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In the face of financial, political, and capacity constraints, leaders within the Baltimore City Public School System and Chicago Public Schools are making progress toward closing post-pandemic gaps in student learning. The executive leadership teams serving Baltimore City Public School System and Chicago Public Schools are using several strategies to help their systems build back after the pandemic including: (1) standardizing key elements of instructional cores and adding instructional staff across their systems, while maintaining some school-level flexibility; (2) supporting existing staff to become teachers and "building the bench" of new principals, while also promoting school leaders and administrators from within; (3) spending federal pandemic-relief money Elementary and Secondary School Emergency Relief Fund (ESSER) and other supplements, while hoping to avoid cutting into funding for instruction; (4) insulating schools as much as possible by taking the political hits themselves and making concessions to demanding stakeholders; and (5) confronting continued enrollment declines, limited central office capacity, constrained teacher labor markets, changes in school boards, shifting local interests, and uncertain state-level financial support. The strong leadership modeled in both districts remains at the heart of moving forward, despite the fact they are straining against their capacities. While there may be nuanced differences, all leaders in maxed-out districts will have to search for new options and forge alliances with new partners within school boards, unions, and state-level governments. This project is part of the American School District Panel (ASDP), a research partnership between the RAND Corporation and the Center on Reinventing Public Education (CRPE) at Arizona State University's Mary Lou Fulton Teachers College.

The COVID-19 pandemic and recovery period is a unique opportunity to understand contemporary issues in high school reform. Evidence has clearly demonstrated lingering COVID impacts on adolescent students that have deepened pre-existing inequities and worsened teen mental health. There's a natural desire to regain normalcy after the pandemic. But it's essential, and urgent, that researchers examine why that "normalcy" failed in the past to support every student's needs. They must identify effective, even new, ways to level the playing field for today's students, and for future generations. Research has shown that many high school educators and administrators experimented with new approaches during the pandemic. Can the lessons learned in this period contribute to more lasting, transformative shifts? Researchers studied a group of New England public high schools during the pandemic recovery period to explore this question and hear directly from students and adults about their experiences. This report presents a set of recommendations for educational leaders and policy makers at the state and local levels -- along with caregivers, employers, higher education, and other community partners -- to help schools overcome barriers and think more broadly about how best to prepare graduates for a variety of paths to success after high school.

In this work, we explore the effect of neutrino nonstandard interactions (NSI) involving the charm quark at SND@LHC. Using an effective description of new physics in terms of four-fermion operators involving a charm quark, we constrain the Wilson coefficients of the effective interaction from two and three-body charmed meson decays. In our fit, we include charmed meson decays not only to pseudoscalar final states but also to vector final states and include decays to the $\eta$ and $\eta^\prime$ final states. We also consider constraints from charmed baryon decays. We then study the effect of new physics in neutrino scattering processes, involving charm production at SND@LHC, for various benchmark new physics couplings obtained from the low energy fits. Finally, we also study the effects of lepton universality violation (LUV) assuming that the new physics coupling is not lepton universal., Comment: 24 pages, 4 figures

Tendon-driven robotic catheters are capable of precise execution of minimally invasive cardiac procedures including ablations and imaging. These procedures require accurate mathematical models of not only the catheter and tendons but also their interactions with surrounding tissue and vasculature in order to control the robot path and interaction. This paper presents a mechanical model of a tendon-driven robotic catheter system based on Cosserat rods and integrated with a stable, implicit Euler scheme. We implement the Cosserat rod as a model for a simple catheter centerline and validate its physical accuracy against a large deformation analytical model and experimental data. The catheter model is then supplemented by adding a second Cosserat rod to model a single tendon, using penalty forces to define the constraints of the tendon-catheter system. All the model parameters are defined by the catheter properties established by the design. The combined model is validated against experimental data to confirm its physical accuracy. This model represents a new contribution to the field of robotic catheter modeling in which both the tendons and catheter are modeled by mechanical Cosserat rods and fully-validated against experimental data in the case of the single rod system., Comment: 24 pages, 23 figures

Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However, a pivotal prerequisite for achieving this level of competitiveness is the significant communication and computation overheads when updating these models, which prohibits the applications of them to real-world scenarios. To address this issue, drawing inspiration from advanced model merging techniques without requiring additional training, we introduce the Decentralized Iterative Merging-And-Training (DIMAT) paradigm--a novel decentralized deep learning framework. Within DIMAT, each agent is trained on their local data and periodically merged with their neighboring agents using advanced model merging techniques like activation matching until convergence is achieved. DIMAT provably converges with the best available rate for nonconvex functions with various first-order methods, while yielding tighter error bounds compared to the popular existing approaches. We conduct a comprehensive empirical analysis to validate DIMAT's superiority over baselines across diverse computer vision tasks sourced from multiple datasets. Empirical results validate our theoretical claims by showing that DIMAT attains faster and higher initial gain in accuracy with independent and identically distributed (IID) and non-IID data, incurring lower communication overhead. This DIMAT paradigm presents a new opportunity for the future decentralized learning, enhancing its adaptability to real-world with sparse and light-weight communication and computation., Comment: CVPR 2024 accepted paper, 22 pages, 12 figures

The temperature structure of a giant planet was traditionally thought to be an adiabat assuming convective mixing homogenizes entropy. The only in-situ measurement made by the Galileo Probe detected a near-adiabatic temperature structure within one of Jupiter's 5$\mu$m hot spots with small but definite local departures from adiabaticity. We analyze Juno's microwave observations near Jupiter's equator (0 ~ 5$^o$N) and find that the equatorial temperature structure is best characterized by a stable super-adiabatic temperature profile rather than an adiabatic one. Water is the only substance with sufficient abundance to alter the atmosphere's mean molecular weight and prevent dynamic instability if a super-adiabatic temperature gradient exists. Thus, from the super-adiabaticity, our results indicate a water concentration (or the oxygen to hydrogen ratio) of about 4.9 times solar with a possible range of 1.5 ~ 8.3 times solar in Jupiter's equatorial region., Comment: 46 pages, 11 figures, accepted by Icraus

Nokomis Regional High School, which draws nearly 600 students from eight different towns in rural Maine, has spent 10 years transforming its instructional model to immerse students in meaningful learning experiences that relate to their interests and passions. Project-based learning, interdisciplinary courses, and career exploration units are designed to help students explore various options and feel secure about their postsecondary choices. As students near graduation in this rural community, some plan to enroll in college, while others envision entering the trades or starting a job. Staff at Nokomis want students to achieve success on their own terms, which means challenging the predominantly college-oriented attitude that persists in high school practice and policy. But many teachers and administrators are still grappling with how to ensure that students choosing different pathways--some involving college, some not (or not yet)--will have an equal shot at long-term success and stability relative to their peers. Some staff think the school should more assertively push students toward college and career options beyond the local rural community. This case wrestles with how high schools can expand students' postsecondary horizons while remaining responsive to their values and interests--especially if those values differ significantly from those of the adults who support them. [For the corresponding Case Studies in High School Redesign, see ED626304 and ED626305.]

Between 2015 and 2019, Holyoke Public Schools in Massachusetts cut its dropout rate nearly in half and increased its graduation rate by 10 percentage points, with notable gains among historically marginalized student groups. The district cites Opportunity Academy (OA), an alternative education program within the district's high school, as a key driver of these improvements. OA surrounds students with supportive relationships and provides multiple pathways to graduation, including a competency-based pathway in which students earn credits through flexible learning experiences inside and outside traditional classrooms. But for OA's design team, getting students to graduate isn't enough. Real success, the principal said, means students have "the ability to live a choice-filled postsecondary life." This case wrestles with what's needed to achieve this higher bar for students who often just want to get high school over with, but whose success beyond the K-12 system depends on life skills, supportive relationships, and concrete postsecondary plans in addition to a diploma. The case concludes with key questions for high school leaders and design teams in both traditional and alternative education. [For the corresponding Case Studies in High School Redesign, see ED626303 and ED626304.]

This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical model (obtained from first principles or a simulator) and a noise model that contains the measurement noise introduced by the real environment. We focus on two types of noise, benign and adversarial noise, and develop a data-driven model for each type using generative models and classifiers, respectively. We show that the trained models perform well according to a variety of evaluation metrics based on downstream tasks such as state estimation and control. Finally, we verify the safety of two systems with high-dimensional data-driven models, namely an image-based version of mountain car (a reinforcement learning benchmark) as well as the F1/10 car, which uses LiDAR measurements to navigate a racing track., Comment: 23 pages, 12 figures, and 3 tables. Submitted to: 6th Annual Learning for Dynamics & Control Conference

The increasing concern surrounding gun violence in the United States has led to a focus on developing systems to improve public safety. One approach to developing such a system is to detect and track shooters, which would help prevent or mitigate the impact of violent incidents. In this paper, we proposed detecting shooters as a whole, rather than just guns, which would allow for improved tracking robustness, as obscuring the gun would no longer cause the system to lose sight of the threat. However, publicly available data on shooters is much more limited and challenging to create than a gun dataset alone. Therefore, we explore the use of domain randomization and transfer learning to improve the effectiveness of training with synthetic data obtained from Unreal Engine environments. This enables the model to be trained on a wider range of data, increasing its ability to generalize to different situations. Using these techniques with YOLOv8 and Deep OC-SORT, we implemented an initial version of a shooter tracking system capable of running on edge hardware, including both a Raspberry Pi and a Jetson Nano., Comment: 11 pages, 6 figures

JWST has ushered in an era of unprecedented ability to characterize exoplanetary atmospheres. While there are over 5,000 confirmed planets, more than 4,000 TESS planet candidates are still unconfirmed and many of the best planets for atmospheric characterization may remain to be identified. We present a sample of TESS planets and planet candidates that we identify as "best-in-class" for transmission and emission spectroscopy with JWST. These targets are sorted into bins across equilibrium temperature $T_{\mathrm{eq}}$ and planetary radius $R{_\mathrm{p}}$ and are ranked by transmission and emission spectroscopy metric (TSM and ESM, respectively) within each bin. In forming our target sample, we perform cuts for expected signal size and stellar brightness, to remove sub-optimal targets for JWST. Of the 194 targets in the resulting sample, 103 are unconfirmed TESS planet candidates, also known as TESS Objects of Interest (TOIs). We perform vetting and statistical validation analyses on these 103 targets to determine which are likely planets and which are likely false positives, incorporating ground-based follow-up from the TESS Follow-up Observation Program (TFOP) to aid the vetting and validation process. We statistically validate 23 TOIs, marginally validate 33 TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and leave the dispositions for 4 TOIs as inconclusive. 14 of the 103 TOIs were confirmed independently over the course of our analysis. We provide our final best-in-class sample as a community resource for future JWST proposals and observations. We intend for this work to motivate formal confirmation and mass measurements of each validated planet and encourage more detailed analysis of individual targets by the community., Comment: Submitted to AJ. Machine-readable versions of Tables 2 and 3 are included. 40 pages, 7 figures, 3 tables

Water and ammonia vapors are known to be the major sources of spectral absorption at pressure levels observed by the microwave radiometer (MWR) on Juno. However, the brightness temperatures and limb darkening observed by the MWR at its longest wavelength channel of 50 cm (600 MHz) in the first 9 perijove passes indicate the existence of an additional source of opacity in the deep atmosphere of Jupiter (pressures beyond 100 bar). The absorption properties of ammonia and water vapor, and their relative abundances in Jupiter's atmosphere do not provide sufficient opacity in deep atmosphere to explain the 600 MHz channel observation. Here we show that free electrons due to the ionization of alkali metals, i.e. sodium, and potassium, with sub-solar metallicity [M/H] (log based 10 relative concentration to solar) in the range of [M/H] = -2 to [M/H] = -5 can provide the missing source of opacity in the deep atmosphere. If the alkali metals are not the source of additional opacity in the MWR data, then their metallicity at 1000 bars can only be even lower. The upper bound of -2 on the metallicity of the alkali metals contrasts with the other heavy elements -- C, N, S, Ar, Kr, and Xe -- which are all enriched relative to their solar abundances having a metallicity of approximately +0.5., Comment: This manuscript has been accepted for publication in The Astrophysical Journal Letters. The final version of the paper will be available in the published journal. This arXiv version is provided for informational purposes

So far no designated mission to either of the two ice giants, Uranus and Neptune, exists. Almost all of our gathered information on these planets comes from remote sensing. In recent years, NASA and ESA have started planning for future mission to Uranus and Neptune, with both agencies focusing their attention on orbiters and atmospheric probes. Whereas information provided by remote sensing is undoubtedly highly valuable, remote sensing of planetary atmospheres also presents some shortcomings, most of which can be overcome by mass spectrometers. In most studies presented to date a mass spectrometer experiment is thus a favored science instrument for in situ composition measurements on an atmospheric probe. Mass spectrometric measurements can provide unique scientific data, i.e., sensitive and quantitative measurements of the chemical composition of the atmosphere, including isotopic, elemental, and molecular abundances. In this review paper we present the technical aspects of mass spectrometry relevant to atmospheric probes. This includes the individual components that make up mass spectrometers and possible implementation choices for each of these components. We then give an overview of mass spectrometers that were sent to space with the intent of probing planetary atmospheres, and discuss three instruments, the heritage of which is especially relevant to Uranus and Neptune probes, in detail. The main part of this paper presents the current state-of-art in mass spectrometry intended for atmospheric probe. Finally, we present a possible descent probe implementation in detail, including measurement phases and associated expected accuracies for selected species.