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Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision. To address these shortcomings, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are often insufficient for instilling self-correction behavior. In particular, we observe that training via SFT falls prey to either a distribution mismatch between mistakes made by the data-collection policy and the model's own responses, or to behavior collapse, where learning implicitly prefers only a certain mode of correction behavior that is often not effective at self-correction on test problems. SCoRe addresses these challenges by training under the model's own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction behavior that is effective at test time as opposed to fitting high-reward responses for a given prompt. This regularization process includes an initial phase of multi-turn RL on a base model to generate a policy initialization that is less susceptible to collapse, followed by using a reward bonus to amplify self-correction. With Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on MATH and HumanEval.

Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. Code and supplementary materials are available at https://e2map.github.io/., Comment: 19 pages, 28 figures. Project page: https://e2map.github.io

In contrast with the diversity of materials found in nature, most robots are designed with some combination of aluminum, stainless steel, and 3D-printed filament. Additionally, robotic systems are typically assumed to follow basic rigid-body dynamics. However, several examples in nature illustrate how changes in physical material properties yield functional advantages. In this paper, we explore how physical materials (non-rigid bodies) affect the functional performance of a hopping robot. In doing so, we address the practical question of how to model and simulate material properties. Through these simulations we demonstrate that material gradients in the limb system of a single-limb hopper provide functional advantages compared to homogeneous designs. For example, when considering incline ramp hopping, a material gradient with increasing density provides a 35\% reduction in tracking error and a 23\% reduction in power consumption compared to isotropic stainless steel. By providing bio-inspiration to the rigid limbs in a robotic system, we seek to show that future fabrication of robots should look to leverage the material anisotropies of moduli and density found in nature. This would allow for reduced vibrations in the system and would provide offsets of joint torques and vibrations while protecting their structural integrity against reduced fatigue and wear. This simulation system could inspire future intelligent material gradients of custom-fabricated robotic locomotive devices.

We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced or smaller datasets due to their simplicity, interpretability, and efficiency. These models are particularly effective in domains such as finance and biology, where traditional methods struggle with high feature dimensionality and varied sequence lengths. Our ensemble-based scoring method enables the comparison of sequences of any length and improves performance on imbalanced datasets. This study focuses on the binary classification problem, particularly in scenarios with data imbalance, where the negative class is the majority (e.g., normal data) and the positive class is the minority (e.g., anomalous data), often with extreme distribution skews. We propose a novel training approach for HMM Ensembles that generalizes to multi-class problems and supports classification and anomaly detection. Our method fits class-specific groups of diverse models using random data subsets, and compares likelihoods across classes to produce composite scores, achieving high average precisions and AUCs. In addition, we compare our approach with neural network-based methods such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), highlighting the efficiency and robustness of HMMs in data-scarce environments. Motivated by real-world use cases, our method demonstrates robust performance across various benchmarks, offering a flexible framework for diverse applications.

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

The radial gradient of gas-phase metallicity is a powerful probe of the chemical and structural evolution of star-forming galaxies, closely tied to disk formation and gas kinematics in the early universe. We present spatially resolved chemical and dynamical properties for a sample of 26 galaxies at $0.5 \lesssim z \lesssim 1.7$ from the MSA-3D survey. These innovative observations provide 3D spectroscopy of galaxies at a spatial resolution approaching JWST's diffraction limit and a high spectral resolution of $R\simeq2700$. The metallicity gradients measured in our galaxy sample range from $-$0.05 to 0.02 dex~kpc$^{-1}$. Most galaxies exhibit negative or flat radial gradients, indicating lower metallicity in the outskirts or uniform metallicity throughout the entire galaxy. We confirm a tight relationship between stellar mass and metallicity gradient at $z\sim1$ with small intrinsic scatter of 0.02 dex~kpc$^{-1}$. Our results indicate that metallicity gradients become increasingly negative as stellar mass increases, likely because the more massive galaxies tend to be more "disky". This relationship is consistent with the predictions from cosmological hydrodynamic zoom-in simulations with strong stellar feedback. This work presents the effort to harness the multiplexing capability of JWST NIRSpec/MSA in slit-stepping mode to map the chemical and kinematic profiles of high-redshift galaxies in large samples and at high spatial and spectral resolution., Comment: 23 pages, 6 figures, 1 table; submitted to ApJL

Measurements of B-mode polarization in the CMB sourced from primordial gravitational waves would provide information on the energy scale of inflation and its potential form. To achieve these goals, one must carefully characterize the Galactic foregrounds, which can be distinguished from the CMB by conducting measurements at multiple frequencies. BICEP Array is the latest-generation multi-frequency instrument of the BICEP/Keck program, which specifically targets degree-scale primordial B-modes in the CMB. In its final configuration, this telescope will consist of four small-aperture receivers, spanning frequency bands from 30 to 270 GHz. The 220/270 GHz receiver designed to characterize Galactic dust is currently undergoing commissioning at Stanford University and is scheduled to deploy to the South Pole during the 2024--2025 austral summer. Here, we will provide an overview of this high-frequency receiver and discuss the integration status and test results as it is being commissioned.

In Sunyaev-Zeldovich (SZ) cluster cosmology, two tools are needed to be able to exploit data from large scale surveys in the millimeter-wave domain. An accurate description of the IntraCluster Medium (ICM) pressure profile is needed along with the scaling relation connecting the SZ brightness to the mass. With its high angular resolution and large field of view, The NIKA2 camera, operating at 150 and 260 GHz, is perfectly suited for precise cluster SZ mapping. The SZ Large Program (LPSZ) of the NIKA2 collaboration is dedicated to the observation of a sample of 38 SZ-selected clusters at intermediate to high redshift and observed both in SZ and X-ray. The current status is that all LPSZ clusters have been observed and the analysis toward the final results is ongoing. We present in detail how NIKA2-LPSZ will obtain a robust estimation of the SZ-Mass scaling relation and how it will be used to obtain cosmological constraints., Comment: 4 pages, 3 figures, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

We answer a question of Bergelson and Lesigne by showing that the notion of van der Corput set does not depend on the F\o lner sequence used to define it. This result has been discovered independently by Sa\'ul Rodr\'iguez Mart\'in. Both ours and Rodr\'iguez's proofs proceed by first establishing a converse to the Furstenberg Correspondence Principle for amenable groups. This involves studying the distributions of Reiter sequences over congruent sequences of tilings of the group. Lastly, we show that many of the equivalent characterizations of van der Corput sets in $\mathbb{N}$ that do not involve F\o lner sequences remain equivalent for arbitrary countably infinite groups., Comment: Many typos were fixed, the content of the introduction was reorganized, the grammar in some statements of results was corrected, and a short proof was added for a result at the end of Section 4

The baryon cycle is crucial for understanding galaxy formation, as gas inflows and outflows vary throughout a galaxy's lifetime and affect its star formation rate. Despite the necessity of accretion for galaxy growth at high redshifts, direct observations of inflowing gas have proven elusive especially at $z\gtrsim2$. We present spectroscopic analysis of a galaxy at redshift $z=2.45$ which exhibits signs of inflow in several ultraviolet interstellar absorption lines, with no clear outflow signatures. The absorption lines are redshifted by $\sim$250 km sec$^{-1}$ with respect to the systemic redshift, and C IV shows a prominent inverse P-Cygni profile. Simple stellar population models suggest that this galaxy has a low metallicity ($\sim$5% solar), with a very young starburst of age $\sim$4 Myr dominating the ultraviolet luminosity. The gas inflow velocity and nebular velocity dispersion suggest an approximate halo mass of order $\sim 10^{11}M_{\odot}$, a regime in which simulations predict that bursty star formation is common at this redshift. We conclude that this system is likely in the beginning of a cycle of bursty star formation, where inflow and star formation rates are high, but where supernovae and other feedback processes have not yet launched strong outflows. In this scenario, we expect the inflow-dominated phase to be observable (e.g., with net redshifted ISM absorption) for only a short timescale after a starburst onset. This result represents a promising avenue for probing the full baryon cycle, including inflows, during the formative phases of low-mass galaxies at high redshifts., Comment: Accepted for publication in ApJL, 12 pages, 4 figures

Blade-tip vortices can lead to wakes, cavitation and noise, and their control remains a significant challenge for tidal and wind turbines. In the present work, we propose and investigate controlling tip vortices through local permeability. Blade-resolved Reynolds-averaged Navier-Stokes simulation has been employed on a model-scale horizontal-axis turbine, following a rigorous validation and verification process. The tip-speed ratio of the turbine varies from 4.52 to 7.54. The tip permeability is modelled by including a porous zone over the blade tip section, within which Darcy's law is applied. The results demonstrate that there is an optimal range of permeability, corresponding to a non-dimensional Darcy number, Da, of around 10^{-5}, that can substantially decrease the tip vortex intensity. The revealed flow physics show that the permeable tip treatment can effectively enlarge the vortex viscous core radius with little change to the vortex circulation. As the tip vortex intensity is significantly reduced, the permeable tip treatment can increase the minimal pressure-coefficient at the vortex core by up to 63%, which significantly alleviates the cavitation risk due to tip vortices. This approach has negligible influence on the turbine's energy-harvesting performance because the spanwise extent of the permeable tip treatment is only in the order of 0.1% turbine diameter. Our findings demonstrate this approach's great promise to break the upper tip-speed ratio limit capped by cavitation for tidal turbines. These will contribute to developing more efficient and resilient turbines.

Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam radiotherapy. Unfortunately, the availability of CT scanners in low- and mid-resource settings is highly variable. Planar x-ray radiography units, in comparison, are far more prevalent, but can only provide limited 2D observations of the 3D anatomy. In this work we propose DIFR3CT, a 3D latent diffusion model, that can generate a distribution of plausible CT volumes from one or few (<10) planar x-ray observations. DIFR3CT works by fusing 2D features from each x-ray into a joint 3D space, and performing diffusion conditioned on these fused features in a low-dimensional latent space. We conduct extensive experiments demonstrating that DIFR3CT is better than recent sparse CT reconstruction baselines in terms of standard pixel-level (PSNR, SSIM) on both the public LIDC and in-house post-mastectomy CT datasets. We also show that DIFR3CT supports uncertainty quantification via Monte Carlo sampling, which provides an opportunity to measure reconstruction reliability. Finally, we perform a preliminary pilot study evaluating DIFR3CT for automated breast radiotherapy contouring and planning -- and demonstrate promising feasibility. Our code is available at https://github.com/yransun/DIFR3CT., Comment: 11 pages, 9 figures

We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point clouds. Given a language instruction and a point cloud of the scene, our framework initiates a hierarchical planning procedure, whereby a language model generates a high-level plan and a sampling-based planner produces constraint-satisfying continuous parameters for manipulation primitives sequenced according to the high-level plan. Key to our approach is the use of a relational dynamics model as a unifying interface between the continuous and symbolic representations of states and actions, thus facilitating language-driven planning from high-dimensional perceptual input such as point clouds. Whereas previous relational dynamics models require training on datasets of multi-step manipulation scenarios that align with the intended test scenarios, Points2Plans uses only single-step simulated training data while generalizing zero-shot to a variable number of steps during real-world evaluations. We evaluate our approach on tasks involving geometric reasoning, multi-object interactions, and occluded object reasoning in both simulated and real-world settings. Results demonstrate that Points2Plans offers strong generalization to unseen long-horizon tasks in the real world, where it solves over 85% of evaluated tasks while the next best baseline solves only 50%. Qualitative demonstrations of our approach operating on a mobile manipulator platform are made available at sites.google.com/stanford.edu/points2plans., Comment: Under review

We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, consumption, and monetary flow. Each agent type has distinct objectives, such as households seeking utility from consumption and the central bank targeting inflation and production. We define this multi-agent economic system using an OpenAI Gym-style environment, enabling agents to optimize their objectives through reinforcement learning. Standard multi-agent reinforcement learning (MARL) schemes, like independent learning, enable agents to learn concurrently but do not address whether the resulting strategies are at equilibrium. This study integrates the Policy Space Response Oracle (PSRO) algorithm, which has shown superior performance over independent MARL in games with homogeneous agents, with economic agent-based modeling. We use PSRO to develop agent policies approximating Nash equilibria of the empirical economic game, thereby linking to economic equilibria. Our results demonstrate that PSRO strategies achieve lower regret values than independent MARL strategies in our economic system with four agent types. This work aims to bridge artificial intelligence, economics, and empirical game theory towards future research., Comment: arXiv admin note: text overlap with arXiv:2402.09563

Over the past few years alone, the lensing community has discovered thousands of strong lens candidates, and spectroscopically confirmed hundreds of them. In this time of abundance, it becomes pragmatic to focus our time and resources on the few extraordinary systems, in order to most efficiently study the universe. In this paper, we present such a system: DESI-090.9854-35.9683, a cluster-scale lens at $z_{\rm l} = 0.49$, with seven observed lensed sources around the core, and additional lensed sources further out in the cluster. From the number and the textbook configuration of the lensed images, a tight constraint on the mass potential of the lens is possible. This would allow for detailed analysis on the dark and luminous matter content within galaxy clusters, as well as a probe into dark energy and high-redshift galaxies. We present our spatially resolved kinematic measurements of this system from the Very Large Telescope Multi Unit Spectroscopic Explorer, which confirm five of these source galaxies (in ascending order, at $z_{\rm s} = 0.962, 0.962, 1.166, 1.432,$ and $1.432$). With previous Hubble Space Telescope imaging in the F140W and F200LP bands, we also present a simple two power-law profile flux-based lens model that, for a cluster lens, well models the five lensed arc families with redshifts. We determine the mass to be $M(< \theta_{\rm E}) = 4.78\times10^{13} M_{\odot}$ for the primary mass potential. From the model, we extrapolate the redshift of one of the two source galaxies not yet spectroscopically confirmed to be at $z_{\rm s}=4.52^{+1.03}_{-0.71}$., Comment: 14 pages, 6 figures

Recent ethnographic research reveals that gang dynamics in Chicago's Southside have evolved with decentralized micro-gang "set" factions and cross-gang interpersonal networks marking the contemporary landscape. However, standard police datasets lack the depth to analyze gang violence with such granularity. To address this, we employed a natural language processing strategy to analyze text from a Chicago gangs message board. By identifying proper nouns, probabilistically linking them to gang sets, and assuming social connections among names mentioned together, we created a social network dataset of 271 individuals across 11 gang sets. Using Louvain community detection, we found that these individuals often connect with gang-affiliated peers from various gang sets that are physically proximal. Hierarchical logistic regression revealed that individuals with ties to homicide victims and central positions in the overall gang network were at increased risk of victimization, regardless of gang affiliation. This research demonstrates that utilizing crowd-sourced information online can enable the study of otherwise inaccessible topics and populations., Comment: 24 pages, 5 figures

We have obtained IFU spectra of 75 SN Ia host galaxies from the Foundation Supernova survey to search for correlations between the properties of individual galaxies and SN Hubble residuals. After standard corrections for light-curve width and SN colour have been applied, we find correlations between Hubble residuals and the equivalent width of the [O II] $\lambda\lambda$ 3727, 3729 doublet (2.3$\sigma$), an indicator of the specific star formation rate (sSFR). When splitting our sample by SN colour, we find no colour dependence impacting the correlation between EW[O II] and Hubble residual. However, when splitting by colour, we reveal a correlation between the Hubble residuals of blue SNe Ia and the Balmer decrement (2.2$\sigma$), an indicator of dust attenuation. These correlations remain after applying a mass-step correction, suggesting that the mass-step correction does not fully account for the limitations of the colour correction used to standardise SNe Ia. Rather than a mass correction, we apply a correction to SNe from star forming galaxies based on their measurable EW[O II]. We find that this correction also removes the host galaxy mass step, while also greatly reducing the significance of the correlation with the Balmer decrement for blue SNe Ia. We find that correcting for EW[O II], in addition to or in place of the mass-step, may further reduce the scatter in the Hubble diagram., Comment: 16 pages, 15 figures

Integral field spectroscopy (IFS) is a powerful tool for understanding the formation of galaxies across cosmic history. We present the observing strategy and first results of MSA-3D, a novel JWST program using multi-object spectroscopy in a slit-stepping strategy to produce IFS data cubes. The program observed 43 normal star-forming galaxies at redshifts $0.5 \lesssim z \lesssim 1.5$, corresponding to the epoch when spiral thin-disk galaxies of the modern Hubble sequence are thought to emerge, obtaining kpc-scale maps of rest-frame optical nebular emission lines with spectral resolution $R\simeq2700$. Here we describe the multiplexed slit-stepping method which is $>15$ times more efficient than the NIRSpec IFS mode for our program. As an example of the data quality, we present a case study of an individual galaxy at $z=1.104$ (stellar mass $M_{*} = 10^{10.3}~M_{\odot}$, star formation rate~$=3~M_{\odot}$ yr$^{-1}$) with prominent face-on spiral structure. We show that the galaxy exhibits a rotationally supported disk with moderate velocity dispersion ($\sigma = 36^{+5}_{-4}$~\kms), a negative radial metallicity gradient ($-0.020\pm0.002$~dex\,kpc$^{-1}$), a dust attenuation gradient, and an exponential star formation rate density profile which closely matches the stellar continuum. These properties are characteristic of local spirals, indicating that mature galaxies are in place at $z\sim1$. We also describe the customized data reduction and original cube-building software pipelines which we have developed to exploit the powerful slit-stepping technique. Our results demonstrate the ability of JWST slit-stepping to study galaxy populations at intermediate to high redshifts, with data quality similar to current surveys of the $z\sim0.1$ universe., Comment: 24 pages, 12 figures, 1 table; submitted to ApJ, comments welcome. Our custom designed data reduction pipeline (MSA3D) and example processed data-cube will be publicly released before the JWST Cycle 4 deadline. MSA3D package on GitHub: https://github.com/barisiciv/msa3d.git

Quantum computers must achieve large-scale, fault-tolerant operation to deliver on their promise of transformational processing power [1-4]. This will require thousands or millions of high-fidelity quantum gates and similar numbers of qubits [5]. Demonstrations using neutral-atom qubits trapped and manipulated by lasers have shown that this modality can provide high two-qubit gate (CZ) fidelities and scalable operation [6-10]. However, the gates in these demonstrations are driven by lasers that do not resolve individual qubits, with universal computation enabled by physical mid-circuit shuttling of the qubits. This relatively slow operation will greatly extend runtimes for useful, large-scale computation. Here we demonstrate a universal neutral-atom quantum computer with gate rates limited by optical switching times, rather than shuttling, by individually addressing tightly focused laser beams at an array of single atoms. We achieve CZ fidelity of 99.35(4)% and local single qubit RZ gate fidelity of 99.902(8)%. Moreover, we demonstrate non-destructive readout of alkali-atom qubits with sub-percent loss, which boosts operational speed. This technique also enables us to measure 99.73(3)% CZ fidelity with atom-loss events excluded, which is a record among long lived neutral-atom qubits and highlights the path to higher fidelity and error correction. Our results represent a critical step towards large-scale, fault-tolerant neutral-atom quantum computers that can execute computations on practical timescales.

While quiescent galaxies have comparable amounts of cool gas in their outer circumgalactic medium (CGM) compared to star-forming galaxies, they have significantly less interstellar gas. However, open questions remain on the processes causing galaxies to stop forming stars and stay quiescent . Theories suggest dynamical interactions with the hot corona prevent cool gas from reaching the galaxy, therefore predicting the inner regions of quiescent galaxy CGMs are devoid of cool gas. However, there is a lack of understanding of the inner regions of CGMs due to the lack of spatial information in quasar-sightline methods. We present integral-field spectroscopy probing 10--20~kpc (2.4--4.8 R\textsubscript{e}) around a massive quiescent galaxy using a gravitationally lensed star-forming galaxy. We detect absorption from Magnesium (MgII) implying large amounts of cool atomic gas (10\textsuperscript{8.4} -- 10\textsuperscript{9.3} M\textsubscript{$\odot$} with T$\sim$10\textsuperscript{4} Kelvin), in comparable amounts to star-forming galaxies. Lens modeling of Hubble imaging also reveals a diffuse asymmetric component of significant mass consistent with the spatial extent of the MgII absorption, and offset from the galaxy light profile. This study demonstrates the power of galaxy-scale gravitational lenses to not only probe the gas around galaxies, but to also independently probe the mass of the CGM due to it's gravitational effect., Comment: accepted nature communications physics

We use JWST/NIRSpec observations from the Assembly of Ultradeep Rest-optical Observations Revealing Astrophysics (AURORA) survey to constrain the shape of the nebular attenuation curve of a star-forming galaxy at z=4.41, GOODSN-17940. We utilize 11 unblended HI recombination lines to derive the attenuation curve spanning optical to near-infrared wavelengths (3751-9550 \r{A}). We then leverage a high-S/N spectroscopic detection of the rest-frame ultraviolet continuum in combination with rest-UV photometric measurements to constrain the shape of the curve at ultraviolet wavelengths. While this UV constraint is predominantly based on stellar emission, the large measured equivalent widths of H$\alpha$ and H$\beta$ indicate that GOODSN-17940 is dominated by an extremely young stellar population <10 Myr in age such that the UV stellar continuum experiences the same attenuation as the nebular emission. The resulting combined nebular attenuation curve spans 1400-9550 \r{A} and has a shape that deviates significantly from commonly assumed dust curves in high-redshift studies. Relative to the Milky Way, SMC, and Calzetti curves, the new curve has a steeper slope at long wavelengths ($\lambda>5000$ \r{A}) while displaying a similar slope across blue-optical wavelengths ($\lambda=3750-5000$ \r{A}). In the ultraviolet, the new curve is shallower than the SMC and Calzetti curves and displays no significant 2175 \r{A} bump. This work demonstrates that the most commonly assumed dust curves are not appropriate for all high-redshift galaxies. These results highlight the ability to derive nebular attenuation curves for individual high-redshift sources with deep JWST/NIRSpec spectroscopy, thereby improving the accuracy of physical properties inferred from nebular emission lines., Comment: 21 pages, 6 figures, 1 table, submitted to ApJ

Using 1533 type Ia supernovae (SNe Ia) from the five-year sample of the Dark Energy Survey (DES), we investigate the effects of projected galactocentric separation between the SNe and their host galaxies on their light curves and standardization. We show, for the first time, that the difference in SN Ia post-standardization brightnesses between high and low-mass hosts reduces from $0.078\pm0.011$ mag in the full sample to $0.036 \pm 0.018$ mag for SNe Ia located in the outer regions of their host galaxies, while increasing to $0.100 \pm 0.014$ mag for SNe in the inner regions. In these inner regions, the step can be reduced (but not removed) using a model where the $R_V$ of dust along the line-of-sight to the SN changes as a function of galaxy properties. To explain the remaining difference, we use the distributions of the SN Ia stretch parameter to test whether the inferred age of SN progenitors are more varied in the inner regions of galaxies. We find that the proportion of high-stretch SNe Ia in red (older) environments is more prominent in outer regions and that the outer regions stretch distributions are overall more homogeneous compared to inner regions, but conclude that this effect cannot explain the reduction in significance of any Hubble residual step in outer regions. We conclude that the standardized distances of SNe Ia located in the outer regions of galaxies are less affected by their global host galaxy properties than those in the inner regions., Comment: 17 pages, 13 figures

Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at inference is critical to warn the clinicians that the model likely failed. This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted magnetic resonance imaging and computed tomography. By reducing the dimensions of the bottleneck features with either principal component analysis or uniform manifold approximation and projection, images the models failed on were detected with high performance and minimal computational load. In addition, this work explored a non-parametric alternative to the MD, a k-th nearest neighbors distance (KNN). KNN drastically improved scalability and performance over MD when both were applied to raw and average-pooled bottleneck features., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:020. Expansion of "Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation" arXiv:2308.03723. Code available at https://github.com/mckellwoodland/dimen_reduce_mahal (https://zenodo.org/records/13881989)

I find that Type Ia supernovae (SNe Ia) with bimodal nebular emission profiles occur almost exclusively in massive ($M_\star \gtrsim 10^{11}~M_\odot$) galaxies with low star-formation rates (SFR~$\lesssim 0.5~M_\odot$/yr). The bimodal profiles are likely produced by two white dwarfs that exploded during a merger or collision, supported by a correlation between the peak-to-peak velocity separation ($v_{\rm sep}$) and the SN Ia peak luminosity ($M_V$) which arises naturally from more massive white dwarf binaries synthesizing more $^{56}$Ni during the explosion. The quiescent hosts are consistent with the long delay times required to form double white dwarf binaries. The distributions of SNe Ia with and without bimodal nebular lines differ in host mass, SFR, and specific SFR with K-S test probabilities of $3.1\%$, $0.03\%$, and $0.02\%$, respectively. Viewing angle effects can fully explain the SNe Ia in quiescent hosts without bimodal emission profiles and the dearth of merger/collision driven SNe Ia in star-forming hosts requires at least two distinct progenitor channels for normal SNe Ia. $30-40\%$ of all SNe Ia originate from mergers or collisions depending on how cleanly host environment distinguishes progenitor scenarios. The bimodal SNe Ia share some characteristics with the underluminous 91bg-like SNe Ia that also prefer older populations, but there is no unambiguous connection between the two classifications. This may suggest separate processes or multiple axes of ejecta (a)symmetry. Existing models for WD mergers and collisions broadly reproduce the $v_{\rm sep} - M_V$ correlation and future analyses may be able to infer the masses/mass-ratios of merging white dwarfs in external galaxies., Comment: 7 pages, 4 figures, 1 table. Submitted to ApJL, comments welcome

Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new methods makes the comparison of methods difficult. To address this, we introduce the Medical Imaging Segmentation Toolkit (MIST), a simple, modular, and end-to-end medical imaging segmentation framework designed to facilitate consistent training, testing, and evaluation of deep learning-based medical imaging segmentation methods. MIST standardizes data analysis, preprocessing, and evaluation pipelines, accommodating multiple architectures and loss functions. This standardization ensures reproducible and fair comparisons across different methods. We detail MIST's data format requirements, pipelines, and auxiliary features and demonstrate its efficacy using the BraTS Adult Glioma Post-Treatment Challenge dataset. Our results highlight MIST's ability to produce accurate segmentation masks and its scalability across multiple GPUs, showcasing its potential as a powerful tool for future medical imaging research and development., Comment: Submitted to BraTS 2024

Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine learning, but representing a grain boundary structure in a manner suitable for machine learning is not a trivial task. There are three key steps common to property prediction in grain boundaries and other variable-sized atom clustered structures. These are: (1) describe the atomic structure as a feature matrix, (2) transform the variable-sized feature matrices of different structures to a fixed length common to all structures, and (3) apply machine learning to predict properties from the transformed feature matrices. We examine these feature engineering steps to understand how they impact the accuracy of grain boundary energy predictions. A database of over 7000 grain boundaries serves to evaluate the different feature engineering combinations. We also examine how these combination of engineered features provide interpretability, or the ability to extract insightful physics from the obtained structure-property relationships., Comment: 18 pages, 8 figures

We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of Austral winter survey data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The signal is most significant at large angular scales, high observing frequency, and low elevation angle. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 years of winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. For the SPT-3G data, downweighting the small fraction of significantly contaminated observations is an effective mitigation strategy. In addition, we present a strategy for further improving sensitivity on large angular scales where maps made in the 220 GHz band are used to measure and subtract the polarized atmosphere signal from the 150 GHz band maps. In observations with the SPT-3G instrument at the South Pole, the polarized atmospheric signal is a well-understood and sub-dominant contribution to the measured noise after implementing the mitigation strategies described here., Comment: 32 pages, 28 figures

There are numerous randomized algorithms to generate spanning trees in a given ambient graph; several target the uniform distribution on trees (UST), while in practice the fastest and most frequently used draw random weights on the edges and then employ a greedy algorithm to choose the minimum-weight spanning tree (MST). Though MST is a workhorse in applications, the mathematical properties of random MST are far less explored than those of UST. In this paper we develop tools for the quantitative study of random MST. We consider the standard case that the weights are drawn i.i.d. from a single distribution on the real numbers, as well as successive generalizations that lead to \emph{product measures}, where the weights are independently drawn from arbitrary distributions.

We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. These dark-matter-dominated objects are intriguing due to potentially unusual formation channels that lead to their diffuse stellar component. Given the faintness of LSBGs, using standard observational techniques to characterize their total masses proves challenging. Weak gravitational lensing, which is less sensitive to the stellar component of galaxies, could be a promising avenue to estimate the masses of LSBGs. Our LSBG sample consists of 23,790 galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i< 0.60$, respectively. Combined with the DES Y3 shear catalog, we measure the tangential shear around these LSBGs and find signal-to-noise ratios of 6.67 for the red sample, 2.17 for the blue sample, and 5.30 for the full sample. We use the clustering redshifts method to obtain redshift distributions for the red and blue LSBG samples. Assuming all red LSBGs are satellites, we fit a simple model to the measurements and estimate the host halo mass of these LSBGs to be $\log(M_{\rm host}/M_{\odot}) = 12.98 ^{+0.10}_{-0.11}$. We place a 95% upper bound on the subhalo mass at $\log(M_{\rm sub}/M_{\odot})<11.51$. By contrast, we assume the blue LSBGs are centrals, and place a 95% upper bound on the halo mass at $\log(M_\mathrm{host}/M_\odot) < 11.84$. We find that the stellar-to-halo mass ratio of the LSBG samples is consistent with that of the general galaxy population. This work illustrates the viability of using weak gravitational lensing to constrain the halo masses of LSBGs., Comment: 20 pages, 14 figures

LAMOST J235456.73+335625 (J2354) is a binary system hosting a $\sim 0.7~\rm M_\odot$ K dwarf and a $\sim 1.4~\rm M_\odot$ dark companion, supposedly a neutron star, in a 0.48d orbit. Here we present high- and low-resolution spectroscopy to better constrain the properties of the system. The low-resolution spectrum confirms that the luminous star is a slightly metal-poor K dwarf and strengthens the limits on any optical flux from the dimmer companion. We use the high-resolution spectra to measure atmospheric parameters ($T_{\rm eff}$, $\log g$, [Fe/H], $v_{\rm rot}\sin i$) and abundances for 8 elements for the K dwarf. We refine the mass of the compact object to $M_{\rm co} \sim 1.3~\rm M_\odot$ with a minimum mass of $M_{\rm co, min} = 1.23\pm0.04~\rm M_\odot$. The expected overabundance of intermediate-mass elements from the incident supernova ejecta is not detected in the K-dwarf atmosphere. This contrasts with known binaries hosting neutron stars where almost all companions show evidence for polluting material. Moving the neutron-star progenitor further from the K-dwarf at the time of explosion to minimize atmospheric pollution requires a finely-tuned kick to produce the current orbital separation of $\sim 3.3~\rm R_\odot$. Instead, we find that a massive white dwarf with a cooling age of $\gtrsim 3~$Gyr satisfies all observational constraints. The system likely experienced two common-envelope phases leading to its current state because the white dwarf progenitor was massive enough to ignite He-shell burning. The system will become a cataclysmic variable in the distant future when the K-dwarf evolves off of the main sequence. These short-period high-$q$ binaries represent an intriguing formation pathway for compact double white dwarf binaries and thermonuclear supernovae. An ultraviolet spectrum is the most promising avenue for directly detecting the white dwarf companion., Comment: 9 pages, 4 figures, and 2 tables in the main text, an additional 3 pages, 1 figure, and 2 tables in the appendix. Submitted to the Open Journal of Astrophysics

Language models exhibit scaling laws, whereby increasing model and dataset size yields predictable decreases in negative log likelihood, unlocking a dazzling array of capabilities. This phenomenon spurs many companies to train ever larger models in pursuit of ever improved performance. Yet, these models are vulnerable to adversarial inputs such as ``jailbreaks'' and prompt injections that induce models to perform undesired behaviors, posing a growing risk as models become more capable. Prior work indicates that computer vision models become more robust with model and data scaling, raising the question: does language model robustness also improve with scale? We study this question empirically in the classification setting, finding that without explicit defense training, larger models tend to be modestly more robust on most tasks, though the effect is not reliable. Even with the advantage conferred by scale, undefended models remain easy to attack in absolute terms, and we thus turn our attention to explicitly training models for adversarial robustness, which we show to be a much more compute-efficient defense than scaling model size alone. In this setting, we also observe that adversarially trained larger models generalize faster and better to modified attacks not seen during training when compared with smaller models. Finally, we analyze the offense/defense balance of increasing compute, finding parity in some settings and an advantage for offense in others, suggesting that adversarial training alone is not sufficient to solve robustness, even at greater model scales., Comment: 36 pages; updated to include new results and analysis

Continuing advances in Large Language Models (LLMs) in artificial intelligence offer important capacities in intuitively accessing and using medical knowledge in many contexts, including education and training as well as assessment and treatment. Most of the initial literature on LLMs in medicine has emphasized that LLMs are unsuitable for medical use because they are non-deterministic, may provide incorrect or harmful responses, and cannot be regulated to assure quality control. If these issues could be corrected, optimizing LLM technology could benefit patients and physicians by providing affordable, point-of-care medical knowledge. Our proposed framework refines LLM responses by restricting their primary knowledge base to domain-specific datasets containing validated medical information. Additionally, we introduce an actor-critic LLM prompting protocol based on active inference principles of human cognition, where a Therapist agent initially responds to patient queries, and a Supervisor agent evaluates and adjusts responses to ensure accuracy and reliability. We conducted a validation study where expert cognitive behaviour therapy for insomnia (CBT-I) therapists evaluated responses from the LLM in a blind format. Experienced human CBT-I therapists assessed responses to 100 patient queries, comparing LLM-generated responses with appropriate and inappropriate responses crafted by experienced CBT-I therapists. Results showed that LLM responses received high ratings from the CBT-I therapists, often exceeding those of therapist-generated appropriate responses. This structured approach aims to integrate advanced LLM technology into medical applications, meeting regulatory requirements for establishing the safe and effective use of special purpose validated LLMs in medicine., Comment: 25 pages, 4 figures

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light-curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-year photometrically-classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3x2 point analyses. With detailed catalog-level simulations of the DES 5-year sample, we find that the simulated w can be recovered within $\pm$0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1$\sigma$ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses., Comment: 19 pages, 9 figures. Submitting to MNRAS, comments welcome

How a neural network (NN) generalizes to novel situations depends on whether it has learned to select actions heuristically or via a planning process. "An investigation of model-free planning" (Guez et al. 2019) found that a recurrent NN (RNN) trained to play Sokoban appears to plan, with extra computation steps improving the RNN's success rate. We replicate and expand on their behavioral analysis, finding the RNN learns to give itself extra computation steps in complex situations by "pacing" in cycles. Moreover, we train linear probes that predict the future actions taken by the network and find that intervening on the hidden state using these probes controls the agent's subsequent actions. Leveraging these insights, we perform model surgery, enabling the convolutional NN to generalize beyond its 10x10 architectural limit to arbitrarily sized inputs. The resulting model solves challenging, highly off-distribution levels. We open-source our model and code, and believe the neural network's small size (1.29M parameters) makes it an excellent model organism to deepen our understanding of learned planning., Comment: Mechanistic Interpretability workshop, ICML 2024

Light echoes occur when light from a luminous transient is scattered by dust back into our line of sight with a time delay due to the extra propagation distance. We introduce a novel approach to estimating the distance to a source by combining light echoes with recent three-dimensional dust maps. We identify light echoes from the historical supernovae Cassiopeia A and SN 1572 (Tycho) in nearly a decade of imaging from the All-Sky Automated Survey for Supernovae (ASAS-SN). Using these light echoes, we find distances of $3.6\pm0.1$ kpc and $3.2^{+0.1}_{-0.2}$ kpc to Cas A and Tycho, respectively, which are generally consistent with previous estimates but are more precise. These distance uncertainties are primarily dominated by the low distance resolution of the 3D dust maps, which will likely improve in the future. The candidate single degenerate explosion donor stars B and G in Tycho are clearly foreground stars. Finally, the inferred reddening towards each SN agrees well with the intervening HI column density estimates from X-ray analyses of the remnants., Comment: 11 pages, 10 figures, 2 tables. The table containing the light echo data presented in this submission is included in machine-readable format as an ancillary file

We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene. Our generation is conditioned by language input (e.g., city name, weather), as well as an underlying map/layout hosting the desired trajectory. Compared to recent models for video generation or 3D view synthesis, our method can scale to much longer-range camera trajectories, spanning several city blocks, while maintaining visual quality and consistency. To achieve this goal, we build on recent work on video diffusion, used within an autoregressive framework that can easily scale to long sequences. In particular, we introduce a new temporal imputation method that prevents our autoregressive approach from drifting from the distribution of realistic city imagery. We train our Streetscapes system on a compelling source of data-posed imagery from Google Street View, along with contextual map data-which allows users to generate city views conditioned on any desired city layout, with controllable camera poses. Please see more results at our project page at https://boyangdeng.com/streetscapes., Comment: *Equal Contributions; Fixed few duplicated references from 1st upload; Project Page: https://boyangdeng.com/streetscapes

Adversarially robust optimization (ARO) has become the de facto standard for training models to defend against adversarial attacks during testing. However, despite their robustness, these models often suffer from severe overfitting. To mitigate this issue, several successful approaches have been proposed, including replacing the empirical distribution in training with: (i) a worst-case distribution within an ambiguity set, leading to a distributionally robust (DR) counterpart of ARO; or (ii) a mixture of the empirical distribution with one derived from an auxiliary dataset (e.g., synthetic, external, or out-of-domain). Building on the first approach, we explore the Wasserstein DR counterpart of ARO for logistic regression and show it admits a tractable convex optimization reformulation. Adopting the second approach, we enhance the DR framework by intersecting its ambiguity set with one constructed from an auxiliary dataset, which yields significant improvements when the Wasserstein distance between the data-generating and auxiliary distributions can be estimated. We analyze the resulting optimization problem, develop efficient solutions, and show that our method outperforms benchmark approaches on standard datasets., Comment: 33 pages, 3 color figures, under review at a conference

We present a study of the weak lensing matter profiles of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel'dovich effect (tSZE) selected galaxy clusters in the redshift range $0.25

Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) performance on standard benchmarks. However, these LLM-based models have a significant drawback due to the large model size, with the number of trainable parameters in the millions. In this paper, we propose an alternative approach to leveraging the success of language modeling in the time series domain. Instead of fine-tuning LLMs, we utilize a language embedding model to embed time series and then pair the embeddings with a simple classification head composed of convolutional neural networks (CNN) and multilayer perceptron (MLP). We conducted extensive experiments on well-established time series classification benchmark datasets. We demonstrated LETS-C not only outperforms the current SOTA in classification accuracy but also offers a lightweight solution, using only 14.5% of the trainable parameters on average compared to the SOTA model. Our findings suggest that leveraging language encoders to embed time series data, combined with a simple yet effective classification head, offers a promising direction for achieving high-performance time series classification while maintaining a lightweight model architecture., Comment: 22 pages, 5 figures, 10 tables

Based on the rate of change of its orbital period, PSR J2043+1711 has a substantial peculiar acceleration of 3.5 $\pm$ 0.8 mm/s/yr, which deviates from the acceleration predicted by equilibrium Milky Way models at a $4\sigma$ level. The magnitude of the peculiar acceleration is too large to be explained by disequilibrium effects of the Milky Way interacting with orbiting dwarf galaxies ($\sim$1 mm/s/yr), and too small to be caused by period variations due to the pulsar being a redback. We identify and examine two plausible causes for the anomalous acceleration: a stellar flyby, and a long-period orbital companion. We identify a main-sequence star in \textit{Gaia} DR3 and Pan-STARRS DR2 with the correct mass, distance, and on-sky position to potentially explain the observed peculiar acceleration. However, the star and the pulsar system have substantially different proper motions, indicating that they are not gravitationally bound. However, it is possible that this is an unrelated star that just happens to be located near J2043+1711 along our line of sight (chance probability of 1.6\%). Therefore, we also constrain possible orbital parameters for a circumbinary companion in a hierarchical triple system with J2043+1711; the changes in the spindown rate of the pulsar are consistent with an outer object that has an orbital period of 80 kyr, a companion mass of 0.3 $M_\odot$ (indicative of a white dwarf or low-mass star), and a semi-major axis of 2000 AU. Continued timing and/or future faint optical observations of J2043+1711 may eventually allow us to differentiate between these scenarios.

A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and generality, along with limited or no hardware safety guarantees. In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy trained entirely in simulation that combines reinforcement learning, geometric fabrics, and teacher-student distillation. We address key challenges in joint arm-hand policy learning, such as high-dimensional observation and action spaces, the sim2real gap, collision avoidance, and hardware constraints. DextrAH-G enables a 23 motor arm-hand robot to safely and continuously grasp and transport a large variety of objects at high speed using multi-modal inputs including depth images, allowing generalization across object geometry. Videos at https://sites.google.com/view/dextrah-g.

We report new radio observations of SDSS J090122.37+181432.3, a strongly lensed star-forming galaxy at $z=2.26$. We image 1.4 GHz (L-band) and 3 GHz (S-band) continuum using the VLA and 1.2 mm (band 6) continuum with ALMA, in addition to the CO(7-6) and CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) lines, all at $\lesssim1.^{\prime\prime}7$ resolution. Based on the VLA integrated flux densities, we decompose the radio spectrum into its free-free (FF) and non-thermal components. The infrared-radio correlation (IRRC) parameter $q_{\rm TIR}=2.65_{-0.31}^{+0.24}$ is consistent with expectations for star forming galaxies. We obtain radio continuum-derived SFRs that are free of dust extinction, finding $\rm {620}_{-220}^{+280}\,M_\odot\,yr^{-1}$, $\rm {230}_{-160}^{+570}\,M_\odot\,yr^{-1}$, and $\rm {280}_{-120}^{+460}\,M_\odot\,yr^{-1}$ from the FF emission, non-thermal emission, and when accounting for both emission processes, respectively, in agreement with previous results. We estimate the gas mass from the CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) line as $M_{\rm gas}=(1.2\pm0.2)\times10^{11}\,M_\odot$, which is consistent with prior CO(1-0)-derived gas masses. Using our new IR and radio continuum data to map the SFR, we assess the dependence of the Schmidt-Kennicutt relation on choices of SFR and gas tracer for $\sim{\rm kpc}$ scales. The different SFR tracers yield different slopes, with the IR being the steepest, potentially due to highly obscured star formation in J0901. The radio continuum maps have the lowest slopes and overall fidelity for mapping the SFR, despite producing consistent total SFRs. We also find that the Schmidt-Kennicutt relation slope is flattest when using CO(7-6) or CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) to trace gas mass, suggesting that those transitions are not suitable for tracing the bulk molecular gas in galaxies like J0901., Comment: 28 pages, 13 figures, 9 tables

We present $griz$ photometric light curves for the full 5 years of the Dark Energy Survey Supernova program (DES-SN), obtained with both forced Point Spread Function (PSF) photometry on Difference Images (DIFFIMG) performed during survey operations, and Scene Modelling Photometry (SMP) on search images processed after the survey. This release contains $31,636$ DIFFIMG and $19,706$ high-quality SMP light curves, the latter of which contains $1635$ photometrically-classified supernovae that pass cosmology quality cuts. This sample spans the largest redshift ($z$) range ever covered by a single SN survey ($0.1

We present extensive observations of the Type II supernova (SN II) 2023ufx which is likely the most metal-poor SN II observed to-date. It exploded in the outskirts of a low-metallicity ($Z_{\rm host} \sim 0.1~Z_\odot$) dwarf ($M_g = -13.23\pm0.15$~mag; $r_e\sim 1$~kpc) galaxy. The explosion is luminous, peaking at $M_g\approx -18.5~$mag, and shows rapid evolution. The $r$-band (pseudo-bolometric) light curve has a shock-cooling phase lasting 20 (17) days followed by a 19 (23)-day plateau. The entire optically-thick phase lasts only $\approx 55~$days following explosion, indicating that the red supergiant progenitor had a thinned H envelope prior to explosion. The early spectra obtained during the shock-cooling phase show no evidence for narrow emission features and limit the pre-explosion mass-loss rate to $\dot{M} \lesssim 10^{-3}~\rm M_\odot$/yr. The photospheric-phase spectra are devoid of prominent metal absorption features, indicating a progenitor metallicity of $\lesssim 0.1~Z_\odot$. The semi-nebular ($\sim 60-130~$d) spectra reveal weak Fe II, but other metal species typically observed at these phases (Ti II, Sc II, Ba II) are conspicuously absent. The late-phase optical and near-infrared spectra also reveal broad ($\approx 10^4~\rm{km}~\rm s^{-1}$) double-peaked H$\alpha$, P$\beta$, and P$\gamma$ emission profiles suggestive of a fast outflow launched during the explosion. Outflows are typically attributed to rapidly-rotating progenitors which also prefer metal-poor environments. This is only the second SN II with $\lesssim 0.1~Z_\odot$ and both exhibit peculiar evolution, suggesting a sizable fraction of metal-poor SNe II have distinct properties compared to nearby metal-enriched SNe II. These observations lay the groundwork for modeling the metal-poor SNe II expected in the early Universe., Comment: 17 pages, 15 figures and 3 tables in main text, an additional 5 pages, 4 figures, and 2 tables in the appendix. Accepted by ApJ, spectra and photometry are included as ancillary data

A large body of evidence indicates that well-implemented social and emotional learning (SEL) programs improve academic, social, and emotional outcomes for students and educators. Education policy has the potential to influence the high-quality implementation of SEL, from the school district, to the school, to the classroom. Before and during the coronavirus disease 2019 pandemic, U.S. states enacted supportive policies and conditions to promote SEL in schools. However, curricula that foster the development of social and emotional competences have been one of many controversies about the instructional content and instructional practices schools should use to teach students. Thus, legislators in nine states have proposed bills to prohibit or inhibit SEL instruction in kindergarten through grade 12 (K-12) schools. In this report, the authors investigate whether states' SEL policies (for and against) are associated with the implementation of SEL by K-12 schools, as reported by teachers and principals. In their approach, the authors merge the survey data they collected from educators with the results of the Collaborative for Academic, Social, and Emotional Learning's (CASEL's) nationwide scan of each state's and the District of Columbia's SEL-relevant policies and conditions. The authors also study whether SEL implementation is correlated with two teacher-reported indicators of positive student experience: supportive climate and student interest in learning. The authors' findings show whether state policymakers' SEL-related policies (including legislation and guidance) connect to schools' on-the-ground decisions and implementation, as reported by principals and teachers.

Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-year Dark Energy Survey (DES) dataset using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select 2,298 high-quality SNe Ia from the DES 5-year dataset an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light-curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light-curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN respectively compared to earlier approaches. Our novel method thus leads to better optimisation of spectroscopic resources for follow-up., Comment: Accepted MNRAS