (Invited Paper)

This research project's objective was to investigate the impacts of transit stop location (near-side versus far-side) on pedestrian safety and traffic operations. Three different video-based behavioral observation data collections at signalized intersections in Utah were utilized, studying: (1) transit vehicle stop events and transit rider crossing behaviors and vehicle conflicts; (2) pedestrian conflicts with right-turning vehicles (driver/pedestrian reactions, conflict severity); and (3) pedestrian crossing behaviors (crossing location, crossing behaviors). These outcomes were statistically compared for near-side versus far-side transit stop locations. Far-side transit stops appear better for general traffic operations. Although transit departure delays are more likely and impactful at far-side stops, actions can be taken to improve transit operations there. On the other hand, far-side transit stops appear to be worse for pedestrian safety, corroborating prior crash-based research findings. Specifically, conflicts at far-side stops were more severe, and drivers were less likely to slow or stop for pedestrians. Reconciling these differing findings likely requires improving pedestrian safety at some far-side transit stops, and prioritizing safety over operational efficiency at other near-side transit stops.

A family of $r$ distinct sets $\{A_1,\ldots, A_r\}$ is an $r$-sunflower if for all $1 \leqslant i < j \leqslant r$ and $1 \leqslant i' < j' \leqslant r$, we have $A_i \cap A_j = A_{i'} \cap A_{j'}$. Erd\H{o}s and Rado conjectured in 1960 that every family $\mathcal{H}$ of $\ell$-element sets of size at least $K(r)^\ell$ contains an $r$-sunflower, where $K(r)$ is some function that depends only on $r$. We prove that if $\mathcal{H}$ is a family of $\ell$-element sets of VC-dimension at most $d$ and $|\mathcal{H}| > (C r (\log d+\log^\ast \ell))^\ell$ for some absolute constant $C > 0$, then $\mathcal{H}$ contains an $r$-sunflower. This improves a recent result of Fox, Pach, and Suk. When $d=1$, we obtain a sharp bound, namely that $|\mathcal{H}| > (r-1)^\ell$ is sufficient. Along the way, we establish a strengthening of the Kahn-Kalai conjecture for set families of bounded VC-dimension, which is of independent interest., Comment: 14 pages