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Cat qubits, a type of bosonic qubit encoded in a harmonic oscillator, can exhibit an exponential noise bias against bit-flip errors with increasing mean photon number. Here, we focus on cat qubits stabilized by two-photon dissipation, where pairs of photons are added and removed from a harmonic oscillator by an auxiliary, lossy buffer mode. This process requires a large loss rate and strong nonlinearities of the buffer mode that must not degrade the coherence and linearity of the oscillator. In this work, we show how to overcome this challenge by coloring the loss environment of the buffer mode with a multi-pole filter and optimizing the circuit to take into account additional inductances in the buffer mode. Using these techniques, we achieve near-ideal enhancement of cat-qubit bit-flip times with increasing photon number, reaching over $0.1$ seconds with a mean photon number of only $4$. Concurrently, our cat qubit remains highly phase coherent, with phase-flip times corresponding to an effective lifetime of $T_{1,\text{eff}} \simeq 70$ $\mu$s, comparable with the bare oscillator lifetime. We achieve this performance even in the presence of an ancilla transmon, used for reading out the cat qubit states, by engineering a tunable oscillator-ancilla dispersive coupling. Furthermore, the low nonlinearity of the harmonic oscillator mode allows us to perform pulsed cat-qubit stabilization, an important control primitive, where the stabilization can remain off for a significant fraction (e.g., two thirds) of a $3~\mathrm{\mu s}$ cycle without degrading bit-flip times. These advances are important for the realization of scalable error-correction with cat qubits, where large noise bias and low phase-flip error rate enable the use of hardware-efficient outer error-correcting codes., Comment: Comments welcome!

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In this study, 250 raw chicken breast fillet samples (normal, mild, severe) were taken, and spatially heterogeneous hardness distribution was first considered when designing HSI processing models. The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data. To achieve a satisfactory classification and regression model, a neural network architecture search (NAS) enabled a wide-deep neural network model named NAS-WD, which was developed. In NAS-WD, NAS was first used to automatically optimize the network architecture and hyperparameters. The classification results show that NAS-WD can classify the three WB levels with an overall accuracy of 95%, outperforming the traditional machine learning model, and the regression correlation between the spectral data and hardness was 0.75, which performs significantly better than traditional regression models.

We present a simple, self-supervised approach to the Tracking Any Point (TAP) problem. We train a global matching transformer to find cycle consistent tracks through video via contrastive random walks, using the transformer's attention-based global matching to define the transition matrices for a random walk on a space-time graph. The ability to perform "all pairs" comparisons between points allows the model to obtain high spatial precision and to obtain a strong contrastive learning signal, while avoiding many of the complexities of recent approaches (such as coarse-to-fine matching). To do this, we propose a number of design decisions that allow global matching architectures to be trained through self-supervision using cycle consistency. For example, we identify that transformer-based methods are sensitive to shortcut solutions, and propose a data augmentation scheme to address them. Our method achieves strong performance on the TapVid benchmarks, outperforming previous self-supervised tracking methods, such as DIFT, and is competitive with several supervised methods., Comment: ECCV 2024. Project link: https://ayshrv.com/gmrw . Code: https://github.com/ayshrv/gmrw/

Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we manipulate input speech to sound as though it was recorded within a different scene, given an audio-visual conditional example recorded from that scene. Our model learns through self-supervision, taking advantage of the fact that natural video contains recurring sound events and textures. We extract an audio clip from a video and apply speech enhancement. We then train a latent diffusion model to recover the original speech, using another audio-visual clip taken from elsewhere in the video as a conditional hint. Through this process, the model learns to transfer the conditional example's sound properties to the input speech. We show that our model can be successfully trained using unlabeled, in-the-wild videos, and that an additional visual signal can improve its sound prediction abilities. Please see our project webpage for video results: https://tinglok.netlify.app/files/avsoundscape/, Comment: ECCV 2024

Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.

Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite significant advancements in bias mitigation techniques using data augmentation, zero-shot prompting, and model fine-tuning, biases continuously persist, including subtle biases that may elude human detection. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning and factuality in LLMs. Building on this approach, we propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs. Our work is the first to introduce and evaluate two distinct approaches within this framework for debiasing LLMs: a centralized method, where the conversation is facilitated by a single central LLM, and a decentralized method, where all models communicate directly. Our findings reveal that our multi-LLM framework significantly reduces bias in LLMs, outperforming the baseline method across several social groups.

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.

We present the latest results from the Chicago Carnegie Hubble Program (CCHP) to measure the Hubble constant using data from the James Webb Space Telescope (JWST). This program is based upon three independent methods: (1) Tip of the Red Giant Branch (TRGB) stars, (2) JAGB (J-Region Asymptotic Giant Branch) stars, and (3) Cepheids. Our program includes 10 nearby galaxies, each hosting Type Ia supernovae, suitable for measuring the Hubble constant (Ho). It also includes NGC 4258, which has a geometric distance, setting the zero point for all three methods. The JWST observations have significantly higher signal-to-noise and finer angular resolution than previous observations with the Hubble Space Telescope (HST). We find three independent values of Ho = 69.85 +/- 1.75 (stat) +/- 1.54 (sys) for the TRGB, Ho = 67.96 +/- 1.85 (stat) +/- 1.90 (sys) for the JAGB, and Ho = 72.05 +/- 1.86 (stat) +/- 3.10 (sys) km/s/Mpc for Cepheids. Tying into supernovae, and combining these methods adopting a flat prior, yields our current estimate of Ho = 69.96 +/- 1.05 (stat) +/- 1.12 (sys) km/s/Mpc. The distances measured using the TRGB and the JAGB method agree at the 1% level, but differ from the Cepheid distances at the 2.5-4% level. The value of Ho based on these two methods with JWST data alone is Ho = 69.03 +/- 1.75 (total error) km/sec/Mpc. These numbers are consistent with the current standard Lambda CDM model, without the need for the inclusion of additional new physics. Future JWST data will be required to increase the precision and accuracy of the local distance scale., Comment: 61 pages, 20 figures

The J-region asymptotic giant branch (JAGB) method is a new standard candle based on the constant luminosities of carbon-rich asymptotic giant branch stars in the J band. The JAGB method is independent of the Cepheid and TRGB distance indicators. Therefore, we can leverage it to both cross-check Cepheid and TRGB distances for systematic errors and use it to measure an independent local Hubble constant. The JAGB method also boasts a number of advantages in measuring distances relative to the TRGB and Cepheids, several of which are especially amplified when combined with JWST's revolutionary resolving power. First, JAGB stars are 1 mag brighter in the NIR than the TRGB, and can be discovered from single-epoch NIR photometry unlike Cepheids which require congruent optical imaging in at least 12 epochs. Thus, JAGB stars can be used to measure significantly farther distances than both the TRGB stars and Cepheids using the same amount of observing time. Further advantages include: JAGB stars are easily identified solely via their colors and magnitudes, dust extinction is reduced in near-infrared observations, and JAGB stars are ubiquitous in all galaxies with intermediate-age populations. In this paper, we present a novel algorithm that identifies the optimal location in a galaxy for applying the JAGB method, so as to minimize effects from crowding. We then deploy this algorithm in JWST NIRCam imaging of seven SN Ia host galaxies to measure their JAGB distances, undertaking a completely blind analysis. The zero-point of this JAGB distance scale is set in the water mega-maser galaxy NGC 4258. In our CCHP overview paper Freedman et al. (2024), we apply the JAGB distances measured in this paper to the Carnegie Supernova Program (CSP) SNe Ia sample, measuring a Hubble constant of H0 = 67.96 +/- 1.85 (stat) +/- 1.90 (sys) km/s/Mpc., Comment: 24 pages, 10 figures, 5 tables, submitted to AAS Journals, v2 now includes systematic uncertainty in abstract

Wide-separation lensed quasars (WSLQs) are a rare class of strongly lensed quasars, magnified by foreground massive galaxy clusters, with typically large magnifications of the multiple quasar images. They are a relatively unexplored opportunity for detailed study of quasar host galaxies. The current small sample of known WSLQs has a median redshift of $z\approx 2.1$, larger than most other samples of quasar host galaxies studied to date. Here, we derive precise constraints on the properties of six WSLQs and their host galaxies, using parametric surface brightness fitting, measurements of quasar emission lines, and stellar population synthesis of host galaxies in six WSLQ systems. Our results, with significant uncertainty, indicate that these six hosts are a mixture of star-forming and quiescent galaxies. To probe for co-evolution between AGNs and host galaxies, we model the offset from the `local' ($z=0$) $M_{\rm{BH}}\unicode{x2013}M_\star$ relation as a simple power-law in redshift. Accounting for selection effects, a WSLQ-based model for evolution in the $M_{\rm{BH}}\unicode{x2013}M_\star$ relation has a power-law index of $\gamma_M=-0.42\pm0.31$, consistent with no evolution. Compared to several literature samples, which mostly probe unlensed quasars at $z<2$, the WSLQ sample shows less evolution from the local relation, at $\sim 4\sigma$. We find that selection affects and choices of $M_{\rm{BH}}$ calibration are the most important systematics in these comparisons. Given that we resolve host galaxy flux confidently even from the ground in some instances, our work demonstrates that WSLQs and highly magnified AGNs are exceptional systems for future AGN$\unicode{x2013}$host co-evolution studies., Comment: Submitted to ApJ. 25 pages + 7-page appendix, 12+4 figures. Key results are shown starting with Figure 6. Comments welcome

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

Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration, to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet most time-series analysis methods that are used in practice, on important problems across science and industry, make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as Parameter Inference from a Non-stationary Unknown Process (PINUP). Given an observed time series, this involves inferring the parameters that drive non-stationarity of the time series, without requiring knowledge or inference of a mathematical model of the underlying system. Here we review and unify a diverse literature of algorithms for PINUP. We formulate the problem, and categorize the various algorithmic contributions. This synthesis will allow researchers to identify gaps in the literature and will enable systematic comparisons of different methods. We also demonstrate that the most common systems that existing methods are tested on - notably the non-stationary Lorenz process and logistic map - are surprisingly easy to perform well on using simple statistical features like windowed mean and variance, undermining the practice of using good performance on these systems as evidence of algorithmic performance. We then identify more challenging problems that many existing methods perform poorly on and which can be used to drive methodological advances in the field. Our results unify disjoint scientific contributions to analyzing non-stationary systems and suggest new directions for progress on the PINUP problem and the broader study of non-stationary phenomena.

Boasting a 6.5m mirror in space, JWST can increase by several times the number of supernovae (SNe) to which a redshift-independent distance has been measured with a precision distance indicator (e.g., TRGB or Cepheids); the limited number of such SN calibrators currently dominates the uncertainty budget in distance ladder Hubble constant (H0) experiments. JWST/NIRCAM imaging of the Virgo Cluster galaxy NGC4536 is used here to preview JWST program GO-1995, which aims to measure H0 using three stellar distance indicators (Cepheids, TRGB, JAGB/carbon stars). Each population of distance indicator was here successfully detected -- with sufficiently large number statistics, well-measured fluxes, and characteristic distributions consistent with ingoing expectations -- so as to confirm that we can acquire distances from each method precise to about 0.05mag (statistical uncertainty only). We leverage overlapping HST imaging to identify TRGB stars, cross-match them with the JWST photometry, and present a preliminary constraint on the slope of the TRGB's F115W-(F115W}-F444W) relation equal to -0.99 +/- 0.16 mag/mag. This slope is consistent with prior slope measurements in the similar 2MASS J-band, as well as with predictions from the BASTI isochrone suite. We use the new TRGB slope estimate to flatten the two-dimensional TRGB feature and measure a (blinded) TRGB distance relative to a set of fiducial TRGB colors, intended to represent the absolute fiducial calibrations expected from geometric anchors such as NGC4258 and the Magellanic Clouds. In doing so, we empirically demonstrate that the TRGB can be used as a standardizable candle at the IR wavelengths accessible with JWST., Comment: Revised version after submission to AAS journals; 20 pages, 12 figures; Fig. 1 compressed to reduce file size

Domain adaptive semantic segmentation aims to generate accurate and dense predictions for an unlabeled target domain by leveraging a supervised model trained on a labeled source domain. The prevalent self-training approach involves retraining the dense discriminative classifier of $p(class|pixel feature)$ using the pseudo-labels from the target domain. While many methods focus on mitigating the issue of noisy pseudo-labels, they often overlook the underlying data distribution p(pixel feature|class) in both the source and target domains. To address this limitation, we propose the multi-prototype Gaussian-Mixture-based (ProtoGMM) model, which incorporates the GMM into contrastive losses to perform guided contrastive learning. Contrastive losses are commonly executed in the literature using memory banks, which can lead to class biases due to underrepresented classes. Furthermore, memory banks often have fixed capacities, potentially restricting the model's ability to capture diverse representations of the target/source domains. An alternative approach is to use global class prototypes (i.e. averaged features per category). However, the global prototypes are based on the unimodal distribution assumption per class, disregarding within-class variation. To address these challenges, we propose the ProtoGMM model. This novel approach involves estimating the underlying multi-prototype source distribution by utilizing the GMM on the feature space of the source samples. The components of the GMM model act as representative prototypes. To achieve increased intra-class semantic similarity, decreased inter-class similarity, and domain alignment between the source and target domains, we employ multi-prototype contrastive learning between source distribution and target samples. The experiments show the effectiveness of our method on UDA benchmarks.

We analyze an optical atomic clock using two-photon $5S_{1/2} \rightarrow 4D_J$ transitions in rubidium. Four one- and two-color excitation schemes to probe the fine-structure states $4D_{3/2}$ and $4D_{5/2}$ are considered in detail. We compare key characteristics of Rb $4D_J$ and $5D_{5/2}$ two-photon clocks. The $4D_J$ clock features a high signal-to-noise ratio due to two-photon decay at favorable wavelengths, low dc electric and magnetic susceptibilities, and minimal black-body shifts. Ac Stark shifts from the clock interrogation lasers are compensated by two-color Rabi-frequency matching. We identify a "magic" wavelength near 1060~nm, which allows for in-trap, Doppler-free clock-transition interrogation with lattice-trapped cold atoms. From our analysis of clock statistics and systematics, we project a quantum-noise-limited relative clock stability at the $10^{-13}/\sqrt{\tau(s)}$-level, with integration time $\tau$ in seconds, and a relative accuracy of $\sim 10^{-13}$. We describe a potential architecture for implementing the proposed clock using a single telecom clock laser at 1550~nm, which is conducive to optical communication and long-distance clock comparisons. Our work could be of interest in efforts to realize small and portable Rb clocks and in high-precision measurements of atomic properties of Rb $4D_J$-states., Comment: A.D. and C.O. contributed equally to this work; 15 pages, 3 figures, 1 table

The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. These data are widely used to model atmospheres of exoplanets, cool stars and other astronomical objects, as well as a variety of terrestrial applications. The 2024 data release reports the current status of the database which contains recommended line lists for 91 molecules and 224 isotopologues giving a total of almost 10$^{12}$ individual transitions. New features of the database include extensive "MARVELization" of line lists to allow them to be used for high resolutions studies, extension of several line lists to ultraviolet wavelengths, provision of photodissociation cross sections and extended provision of broadening parameters. Some of the in-house data specifications have been rewritten in JSON and moved to conformity with other international standards. Data products, including specific heats, a database of lifetimes for plasma studies, and the ExoMolHR web app which allows exclusively high resolution data to be extracted, are discussed.

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount.

Spectrograms are 2D representations of sound that look very different from the images found in our visual world. And natural images, when played as spectrograms, make unnatural sounds. In this paper, we show that it is possible to synthesize spectrograms that simultaneously look like natural images and sound like natural audio. We call these spectrograms images that sound. Our approach is simple and zero-shot, and it leverages pre-trained text-to-image and text-to-spectrogram diffusion models that operate in a shared latent space. During the reverse process, we denoise noisy latents with both the audio and image diffusion models in parallel, resulting in a sample that is likely under both models. Through quantitative evaluations and perceptual studies, we find that our method successfully generates spectrograms that align with a desired audio prompt while also taking the visual appearance of a desired image prompt. Please see our project page for video results: https://ificl.github.io/images-that-sound/, Comment: Project site: https://ificl.github.io/images-that-sound/

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training, we randomly mask clusters of visually similar image patches, as measured by their raw pixel intensities. This provides an extra learning signal, beyond the contrastive training itself, since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks, finding that it outperforms other masking strategies, such as FLIP, on the quality of the learned representation., Comment: CVPR 2024, Project page: https://zxp46.github.io/cluster-masking/ , Code: https://github.com/Zi-hao-Wei/Efficient-Vision-Language-Pre-training-by-Cluster-Masking

"Godzilla" is a peculiar object within the gravitationally lensed Sunburst Arc at $z=2.37$. Despite being very bright, it appears in only one of the twelve lensed images of the source galaxy, and shows exotic spectroscopic properties not found elsewhere in the galaxy. We use JWST's unique combination of spatial resolution and spectroscopic sensitivity to provide a unified, coherent explanation of the physical nature of Godzilla. We measure fluxes and kinematic properties of rest-optical emission lines in Godzilla and surrounding regions. Using standard line ratio-based diagnostic methods in combination with NIRCam imaging and ground based rest-UV spectra, we characterize Godzilla and its surroundings. We find that Godzilla is most likely an extremely magnified, non-erupting LBV star with dense gas condensations in close proximity. Among around 60 detected lines, we find a cascade of strong O I lines pumped by intense Ly$\beta$ emission, as well as Ly$\alpha$-pumped rest-optical Fe II lines, reminiscent of the Weigelt blobs in the local LBV star Eta Carinae. Godzilla is surrounded by dusty, inhomogeneous gas common to massive, evolved stars. Spectra and images of Godzilla and adjacent objects and the detection of a low-surface brightness foreground galaxy in the NIRCam data support the interpretation that Godzilla is a stellar-scale object extremely magnified by alignment with lensing caustics. To explain the dusty surroundings, strong [Ne III] and line kinematics simultaneously, we argue that Godzilla is a post-eruption LBV accompanied by a hotter companion and/or gas condensations exposed to more intense radiation compared to the Weigelt blobs. We expect periodic spectroscopic variations if Godzilla is a binary system. If Godzilla is confirmed to be an LBV star, it expands the distance to the furthest known LBV from a dozen Mpc to several Gpc., Comment: 18 pages, 16 figures. Submitted to A&A

We measure the gas-phase abundances of the elements He, N, O, Ne, S, Ar, and Fe in the Lyman-continuum emitting region of the Sunburst Arc, a highly magnified galaxy at redshift $z=2.37$. We detect the temperature-sensitive auroral lines [SII]$\lambda\lambda4069,4076$, [OII]$\lambda\lambda7320,7330$, [SIII]$\lambda6312$, [OIII]$\lambda4363$, and [NeIII]$\lambda3343$ in a stacked spectrum of 5 multiple images of the Lyman-continuum emitter (LCE), from which we directly measure the electron temperature in the low, intermediate, and high ionization zones. We also detect the density-sensitive doublets of [OII]$\lambda\lambda3727,3729$, [SII]$\lambda\lambda6717,6731$, and [ArIV]$\lambda\lambda4713,4741$, which constrain the density in both the low- and high-ionization gas. With these temperature and density measurements, we measure gas-phase abundances with similar rigor as studies of local galaxies. We measure a gas-phase metallicity for the LCE of $12+\log(\textrm{O}/\textrm{H}) = 7.97 \pm 0.05$, and find an enhanced nitrogen abundance $\log(\textrm{N}/\textrm{O}) = -0.65^{+0.16}_{-0.25}$. This nitrogen abundance is consistent with enrichment from a population of Wolf-Rayet stars, additional signatures of which are reported in a companion paper. Abundances of sulfur, argon, neon, and iron are consistent with local low-metallicity HII regions and low-redshift galaxies. This study represents the most complete chemical abundance analysis of a galaxy at Cosmic Noon to date, which enables direct comparisons between local HII regions and those in the distant universe., Comment: 15 pages, 4 figures, 3 tables. Submitted to ApJ

We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to estimate the visual and tactile signals for a given 3D position within a scene. We capture a scene's TaRF from a collection of photos and sparsely sampled touch probes. Our approach makes use of two insights: (i) common vision-based touch sensors are built on ordinary cameras and thus can be registered to images using methods from multi-view geometry, and (ii) visually and structurally similar regions of a scene share the same tactile features. We use these insights to register touch signals to a captured visual scene, and to train a conditional diffusion model that, provided with an RGB-D image rendered from a neural radiance field, generates its corresponding tactile signal. To evaluate our approach, we collect a dataset of TaRFs. This dataset contains more touch samples than previous real-world datasets, and it provides spatially aligned visual signals for each captured touch signal. We demonstrate the accuracy of our cross-modal generative model and the utility of the captured visual-tactile data on several downstream tasks. Project page: https://dou-yiming.github.io/TaRF, Comment: CVPR 2024, Project page: https://dou-yiming.github.io/TaRF, Code: https://github.com/Dou-Yiming/TaRF/

Decisions about who is in charge of school systems often make the news. High-profile cases such as the recent takeover of the Houston Independent School District by the State of Texas -- where one of the first steps by state leaders was to replace the superintendent -- highlight the central importance of district leadership as well as the public perception of superintendents as critical contributors to the quality of a school system. Yet decision-makers who want to rely on research-based evidence about who to select or even how to conceptualize the characteristics required to be successful as superintendents have relatively little to draw on in the education research literature. In the spring of 2023, The Broad Center at the Yale School of Management partnered with the Annenberg Institute at Brown University to bring together a group of scholars who have made recent contributions to research on superintendency and to educational leadership more broadly. These conversations resulted in a strong consensus on the need for a renewed push to build knowledge around specific aspects of the superintendency, anchored in new data that broaden the research that is possible in the field. Future research, in particular, could inform the field by investigating superintendents' roles, including their time use, decision-making, priorities, management styles, and behaviors; the routes to the superintendency and factors associated with retention; how and why superintendents improve; and the causal impact of superintendents on key outcomes. [This report was co-produced by The Broad Center at Yale School of Management.]

Changing clinical algorithms to remove race adjustment has been proposed and implemented for multiple health conditions. Removing race adjustment from estimated glomerular filtration rate (eGFR) equations may reduce disparities in chronic kidney disease (CKD), but has not been studied in clinical practice after implementation. Here, we assessed whether implementing an eGFR equation (CKD-EPI 2021) without adjustment for Black or African American race modified quarterly rates of nephrology referrals and visits within a single healthcare system, Stanford Health Care (SHC). Our cohort study analyzed 547,194 adult patients aged 21 and older who had at least one recorded serum creatinine or serum cystatin C between January 1, 2019 and September 1, 2023. During the study period, implementation of CKD-EPI 2021 did not modify rates of quarterly nephrology referrals in those documented as Black or African American or in the overall cohort. After adjusting for capacity at SHC nephrology clinics, estimated rates of nephrology referrals and visits with CKD-EPI 2021 were 34 (95% CI 29, 39) and 188 (175, 201) per 10,000 patients documented as Black or African American. If race adjustment had not been removed, estimated rates were nearly identical: 38 (95% CI: 28, 53) and 189 (165, 218) per 10,000 patients. Changes to the eGFR equation are likely insufficient to achieve health equity in CKD care decision-making as many other structural inequities remain., Comment: Accepted to Conference on Health, Inference, and Learning (CHIL) 2024

Characterizing and predicting the training performance of modern machine learning (ML) workloads on compute systems with compute and communication spread between CPUs, GPUs, and network devices is not only the key to optimization and planning but also a complex goal to achieve. The primary challenges include the complexity of synchronization and load balancing between CPUs and GPUs, the variance in input data distribution, and the use of different communication devices and topologies (e.g., NVLink, PCIe, network cards) that connect multiple compute devices, coupled with the desire for flexible training configurations. Built on top of our prior work for single-GPU platforms, we address these challenges and enable multi-GPU performance modeling by incorporating (1) data-distribution-aware performance models for embedding table lookup, and (2) data movement prediction of communication collectives, into our upgraded performance modeling pipeline equipped with inter-and intra-rank synchronization for ML workloads trained on multi-GPU platforms. Beyond accurately predicting the per-iteration training time of DLRM models with random configurations with a geomean error of 5.21% on two multi-GPU platforms, our prediction pipeline generalizes well to other types of ML workloads, such as Transformer-based NLP models with a geomean error of 3.00%. Moreover, even without actually running ML workloads like DLRMs on the hardware, it is capable of generating insights such as quickly selecting the fastest embedding table sharding configuration (with a success rate of 85%)., Comment: 12 pages, 11 figures, 4 tables

Given a factorization of an image into a sum of linear components, we present a zero-shot method to control each individual component through diffusion model sampling. For example, we can decompose an image into low and high spatial frequencies and condition these components on different text prompts. This produces hybrid images, which change appearance depending on viewing distance. By decomposing an image into three frequency subbands, we can generate hybrid images with three prompts. We also use a decomposition into grayscale and color components to produce images whose appearance changes when they are viewed in grayscale, a phenomena that naturally occurs under dim lighting. And we explore a decomposition by a motion blur kernel, which produces images that change appearance under motion blurring. Our method works by denoising with a composite noise estimate, built from the components of noise estimates conditioned on different prompts. We also show that for certain decompositions, our method recovers prior approaches to compositional generation and spatial control. Finally, we show that we can extend our approach to generate hybrid images from real images. We do this by holding one component fixed and generating the remaining components, effectively solving an inverse problem.

In this work, we propose a unified abstraction for graph algorithms: the Extended General Einsums language, or EDGE. The EDGE language expresses graph algorithms in the language of tensor algebra, providing a rigorous, succinct, and expressive mathematical framework. EDGE leverages two ideas: (1) the well-known foundations provided by the graph-matrix duality, where a graph is simply a 2D tensor, and (2) the power and expressivity of Einsum notation in the tensor algebra world. In this work, we describe our design goals for EDGE and walk through the extensions we add to Einsums to support more complex operations common in graph algorithms. Additionally, we provide a few examples of how to express graph algorithms in our proposed notation. We hope that a single, mathematical notation for graph algorithms will (1) allow researchers to more easily compare different algorithms and different implementations of a graph algorithm; (2) enable developers to factor complexity by separating the concerns of what to compute (described with the extended Einsum notation) from the lower level details of how to compute; and (3) enable the discovery of different algorithmic variants of a problem through algebraic manipulations and transformations on a given EDGE expression., Comment: 79 pages, 14 figures

We report the detection of a population of Wolf-Rayet (WR) stars in the Sunburst Arc, a strongly gravitationally lensed galaxy at redshift $z=2.37$. As the brightest known lensed galaxy, the Sunburst Arc has become an important cosmic laboratory for studying star and cluster formation, Lyman $\alpha$ radiative transfer, and Lyman Continuum (LyC) escape. Here, we present the first results of JWST/NIRSpec IFU observations of the Sunburst Arc, focusing on a stacked spectrum of the 12-fold imaged LyC-emitting (Sunburst LCE) cluster. In agreement with previous studies, we find that the cluster is massive and compact, with $M_{\text{dyn}} = (9\pm1) \times 10^{6} M_{\odot}$, Our age estimate of 4.2--4.5 Myr is much larger than the crossing time of $t_{\text{cross}} = 183 \pm 9 $ kyr, indicating that the cluster is dynamically evolved and consistent with being gravitationally bound. We find a significant nitrogen enhancement of the low ionization state ISM, with $\log(N/O) = -0.74 \pm 0.09$, which is $\approx 0.8$ dex above typical values for H II regions of similar metallicity in the local Universe. We find broad stellar emission complexes around He II$\lambda 4686$ and C IV$\lambda 5808$ with associated nitrogen emission -- this is the first time WR signatures have been directly observed at redshifts above $\sim 0.5$. The strength of the WR signatures cannot be reproduced by stellar population models that only include single-star evolution. While models with binary evolution better match the WR features, they still struggle to reproduce the nitrogen-enhanced WR features. JWST reveals the Sunburst LCE to be a highly ionized, proto-globular cluster with low oxygen abundance and extreme nitrogen enhancement that hosts a population of Wolf-Rayet stars, and possibly Very Massive stars (VMSs), which are rapidly enriching the surrounding medium., Comment: 8 pages, 4 figures, 3 tables. Accepted for publication in A&A

We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured room impulse response data paired with multi-view images, and precise 6DoF pose tracking data for sound emitters and listeners in the rooms. We used this dataset to evaluate existing methods for novel-view acoustic synthesis and impulse response generation which previously relied on synthetic data. In our evaluation, we thoroughly assessed existing audio and audio-visual models against multiple criteria and proposed settings to enhance their performance on real-world data. We also conducted experiments to investigate the impact of incorporating visual data (i.e., images and depth) into neural acoustic field models. Additionally, we demonstrated the effectiveness of a simple sim2real approach, where a model is pre-trained with simulated data and fine-tuned with sparse real-world data, resulting in significant improvements in the few-shot learning approach. RAF is the first dataset to provide densely captured room acoustic data, making it an ideal resource for researchers working on audio and audio-visual neural acoustic field modeling techniques. Demos and datasets are available on our project page: https://facebookresearch.github.io/real-acoustic-fields/, Comment: Accepted to CVPR 2024. Project site: https://facebookresearch.github.io/real-acoustic-fields/

The jamming transition is an important feature of granular materials, with prior work showing an excess of low frequency modes in the granular analog to the density of states, the granular density of modes. In this work, we present an experimental method for acoustically measuring the granular density of modes using a single impact event to excite vibrational modes in an experimental, three dimensional, granular material. We test three different granular materials, all of which are composed of spherical beads. The first two systems are monodisperse collections of either 6 mm or 8 mm diameter beads. The third system is a bidisperse mixture of the previous two bead sizes. During data collection, the particles are confined to a box; on top of this box, and resting on the granular material, is a light, rigid sheet onto which pressure can be applied to the system. To excite the material, a steel impactor ball is dropped on top of the system. The response of the granular material to the impact pulse is recorded by piezoelectric sensors buried throughout the material, and the density of modes is computed from the spectrum of the velocity autocorrelation of these sensors. Our measurements of the density of modes show more low frequency modes at low pressure, consistent with previous experimental and numerical results, as well as several low frequency peaks in the density of modes that shift with applied pressure. Our method represents an experimentally simple technique for investigating the granular density of modes and may increase the accessibility and number of such measurements., Comment: 11 pages, 8 figures

Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient and interpretable model for representing and reasoning over such molecules in terms of graph grammars that explicitly describe the hierarchical design space featuring motifs to be the design basis. We present a novel representation in the form of random walks over the design space, which facilitates both molecule generation and property prediction. We demonstrate clear advantages over existing methods in terms of performance, efficiency, and synthesizability of predicted molecules, and we provide detailed insights into the method's chemical interpretability.

How the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large-scale dynamics internal to the magnetosphere. To compensate for the relative lack of in-situ data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in-situ measurements. This introduces another complication: the propagation of near-Earth solar wind measurements introduces difficult-to-assess uncertainties. Here, we present the Multi-Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi-model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in-situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the case of the Jupiter-MME presented here, three solar wind propagation models were compared to in-situ measurements from the near-Jupiter spacecraft Ulysses and Juno which span diverse geometries and phases of the solar cycle, amounting to more than 14,000 hours of data over 2.5 decades. The MME gives the most-probable near-Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty., Comment: 31 pages, 10 figures

We present near-infrared JHK photometry for the resolved stellar populations in 13 nearby galaxies: NGC 6822, IC 1613, NGC 3109, Sextans B, Sextans A, NGC 300, NGC 55, NGC 7793, NGC 247, NGC 5253, Cen A, NGC 1313, and M83, acquired from the 6.5m Baade-Magellan telescope. We measure distances to each galaxy using the J-region asymptotic giant branch (JAGB) method, a new standard candle that leverages the constant luminosities of color-selected, carbon-rich AGB stars. While only single-epoch, random-phase photometry is necessary to derive JAGB distances, our photometry is time-averaged over multiple epochs, thereby decreasing the contribution of the JAGB stars' intrinsic variability to the measured dispersions in their observed luminosity functions. To cross-validate these distances, we also measure near-infrared tip of the red giant branch (TRGB) distances to these galaxies. The residuals obtained from subtracting the distance moduli from the two methods yield an RMS scatter of $\sigma_{JAGB - TRGB}= \pm 0.07$ mag. Therefore, all systematics in either the JAGB method and TRGB method (e.g., crowding, differential reddening, star formation histories) must be contained within these $\pm0.07$ mag bounds for this sample of galaxies because the JAGB and TRGB distance indicators are drawn from entirely distinct stellar populations, and are thus affected by these systematics independently. Finally, the composite JAGB star luminosity function formed from this diverse sample of galaxies is well-described by a Gaussian function with a modal value of $M_J = -6.20 \pm 0.003$ mag (stat), indicating the underlying JAGB star luminosity function of a well-sampled full star formation history is highly symmetric and Gaussian, based on over 6,700 JAGB stars in the composite sample., Comment: 31 pages, 11 figures, 6 tables, accepted to ApJ. Photometry catalogs for 13 galaxies available at https://zenodo.org/records/10606945

Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in-situ measured OMF and that derived from remote sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period from September to October, where a sudden and significant OMF increase was reported. By deriving the OMF evolution at 1au, modeling it at the source surface, and analyzing solar photospheric data, we provide a comprehensive analysis of the observed phenomenon. First, we establish a strong correlation between the OMF increase and the solar magnetic field derived from a Potential Field Source Surface (PFSS) model ($cc_{\mathrm{Pearson}}=0.94$). Moreover, we find a good correlation between the OMF and the open flux derived from solar coronal holes ($cc_{\mathrm{Pearson}}=0.88$), although the coronal holes only contain $14-32\%$ of the Sun's total open flux. However, we note that while the OMF evolution correlates with coronal hole open flux, there is no correlation with the coronal hole area evolution ($cc_{\mathrm{Pearson}}=0.0$). The temporal increase in OMF correlates with the vanishing remnant magnetic field at the southern pole, caused by poleward flux circulations from the decay of numerous active regions months earlier. Additionally, our analysis suggests a potential link between the OMF enhancement and the concurrent emergence of the largest active region in solar cycle 24. In conclusion, our study provides insights into the strong increase in OMF observed during September to October 2014., Comment: accepted in ApJ

Solar radio bursts (SRBs), are emitted by electrons propagating through the corona and interplanetary space. Tracking such bursts is key to understanding the properties of accelerated electrons and radio wave propagation as well as the local plasma environment that they propagate through. Here, we present a novel multilateration algorithm called BayEsian LocaLisation Algorithm (BELLA). In addition, apparent SRB positions from BELLA are compared with comparable localisation methods and the predictions of solar wind models. BELLA uses Bayesian inference to create probabilistic distributions of source positions and their uncertainties. This facilitates the estimation of algorithmic, instrumental, and physical uncertainties in a quantitative manner. We validated BELLA using simulations and a Type III SRB observed by STEREO A/B and Wind. BELLA tracked the Type III source from $\sim$ 10--150 $R_{sun}$ (2-0.15 MHz) along a spiral trajectory. This allowed for an estimate of an apparent solar wind speed of $v_{sw} \sim$ 400 km s$^{-1}$ and a source longitude of $\phi_0 \sim$ 30deg. We compared these results with well-established methods of positioning: Goniopolarimetric (GP), analytical time-difference-of-arrival (TDOA), and Solar radio burst Electron Motion Tracker (SEMP). We found them to be in agreement with the results obtained by BELLA. Additionally, the results aligned with solar wind properties assimilated by the Heliospheric Upwind Extrapolation with time dependence (HUXt) model. We have validated BELLA and used it to identify apparent source positions as well as velocities and densities of the solar wind. Furthermore, we identified higher than expected electron densities, suggesting that the true emission sources were at lower altitudes than those identified by BELLA, an effect that may be due to appreciable scattering of electromagnetic waves by electrons in interplanetary space., Comment: 13 pages, 12 figures, in press, accepted by Astronomy and Astrophysics for publication