Search

One of the primary objectives of the SPHEREx mission is to understand the origin of molecules such as H2O, CO2, and other volatile compounds at the early stages of planetary system formation. Because the vast majority of these compounds -- typically exceeding 95% -- exist in the solid phase rather than the gaseous phase in the systems of concern here, the observing strategy planned to characterize them is slightly unusual. Specifically, SPHEREx will target highly obscured sources throughout the Milky Way, and observe the species of concern in absorption against background illumination. SPHEREx spectrophotometry will yield ice column density measurements for millions of obscured Milky Way sources of all ages and types. By correlating those column densities with source ages, the SPHEREx mission will shed light on whether those molecules were formed in situ along with their nascent stellar systems, or whether instead they formed elsewhere and were introduced into those systems after their formation. To that end, this work describes version 7$.$1 of the SPHEREx Target List of Ice Sources (SPLICES) for the community. It contains about 8$.$6 million objects brighter than W2~12 Vega mag over much of the sky, principally within a broad strip running the length of the Milky Way midplane, but also within high-latitude molecular clouds and even the Magellanic Clouds., Comment: Published by ApJ. 21 pages, 6 figures. This article documents the original version of SPLICES (7.1). The current version as well as the complete catalog is publicly available along with release notes documenting all additions and changes at the NASA/IPAC Infrared Science Archive (IRSA) at this URL: https://irsa.ipac.caltech.edu/data/SPHEREx/SPLICES/

The circumstellar envelope of the carbon star CW Leo exhibited various unexpected changes in recent optical imaging observations. We have performed a follow-up observation using the Near-infrared Integral-Field Spectrograph (NIFS) equipped on the Gemini-North telescope. We report the near-infrared counterparts of a local brightness peak in the optical at the stellar position of CW Leo. On the other hand, a second peak detected at short wavelengths in the J band coincides with the brightest, bluest position in the optical images. The absorption features in the K band are minimized at a radius of 0.2 arcsec from the predicted stellar position. The reduction of the absorption depths likely indicates dilution of the absorption features by thermal emission of dust grains newly formed at such a radius and heated by radiation from the central star. The broad absorption feature at 1.53 um is significantly deeper than in template carbon stars, consistent with the presence of a substantial amount of circumstellar material around CW Leo. Its northeastern quadrant lacks circumstellar absorption features and scattered light in the near-infrared regime, which are possibly manifestations of its conical cavity in both gas and dust. In addition, a cross correlation of CO overtone bands indicates that the average expansion velocity of dust grains is smaller to the northern direction, likewise the velocity of transverse wind components derived using the differential proper motion of a circumstellar whirled pattern. The gradual brightening of CW Leo and the changes in its innermost circumstellar envelope need further continuous monitoring observations to properly understand its transitional phase toward the post-asymptotic-giant-branch stage., Comment: 18 pages, 13 figures, 1 table, Published in AJ

Young planetary nebulae (PNe) are characterized by their hot central stars and the presence of abundant neutral and molecular components, which result from significant mass loss during the asymptotic giant branch (AGB) phase of stellar evolution. Far-UV \ion{He}{2}$\lambda$1025 line photons produced near the central star can undergo Raman scattering by hydrogen atoms, creating a broad emission feature centered at $\sim$ 6545~\AA. We conducted high-resolution spectroscopy of 12 young PNe from April 2019 to March 2020 using the Bohyunsan Observatory Echelle Spectrograph ({\it BOES}). Building on the study by Choi and Lee, who identified Raman-scattered \ion{He}{2} at 6545~\AA\ in NGC~6881 and NGC~6886, we report new detections of this feature in NGC~6741 and NGC~6884. Profile fitting reveals that the velocity of the \ion{H}{1} component relative to the \ion{He}{2} emission region ranges from $26-33~{\rm km~s^{-1}}$ in these PNe. Using photoionization modeling, we estimate the line flux of \ion{He}{2}$\lambda$1025 and derive Raman conversion efficiencies of 0.39, 0.21, 0.24, and 0.07 for NGC~6881, NGC~6741, NGC~6886, and NGC~6884, respectively. These results, combined with radiative transfer modeling, suggest the presence of \ion{H}{1} components with masses around $10^{-2}~M_\odot$, moving outward from the central \ion{He}{2} emission region at speeds characteristic of the slow stellar wind from a mass-losing giant star., Comment: 15 pages, 10 figures, accepted for publication in ApJ

In Redirected Walking (RDW), resets are an overt method that explicitly interrupts users, and they should be avoided to provide a quality user experience. The number of resets depends on the configuration of the physical environment; thus, inappropriate object placement can lead to frequent resets, causing motion sickness and degrading presence. However, estimating the number of resets based on the physical layout is challenging. It is difficult to measure reset frequency with real users repeatedly testing different layouts, and virtual simulations offer limited real-time verification. As a result, while rearranging objects can reduce resets, users have not been able to fully take advantage of this opportunity, highlighting the need for rapid assessment of object placement. To address this, in Study 1, we collected simulation data and analyzed the average number of resets for various object placements. In study 2, we developed a system that allows users to evaluate reset frequency using a real-time placement interface powered by the first learning-based reset prediction model. Our model predicts resets from top-down views of the physical space, leveraging a Vision Transformer architecture. The model achieved a root mean square error (RMSE) of $23.88$. We visualized the model's attention scores using heatmaps to analyze the regions of focus during prediction. Through the interface, users can reorganize furniture while instantly observing the change in the predicted number of resets, thus improving their interior for a better RDW experience with fewer resets.

Eye gaze is considered a promising interaction modality in extende reality (XR) environments. However, determining selection intention from gaze data often requires additional manual selection techniques. We present a Bayesian-based machine learning (ML) model to predict user selection intention in real-time using only gaze data. Our model uses a Bayesian approach to transform gaze data into selection probabilities, which are then fed into an ML model to discriminate selection intentions. In Study 1, our model achieved real-time inference with an accuracy of 0.97 and an F1 score of 0.96. In Study 2, we found that the selection intention inferred by our model enables more comfortable and accurate interactions compared to traditional techniques., Comment: 18 pages, 6 figures, 2 Tables

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that minimizes the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information minimized, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two state-of-the-art T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD). Approximately 4 years before MCI diagnosis, dMRI-based brain age yields better performance than T1w MRI-based brain ages in predicting transition from CN to MCI.

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature $\mathrm{T_c}$, but it has proven technically challenging to probe the relevant length and time scales with most conventional measurement techniques. In this study, we employ scanning nitrogen-vacancy center based magnetometry and relaxometry to reveal the critical behavior of a high-$\mathrm{T_c}$ ferromagnetic oxide near its Curie temperature. Cluster analysis of the measured temperature-dependent nanoscale magnetic textures points to a 3D universality class with a correlation length that diverges near $\mathrm{T_c}$. Meanwhile, the temperature-dependent spin dynamics, measured through all optical relaxometry suggest that the phase transition is in the XY universality class. Our results capture both static and dynamic aspects of critical behavior, providing insights into universal properties that govern phase transitions in magnetic materials.

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

The primary objective of this work is to establish pointwise gradient estimates for solutions to a class of parabolic nonlinear nonlocal measure data problems, expressed in terms of caloric Riesz potentials of the data. As a consequence of our pointwise estimates, we obtain that the first-order regularity properties of solutions to such general parabolic nonlinear nonlocal equations, both in terms of size and oscillations of the spatial gradient, closely resemble the ones of the fractional heat equation even at highly refined scales. Along the way, we show that solutions to homogeneous parabolic nonlinear nonlocal equations have H\"older continuous spatial gradients under optimal assumptions on the nonlocal tails., Comment: 76 pages

We study massless solutions to the Einstein equations coupled to different matter models with a magnetic field and a conformal gauge singularity assuming spatial homogeneity with three commuting spatial translations. We show that there are no solutions in the case that the matter model is a radiation fluid. If the matter is described via kinetic theory we obtain that there exist unique solutions to the Einstein-Vlasov system and the Einstein-Boltzmann system for a certain range of soft potentials. For both the Vlasov and the Boltzmann case we also obtain asymptotic expansions close to the initial conformal gauge singularity., Comment: 34 pages

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi, an extension of the CasADi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training., Comment: RAL 2024 submission

In this work, we introduce a control framework that combines model-based footstep planning with Reinforcement Learning (RL), leveraging desired footstep patterns derived from the Linear Inverted Pendulum (LIP) dynamics. Utilizing the LIP model, our method forward predicts robot states and determines the desired foot placement given the velocity commands. We then train an RL policy to track the foot placements without following the full reference motions derived from the LIP model. This partial guidance from the physics model allows the RL policy to integrate the predictive capabilities of the physics-informed dynamics and the adaptability characteristics of the RL controller without overfitting the policy to the template model. Our approach is validated on the MIT Humanoid, demonstrating that our policy can achieve stable yet dynamic locomotion for walking and turning. We further validate the adaptability and generalizability of our policy by extending the locomotion task to unseen, uneven terrain. During the hardware deployment, we have achieved forward walking speeds of up to 1.5 m/s on a treadmill and have successfully performed dynamic locomotion maneuvers such as 90-degree and 180-degree turns., Comment: 8 pages

In this work, we study the higher differentiability of solutions to the inhomogeneous fractional $p$-Laplace equation under different regularity assumptions on the data. In the superquadratic case, we extend and sharpen several previous results, while in the subquadratic regime our results constitute completely novel developments even in the homogeneous case. In particular, in the local limit our results are consistent with well-known higher differentiability results for the standard inhomogeneous $p$-Laplace equation. All of our main results remain valid in the vectorial context of fractional $p$-Laplace systems., Comment: 48 pages

2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure. However, single-slice data necessitates the use of 2D networks for segmentation, but these networks often struggle to capture contextual information effectively. Consequently, even when trained on identical datasets, 3D networks typically achieve superior segmentation results. In this work, we propose a novel 3D-to-2D distillation framework, leveraging pre-trained 3D models to enhance 2D single-slice segmentation. Specifically, we extract the prediction distribution centroid from the 3D representations, to guide the 2D student by learning intra- and inter-class correlation. Unlike traditional knowledge distillation methods that require the same data input, our approach employs unpaired 3D CT scans with any contrast to guide the 2D student model. Experiments conducted on 707 subjects from the single-slice Baltimore Longitudinal Study of Aging (BLSA) dataset demonstrate that state-of-the-art 2D multi-organ segmentation methods can benefit from the 3D teacher model, achieving enhanced performance in single-slice multi-organ segmentation. Notably, our approach demonstrates considerable efficacy in low-data regimes, outperforming the model trained with all available training subjects even when utilizing only 200 training subjects. Thus, this work underscores the potential to alleviate manual annotation burdens.

Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the classification of cells on H&E. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We use inter-modality learning to label previously un-labelable cell types on H&E. We take advantage of multiplexed immunofluorescence (MxIF) histology to label 14 cell subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on private virtual H&E and public real H&E. On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively, when using ground truth centroid information. On real H&E we could classify helper T cells and epithelial progenitors with upper bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence 0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground truth centroid information. This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E., Comment: arXiv admin note: text overlap with arXiv:2401.05602

We establish Zaremba problem for Laplacian and $p$-Laplacian with degenerate weights when the Dirichlet condition is only imposed in a set of positive weighted capacity. We prove weighted Sobolev-Poincar\'{e} inequality with sharp scaling-invariant constants involving weighted capacity. Then we show higher integrability of the gradient of the solution (Meyers estimate) with minimal conditions on the part of the boundary where the Dirichlet condition is assumed. Our results are new both for the linear $p=2$ and nonlinear case and include problems with the weight not only as a measure but also as a multiplier of the gradient of the solution.

In the era of modern education, addressing cross-school learner diversity is crucial, especially in personalized recommender systems for elective course selection. However, privacy concerns often limit cross-school data sharing, which hinders existing methods' ability to model sparse data and address heterogeneity effectively, ultimately leading to suboptimal recommendations. In response, we propose HFRec, a heterogeneity-aware hybrid federated recommender system designed for cross-school elective course recommendations. The proposed model constructs heterogeneous graphs for each school, incorporating various interactions and historical behaviors between students to integrate context and content information. We design an attention mechanism to capture heterogeneity-aware representations. Moreover, under a federated scheme, we train individual school-based models with adaptive learning settings to recommend tailored electives. Our HFRec model demonstrates its effectiveness in providing personalized elective recommendations while maintaining privacy, as it outperforms state-of-the-art models on both open-source and real-world datasets.

The aim of this work is to establish numerous interrelated gradient estimates in the nonlinear nonlocal setting. First of all, we prove that weak solutions to a class of homogeneous nonlinear nonlocal equations of possibly arbitrarily low order have H\"{o}lder continuous gradients. Using these estimates in the homogeneous case, we then prove sharp higher differentiability as well as pointwise gradient potential estimates for nonlinear nonlocal equations of order larger than one in the presence of general measure data. Our pointwise estimates imply that the first-order regularity properties of such nonlinear nonlocal equations coincide with the sharp ones of the fractional Laplacian., Comment: 58 pages. To appear in Journal of the European Mathematical Society

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology.

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference. Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases. First, to restore spatial details from scans with a low through-plane resolution compared to a high in-plane resolution, we apply a deep learning-based super-resolution algorithm. Then, we generate an initial unbiased reference with an iterative metric-based registration using a small portion of subject scans. We register the remaining scans to this template and refine the template using an unsupervised deep probabilistic approach that generates a more expansive deformation field to enhance the organ boundary alignment. We demonstrate this framework using magnetic resonance images across four different tissue contrasts, generating four atlases in separate spatial alignments. Results: For each tissue contrast, we find a significant improvement using the Wilcoxon signed-rank test in the average Dice score across four labeled regions compared to a standard registration framework consisting of rigid, affine, and deformable transformations. These results highlight the effective alignment of eye organs and boundaries using our proposed process. Conclusions: By combining super-resolution preprocessing and deep probabilistic models, we address the challenge of generating an eye atlas to serve as a standardized reference across a largely variable population., Comment: Published in SPIE Journal of Medical Imaging (https://doi.org/10.1117/1.JMI.11.6.064004). 27 pages, 6 figures

Strong accretion outbursts onto protostars are associated with emission dominated by a viscously heated disk, which is characterized by high luminosities. We report the discovery and characterization of a strong mid-IR (3.4, 4.6 $\mu$m) outburst in the embedded protostar SSTgbs J21470601+4739394 (hereafter SSTgbsJ214706). SSTgbsJ214706 has steadily brightened in the mid-infrared by $\sim2$ magnitudes over the past decade, as observed by NEOWISE. Follow-up investigations with the Gemini near-IR spectrograph reveal that SSTgbsJ214706 is a binary system with a spatially extended outflow. The outburst is occurring on the more embedded southeast (SE) component, which dominates the mid- and far-infrared emission from the source. The outbursting component exhibits a spectrum consistent with an FU Ori-type outburst, including the presence of enhanced absorption observed in the molecular bands of CO. The luminosity of the SE component is estimated to be $\sim 0.23\,$ L$_\odot$ before the outburst and $\sim 0.95\,$ L$_\odot$ during the outburst, which is 1 to 2 orders of magnitude fainter than bonafide FU Ori outbursts. We interpret this eruption as an FU Ori-type outburst, although the possibility of brightening following an extinction episode cannot be ruled out. We discuss the implications and potential explanations for such a low-luminosity eruption.

In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like ImageNet. In this work, we propose a novel framework leveraging the visual-linguistic CLIP model to adeptly train a backbone model tailored to the manufacturing domain. Our approach concurrently considers visual and text-aligned embedding spaces for normal and abnormal conditions. The resulting pre-trained backbone markedly enhances performance in industrial downstream tasks, particularly in anomaly detection and localization. Notably, this improvement is substantiated through experiments conducted on multiple datasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained backbone weights allows previous works to achieve superior performance in few-shot scenarios with less training data. The proposed anomaly backbone provides a foundation model for more precise anomaly detection and localization.

We show under the assumption of small data that solutions to the Einstein-Vlasov system with a pure magnetic field and Bianchi I symmetry isotropise and tend to dust solutions. We also obtain the decay rates for the main variables. This generalises part of the work [V.~G.~LeBlanc, Classical Quantum Gravity 14, 2281-2301 (1997)] concerning the future behaviour of orthogonal perfect fluids with a linear equation of state in the presence of a magnetic field to the Vlasov case., Comment: 21 pages, presentation improved, references added