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This is the technical document for the report, "Centering Quality, Centering Equity: Lessons Learned in Increasing Early Childhood Educator Credentials." The report explores Early Childhood Education (ECE) credential requirements in California (CA) and Washington, D.C. (D.C.) for two key reasons: 1) both recently raised these requirements and 2) both are in the process of implementing the new changes. The study does not seek to advocate in favor of or against the adoption of increased credential requirements. Rather, its purpose is to shed light on the need to center racial and economic equity in policy design and implementation--focusing on the impact of increased credential requirements on early childhood educators in states pursuing these changes. The appendices in this technical document are: (1) Make-up of Early Care and Education Workforce; (2) Center-Based Early Educator Characteristics by Role; (3) Center-Based Early Educator Work Experience and Credentials by Role; (4) Center-Based Early Educator Time Worked, Income, and Wages by Role; (5) Lead Teachers' Work Experience and Credentials by Race and Ethnicity; (6) Lead Teachers without a Postsecondary Degree, Characteristics by Race and Ethnicity; (7) Focus Group Interview Protocol for California; (8) Focus Group Interview Protocol for Washington, D.C.; (9) Hourly Wages for Early Childhood Educators; and (10) Comparison of D.C. Teacher Salaries in 2022 and Minimum Salaries Required in Fiscal Year 2024 for Programs Receiving Pay Equity Funds.

Thriving communities depend on a strong early childhood education (ECE) system--one where both young children and members of the workforce are served and supported. In recent years, state government leaders have increasingly focused on changing qualifications for specific ECE roles, as increasing credential requirements has sometimes been associated with increasing quality. However, across the country, early childhood educators face significant barriers to economic security and continuing education--all while supporting children, parents, and their communities with specialized education services. This report examines the barriers that even highly qualified early child educators experience in the face of increased credential requirements. Featuring case studies of California and Washington, D.C., the report offers implications for policies that protect educators and advance racial and economic equity in the profession. Included with the report are an Executive Summary encapsulating the findings and a Technical Document describing the methodology, data collection, and quantitative analysis.

As the infrastructure of police surveillance grows in public schools, communities must be prepared to safeguard the rights and freedoms of students and families. This report is designed to help youth justice advocates, youth leaders, educators, caregivers, and policymakers understand and challenge the impact of school surveillance, data criminalization, and police surveillance technologies in schools. This report includes: (1) An analysis of six key facts about the impacts of data criminalization and school surveillance technologies on education equity; (2) A case study of an AI school surveillance technology that can land children in adult misdemeanor court; and (3) Key recommendations for education policymakers and school district leaders for advancing youth data justice.

In an effort to reduce state spending on special education in public schools, New Jersey moved to census-based funding as part of the new school funding formula, the School Funding Reform Act (SFRA), adopted in 2008. The census approach funds all districts using the statewide average classification rate and a statewide average "excess cost." This report found that: (1) Classification rates for special education vary greatly among school districts, resulting in the inequitable distribution of resources to fund districts' actual special education enrollment; (2) In 2022-23, districts with classification rates higher than the statewide average received $378 million less than if they were funded on their actual special education enrollment; and (3) Districts with higher-than-average classification rates must divert funding from general education programs or raise additional local revenue to fill the gap for unfunded special education students. In this report, the authors show that census-based funding does not meet the needs of New Jersey school districts and the students, in both special and general education, they serve. The analyses presented in this report demonstrate the need for New Jersey to convene school finance and special education experts to explore and recommend alternative funding models for special education that would better meet the needs of the state's school districts.

The School Funding Reform Act (SFRA), New Jersey's school finance law, requires the Governor, in consultation with the Commissioner of Education, to review certain components of the school funding formula every three years. The New Jersey Department of Education (NJDOE) issued four Educational Adequacy Reports (EAR) between 2013 and 2022, but none provided the in-depth analysis that is required to ensure the formula is delivering adequate resources to all students. These reports updated the underlying costs of the formula while failing to consider whether the inputs themselves are adequate for delivering the state's evolving learning standards. These surface-level reviews have left the SFRA out of step with current thinking on the resources needed to meet the state's academic and social-emotional standards. To ensure New Jersey students are receiving a constitutional "thorough and efficient" education, it is time for the NJDOE to conduct a much more thorough analysis. The FY2026 EAR, due to the Legislature in early 2025, provides the long overdue opportunity to do a thorough analysis of the formula and its components to ensure it is calibrated to the resources needed to provide all students with an adequate education aligned with New Jersey's current standards and curriculum.

This is the executive summary for the full report, "Centering Quality, Centering Equity: Lessons Learned in Increasing Early Childhood Educator Credentials." Thriving communities depend on a strong early childhood education (ECE) system--one where both young children and members of the workforce are served and supported. In recent years, state government leaders have increasingly focused on changing qualifications for specific ECE roles, as increasing credential requirements has sometimes been associated with increasing quality. However, across the country, early childhood educators face significant barriers to economic security and continuing education--all while supporting children, parents, and their communities with specialized education services.

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. In traditional computer vision tasks, creative or approximate answers may be acceptable, but in healthcare, precision is paramount.Current large multimodal models like Gemini and GPT-4o are insufficient for medical tasks due to their reliance on memorized internet knowledge rather than the nuanced expertise required in healthcare. VLMs are usually trained in three stages: vision pre-training, vision-language pre-training, and instruction fine-tuning (IFT). IFT has been typically applied using a mixture of generic and healthcare data. In contrast, we propose that for medical VLMs, a fourth stage of specialized IFT is necessary, which focuses on medical data and includes information from domain expert models. Domain expert models developed for medical use are crucial because they are specifically trained for certain clinical tasks, e.g. to detect tumors and classify abnormalities through segmentation and classification, which learn fine-grained features of medical data$-$features that are often too intricate for a VLM to capture effectively especially in radiology. This paper introduces a new framework, VILA-M3, for medical VLMs that utilizes domain knowledge via expert models. Through our experiments, we show an improved state-of-the-art (SOTA) performance with an average improvement of ~9% over the prior SOTA model Med-Gemini and ~6% over models trained on the specific tasks. Our approach emphasizes the importance of domain expertise in creating precise, reliable VLMs for medical applications.

The ongoing physical and chemical processes in planet-forming disks set the stage for planet formation. The asymmetric disk around the young star Oph-IRS 48 has one of the most well-characterised chemical inventories, showing molecular emission from a wide variety of species at the dust trap. One of the explanations for the asymmetric structure is dust trapping by a perturbation-induced vortex. We aim to constrain the excitation properties of the molecular species SO$_2$, CH$_3$OH, and H$_2$CO. We further characterise the extent of the molecular emission, through the determination of important physical and chemical timescales at the location of the dust trap. We also investigate whether the potential vortex can influence the observable temperature structure of the gas. Through a pixel-by-pixel rotational diagram analysis, we create rotational temperature and column density maps for SO$_2$ and CH$_3$OH, while temperature maps for H$_2$CO are created using line ratios. We find temperatures of $T\sim$55 K and $T\sim$125 K for SO$_2$ and CH$_3$OH, respectively, while the line ratios point towards temperatures of T$\sim$150-300 K for H$_2$CO. The rotational diagram of CH$_3$OH is dominated by scatter and subsequent non-LTE RADEX calculations suggest that both CH$_3$OH and H$_2$CO must be sub-thermally excited. The temperatures suggest that SO$_2$ comes from a layer deep in the disk, while CH$_3$OH and H$_2$CO originate from a higher layer. While a potential radial gradient is seen in the temperature map of SO$_2$, we do not find any hints of a vortex influencing the temperature structure. The determined turbulent mixing timescale is not able to explain the emitting heights of the molecules, but the photodissociation timescales are able to explain the wider azimuthal extents of SO$_2$ and H$_2$CO compared to CH$_3$OH, where a secondary, gas-phase formation reservoir is required for H$_2$CO., Comment: Accepted for publication in Astronomy and Astrophysics

The formalism of quantum estimation theory with a specific focus on the classical postprocessing of data is applied to a two-level system driven by an external gyrating magnetic field. We employed both Bayesian and frequentist approaches to estimate the unknown transition frequency. In the frequentist approach, we have shown that reducing the distance between the classical and the quantum Fisher information does not necessarily mean that the estimators as functions of the data deliver an estimate with desirable precision. Due to the nonlinearity of the probability mass function of the data on the transition frequency, the minimum variance unbiased estimator may not exist. The maximum likelihood and the maximum a posteriori estimators often result in ambiguous estimates, which in certain cases can be made unambiguous upon changing the parameters of the external field. It is demonstrated that the most promising solution is given by the minimum mean-square error estimator of the Bayesian statistics, which is efficient even for finite data points. In the Bayesian approach, we have considered both informative and noninformative priors, including a uniform prior, Jeffrey's prior, and a Gaussian prior., Comment: 14 pages, 7 figures

The nonlinear Hall effect refers to the nonlinear voltage response that is transverse to the applied electric field. Recent studies have shown that the quantum geometric quantities on Fermi surfaces serve as fundamental contributors to the nonlinear Hall effect, suggesting that the nonlinear Hall effect occurs mainly in metals. However, in this work, we demonstrate that insulators can also exhibit the nonlinear Hall effect. We find that for an insulator driven at a finite frequency, a series of frequency dependent quantum geometric quantities from the occupied bands can give rise to a nonvanishing nonlinear Hall conductivity. The nonlinear Hall conductivity is frequency dependent: at resonance, it represents the inter-band transition enabled nonlinear Hall current; near resonance, it represents the nonlinear order polarization transverse to the electric field. We further connect the nonlinear Hall conductivity to the Kleinman conjecture in nonlinear optics and point out that the nonlinear Hall effect is generally allowed in insulators given the driving frequency near resonance. For the candidate materials, we consider the biased Bernal bilayer graphene under uniaxial strain and propose polarization resolved second harmonic microscopy to detect the nonlinear Hall effect there., Comment: 7 pages, 4 figures. Comments are welcome

Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5-15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model's advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://github.com/xlwang233/CaLLiPer., Comment: 19 pages, 5 figures, 7 tables

To a graph $\Gamma$, one can associate a hypertoric variety $\mathcal{M}(\Gamma)$ and its multiplicative version $\mathcal{M}^{\mathrm{mul}}(\Gamma)$. It was shown in [DMS24] that the cohomology of $\mathcal{M}^{\mathrm{mul}}(\Gamma)$ is computed by the CKS complex, which is a finite dimensional complex attached to $\Gamma$. The multiplicative hypertoric variety can be realized as the quotient of a periodized hypertoric variety by a lattice action. In this paper, we show that the group cohomology of the lattice with coefficients in the cohomology of the prequotient is isomorphic to the cohomology of the CKS complex using a spectral sequence argument. Therefore, the group cohomology can serve as an alternative way to compute the cohomology of multiplicative hypertoric varieties. We also found graph-theoretic descriptions for the Euler characteristics of the graded pieces in a certain decomposition of $\mathrm{H}^\bullet(\mathcal{M}^{\mathrm{mul}}(\Gamma))$., Comment: 31 pages, 5 figures

We extend our recently identified dynamical edge mode boundary condition to $p$-form gauge theories, revealing their edge modes as Goldstone bosons arising from gauge transformations with support on the boundary. The symplectic conjugates of these edge modes correspond to the electric-field-like components normal to the boundary. We demonstrate that both the symplectic form and the Hamiltonian naturally decompose into bulk and edge parts. When the boundary is a stretched horizon, we show that the thermal edge partition function reduces to that of a codimension-two ghost $(p-1)$-form residing on the bifurcation surface. These findings provide a dynamical framework that elucidates observations made by several authors. Additionally, we generalize Donnelly and Wall's non-dynamical approach to obtain edge partition functions for both massive and massless $p$-forms. In the context of a de Sitter static patch, these results are consistent with the edge partition functions found by several authors in arbitrary dimensions., Comment: 36 pages + 3 appendices; 1 figure

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

Different camera sensors have different noise patterns, and thus an image denoising model trained on one sensor often does not generalize well to a different sensor. One plausible solution is to collect a large dataset for each sensor for training or fine-tuning, which is inevitably time-consuming. To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain). The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain (some data are harmful as adding them during training lowers the performance due to domain gaps). Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising. We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor., Comment: 13 pages, 3 figures, accepted by neurips 2024

Massive star-forming regions exhibit a rich chemistry with complex gas distributions, especially on small scales. While surveys have yielded constraints on typical gas conditions, they often have coarse spatial resolution and limited bandwidths. Thus, to establish an interpretative framework for these efforts, detailed observations that simultaneously provide high sensitivity, spatial resolution, and large bandwidths for a subset of diverse sources are needed. Here, we present wideband (32 GHz) Submillimeter Array observations of four high-mass star-forming regions (G28.20-0.05, G20.08-0.14 N, G35.58-0.03, W33 Main) at subarcsecond resolution, where we detect and spatially-resolve 100s of lines from over 60 molecules, including many complex organic molecules (COMs). The chemical richness of our sample is consistent with an evolutionary sequence from the line-rich hot cores and HC HII regions of G28.20-0.05 and G20.08-0.14 N to the more chemically-modest UC HII regions in G35.58-0.03, followed by the molecule-poor HII region W33 Main. We detect lines across a range of excitation conditions (Eu=20 to >800 K) and from numerous isotopologues, which enables robust estimates of gas properties. We derive nearly constant COM column density ratios that agree with literature values in other low- and high-mass protostellar cores, supporting the idea that COM abundances are set during the pre-stellar phase. In all regions, we identify spatial offsets among different molecular families, due to a combination of source physical structure and chemistry. In particular, we find potential evidence of carbon grain sublimation in G28.20-0.05 and identify an elemental oxygen gradient and rich sulfur-chemistry in G35.58-0.03. Overall, these results demonstrate that the SMA's wide bandwidth is a powerful tool to untangle the complex molecular gas structures associated with massive star formation., Comment: 47 pages, 36 figures, accepted for publication in ApJS. Data and image cubes available at https://zenodo.org/records/13342640

3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, leading to poor performance on unfamiliar scenes. We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. These uncertainty estimates require minimal computational overhead and are generalizable across different architectures. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections; our framework consistently improves over baselines by 10-20% on average. Finally, we integrate this suite of tasks into a system where a 3D object detector auto-labels driving scenes and our uncertainty estimates verify label correctness before the labels are used to train a second model. Here, our uncertainty-driven verification results in a 1% improvement in mAP and a 1-2% improvement in NDS.

Evidence for similar chemical characteristics around low- and high-mass protostars has been found: in particular, a variety of carbon-chain species and complex organic molecules (COMs) are formed around them. On the other hand, the chemical compositions around intermediate-mass (IM; $2 M_{\odot} < m_* <8 M_{\odot}$) protostars have not been studied with large samples. In particular, it is unclear the extent to which carbon-chain species are formed around them. We aim to obtain the chemical compositions, particularly focusing on carbon-chain species, towards a sample of IM protostars. We have conducted Q-band (31.5-50 GHz) line survey observations towards eleven mainly intermediate-mass protostars with the Yebes 40 m radio telescope. The target protostars were selected from a sub-sample of the source list of the SOFIA Massive (SOMA) Star Formation project. Nine carbon-chain species (HC$_3$N, HC$_5$N, C$_3$H, C$_4$H, $linear-$H$_2$CCC, $cyclic-$C$_3$H$_2$, CCS, C$_3$S, and CH$_3$CCH), three COMs (CH$_3$OH, CH$_3$CHO, and CH$_3$CN), H$_2$CCO, HNCO, and four simple sulfur (S)-bearing species ($^{13}$CS, C$^{34}$S, HCS$^+$, H$_2$CS) have been detected. The rotational temperatures of HC$_5$N are derived to be $\sim20-30$ K in three IM protostars and they are very similar compared to those around low- and high-mass protostars. These results indicate that carbon-chain molecules are formed in lukewarm ($\sim20-30$ K) gas around the IM protostars by the Warm Carbon-Chain Chemistry (WCCC) process. Carbon-chain formation occurs ubiquitously in the warm gas around protostars across a wide range of stellar masses. Carbon-chain molecules and COMs coexist around most of the target IM protostars, which is similar to the situation in low- and high-mass protostars. The chemical characteristics around protostars are common in the low-, intermediate- and high-mass regimes., Comment: Accepted for publication in the Astronomy and Astrophysics (A&A)

Aligning AI agents with human values is challenging due to diverse and subjective notions of values. Standard alignment methods often aggregate crowd feedback, which can result in the suppression of unique or minority preferences. We introduce Interactive-Reflective Dialogue Alignment, a method that iteratively engages users in reflecting on and specifying their subjective value definitions. This system learns individual value definitions through language-model-based preference elicitation and constructs personalized reward models that can be used to align AI behaviour. We evaluated our system through two studies with 30 participants, one focusing on "respect" and the other on ethical decision-making in autonomous vehicles. Our findings demonstrate diverse definitions of value-aligned behaviour and show that our system can accurately capture each person's unique understanding. This approach enables personalized alignment and can inform more representative and interpretable collective alignment strategies., Comment: 19 pages, 16 figures

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physical scales. In this work, we seek to use a recently proposed approach called the Velocity Gradient Technique (VGT) to study B-fields in star-forming regions and compare the results with dust polarization observations in different wavelengths. The VGT is based on the anisotropic properties of eddies in magnetized turbulence to derive B-field properties in the ISM. We investigate that this technique is synergistic with dust polarimetry when applied to a turbulent diffused medium for the purpose of measuring its magnetization. Specifically, we use the VGT on molecular line data toward the NGC~1333 star-forming region ($\rm ^{12}CO$, $\rm ^{13}CO$, $\rm C^{18}O$, and $\rm N_{2}H^{+}$), and we compare the derived B-field properties with those inferred from 214 and 850~$\mu$m dust polarization observations of the region using SOFIA/HAWC+ and JCMT/POL-2, respectively. We estimate both the inclination angle and the 3D Alfv\'enic Mach Number $M_A$ from the molecular line gradients. Crucially, testing this technique on gravitationally bound, dynamic, and turbulent regions, and comparing the results with those obtained from polarization observations at different wavelength, such as the plane-of-the-sky field orientation, is an important test on the applicability of the VGT in various density regimes of the ISM., Comment: 16 pages, 6 figures, Accepted for publication in ApJ

We present James Webb Space Telescope (JWST)-NIRCam observations of the massive star-forming molecular cloud Sagittarius C (Sgr C) in the Central Molecular Zone (CMZ). In conjunction with ancillary mid-IR and far-IR data, we characterize the two most massive protostars in Sgr C via spectral energy distribution (SED) fitting, estimating that they each have current masses of $m_* \sim 20\:M_\odot$ and surrounding envelope masses of $\sim 100\:M_\odot$. We report a census of lower-mass protostars in Sgr C via a search for infrared counterparts to mm continuum dust cores found with ALMA. We identify 88 molecular hydrogen outflow knot candidates originating from outflows from protostars in Sgr C, the first such unambiguous detections in the infrared in the CMZ. About a quarter of these are associated with flows from the two massive protostars in Sgr C; these extend for over 1 pc and are associated with outflows detected in ALMA SiO line data. An additional $\sim 40$ features likely trace shocks in outflows powered by lower-mass protostars throughout the cloud. We report the discovery of a new star-forming region hosting two prominent bow shocks and several other line-emitting features driven by at least two protostars. We infer that one of these is forming a high-mass star given an SED-derived mass of $m_* \sim 9\:M_\odot$ and associated massive ($\sim 90\:M_\odot$) mm core and water maser. Finally, we identify a population of miscellaneous Molecular Hydrogen Objects (MHOs) that do not appear to be associated with protostellar outflows., Comment: Appendix figures B1 and B2 will be made into online-only figure sets for the eventual ApJ publication

Dust from core-collapse supernovae (CCSNe), specifically Type IIP SNe, has been suggested to be a significant source of the dust observed in high-redshift galaxies. CCSNe eject large amounts of newly formed heavy elements, which can condense into dust grains in the cooling ejecta. However, infrared (IR) observations of typical CCSNe generally measure dust masses that are too small to account for the dust production needed at high redshifts. Type IIn SNe, classified by their dense circumstellar medium (CSM), are also known to exhibit strong IR emission from warm dust, but the dust origin and heating mechanism have generally remained unconstrained because of limited observational capabilities in the mid-IR. Here, we present a JWST/MIRI Medium Resolution Spectrograph (MRS) spectrum of the Type IIn SN 2005ip nearly 17 years post-explosion. The Type IIn SN 2005ip is one of the longest-lasting and most well-studied SNe observed to date. Combined with a Spitzer mid-IR spectrum of SN 2005ip obtained in 2008, this data set provides a rare 15-year baseline, allowing for a unique investigation of the evolution of dust. The JWST spectrum shows a new high-mass dust component ($\gtrsim0.08$ M$_{\odot}$) that is not present in the earlier Spitzer spectrum. Our analysis shows dust likely formed over the past 15 years in the cold, dense shell (CDS), between the forward and reverse shocks. There is also a smaller mass of carbonaceous dust ($\gtrsim0.005$ M$_{\odot}$) in the ejecta. These observations provide new insights into the role of SN dust production, particularly within the CDS, and its potential contribution to the rapid dust enrichment of the early Universe.

Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, dust continuum polarization vector orientation angles, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis-Chandrasekhar-Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM., Comment: submitted to ApJ, comments welcome

We report the detection of broad, flat-topped emission in the fine-structure lines of [Ne V], [Ne VI], and [O IV] in mid-infrared spectra of the O9 V star 10 Lacertae obtained with JWST/MIRI. Optically thin emission in these high ions traces a hot, low-density component of the wind. The observed line fluxes imply a mass-loss rate of > 3 x 10^8 Msun/yr, which is an order of magnitude larger than previous estimates based on UV and optical diagnostics. The presence of this hot component reconciles measured values of the mass-loss rate with theoretical predictions, and appears to solve the "weak wind" problem for the particular case of 10 Lac., Comment: 8 pages, 5, figures, ApJL in press

The most powerful stellar flares driven by magnetic energy occur during the early pre-main sequence (PMS) phase. The Orion Nebula represents the nearest region populated by young stars, showing the greatest number of flares accessible to a single pointing of Chandra. This study is part of a multi-observatory project to explore stellar surface magnetic fields (with HET-HPF), particle ejections (VLBA), and disk ionization (ALMA) immediately following the detection of PMS super-flares with Chandra. In December 2023, we successfully conducted such a multi-telescope campaign. Additionally, by analyzing Chandra data from 2003, 2012, and 2016, we examine the multi-epoch behavior of PMS X-ray emission related to PMS magnetic cyclic activity and ubiquitous versus sample-confined mega-flaring. Our findings follow. 1) We report detailed stellar quiescent and flare X-ray properties for numerous HET/ALMA/VLBA targets, facilitating ongoing multi-wavelength analyses. 2) For numerous moderately energetic flares, we report correlations (or lack thereof) between flare energies and stellar mass/size (presence/absence of disks) for the first time. The former is attributed to the correlation between convection-driven dynamo and stellar volume, while the latter suggests the operation of solar-type flare mechanisms in PMS stars. 3) We find that most PMS stars exhibit minor long-term baseline variations, indicating the absence of intrinsic magnetic dynamo cycles or observational mitigation of cycles by saturated PMS X-rays. 4) We conclude that X-ray mega-flares are ubiquitous phenomena in PMS stars, which suggests that all protoplanetary disks and nascent planets are subject to violent high-energy emission and particle irradiation events., Comment: 32 pages, 9 figures, 8 tables. Accepted for publication in The Astrophysical Journal, October 7, 2024

The composition of a planet's atmosphere is intricately linked to the chemical makeup of the protoplanetary disk in which it formed. Determining the elemental abundances from key volatiles within disks is therefore essential for establishing connections between the composition of disks and planets. The disk around the Herbig Ae star HD 169142 is a compelling target for such a study due to its molecule-rich nature and the presence of a newly-forming planet between two prominent dust rings. In this work, we probe the chemistry of the HD 169142 disk at small spatial scales, drawing links between the composition of the disk and the planet-accreted gas. Using thermochemical models and archival data, we constrain the elemental abundances of volatile carbon, oxygen, and sulfur. Carbon and oxygen are only moderately depleted from the gas phase relative to their interstellar abundances, with the inner 60 au appearing enriched in volatile oxygen. The C/O ratio is approximately solar within the inner disk and rises above this in the outer disk, as expected across the H2O snowline. The gas-phase sulfur abundance is depleted by a factor of 1000, consistent with a number of other protoplanetary disks. Interestingly, the observed SiS emission near the HD 169142 b protoplanet vastly exceeds chemical model predictions, supporting previous hypotheses suggesting its origin in shocked gas or a localised outflow. We contextualise our findings in terms of the potential atmospheric composition of the embedded planet, and highlight the utility of sulfur-bearing molecules as probes of protoplanetary disk chemistry., Comment: Accepted for publication in MNRAS

Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or deformation of a template, or as a disordered triangle soup lacking local structure. This work presents a scheme to directly generate manifold, polygonal meshes of complex connectivity as the output of a neural network. Our key innovation is to define a continuous latent connectivity space at each mesh vertex, which implies the discrete mesh. In particular, our vertex embeddings generate cyclic neighbor relationships in a halfedge mesh representation, which gives a guarantee of edge-manifoldness and the ability to represent general polygonal meshes. This representation is well-suited to machine learning and stochastic optimization, without restriction on connectivity or topology. We first explore the basic properties of this representation, then use it to fit distributions of meshes from large datasets. The resulting models generate diverse meshes with tessellation structure learned from the dataset population, with concise details and high-quality mesh elements. In applications, this approach not only yields high-quality outputs from generative models, but also enables directly learning challenging geometry processing tasks such as mesh repair., Comment: published at SIGGRAPH Asia 2024

The Neptunian desert and savanna have been recently found to be separated by a ridge, an overdensity of planets in the $\simeq$3-5 days period range. These features are thought to be shaped by dynamical and atmospheric processes. However, their relative roles are not yet well understood. We intend to confirm and characterise the super-Neptune TESS candidate TOI-5005.01, which orbits a moderately bright (V = 11.8) solar-type star (G2 V) with an orbital period of 6.3 days. We confirm TOI-5005 b to be a transiting super-Neptune with a radius of $R_{\rm p}$ = $6.25\pm 0.24$ $\rm R_{\rm \oplus}$ ($R_{\rm p}$ = $0.558\pm 0.021$ $\rm R_{\rm J}$) and a mass of $M_{\rm p}$ = $32.7\pm 5.9$ $\rm M_{\oplus}$ ($M_{\rm p}$ = $0.103\pm 0.018$ $\rm M_{\rm J}$), which corresponds to a mean density of $\rho_{\rm p}$ = $0.74 \pm 0.16$ $\rm g \, cm^{-3}$. Our internal structure modelling indicates that the overall metal mass fraction is well constrained to a value slightly lower than that of Neptune and Uranus ($Z_{\rm planet}$ = $0.76^{+0.04}_{-0.11}$). We also estimated the present-day atmospheric mass-loss rate of TOI-5005 b but found contrasting predictions depending on the choice of photoevaporation model. At a population level, we find statistical evidence ($p$-value = $0.0092^{+0.0184}_{-0.0066}$) that planets in the savanna such as TOI-5005 b tend to show lower densities than planets in the ridge, with a dividing line around 1 $\rm g \, cm^{-3}$, which supports the hypothesis of different evolutionary pathways populating both regimes. TOI-5005 b is located in a key region of the period-radius space to study the transition between the Neptunian ridge and the savanna. It orbits the brightest star of all such planets, which makes it a target of interest for atmospheric and orbital architecture observations that will bring a clearer picture of its overall evolution., Comment: Accepted for publication in A&A. Abstract shortened. 35 pages, 26 figures

Fast Radio Bursts (FRBs) are millisecond-duration events detected from beyond the Milky Way. FRB emission characteristics favor highly magnetized neutron stars, or magnetars, as the sources, as evidenced by FRB-like bursts from a galactic magnetar, and the star-forming nature of FRB host galaxies. However, the processes that produce FRB sources remain unknown. Although galactic magnetars are often linked to core-collapse supernovae (CCSNe), it's uncertain what determines which supernovae result in magnetars. The galactic environments of FRB sources can be harnessed to probe their progenitors. Here, we present the stellar population properties of 30 FRB host galaxies discovered by the Deep Synoptic Array. Our analysis shows a significant deficit of low-mass FRB hosts compared to the occurrence of star-formation in the universe, implying that FRBs are a biased tracer of star-formation, preferentially selecting massive star-forming galaxies. This bias may be driven by galaxy metallicity, which is positively correlated with stellar mass. Metal-rich environments may favor the formation of magnetar progenitors through stellar mergers, as higher metallicity stars are less compact and more likely to fill their Roche lobes, leading to unstable mass transfer. Although massive stars do not have convective interiors to generate strong magnetic fields by dynamo, merger remnants are thought to have the requisite internal magnetic-field strengths to result in magnetars. The preferential occurrence of FRBs in massive star-forming galaxies suggests that CCSN of merger remnants preferentially forms magnetars., Comment: Accepted for publication in Nature. The final version will be published by the journal

Approximately half of the Universe's dark matter resides in collapsed halos; significantly less than half of the baryonic matter (protons and neutrons) remains confined to halos. A small fraction of baryons are in stars and the interstellar medium within galaxies. The lion's share are diffuse (less than $10^{-3}$ cm$^{-3}$) and ionized (neutral fraction less than $10^{-4}$), located in the intergalactic medium (IGM) and in the halos of galaxy clusters, groups, and galaxies. The quantity and spatial distribution of this diffuse ionized gas is notoriously difficult to measure, but has wide implications for galaxy formation, astrophysical feedback, and precision cosmology. Recently, the dispersion of extragalactic Fast Radio Bursts (FRBs) has been used to measure the total content of cosmic baryons. However, past efforts had modest samples and methods that cannot discriminate between IGM and halo gas, which is critical for studying feedback and for observational cosmology. Here, we present a large cosmological sample of FRB sources localized to their host galaxies. We have robustly partitioned the missing baryons into the IGM, galaxy clusters, and galaxies, providing a late-Universe measurement of the total baryon density of $\Omega_b h_{70}$=0.049$\pm$0.003. Our results indicate efficient feedback processes that can expel gas from galaxy halos and into the intergalactic medium, agreeing with the enriched cosmic web scenario seen in cosmological simulations. The large diffuse baryon fraction that we have measured disfavours bottom-heavy stellar initial mass functions, which predict a large total stellar density, $\Omega_*$.

We describe the spectrophotometric calibration of the Mid-Infrared Instrument's (MIRI) Medium Resolution Spectrometer (MRS) aboard the James Webb Space Telescope (JWST). This calibration is complicated by a time-dependent evolution in the effective throughput of the MRS; this evolution is strongest at long wavelengths, approximately a factor of 2 at 25um over the first two years of the mission. We model and correct for this evolution through regular observations of internal calibration lamps. Pixel flatfields are constructed from observations of the infrared-bright planetary nebula NGC 7027, and photometric aperture corrections from a combination of theoretical models and observations of bright standard stars. We tie the 5--18um flux calibration to high signal/noise (S/N; ~ 600-1000) observations of the O9 V star 10 Lacertae, scaled to the average calibration factor of nine other spectrophotometric standards. We calibrate the 18--28um spectral range using a combination of observations of main belt asteroid 515 Athalia and the circumstellar disk around young stellar object SAO 206462. The photometric repeatability is stable to better than 1% in the wavelength range 5--18um, and the S/N ratio of the delivered spectra is consistent between bootstrapped measurements, pipeline estimates, and theoretical predictions. The MRS point-source calibration agrees with that of the MIRI imager to within 1% from 7 to 21um and is approximately 1% fainter than prior Spitzer observations, while the extended source calibration agrees well with prior Cassini/CIRS and Voyager/IRIS observations., Comment: 29 pages, 32 figures. Submitted to AJ

Recent work on the quantum mechanics of near-extremal non-supersymmetric black holes has identified a characteristic $T^{3/2}$ scaling of the low temperature black hole partition function. This result has only been derived using the path integral in the near-horizon region and relies on many assumptions. We discuss how to derive the $T^{3/2}$ scaling for the near-extremal rotating BTZ black hole from a calculation in the full black hole background using the Denef-Hartnoll-Sachdev (DHS) formula, which expresses the 1-loop determinant of a thermal geometry in terms of a product over the quasinormal mode spectrum. We also derive the spectral measure for fields of any spin in Euclidean BTZ and use it to provide a new proof of the DHS formula and a new, direct derivation of the BTZ heat kernel. The computations suggest a path to proving the $T^{3/2}$ scaling for the asymptotically flat 4d Kerr black hole., Comment: 32 pages

The identification of persistent radio sources (PRSs) coincident with two repeating fast radio bursts (FRBs) supports FRB theories requiring a compact central engine. However, deep non-detections in other cases highlight the diversity of repeating FRBs and their local environments. Here, we perform a systematic search for radio sources towards 37 CHIME/FRB repeaters using their arcminute localizations and a combination of archival surveys and targeted observations. Through multi-wavelength analysis of individual radio sources, we identify two (20181030A-S1 and 20190417A-S1) for which we disfavor an origin of either star formation or an active galactic nucleus in their host galaxies and thus consider them candidate PRSs. We do not find any associated PRSs for the majority of the repeating FRBs in our sample. For 8 FRB fields with Very Large Array imaging, we provide deep limits on the presence of PRSs that are 2--4 orders of magnitude fainter than the PRS associated with FRB\,20121102A. Using Very Large Array Sky Survey imaging of all 37 fields, we constrain the rate of luminous ($\gtrsim$10$^{40}$ erg s$^{-1}$) PRSs associated with repeating FRBs to be low. Within the context of FRB-PRS models, we find that 20181030A-S1 and 20190417A-S1 can be reasonably explained within the context of magnetar, hypernebulae, gamma-ray burst afterglow, or supernova ejecta models -- although we note that both sources follow the radio luminosity versus rotation measure relationship predicted in the nebula model framework. Future observations will be required to both further characterize and confirm the association of these PRS candidates with the FRBs., Comment: 37 pages, 12 figures

The absolute flux calibration of the Mid-Infrared Instrument Imaging and Coronagraphy is based on observations of multiple stars taken during the first 2.5 years of JWST operations. The observations were designed to ensure that the flux calibration is valid for a range of flux densities, different subarrays, and different types of stars. The flux calibration was measured by combining observed aperture photometry corrected to infinite aperture with predictions based on previous observations and models of stellar atmospheres. A subset of these observations were combined with model point-spread-functions to measure the corrections to infinite aperture. Variations in the calibration factor with time, flux density, background level, type of star, subarray, integration time, rate, and well depth were investigated, and the only significant variations were with time and subarray. Observations of the same star taken approximately every month revealed a modest time-dependent response loss seen mainly at the longest wavelengths. This loss is well characterized by a decaying exponential with a time constant of ~200 days. After correcting for the response loss, the band-dependent scatter around the corrected average (aka repeatability) was found to range from 0.1 to 1.2%. Signals in observations taken with different subarrays can be lower by up to 3.4% compared to FULL frame. After correcting for the time and subarray dependencies, the scatter in the calibration factors measured for individual stars ranges from 1 to 4% depending on the band. The formal uncertainties on the flux calibration averaged for all observations are 0.3 to 1.0%, with longer-wavelength bands generally having larger uncertainties., Comment: 26 pages, 17 figures, submitted to AJ

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes.

Planet formation is strongly influenced by the composition and distribution of volatiles within protoplanetary disks. With JWST, it is now possible to obtain direct observational constraints on disk ices, as recently demonstrated by the detection of ice absorption features towards the edge-on HH 48 NE disk as part of the Ice Age Early Release Science program. Here, we introduce a new radiative transfer modeling framework designed to retrieve the composition and mixing status of disk ices using their band profiles, and apply it to interpret the H2O, CO2, and CO ice bands observed towards the HH 48 NE disk. We show that the ices are largely present as mixtures, with strong evidence for CO trapping in both H2O and CO2 ice. The HH 48 NE disk ice composition (pure vs. polar vs. apolar fractions) is markedly different from earlier protostellar stages, implying thermal and/or chemical reprocessing during the formation or evolution of the disk. We infer low ice-phase C/O ratios around 0.1 throughout the disk, and also demonstrate that the mixing and entrapment of disk ices can dramatically affect the radial dependence of the C/O ratio. It is therefore imperative that realistic disk ice compositions are considered when comparing planetary compositions with potential formation scenarios, which will fortunately be possible for an increasing number of disks with JWST., Comment: Accepted to ApJ. 24 pages, 15 figures

Let $p\ge 5$ be a prime and let $P$ be a Sylow $p$-subgroup of a finite symmetric group. To every irreducible character of $P$ we associate a collection of labelled, complete $p$-ary trees. The main results of this article describe Sylow branching coefficients for symmetric groups for all irreducible characters of $P$ in terms of some combinatorial properties of these trees, extending previous work on the linear characters of $P$., Comment: 30 pages

Image vectorization is a process to convert a raster image into a scalable vector graphic format. Objective is to effectively remove the pixelization effect while representing boundaries of image by scaleable parameterized curves. We propose new image vectorization with depth which considers depth ordering among shapes and use curvature-based inpainting for convexifying shapes in vectorization process.From a given color quantized raster image, we first define each connected component of the same color as a shape layer, and construct depth ordering among them using a newly proposed depth ordering energy. Global depth ordering among all shapes is described by a directed graph, and we propose an energy to remove cycle within the graph. After constructing depth ordering of shapes, we convexify occluded regions by Euler's elastica curvature-based variational inpainting, and leverage on the stability of Modica-Mortola double-well potential energy to inpaint large regions. This is following human vision perception that boundaries of shapes extend smoothly, and we assume shapes are likely to be convex. Finally, we fit B\'{e}zier curves to the boundaries and save vectorization as a SVG file which allows superposition of curvature-based inpainted shapes following the depth ordering. This is a new way to vectorize images, by decomposing an image into scalable shape layers with computed depth ordering. This approach makes editing shapes and images more natural and intuitive. We also consider grouping shape layers for semantic vectorization. We present various numerical results and comparisons against recent layer-based vectorization methods to validate the proposed model.

Stable isotopic ratios constitute powerful tools for unraveling the thermal and irradiation history of volatiles. In particular, we can use our knowledge of the isotopic fractionation processes active during the various stages of star, disk and planet formation to infer the origins of different volatiles with measured isotopic patterns in our own solar system. Observations of planet-forming disks with the Atacama Large Millimeter/submillimeter Array (ALMA) now readily detect the heavier isotopologues of C, O and N, while the isotopologue abundances and isotopic fractionation mechanisms of sulfur species are less well understood. Using ALMA observations of the SO and SO2 isotopologues in the nearby, molecule-rich disk around the young star Oph-IRS 48 we present the first constraints on the combined 32S/34S and 32S/33S isotope ratios in a planet-forming disk. Given that these isotopologues likely originate in relatively warm gas (>50 K), like most other Oph-IRS 48 volatiles, SO is depleted in heavy sulfur while SO2 is enriched compared to solar system values. However, we cannot completely rule out a cooler gas reservoir, which would put the SO sulfur ratios more in line with comets and other solar system bodies. We also constrain the S18O/SO ratio and find the limit to be consistent with solar system values given a temperature of 60 K. Together these observations show that we should not assume solar isotopic values for disk sulfur reservoirs, but additional observations are needed to determine the chemical origin of the abundant SO in this disk, inform on what isotopic fractionation mechanism(s) are at play, and aid in unravelling the history of the sulfur budget during the different stages of planet formation., Comment: Accepted AJ on 30th August 2024

The increasing number of observations towards different environments in the Milky Way, as well as theoretical and experimental works, are improving our knowledge of the astrochemical processes in the interstellar medium (ISM). In this chapter we report some of the main projects to study the chemical complexity and isotopic ratios across the Galaxy. High-sensitivity spectral surveys covering broad bandwidths towards Galactic Center molecular clouds (e.g. G+0.693-0.027) and star-forming regions (e.g. the hot core G31.41+0.31) are revealing very rich astrochemical reservoirs, which include molecules of prebiotic interest. At the same time, isotopic ratios (e.g. $^{12}$C/$^{13}$C and $^{14}$N/$^{15}$N) can give important information on the Galactic chemical evolution, as well as on chemical local processes due to the physical conditions of the molecular clouds. We also highlight the role of cosmic rays as a key agent affecting the interstellar chemistry described above., Comment: 9 pages, 1 figures - Chapter II of the National Congress of (proto-)Planetary Astrochemistry (CNAP II) Proceedings - Accepted for publication on MemSAIt, in print

Many educators in California are unaware of just how harmful out of school suspensions can be. When suspended students are barred from attending school, more often than not, the rule broken was some form of minor misconduct. This update of "Lost Instruction Time in California Schools" demonstrates that despite the important efforts by the state of California to reduce suspensions, those efforts are seriously insufficient. The most recent statewide rates of lost instruction due to out of school suspensions show a widespread increase in the days lost per 100 students. The increase in these lost instruction rates, is not large for every group, but the 2023 data reverses a consistent downward 6-year trend. In 2023, nearly every racial and ethnic group, as well as students with disabilities, experienced an increase over their rate of lost instruction from the prior year. Using the recently released data for 2023, show that not only are the rates for homeless youth increasing for every demographic, but they increased far more for Black and Native American homeless youth than any other groups.

Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning durations, and patient discomfort. Current synthesis methods, typically focused on single-image contrasts, fall short in capturing the collective nuances across various contrasts. Moreover, existing methods for multi-contrast MRI synthesis often fail to accurately map feature-level information across multiple imaging contrasts. We introduce McCaD (Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion), a novel framework leveraging an adversarial diffusion model conditioned on multiple contrasts for high-fidelity MRI synthesis. McCaD significantly enhances synthesis accuracy by employing a multi-scale, feature-guided mechanism, incorporating denoising and semantic encoders. An adaptive feature maximization strategy and a spatial feature-attentive loss have been introduced to capture more intrinsic features across multiple contrasts. This facilitates a precise and comprehensive feature-guided denoising process. Extensive experiments on tumor and healthy multi-contrast MRI datasets demonstrated that the McCaD outperforms state-of-the-art baselines quantitively and qualitatively. The code is provided with supplementary materials.

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as events in a spacetime manifold. While most works in the literature focus on undirected graph representation learning or causality embedding separately, our differentiable geometry can encode both graph edge weights in its spatial dimensions and causality in the form of edge directionality in its temporal dimensions. We use a product manifold that combines a quasi-metric (for space) and a partial order (for time). NSTs are implemented as three neural networks trained in an end-to-end manner: an embedding network, which learns to optimize the location of nodes as events in the spacetime manifold, and two other networks that optimize the space and time geometries in parallel, which we call a neural (quasi-)metric and a neural partial order, respectively. The latter two networks leverage recent ideas at the intersection of fractal geometry and deep learning to shape the geometry of the representation space in a data-driven fashion, unlike other works in the literature that use fixed spacetime manifolds such as Minkowski space or De Sitter space to embed DAGs. Our main theoretical guarantee is a universal embedding theorem, showing that any $k$-point DAG can be embedded into an NST with $1+\mathcal{O}(\log(k))$ distortion while exactly preserving its causal structure. The total number of parameters defining the NST is sub-cubic in $k$ and linear in the width of the DAG. If the DAG has a planar Hasse diagram, this is improved to $\mathcal{O}(\log(k)) + 2)$ spatial and 2 temporal dimensions. We validate our framework computationally with synthetic weighted DAGs and real-world network embeddings; in both cases, the NSTs achieve lower embedding distortions than their counterparts using fixed spacetime geometries., Comment: 12 pages: main body and 19 pages: appendix

The emergence of Large Language Models (LLMs) and multimodal foundation models (FMs) has generated heightened interest in their applications that integrate vision and language. This paper investigates the capabilities of ChatGPT-4V and Gemini Pro for Street View Imagery, Built Environment, and Interior by evaluating their performance across various tasks. The assessments include street furniture identification, pedestrian and car counts, and road width measurement in Street View Imagery; building function classification, building age analysis, building height analysis, and building structure classification in the Built Environment; and interior room classification, interior design style analysis, interior furniture counts, and interior length measurement in Interior. The results reveal proficiency in length measurement, style analysis, question answering, and basic image understanding, but highlight limitations in detailed recognition and counting tasks. While zero-shot learning shows potential, performance varies depending on the problem domains and image complexities. This study provides new insights into the strengths and weaknesses of multimodal foundation models for practical challenges in Street View Imagery, Built Environment, and Interior. Overall, the findings demonstrate foundational multimodal intelligence, emphasizing the potential of FMs to drive forward interdisciplinary applications at the intersection of computer vision and language.