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This study analyzed Pennsylvania School Climate Survey data from students and staff in the 2021/22 school year to assess the validity and reliability of the elementary school student version of the survey; approaches to scoring the survey in individual schools at all grade levels; and perceptions of school climate across student, staff, and school groups. The survey encourages data-informed efforts in participating Pennsylvania schools to foster supportive learning environments that promote social and emotional wellness for students and staff. The study validated the elementary school student survey but found that one domain--safe and respectful school climate--did not meet the reliability threshold and thus suggests that revisions are needed. At all grade levels noninstructional staff had the most positive perceptions of school climate, followed by classroom teachers then students. The study found that different approaches to combining the school climate scores of students, teachers, and noninstructional staff within schools yielded slightly different distributions of school climate summary index scores. It also found that different performance category thresholds resulted in similar distributions of schools across categories. Scores calculated using simple averages were strongly and positively correlated with scores calculated using a more complex approach (Rasch models), suggesting that both approaches deliver similar information. School climate scores varied across student groups (defined by race/ethnicity, gender, and grade level) within schools and across school groups. Larger schools and schools with higher percentages of Black students tended to have lower school climate scores than other schools. The findings can inform the Pennsylvania Department of Education's decisionmaking on revisions to the elementary school student survey, approaches to scoring and reporting survey results, and efforts to increase participation in future survey administrations.

Assigning upper elementary grade teachers to teach their strongest subjects to multiple classes ("departmentalizing"), rather than teaching all subjects to a single class, ("self-contained instruction") could mean more specialized instructional expertise in the classroom or focus for teacher planning time and professional development. This study examined the experiences of 90 schools that either voluntarily switched to departmentalized instruction for up to two years or continued with self-contained teaching in 4th and 5th grade classes beginning in fall 2019. The findings show that schools that switched were able to implement the key steps needed to departmentalize instruction but struggled with aspects of the approach. Teachers' reported experiences were consistent with some of both the benefits and challenges that prior research had hypothesized. Although the pandemic significantly disrupted instruction and the study activities, school and teacher experiences during this challenging time may inform schools and districts considering adopting departmentalized instruction.

This appendix is a companion to the report "Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences." The appendix provides additional information about the study, which sought to explore schools' implementation of departmentalized instruction, teachers' experiences with the strategy, and how the strategy was related to key outcomes. This additional information includes details on departmentalized instruction (Section A), study design and execution (Section B), key findings presented in the report (Section C), and additional findings that are not included in the report but provide important context (Section D).

Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space.

Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires meticulous RAG pipeline engineering. This includes fine-tuning embeddings and LLMs, extracting documents from vector databases, rephrasing queries, reranking results, designing prompts, honoring document access controls, providing concise responses, including references, safeguarding personal information, and building orchestration agents. We present a framework for building RAG-based chatbots based on our experience with three NVIDIA chatbots: for IT/HR benefits, financial earnings, and general content. Our contributions are three-fold: introducing the FACTS framework (Freshness, Architectures, Cost, Testing, Security), presenting fifteen RAG pipeline control points, and providing empirical results on accuracy-latency tradeoffs between large and small LLMs. To the best of our knowledge, this is the first paper of its kind that provides a holistic view of the factors as well as solutions for building secure enterprise-grade chatbots.", Comment: 8 pages, 6 figures, 2 tables, Preprint submission to ACM CIKM 2024

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects., Comment: preprint

Explainable Artificial Intelligence (XAI) systems aim to improve users' understanding of AI but rarely consider the inclusivity aspects of XAI. Without inclusive approaches, improving explanations might not work well for everyone. This study investigates leveraging users' diverse problem-solving styles as an inclusive strategy to fix an XAI prototype, with the ultimate goal of improving users' mental models of AI. We ran a between-subject study with 69 participants. Our results show that the inclusivity fixes increased participants' engagement with explanations and produced significantly improved mental models. Analyzing differences in mental model scores further highlighted specific inclusivity fixes that contributed to the significant improvement in the mental model.

Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike 'fixed' automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundamental hardware setup. To make these radial velocities observable, we modified the firmware of a commercial spinning radar to use triangular frequency modulation. In this paper, we develop a novel way to use this modulation to extract radial Doppler velocity measurements from consecutive azimuths of a radar intensity scan, without any data association. We show that these noisy, error-prone measurements contain enough information to provide good ego-velocity estimates, and incorporate these estimates into different modern odometry pipelines. We extensively evaluate the pipelines on over 110 km of driving data in progressively more geometrically challenging autonomous-driving environments. We show that Doppler velocity measurements improve odometry in well-defined geometric conditions and enable it to continue functioning even in severely geometrically degenerate environments, such as long tunnels., Comment: 8 pages, 7 figures, 2 tables, submitted to Robotics and Automation Letters (RA-L)

This paper introduces a framework by which the nonlinear trajectory optimization problem is posed as a path-planning problem in a space liberated of dynamics. In this space, general state constraints for continuous and impulsive control problems are encoded as linear constraints on the native overparameterized variables. This framework is enabled by nonlinear expansion in the vicinity of a reference in terms of fundamental solutions and a minimal nonlinear basis of mixed monomials in problem initial conditions. The former can be computed using state transition tensors, differential algebra, or analytic approaches, and the latter is computed analytically. Nonlinear guidance schemes are proposed taking advantage of this framework, including a successive convex programming scheme for delta-V minimizing trajectory optimization. This work enables a stable, easy to implement, and highly rapid nonlinear guidance implementation without the need for collocation or real-time integration., Comment: 34 pages, 16 figures

We report the magnetic structure and properties of a thiocyanate-based honeycomb magnet [Na(OH$_2$)$_3$]Mn(NCS)$_3$ which crystallises in the unusual low-symmetry trigonal space group $P\overline{3}$. Magnetic measurements on powder samples show this material is an antiferromagnet (ordering temperature ($T_\mathrm{N,mag} = 18.1(6)\,$)K) and can be described by nearest neighbour antiferromagnetic interactions $J=-11.07(4)\,$K. A method for growing neutron-diffraction sized single crystals (\textgreater10 mm$^3$) is demonstrated. Low temperature neutron single crystal diffraction shows that the compound adopts the collinear antiferromagnetic structure with $T_\mathrm{N,neut}= 18.94(7)\,$K, magnetic space group $P \bar{3}'$. Low temperature second-harmonic generation (SHG) measurements provide no evidence of breaking of the centre of symmetry., Comment: 4 pages +references, 3 figures

Using density functional theory (DFT) and linear response approaches, we compute the on-site Hubbard interaction $U$ of elemental Terbium (Tb) metal in the pressure range $\sim 0-65$ GPa. The resulting first-principles $U$ values with experimental crystal structures enable us to examine the magnetic properties of Tb using a self-consistent DFT+U method. The lowest-energy magnetic states in our calculations for different high-pressure Tb phases -- including hcp, $\alpha$-Sm, and dhcp -- are found to be compatible with the corresponding magnetic ordering vectors reported in experiments. The result shows that the inclusion of Hubbard $U$ substantially improves the accuracy and efficiency in modeling correlated rare-earth materials. Our study also provides the necessary $U$ information for other quantum many-body techniques to study Tb under extreme pressure conditions., Comment: 7 pages, 4 figures

In this technical report, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. Treating IMU measurements as inputs to a motion model and then preintegrating these measurements has almost become a de-facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple IMUs. In this work, we methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method's hyperparameters have been tuned on a training set. We show how to preintegrate heterogeneous factors using Gaussian process interpolation. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icp, Comment: Submitted to Robotica March 9th, 2024

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Conventional SFR methods using basis function decomposition are straightforward and computationally efficient, but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact, and is able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Numerical experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylinder harmonic decomposition and the singular value decomposition methods.

In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while preintegrating accelerometer measurements to form relative velocity factors. Our odometry is implemented using sliding-window batch trajectory estimation. To our knowledge, our work is the first to demonstrate radar-inertial odometry with a spinning mechanical radar using both gyroscope and accelerometer measurements. We improve the performance of our radar odometry by 19\% by incorporating an IMU. Our approach is efficient and we demonstrate real-time performance. Code for this project can be found at: https://github.com/utiasASRL/steam_icp, Comment: Submitted to IEEE Transactions on Robotics (2024-02-08)

In 2019, more than 5 million providers cared for one or more children either in their own home or in a child's home. Home-based child care (HBCC) providers are a varied group that includes listed providers and unlisted providers who do and do not receive payment. HBCC is especially prevalent in communities of color, communities with high concentrations of people from immigrant backgrounds, areas of concentrated poverty, and rural communities. Yet, research on HBCC lags behind research on center-based child care and early education (CCEE), and the least is known about unlisted providers who do not appear on state or national provider lists and work outside the formal systems supporting CCEE programs. This brief focuses on describing the characteristics of the communities in which unlisted HBCC providers live and, for many, where they typically care for children. It uses the 2019 National Survey of Early Care and Education (NSECE), the American Community Survey (ACS), and the Child Opportunity Index (COI) 2.0.

A robust drift-safe rendezvous trajectory optimization tool is developed in this work, with applications to orbital rendezvous and proximity operations. The method is based on direct collocation and utilizes a sequential convex programming framework, and is extended from previous work to include passive safety constraints. The tool is then paired with a dispersion analysis framework to allow trajectories to be optimized subject to plant, navigation, and actuator uncertainties. The timing, direction, and magnitude of orbital maneuvers are optimized subject to the expected propellant usage, for a given navigation system performance. Representative trajectories are presented for the LEO flight regime, but the approach can also be applied to GEO and NRHO with minimal modification., Comment: AAS Rocky Mountain Guidance, Navigation and Control Conference, 2023

The antiferromagnetic layered compound EuCd$_2$As$_2$ is widely considered as a leading candidate of ideal Weyl semimetal, featuring a single pair of Weyl nodes in its field-induced ferromagnetic (FM) state. Nevertheless, this view has recently been challenged by an optical spectroscopy study, which suggests that it is a magnetic semiconductor. In this study, we have successfully synthesized highly insulating EuCd$_2$As$_2$ crystals with carrier density reaching as low as $2\times 10^{15}$ $\text{cm}^{-3}$. The magneto-transport measurements revealed a progressive decrease of the anomalous Hall conductivity (AHC) by several orders of magnitude as the carrier density decreases. This behavior contradicts with what is expected from the intrinsic AHC generated by the Weyl points, which is independent of carrier density as the Fermi level approaches the charge neutrality point. In contrast, the scaling relationship between AHC and longitudinal conductivity aligns with the characteristics of variable range hopping insulators. Our results suggest that EuCd$_2$As$_2$ is a magnetic semiconductor rather than a topological Weyl semimetal.

A Personal Sound Zones (PSZ) system aims to generate two or more independent listening zones that allow multiple users to listen to different music/audio content in a shared space without the need for wearing headphones. Most existing studies assume that the acoustic paths between loudspeakers and microphones are measured beforehand in a stationary environment. Recently, adaptive PSZ systems have been explored to adapt the system in a time-varying acoustic environment. However, because a PSZ system usually requires multiple loudspeakers, the multichannel adaptive algorithms impose a high computational load on the processor. To overcome that problem, this paper proposes an efficient distributed algorithm for PSZ systems, which not only spreads the computational burden over multiple nodes but also reduces the overall computational complexity, at the expense of a slight decrease in performance. Simulation results with true room impulse responses measured in a Hemi-Anechoic chamber are performed to verify the proposed distributed PSZ system.

Intuitive interaction has long been seen as a highly user-friendly method. There are attempts to implement intuitive interfaces in vehicles in both research and industrial, such as voice commands. However, there is a lack of exploration in the in-vehicle multi-modal intuitive interaction, especially under a dynamic driving scenario. In this research, we conducted a design workshop (N=6) to understand user's needs and designers' considerations on the in-vehicle multi-modal intuitive interface, based on which we implemented our design on both a simulator and a real autonomous vehicle using Wizard-of-Oz. We conducted a user experiment (N=12) on the simulator to explore determinants of users' acceptance, experience, and behavior. We figured that acceptance was significantly influenced by six determinants. Drivers' behavior has an obvious pattern of change. Drivers have been proven to have less workload but distractions were also reported. Our findings offered empirical evidence which could give insights into future vehicle design.

In 2019, more than 5 million providers cared for one or more children either in their own home or in a child's home. Home-based child care (HBCC) providers are a varied group that includes both listed providers and unlisted providers who do and do not receive payment. HBCC is especially prevalent in communities of color, communities with high concentrations of people from immigrant backgrounds, areas of concentrated poverty, and rural communities. Yet, research on HBCC lags behind research on center-based child care and early education (CCEE), and the least is known about unlisted providers who do not appear on state or national provider lists and work outside the formal systems supporting CCEE programs. Using the 2019 National Survey of Early Care and Education (NSECE), this brief focuses on unlisted providers' caregiving histories, motivations, and recent experiences with professional supports.

In 2019, more than 5 million providers cared for one or more children either in their own home or in a child's home. Home-based child care (HBCC) providers are a varied group that includes both listed providers and unlisted providers who do and do not receive payment. HBCC is especially prevalent in communities of color, communities with high concentrations of people from immigrant backgrounds, areas of concentrated poverty, and rural communities. Yet, research on HBCC lags behind research on center-based child care and early education (CCEE), and the least is known about unlisted providers who do not appear on state or national provider lists and work outside the formal systems supporting CCEE programs. Using the 2019 National Survey of Early Care and Education (NSECE), this brief focuses on the demographic, educational, economic, and health and wellbeing characteristics of unlisted HBCC providers.

In 2019, more than 5 million providers cared for one or more children either in their own home or in a child's home. Home-based child care (HBCC) providers are a varied group that includes both listed providers and unlisted providers who do and do not receive payment. HBCC is especially prevalent in communities of color, communities with high concentrations of people from immigrant backgrounds, areas of concentrated poverty, and rural communities. Yet, research on HBCC lags behind research on center-based child care and early education (CCEE), and the least is known about unlisted providers who do not appear on state or national provider lists and work outside the formal systems supporting CCEE programs. Using the 2019 National Survey of Early Care and Education (NSECE), this brief focuses on how unlisted HBCC providers spend their caregiving time, what kinds of caregiving services they provide, and the characteristics of the children they serve.

Uncommon Schools is a nonprofit charter management organization that starts and manages public charter schools, primarily in traditionally underserved communities. As part of a 2016 grant awarded to Uncommon from the U.S. Department of Education's Charter Schools Program to support replication and expansion of its school model, we conducted a rigorous external evaluation of the network's effects on student outcomes and the key practices implemented in schools that are part of it. The study aimed to address gaps in the existing evidence base for Uncommon's impacts, focusing on the network's two largest regions. We used a rigorous propensity score matching approach to examine the impacts of Uncommon middle schools in Newark and a lottery-based experimental approach to examine the impacts of Uncommon middle schools in NYC. We found that Uncommon produced substantial positive effects on student achievement in math and English language arts in Newark and no discernable effects on student achievement in NYC.

This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both sensors; radar is resilient against adverse weather, while lidar produces high-quality maps in clear conditions. However, owing in part to the unique artefacts present in radar measurements, radar-lidar localization has struggled to achieve comparable performance to lidar-lidar systems, preventing it from being viable for autonomous driving. This work builds on ICP-based radar-lidar localization by including a learned preprocessing step that weights radar points based on high-level scan information. To train the weight-generating network, we present a novel, stand-alone, open-source differentiable ICP library. The learned weights facilitate ICP by filtering out harmful radar points related to artefacts, noise, and even vehicles on the road. Combining an analytical approach with a learned weight reduces overall localization errors and improves convergence in radar-lidar ICP results run on real-world autonomous driving data. Our code base is publicly available to facilitate reproducibility and extensions., Comment: 8 pages, 4 figures, 1 table. Submitted to Robotics and Automation Letters (RA-L)