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The goal of Smithsonian Science for North and South Carolina Classrooms was to provide teachers with ongoing, differentiated professional development and research-based curricular materials to improve elementary student achievement in science, mathematics, and reading. This work used an inquiry-based science curriculum, Smithsonian Science for the Classroom, which is aligned with the Next Generation Science Standards (NGSS), and was implemented in Grades 3-5 within predominantly rural North and South Carolina school districts serving high-needs students. The Center for Research in Educational Policy (CREP) at the University of Memphis, a State of Tennessee Center of Excellence, was the independent third-party evaluator. CREP implemented a Randomized Controlled Trial (RCT) to collect and analyze data using a rigorous, mixed-methods approach, including quantitative analyses of student achievement data from the Stanford-10 and state assessments, and qualitative analyses of classroom observation data, as well as teacher feedback from professional development surveys, module logs, and focus groups. For this study, 36 schools (including one split cohort being treated as one school) in seven districts within North and South Carolina were randomly assigned to treatment and comparison groups. The study cohort, followed over three academic years (2020-21 through 2022-23), was composed of more than 1,600 third grade (2020-21) students in these schools. In the comparison condition, schools used their business-as-usual science curricula and teacher professional development and did not receive Smithsonian Science curricular materials or professional development. As the pretests for achievement outcomes, students took the Abbreviated Battery of the Stanford Achievement Test Series, Tenth Edition (Stanford-10) in Reading and Math in spring of 2021 (the cohort's third grade year). The main findings and posttests included the combined sample (i.e., both states combined) for (a) the Stanford-10 Science subtest, which was administered in the spring of 2023 (the cohort's fifth grade year), and (b) spring 2023 standardized state assessment scores in Reading and Math. Using a hierarchical linear model (HLM) with school as a random effect, the analysis of the effect of Smithsonian Science for the three main findings after several years of disruptions from the COVID-19 pandemic was (a) positive (g = 0.18) and statistically significant (p = 0.032) on the Stanford-10 science assessment and (b) positive for both Reading (g = 0.10) and Math (g = 0.16), meaning students in the treatment group performed better than the control group, but the effects were not statistically significant. Teachers were generally positive in terms of the professional development provided, and feedback on the module logs largely supported the effectiveness of the professional development and fidelity of implementation. Classroom observations further supported the effectiveness of the professional development and fidelity to inquiry-based practices in treatment classrooms (e.g., use of experiential hands-on learning and level of class time dedicated to inquiry-based science). With instructional time for science lagging other subject areas, and as learning recovery in reading and math after COVID-19 receive national attention, the results demonstrate that ongoing, differentiated, high-quality professional learning and high-quality instructional materials for science instruction can support student achievement in science, math, and reading.

The completed WISC-R's (Wechsler Intelligence Scale for Children-Revised) of 76 white urban children with academic problems (6 years, 0 months to 15 years, 9 months) were rescored utilizing the Satz and Mogel criteria for an abbreviated intelligence measure. Extremely high correlations for IQ's (.96 - .98) and subtests (.66 - .95) were found. However, when mean differences between complete WISC-R and the shortened form were examined, significant differences between administrations were found. Furthermore, one third of the subjects showed changes in intelligence classification levels when the abbreviated form was used. Thus, two of the three criteria previously suggested for a valid abbreviated intelligence test of: (1) high correlation between administration forms; (2) non significant t-tests between the abbreviated and standard form mean IQ; and (3) low percentage of IQ classification change with the administration of the short form were not met. It was concluded, however, that the abbreviated WISC-R may be appropriate when intelligence is a question relative to candidacy for therapy or as a non-critical, general indication of intelligence when IQ classification is not important and/or assessment time is limited. (Author/RC)

Logical predictions about relationships between school aptitude and standardized achievement, aptitude and teachers' grades, and teachers' grades and standardized achievement can be made from the literature. These predictions are that (1) conventional school aptitude measures should predict standardized achievement test scores equally well for boys and girls but better for advantaged than disadvantaged children; (2) teachers' marks are more accurate for girls than for boys when judged against the sexes' standardized achievement test scores; are more accurate for middle-class than for disadvantaged children; and are least accurate for disadvantaged black males; and (3) teachers consistently give girls higher grades than boys but there are no important differences between boys' and girls' achievement when measured by standard achievement tests. The present study was conducted to test the strength of the relationships between pupil aptitude, standardized achievement and teachers' grades and to determine the percent of variance in grade point average accounted for by aptitude and standardized achievement in a representative sample of fifth- and ninth-grade Mexican-American, Black and Anglo students. (Author/RC)

A three-day seminar for nurses was conducted to improve their ability to communicate effectively with other people. The method used in this seminar was microteaching. The basic ingredients of the microteaching concept are the communication techniques dimension, the teach-reteach dimension, and the immediate feedback dimension. Under the direction of a supervisor and working with others in the seminar, each participant demonstrated and spoke on the topic of her choice, with the aim of instructing the listeners. Each demonstration was recorded on video tape. The tape was viewed and analysed, followed by direct question and answer sessions and suggestions for improvement by the supervisor. The benefit of immediate feedback and reaction was apparent, and each participant developed more self-confidence as the seminar progressed. The concensus of opinion at the end of the session was that microteaching is a valuable tool not only for improving communication skills but also for improving teaching techniques. (JD)

Preceding the body of the report, a brief review of the literature is provided to acquaint the reader with similarities and differences between national and local trends regarding the demographic characteristics of the physician population. The goal of the statewide residency study was to develop a strategy for the design and allocation of physician residencies consistent with statewide needs. A graphic model of the physician manpower production process was developed which enhanced understanding of the manpower problems facing Louisiana. Through the model, available data, and personal interviews with senior medical students and postgraduate trainees, data were compiled and analyzed regarding the need for physicians in Louisiana in 1982, where and how the number of physicians can be increased, the status of primary care treatment, likely sites for practicing physicians, and related concerns. The general conclusions point to several significant manpower production problems in Louisiana in its preparation for future needs for physician services. The study committee proposed several recommendations to meet the problem. (Appendixes contain supplementary tables and interview forms.) (AG)

The author's statement to the U.S. Senate Committee on Health, Education, Labor & Pensions on the challenges faced by America's classroom teachers focuses on a few of the factors that lead to teacher frustration and burnout that higher pay, however well-intended, does not change. They include, but are not limited to, poor teacher preparation, deteriorating classroom conditions--specifically classroom disorder and disruption--shoddy curriculum, and increasingly the expectation that they will not just teach reading, math, or their subject areas effectively, but also play a quasi-therapeutic role in response to students' behavioral and mental health needs. We are asking teachers to do too many things to do any of them well at any salary.

This report describes results from the Long-Term Impact of Early College High Schools Study--funded by the Institute of Education Sciences (IES) (#R305A210017)--which aimed to explore the long-term impacts of early college (EC) high schools on students' academic outcomes (e.g., college enrollment, degree attainment) and workforce, financial, and later life outcomes after schooling. In this report--the second follow-up report on our initial EC impact study--we focus specifically on key questions about workforce, financial, and other life outcomes: What were the impacts of ECs on workforce, financial, and other life outcomes in the 12th to 14th years after expected high school graduation? Did the impacts of ECs vary by participant characteristics? This report describes findings which build on a previous EC impact study, which was a multisite student-level randomized controlled trial with randomization based on admission lotteries. The analyses in this report focus on individuals who originally participated in 17 admission lotteries conducted by seven ECs for three cohorts of students, and examined outcomes after formal schooling. Key takeaways include the following: (1) Participants who were admitted to an EC, regardless of whether they attended the EC, did not experience a significant effect on any of the workforce, financial, and other life outcomes measured with survey data 12 to 14 years after expected high school graduation; and (2) EC impacts on workforce, financial, and other life outcomes measured 12 to 14 years after expected high school graduation did not differ significantly by individuals' race/ethnicity, low-income status, or prior achievement. While our initial impact study and first follow-up study found that attending an EC had effects on secondary and postsecondary enrollment, and completion for participants with different background characteristics, we found that attending an EC had no impact on any of the workforce, financial, and other life outcomes that we analyzed 12 to 14 years after participants' expected high school graduation. Future studies could consider examining workforce, financial, and other life outcomes in years before the 12th to 14th year after expected high school graduation using administrative data sources (e.g., IRS or unemployment insurance records). Further research may also examine if other factors, such as EC students' college major or labor market opportunities, influence the impact of ECs on longer-term outcomes.

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

Boundary conforming coordinates are commonly used in plasma physics to describe the geometry of toroidal domains, for example, in three-dimensional magnetohydrodynamic equilibrium solvers. The magnetohydrodynamic equilibrium configuration can be approximated with an inverse map, defining nested surfaces of constant magnetic flux. For equilibrium solvers that solve for this inverse map iteratively, the initial guess for the inverse map must be well defined and invertible. Even if magnetic islands are to be included in the representation, boundary conforming coordinates can still be useful, for example to parametrize the interface surfaces in multi-region, relaxed magnetohydrodynamics. Given a fixed boundary shape, finding a valid boundary conforming mapping can be challenging, especially for the non-convex boundaries from recent developments in stellarator optimization. In this work, we propose a new algorithm to construct such a mapping, by solving two Dirichlet-Laplace problems via a boundary integral method. We can prove that the generated harmonic map is always smooth and has a smooth inverse. Furthermore, we can find a discrete approximation of the mapping that preserves this property.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Objective: To design and implement VIEWER, a versatile toolkit for visual analytics of clinical data, and to systematically evaluate its effectiveness across various clinical applications while gathering feedback for iterative improvements. Materials and Methods: VIEWER is an open-source and extensible toolkit that employs distributed natural language processing and interactive visualisation techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualisation at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in a large mental health institution, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods. Results: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. Users and stakeholders reported high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice. Discussion: VIEWER provides a cost-effective enhancement to the functionalities of standard clinical information systems, with evaluation offering valuable feedback for future improvements. Conclusion: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

With the oncologist acting as the ``game leader'', we employ a Stackelberg game-theoretic model involving multiple populations to study prostate cancer. We refine the drug dosing schedule using an empirical Bayes feed-forward analysis, based on clinical data that reflects each patient's prostate-specific drug response. Our methodology aims for a quantitative grasp of the parameter landscape of this adaptive multi-population model, focusing on arresting the growth of drug-resistant prostate cancer by promoting competition across drug-sensitive cancer cell populations. Our findings indicate that not only is it is feasible to considerably extend cancer suppression duration through careful optimization, but even transform metastatic prostate cancer into a chronic condition instead of an acute one for most patients, with supporting clinical and analytical evidence.

On 2022 March 10, a coronal mass ejection (CME) erupted from the Sun, resulting in Solar Orbiter observations at 0.45 au of both dispersive solar energetic particles arriving prior to the interplanetary CME (ICME) and locally accelerated particles near the ICME-associated shock structure as it passed the spacecraft on 2022 March 11. This shock was later detected on 2022 March 14 by the Advanced Composition Explorer (ACE), which was radially aligned with Solar Orbiter, at 1 au. Ion composition data from both spacecraft -- via the Solar Orbiter Energetic Particle Detector/ Suprathermal Ion Spectrograph (EPD/SIS) and the Ultra Low Energy Isotope Spectrometer (ULEIS) on ACE -- allows for in-depth analysis of the radial evolution of species-dependent ICME shock-associated acceleration processes for this event. We present a study of the ion spectra observed at 0.45 and 1 au during both the gradual solar energetic particle (SEP) and energetic storm particle (ESP) phases of the event. We find that the shapes of the spectra seen at each spacecraft have significant differences that were likely caused by varying shock geometry: Solar Orbiter spectra tend to lack spectral breaks, and the higher energy portions of the ACE spectra have comparable average flux to the Solar Orbiter spectra. Through an analysis of rigidity effects on the spectral breaks observed by ACE, we conclude that the 1 au observations were largely influenced by a suprathermal pool of $\mathrm{He}^{+}$ ions that were enhanced due to propagation along a stream interaction region (SIR) that was interacting with the ICME at times of observation., Comment: 15 pages, 7 figures, submitted to A&A

In this work, the Divergent Component of Motion (DCM) method is expanded to include angular coordinates for the first time. This work introduces the idea of spatial DCM, which adds an angular objective to the existing linear DCM theory. To incorporate the angular component into the framework, a discussion is provided on extending beyond the linear motion of the Linear Inverted Pendulum model (LIPM) towards the Single Rigid Body model (SRBM) for DCM. This work presents the angular DCM theory for a 1D rotation, simplifying the SRBM rotational dynamics to a flywheel to satisfy necessary linearity constraints. The 1D angular DCM is mathematically identical to the linear DCM and defined as an angle which is ahead of the current body rotation based on the angular velocity. This theory is combined into a 3D linear and 1D angular DCM framework, with discussion on the feasibility of simultaneously achieving both sets of objectives. A simulation in MATLAB and hardware results on the TORO humanoid are presented to validate the framework's performance.

The modern technological landscape has trended towards increased precision and greater digitization of information. However, the methods used to record and communicate scientific procedures have remained largely unchanged over the last century. Written text as the primary means for communicating scientific protocols poses notable limitations in human and machine information transfer. In this work, we present the Universal Workflow Language (UWL) and the open-source Universal Workflow Language interface (UWLi). UWL is a graph-based data architecture that can capture arbitrary scientific procedures through workflow representation of protocol steps and embedded procedure metadata. It is machine readable, discipline agnostic, and compatible with FAIR reporting standards. UWLi is an accompanying software package for building and manipulating UWL files into tabular and plain text representations in a controlled, detailed, and multilingual format. UWL transcription of protocols from three high-impact publications resulted in the identification of substantial deficiencies in the detail of the reported procedures. UWL transcription of these publications identified seventeen procedural ambiguities and thirty missing parameters for every one hundred words in published procedures. In addition to preventing and identifying procedural omission, UWL files were found to be compatible with geometric learning techniques for representing scientific protocols. In a surrogate function designed to represent an arbitrary multi-step experimental process, graph transformer networks were able to predict outcomes in approximately 6,000 fewer experiments than equivalent linear models. Implementation of UWL and UWLi into the scientific reporting process will result in higher reproducibility between both experimentalists and machines, thus proving an avenue to more effective modeling and control of complex systems., Comment: 17 pages, 5 figures

The EdResearch for Action "Overview Series" summarizes the research on key topics to provide K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students. This overview brief discusses the central question: What is the evidence on strategies to build a sense of school connectedness and engagement among all students? Key insights are arranged in the following sections: (1) breaking down the issue; (2) evidence-based practices; and (3) practices to avoid. [The BARR Center and Network for College Success are additional collaborators for this report.]

High-quality, stable child care and early education (CCEE) can have lasting, positive impacts on children. However, the challenges of recruiting, strengthening, and retaining the CCEE workforce are well documented. CCEE educators typically have low levels of formal education and compensation; limited opportunities for education, training, and professional development; inconsistent working conditions; and high levels of stress and burnout. Additionally, the CCEE sector is well known for high turnover rates, which can strain remaining educators and decrease the quality of care they offer. Turnover can also lead to diminishing returns on an organization's professional development investments. The COVID-19 pandemic has exacerbated these issues. The Building and Sustaining the Child Care and Early Education Workforce (BASE) project conducted an environmental scan and a literature review to identify and document existing knowledge about the CCEE workforce and strategies to strengthen it. One of the main themes identified from this effort was a need for more and better data on the workforce dynamics of CCEE educators. In particular, data are needed on who enters, advances in, stays in, and exits different roles, settings, and types of CCEE care or leaves the field altogether--as well as when, how, and why they do. This information is important for understanding workforce dynamics and informing the development, evaluation, and improvement of strategies that effectively build and sustain a qualified and stable CCEE workforce. For this reason, the BASE project team conducted a data scan to summarize the landscape of existing data sources that may address these gaps and identify areas where future data collection may be most useful. This brief summarizes the findings from the data scan.

The $\sim$5 Myr PDS 70 is the only known system with protoplanets residing in the cavity of the circumstellar disk from which they formed, ideal for studying exoplanet formation and evolution within its natal environment. Here we report the first spin constraint and C/O measurement of PDS 70b from Keck/KPIC high-resolution spectroscopy. We detected CO (3.8 $\sigma$) and H$_2$O (3.5 $\sigma$) molecules in the PDS 70b atmosphere via cross-correlation, with a combined CO and H$_2$O template detection significance of 4.2 $\sigma$. Our forward model fits, using BT-Settl model grids, provide an upper limit for the spin-rate of PDS 70b ($<$29 km s$^{-1}$). The atmospheric retrievals constrain the PDS 70b C/O ratio to ${0.28}^{+0.20}_{-0.12}$ ($<$0.63 under 95$\%$ confidence level) and a metallicity [C/H] of ${-0.2}^{+0.8}_{-0.5}$ dex, consistent with that of its host star. The following scenarios can explain our measured C/O of PDS 70b in contrast with that of the gas-rich outer disk (for which C/O $\gtrsim$ 1). First, the bulk composition of PDS 70b might be dominated by dust+ice aggregates rather than disk gas. Another possible explanation is that the disk became carbon-enriched $\textit{after}$ PDS 70b was formed, as predicted in models of disk chemical evolution and as observed in both very low mass star and older disk systems with $\textit{JWST}$/MIRI. Because PDS 70b continues to accrete and its chemical evolution is not yet complete, more sophisticated modeling of the planet and the disk, and higher quality observations of PDS 70b (and possibly PDS 70c), are necessary to validate these scenarios., Comment: Accepted to ApJ Letters; 15 pages, 3 figures

We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applications. More precisely, we demonstrate that neural networks with a simple $L^2$-minimizing loss achieve a rate of $n^{-1/(4+r)}$ in nonparametric density estimation when the underlying density is Markov to a graph whose maximum clique size is at most $r$, and we provide evidence that in the aforementioned applications, this size is typically constant, i.e., $r=O(1)$. We then establish that the optimal rate in $L^1$ is $n^{-1/(2+r)}$ which, compared to the standard nonparametric rate of $n^{-1/(2+d)}$, reveals that the effective dimension of such problems is the size of the largest clique in the Markov random field. These rates are independent of the data's ambient dimension, making them applicable to realistic models of image, sound, video, and text data. Our results provide a novel justification for deep learning's ability to circumvent the curse of dimensionality, demonstrating dimension-independent convergence rates in these contexts.

The intense debate about the presence of methane in the Martian atmosphere has stimulated the study of methanogens adapted to terrestrial habitats that mimic Martian environments. We examinate the environmental conditions, energy sources and ecology of terrestrial methanogens thriving in deep crystalline fractures, sub-sea hypersaline lakes and subglacial water bodies considered as analogs of a hypothetical habitable Martian subsurface. We combine this information with recent data on the distribution of buried water or ice and radiogenic elements on Mars and with models of the subsurface thermal regime of this planet to identify a 4.3-8.8 km-deep regolith habitat at the mid-latitude location of Acidalia Planitia, that might fit the requirements for hosting putative Martian methanogens analogous to the methanogenic families Methanosarcinaceae and Methanomicrobiaceae., Comment: 75 pages, 6 Figures, 3 Tables, Submitted to AstroBiology

Inference for locally stationary processes is often based on some local Whittle-type approximation of the likelihood function defined in the frequency domain. The main reasons for using such a likelihood approximation is that i) it has substantially lower computational cost and better scalability to long time series compared to the time domain likelihood, particularly when used for Bayesian inference via Markov Chain Monte Carlo (MCMC), ii) convenience when the model itself is specified in the frequency domain, and iii) it provides access to bootstrap and subsampling MCMC which exploits the asymptotic independence of Fourier transformed data. Most of the existing literature compares the asymptotic performance of the maximum likelihood estimator (MLE) from such frequency domain likelihood approximation with the exact time domain MLE. Our article uses three simulation studies to assess the finite-sample accuracy of several frequency domain likelihood functions when used to approximate the posterior distribution in time-varying parameter models. The methods are illustrated on an application to egg price data.

In a companion paper, we have developed a phase-space electronic structure theory of molecules in magnetic fields, whereby the electronic energy levels arise from diagonalizing a phase-space Hamiltonian $\hat H_{PS}(\bf{X},\bf{\Pi})$ that depends parametrically on nuclear position and momentum. The resulting eigenvalues are translationally invariant; moreover, if the magnetic field is in the $z-$direction, then the eigenvalues are also invariant to rotations around the $z-$direction. However, like all Hamiltonians in a magnetic field, the theory has a gauge degree of freedom (corresponding to the position of the magnetic origin in the vector potential), and requires either $(i)$ formally, a complete set of electronic states or $(ii)$ in practice, gauge invariant atomic orbitals (GIAOs) in order to realize such translational and rotational invariance. Here we describe how to implement a phase-space electronic Hamiltonian using GIAOs within a practical electronic structure package (in our case, Q-Chem). We further show that novel phenomena can be observed with finite $\bf{B}-$fields, including minimum energy structures with $\bf{\Pi}_{min} \ne 0$, indicating non-zero electronic motion in the ground-state., Comment: Added reference to paper I

We develop a phase-space electronic structure theory of molecules in magnetic fields. For a system of electrons in a magnetic field with vector potential $\bf{A}(\hat{\bf{r}})$, the usual Born-Oppenheimer Hamiltonian is the sum of the nuclear kinetic energy and the electronic Hamiltonian, $\frac{(\bf{P} - q\bf{A}(\bf{X}) )^2}{2M} + \hat{H}_{e}(\bf{X})$ (where $q$ is a nuclear charge). To include the effects of coupled nuclear-electron motion in the presence of magnetic field, we propose that the proper phase-space electronic structure Hamiltonian will be of the form $\frac{(\bf{P} - q^{\textit{eff}}\bf{A}(\bf{X}) - e\hat{\bf{\Gamma}})^2}{2M} + \hat{H}_{e}(\bf{X})$. Here, $q^{\textit{eff}}$ represents the {\em screened} nuclear charges and the $\hat{\bf{\Gamma}}$ term captures the local pseudomomentum of the electrons. This form reproduces exactly the energy levels for a hydrogen atom in a magnetic field; moreover, single-surface dynamics along the eigenstates is guaranteed to conserve both the total pseudomomentum as well as the total angular momentum in the direction of the magnetic field. This Hamiltonian form can be immediately implemented within modern electronic structure packages (where the electronic orbitals will now depend on nuclear position ($\bf{X}$) and nuclear momentum ($\bf{P}$)). One can expect to find novel beyond Born-Oppenheimer magnetic field effects for strong enough fields and/or nonadiabatic systems., Comment: Added reference to paper II

Accurate calculations of starspot spectra are essential for multiple applications in astronomy. The current standard is to represent starspot spectra by spectra of stars that are cooler than the quiet star regions. This implies approximating a starspot as a non-magnetic 1D structure in radiative-convective equilibrium, parametrizing convective energy transport by mixing length theory. It is the inhibition of convection by the starspot magnetic field that is emulated by using a lower spot temperature relative to the quiet stellar regions. Here, we take a different approach avoiding the approximate treatment of convection and instead self-consistently accounting for the interaction between matter, radiation, and the magnetic field. We simulate spots on G2V, K0V, M0V stars with the 3D radiative magnetohydrodynamics code MURaM and calculate spectra ($R \approx 500$ from 250~nm to 6000~nm) using ray-by-ray radiative transfer with the MPS-ATLAS code. We find that the 1D models fail to return accurate umbral and penumbral spectra on K0V and M0V stars where convective and radiative transfer of energy is simultaneously important over a broad range of atmospheric heights rendering mixing length theory inaccurate. However, 1D models work well for G2V stars, where both radiation and convection significantly contribute to energy transfer only in a narrow region near the stellar surface. Quantitatively, the 1D approximation leads to errors longward of 500 nm of about 50\% for both umbral and penumbral flux contrast relative to quiet star regions on M0V stars, and less than 2\% (for umbrae) and 10\% (for penumbrae) for G2V stars., Comment: Accepted for publication in the Astrophysical Journal Letters

We propose a new method to determine the quark mass by using bilinear operators of the flowed quark field defined within the gradient-flow formalism. This method enables the quark mass determination through a comparison of perturbative calculations with lattice data. The gauge-invariant nature of the observable should allow clear control over perturbative errors. At the same time, the gradient flow suppresses the noise in the lattice measurements of the observable, which simply consists of one-point functions. Concerning the perturbative input in this framework, we study the mass dependence of the flowed quark condensate $\langle \bar{\chi}(t,x) \chi(t,x) \rangle$ at the two-loop level. For this purpose, we develop a novel approach for expanding massive gradient-flow integrals in the limit of small and large $(m^2t)$. We also present a fully numerical result which includes the full mass dependence., Comment: 8 pages, 1 figure. Proceedings of the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August, 2024 Liverpool, UK

Vibronic interactions between nuclear motion and electronic states are critical for the accurate modeling of photochemistry. However, accurate simulations of fully quantum non-adiabatic dynamics are often prohibitively expensive for classical methods beyond small systems. In this work, we present a quantum algorithm based on product formulas for simulating time evolution under a general vibronic Hamiltonian in real space, capable of handling an arbitrary number of electronic states and vibrational modes. We develop the first trotterization scheme for vibronic Hamiltonians beyond two electronic states and introduce an array of optimization techniques for the exponentiation of each fragment in the product formula, resulting in a remarkably low cost of implementation. To demonstrate practical relevance, we outline a proof-of-principle integration of our algorithm into a materials discovery pipeline for designing more efficient singlet fission-based organic solar cells. Based on commutator bounds, we estimate that a $100$ femtosecond evolution using a second-order Trotter product formula of a $4$-state model of an anthracene-fullerene interface requires $117$ qubits and $1.5 \times 10^7$ Toffoli gates in a reduced dimensionality of $11$ modes. In its full dimensionality of $246$ modes, it requires $1065$ qubits and $2.7 \times 10^9$ Toffoli gates.

In light of recent observations, we study evidence for thawing quintessence over a cosmological constant as dark energy, with emphasis on the effect of the choice of priors. Working with a parametrization for the equation of state parameter motivated by the theory, we find a preference for thawing quintessence compared to a bare cosmological constant {\it only} if we use priors which are heavily informed by the data itself. If we extend the priors to physically better motivated ranges, the evidence for thawing quintessence disappears., Comment: 12 pages, 8 figures

Video understanding has made huge strides in recent years, relying largely on the power of the transformer architecture. As this architecture is notoriously expensive and video is highly redundant, research into improving efficiency has become particularly relevant. This has led to many creative solutions, including token merging and token selection. While most methods succeed in reducing the cost of the model and maintaining accuracy, an interesting pattern arises: most methods do not outperform the random sampling baseline. In this paper we take a closer look at this phenomenon and make several observations. First, we develop an oracle for the value of tokens which exposes a clear Pareto distribution where most tokens have remarkably low value, and just a few carry most of the perceptual information. Second, we analyze why this oracle is extremely hard to learn, as it does not consistently coincide with visual cues. Third, we observe that easy videos need fewer tokens to maintain accuracy. We build on these and further insights to propose a lightweight video model we call LITE that can select a small number of tokens effectively, outperforming state-of-the-art and existing baselines across datasets (Kinetics400 and Something-Something-V2) in the challenging trade-off of computation (GFLOPs) vs accuracy.

The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with using LLMs in medical applications have not been systematically characterized. We propose using five key principles for safe and trustworthy medical AI: Truthfulness, Resilience, Fairness, Robustness, and Privacy, along with ten specific aspects. Under this comprehensive framework, we introduce a novel MedGuard benchmark with 1,000 expert-verified questions. Our evaluation of 11 commonly used LLMs shows that the current language models, regardless of their safety alignment mechanisms, generally perform poorly on most of our benchmarks, particularly when compared to the high performance of human physicians. Despite recent reports indicate that advanced LLMs like ChatGPT can match or even exceed human performance in various medical tasks, this study underscores a significant safety gap, highlighting the crucial need for human oversight and the implementation of AI safety guardrails.

In mergoassociation, two atoms in separate optical traps are combined to form a molecule when the traps are merged. Previous theoretical treatments have considered only the relative motion of the atoms, neglecting coupling to the motion of the center of mass. We develop a theoretical method to include the coupling to center-of-mass motion and consider its consequences for experiments for both weak and strong coupling. We consider the example of RbCs and then extend the treatment to other systems where mergoassociation may be effective, namely RbSr, RbYb and CsYb. We consider the role of the coupling when the traps are anisotropic and the potential use of moveable traps to construct quantum logic gates.

This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicability, the \textit{Pril} method incorporates differential DO equations for each lake layer, modeling it as a first-order linear solution using a forward Euler scheme with a daily timestep. However, this method is sensitive to numerical instabilities. When drastic fluctuations occur, the numerical integration is neither mass-conservative nor stable. Especially during stratified conditions, exogenous fluxes into each layer cause significant within-day changes in DO concentrations. To address this challenge, we further propose an \textit{Adaptive Process-Guided Learning (April)} model, which dynamically adjusts timesteps from daily to sub-daily intervals with the aim of mitigating the discrepancies caused by variations in entrainment fluxes. \textit{April} uses a generator-discriminator architecture to identify days with significant DO fluctuations and employs a multi-step Euler scheme with sub-daily timesteps to effectively manage these variations. We have tested our methods on a wide range of lakes in the Midwestern USA, and demonstrated robust capability in predicting DO concentrations even with limited training data. While primarily focused on aquatic ecosystems, this approach is broadly applicable to diverse scientific and engineering disciplines that utilize process-based models, such as power engineering, climate science, and biomedicine.