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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).

Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data. To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time. Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors. Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data. Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a ''digital twin'' for online education., Comment: 26 pages

There has been an enduring interest and controversy about whether or not one can define physically meaningful energy density and stress fields, $e({\bf r})$ and $\sigma_{\alpha \beta}({\bf r})$ in quantum systems. A key issue is kinetic energy since $\frac{1}{2}|\nabla \Psi|^2$ and $-\frac{1}{2}\Psi\nabla^2 \Psi$ lead to different densities, and analogous issues arise interaction energies. This paper presents a resolution to the problems in two steps: 1) All effects of exchange and correlation are shown to be unique functions defined at each point ${\bf r}$; all issues of non-uniqueness involve only the density $n({\bf r})$ and are equivalent to a single-particle problem with wavefunction $s({\bf r}) = \sqrt{n({\bf r})/N}$. 2) Forms for the latter terms are proposed based on the nature of energy and stress, where energy has two distinct roles. Because the energy determines the ground state itself through the variational principle, the appropriate density involves the terms in the hamiltonian: $-\frac{1}{2}\Psi\nabla^2 \Psi$ and interactions in terms of potentials acting on the particles. This leads naturally to density functional theory with the interpretation that the energy density $e({\bf r})$ is equilibrated to minimize fluctuations with the same chemical potential throughout the system. On the other hand, the energy and stress (derivative of the energy with respect to strain) are properties of the ground state, and simple examples to show that the only acceptable expressions involve the combination $\frac{1}{4}[|\nabla \Psi|^2 - \Psi\nabla^2 \Psi]$, as derived by Schrodinger, Pauli and others, and Coulomb interactions in the Maxwell form in terms of electric fields, not potentials. Together these results lead to well-defined formulations of energy density and stress fields that are physically motivated and based on a clear set of arguments.

The Inflation Reduction Act subsidizes the deployment of clean electricity, hydrogen production, and carbon capture and storage, which could enable additional actions by other federal, state, and local policymakers to reduce emissions. Power plant rules finalized by the Environmental Protection Agency (EPA) in 2024 are one such example of complementary policies. The rules establish emissions intensity standards, not technology mandates, meaning power plant owners can choose from a range of technologies and control options provided that emissions standards are met. This flexibility makes electricity systems modeling important to understand the potential effects of these regulations. We report below a multi-model analysis of the EPA power plant rules that can provide timely information, including for other countries and states, on emissions impacts, policy design for electricity decarbonization, power sector investments and retirements, cost impacts, and load growth. We also discuss related technical, political, and legal uncertainties.

Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals using data from NASA's Cyclone GNSS (CYGNSS) mission. In this article, we describe the input datasets, preprocessing methods, model architecture, development methods, and detail the soil moisture products generated from these retrievals. The performance of this product is quantified against in situ measurements and compared to both the target dataset (retrievals from the Soil Moisture Active-Passive (SMAP) satellite) and the v1.0 soil moisture product from the CYGNSS mission. The Muon Space product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm$^3$ cm$^{-3}$ for in situ soil moisture observations from SMAP core validation sites is shown, though performance is lower than SMAP's when comparing in forests and/or mountainous terrain. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. This initial release serves as the foundation of our operational soil moisture product, which soon will additionally include data from Muon Space satellites., Comment: 23 pages, 10 figures

Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but rather includes audio features and speaker turns for a subset of 370K episodes, and speaker role inferences and other metadata for all 1.1M episodes. Using this data, we also conduct a foundational investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to continued computational research of this popular and impactful medium., Comment: 9 pages, 3 figures

We present results from the first application of the Global Navigation Satellite System (GNSS; GPS is one example of a collection of satellites in GNSS) for radio beam calibration using a commercial GNSS receiver with the Deep Dish Development Array (D3A) at the Dominion Radio Astrophysical Observatory (DRAO). Several GNSS satellites pass through the main and side lobes of the beam each day, enabling efficient mapping of the 2D beam structure. Due to the high SNR and abundance of GNSS satellites, we find evidence that GNSS can probe several side lobes of the beam through repeatable measurements of the beam over several days. Over three days of measurements, we find a measured difference reaching a minimum of 0.56 db-Hz in the main lobe of the primary beam. These results show promise for the use of GNSS in beam mapping for the Canadian Hydrogen Observatory and Radio-transient Detector (CHORD) and other future "large-N" radio interferometers. They also motivate future development of the technique within radio astronomy., Comment: Submitted to PASA. Comments welcome!

The study of information scrambling has profoundly deepened our understanding of many-body quantum systems. Much recent research has been devote to understanding the interplay between scrambling and decoherence in open systems. Continuing in this vein, we investigate scrambling in a noisy encoding-decoding circuit model. Specifically, we consider an $L$-qubit circuit consisting of a Haar-random unitary, followed by noise acting on a subset of qubits, and then by the inverse unitary. Scrambling is measured using the bipartite algebraic out-of-time-order correlator ($\mathcal{A}$-OTOC), which allows us to track information spread between extensively sized subsystems. We derive an analytic expression for the $\mathcal{A}$-OTOC that depends on system size and noise strength. In the thermodynamic limit, this system displays a \textit{butterfly effect} in which infinitesimal noise induces macroscopic information scrambling. We also perform numerical simulations while relaxing the condition of Haar-randomness, which preliminarily suggest that this effect may manifest in a larger set of circuits., Comment: 11 pages, 5 figures

As ecologists increasingly adopt stochastic models over deterministic ones, the question arises: when is this a positive development and when is this an unnecessary complication? While deterministic models -- like the Lotka-Volterra model -- provide straightforward predictions about competitive outcomes, they are often unrealistic. Stochastic models are more realistic, but their complexity can limit their usefulness in explaining coexistence. Here, we investigate the relative importance of deterministic and stochastic processes in competition between two flour beetle species, Tribolium castaneum and Tribolium confusum. Specifically, we use highly-replicated one-generation experiments (784 microcosms) to parameterize a mechanistic model. Both the full stochastic model and the underlying deterministic skeleton exhibit priority effects, where one species excludes the other, but the identity of the winning species depends on initial abundances. Stochasticity makes the identity of the winner less predictable, but deterministic dynamics still make reliable predictions (94% accuracy across a range of reasonable initial abundances). We conclude that deterministic population dynamics are sufficient to account for patterns of coexistence (or lack thereof), a potentially general finding that is supported by recent field studies. Additionally, we resolve longstanding issues in flour beetle research by identifying selective egg predation as the mechanism for priority effects, demonstrating the primacy of demographic stochasticity (compared to environmental stochasticity), and reinterpreting classic competition experiments to show that apparent coexistence often represents long-term transient dynamics., Comment: 22 pages, 6 figures, 1 table

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

The main challenge of 21 cm cosmology experiments is astrophysical foregrounds which are difficult to separate from the signal due to telescope systematics. An earlier study has shown that foreground residuals induced by antenna gain errors can be estimated and subtracted using the hybrid foreground residual subtraction (HyFoReS) technique which relies on cross-correlating linearly filtered data. In this paper, we apply a similar technique to the CHIME stacking analysis to subtract beam-induced foreground contamination. Using a linear high-pass delay filter for foreground suppression, the CHIME collaboration reported a $11.1\sigma$ detection in the 21 cm signal stacked on eBOSS quasar locations, despite foreground residual contamination mostly due to the instrument chromatic transfer function. We cross-correlate the foreground-dominated data at low delay with the contaminated signal at high delay to estimate residual foregrounds and subtract them from the signal. We find foreground residual subtraction can improve the signal-to-noise ratio of the stacked 21 cm signal by $ 10 - 20\%$ after the delay foreground filter, although some of the improvement can also be achieved with an alternative flagging technique. We have shown that it is possible to use HyFoReS to reduce beam-induced foreground contamination, benefiting the analysis of the HI auto power spectrum with CHIME and enabling the recovery of large scale modes.

For a given entire function $f(z)=\sum_{j=0}^{\infty}a_{j}z^{j}$, we study the zero distribution of $f_{r}(z)=\sum_{j\equiv r\pmod m}a_{j}z^{j}$ where $m\in\mathbb{N}$ and $0\le r

We present the first results of the holographic beam mapping program for the Canadian Hydrogen Intensity Mapping Experiment (CHIME). We describe the implementation of the holographic technique as adapted for CHIME, and introduce the processing pipeline which prepares the raw holographic timestreams for analysis of beam features. We use data from six bright sources across the full 400-800\,MHz observing band of CHIME to provide measurements of the co-polar and cross-polar beam response of CHIME in both amplitude and phase for the 1024 dual-polarized feeds instrumented on CHIME. In addition, we present comparisons with independent probes of the CHIME beam which indicate the presence of polarized beam leakage in CHIME. Holographic measurements of the CHIME beam have already been applied in science with CHIME, e.g. in estimating detection significance of far sidelobe FRBs, and in validating the beam models used for CHIME's first detections of \tcm emission (in cross-correlation with measurements of large-scale structure from galaxy surveys and the Lyman-$\alpha$ forest). Measurements presented in this paper, and future holographic results, will provide a unique data set to characterize the CHIME beam and improve the experiment's prospects for a detection of BAO., Comment: submitted to ApJ

We present beam measurements of the CHIME telescope using a radio calibration source deployed on a drone payload. During test flights, the pulsing calibration source and the telescope were synchronized to GPS time, enabling in-situ background subtraction for the full $N^{2}$ visibility matrix for one CHIME cylindrical reflector. We use the autocorrelation products to estimate the primary beam width and centroid location, and compare these quantities to solar transit measurements and holographic measurements where they overlap on the sky. We find that the drone, solar, and holography data have similar beam parameter evolution across frequency and both spatial coordinates. This paper presents the first drone-based beam measurement of a large cylindrical radio interferometer. Furthermore, the unique analysis and instrumentation described in this paper lays the foundation for near-field measurements of experiments like CHIME., Comment: Submitted to IEEE OJAP June 30, 2024

Social media enables activists to directly communicate with the public and provides a space for movement leaders, participants, bystanders, and opponents to collectively construct and contest narratives. Focusing on Twitter messages from social movements surrounding three issues in 2018-2019 (guns, immigration, and LGBTQ rights), we create a codebook, annotated dataset, and computational models to detect diagnostic (problem identification and attribution), prognostic (proposed solutions and tactics), and motivational (calls to action) framing strategies. We conduct an in-depth unsupervised linguistic analysis of each framing strategy, and uncover cross-movement similarities in associations between framing and linguistic features such as pronouns and deontic modal verbs. Finally, we compare framing strategies across issues and other social, cultural, and interactional contexts. For example, we show that diagnostic framing is more common in replies than original broadcast posts, and that social movement organizations focus much more on prognostic and motivational framing than journalists and ordinary citizens., Comment: Published in ICWSM Special Issue of the Journal of Quantitative Description: Digital Media

Operator entanglement is a well-established measure of operator complexity across a system bipartition. In this work, we introduce a measure for the ability of a unitary channel to generate operator entanglement, representing an operator-level generalization of the state-space entangling power. This operator space entangling power is demonstrated to be linked to the scrambling properties of the unitary channel via the recently introduced concept of mutual averaged non-commutativity of quantum operator algebras. An upper bound for the operator space entangling power is identified, corresponding to unitary channels with scrambling properties akin to those of typical unitaries. Additionally, for Hamiltonian dynamics, we find that the short-time growth rate of the operator space entangling power matches the Gaussian scrambling rate of the bipartite out-of-time-order-correlator, establishing a direct link between information scrambling and operator entanglement generation for short time scales. Finally, we examine the average growth of local operator entanglement across a symmetric bipartition of a spin-chain. For dual-unitary circuits, a combination of analytical and numerical investigations demonstrates that the average growth of local operator entanglement exhibits two distinct regimes in relation to the operator space entangling power of the building-block gate., Comment: Accepted version. 10+5 pages, 4 figures

A direct consequence of Faraday rotation is that the polarized radio sky does not resemble the total intensity sky at long wavelengths. We analyze G137+7, which is undetectable in total intensity but appears as a depolarization feature. We use the first polarization maps from the Canadian Hydrogen Intensity Mapping Experiment. Our $400-729$ MHz bandwidth and angular resolution, $17'$ to $30'$, allow us to use Faraday synthesis to analyze the polarization structure. In polarized intensity and polarization angle maps, we find a "tail" extending $10^\circ$ from the "head" and designate the combined object the "tadpole". Similar polarization angles, distinct from the background, indicate that the head and tail are physically associated. The head appears as a depolarized ring in single channels, but wideband observations show that it is a Faraday rotation feature. Our investigations of H I and H$\alpha$ find no connections to the tadpole. The tail suggests motion of either the gas or an ionizing star through the ISM; the B2(e) star HD 20336 is a candidate. While the head features a coherent, $\sim -8$ rad m$^2$ Faraday depth, Faraday synthesis also identifies multiple components in both the head and tail. We verify the locations of the components in the spectra using QU fitting. Our results show that $\sim$octave-bandwidth Faraday rotation observations at $\sim 600$ MHz are sensitive to low-density ionized or partially-ionized gas which is undetectable in other tracers., Comment: ApJ in press. Replacement corrects typographical error in equation 6

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a data set of the melting points of 476 non-metallic crystalline binary compounds, using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180~K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability., Comment: 21 pages and 3 figures for the main manuscript, and 11 pages and 15 figures of supplementary information

The purpose of this paper is to develop a practical strategy to accelerate Newton's method in the vicinity of singular points. We present an adaptive safeguarding scheme with a tunable parameter, which we call adaptive gamma-safeguarding, that one can use in tandem with Anderson acceleration to improve the performance of Newton's method when solving problems at or near singular points. The key features of adaptive gamma-safeguarding are that it converges locally for singular problems, and it can detect nonsingular problems automatically, in which case the Newton-Anderson iterates are scaled towards a standard Newton step. The result is a flexible algorithm that performs well for singular and nonsingular problems, and can recover convergence from both standard Newton and Newton-Anderson with the right parameter choice. This leads to faster local convergence compared to both Newton's method, and Newton- Anderson without safeguarding, with effectively no additional computational cost. We demonstrate three strategies one can use when implementing Newton-Anderson and gamma-safeguarded Newton-Anderson to solve parameter-dependent problems near singular points. For our benchmark problems, we take two parameter-dependent incompressible flow systems: flow in a channel and Rayleigh-Benard convection., Comment: 22 pages, 14 figures

The physical quantities in a gas should vary continuously across a shock. However, the physics inherent in the compressible Euler equations is insufficient to describe the width or structure of the shock. We demonstrate the existence of weak shock profiles to the kinetic Landau equation, that is, traveling wave solutions with Maxwellian asymptotic states whose hydrodynamic quantities satisfy the Rankine-Hugoniot conditions. These solutions serve to capture the structure of weak shocks at the kinetic level. Previous works considered only the Boltzmann equation with hard sphere and angular cut-off potentials., Comment: 75 pages, 0 figures

Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion--called "kinosons"--and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and elucidates kinetic mechanisms for diffusion. The distribution of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials., Comment: 16 pages, 5 figures, 38 pages supplemental material

Much recent work has been devoted to the study of information scrambling in quantum systems. In this paper, we study the long-time properties of the algebraic out-of-time-order-correlator ("$\mathcal{A}$-OTOC") and derive an analytical expression for its long-time average under the non-resonance condition. The $\mathcal{A}$-OTOC quantifies quantum scrambling with respect to degrees of freedom described by an operator subalgebra $\mathcal{A}$, which is associated with a partitioning of the corresponding system into a generalized tensor product structure. Recently, the short-time growth of the $\mathcal{A}$-OTOC was proposed as a criterion to determine which partition arises naturally from the system's unitary dynamics. In this paper, we extend this program to the long-time regime where the long-time average of the $\mathcal{A}$-OTOC serves as the metric of subsystem emergence. Under this framework, natural system partitions are characterized by the tendency to minimally scramble information over long time scales. We consider several physical examples, ranging from quantum many-body systems and stabilizer codes to quantum reference frames, and perform the minimization of the $\mathcal{A}$-OTOC long-time average both analytically and numerically over relevant families of algebras. For simple cases subject to the non-resonant condition, minimal $\mathcal{A}$-OTOC long-time average is shown to be related to minimal entanglement of the Hamiltonian eigenstates across the emergent system partition. Finally, we conjecture and provide evidence for a general structure of the algebra that minimizes the average for non-resonant Hamiltonians., Comment: Significant changes in the structure and presentation. 10+5 pages, 4 figures

The generalised two-dimensional (2D) fluid is characterised by a relationship between a scalar field $q$, called generalised vorticity, and the stream function $\psi$, namely $q = -(-\nabla^2)^\frac{\alpha}{2} \psi$. We study the transition of cascades in generalised 2D turbulence by systematically varying the parameter $\alpha$ and investigating its influential role in determining the directionality (inverse, forward, or bidirectional) of these cascades. We identify a finite critical threshold $\alpha_c$, where a transition occurs in cascade directionality, emphasising that $\alpha = 0$ signifies a laminar flow state with a vanishing nonlinear term. At a critical value of $\alpha_c$ a small wavelength instability is induced in the laminar flow. Using perturbation analysis we obtain that $\alpha_c$ is inversely proportional to the square of the Reynolds number -- a scaling validated through numerical simulations. We derive upper bounds for the dimensionless dissipation rates of generalised energy $E_G$ and enstrophy $\Omega_G$ as the Reynolds number tends to infinity. These findings corroborate numerical simulations, illustrating the inverse cascade of $E_G$ and forward cascade of $\Omega_G$ for $\alpha > 0$, contrasting with the reverse behaviour for $\alpha < 0$. The dependence of dissipation rates on system parameters reinforces these observed transitions, substantiated by spectral fluxes and energy spectra, which hint at Kolmogorov-like scalings at large scales but discrepancies at smaller scales. These discrepancies are possibly due to nonlocal transfers, which dominate the dynamics as we go from positive to negative values of $\alpha$. Intriguingly, the forward cascade of $E_G$ for $\alpha < 0$ reveals similarities to three-dimensional turbulence, notably the emergence of vortex filaments within a 2D framework, marking a unique feature of this generalised model., Comment: 18 pages, 6 figures

Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pairwise article similarity model, allowing us to identify story clusters in corpora covering local and national online news, and thereby create a comprehensive corpus of media storms over a nearly two year period. Using this corpus, we investigate media storms at a new level of granularity, allowing us to validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting., Comment: Findings of EMNLP 2023; 16 pages; 12 figures; 4 tables