Search

The COVID-19 pandemic forced widespread school closures and a shift to remote learning. A growing body of research has examined the effects of remote learning on student outcomes. But the accuracy of the school modality measures used in these studies is questionable. The most common measures--based on self-reports or district website information--are often inconsistent and lack nationwide coverage. Some studies have used cell phone mobility data to identify school modalities, but there is no consensus yet on how to translate device pings into modality measures. This paper contributes to the literature on modality measurement by examining the relationship between mobile device signals and school modality prior to the pandemic and applies those findings to the pandemic period in Michigan and Washington. We compare our results to state-provided closure data and other nationwide sources, including the Return to Learn Tracker and the COVID-19 School Data Hub. Our findings indicate that cell phone mobility data can accurately predict school modality under normal conditions, but the accuracy drops during the pandemic. These results have implications for future research on educational and health outcomes during both pandemic and non-pandemic-related school closures.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

The purpose of this technical appendix is to share detailed results and more fully describe the sample and methods used to produce the research brief, "COVID's Impact on Science Achievement: Trends from 2019 through 2024. We investigated three main research questions in this brief: 1) How did science achievement in 2021 and 2024 compare to achievement in 2019, before the pandemic? 2) How much additional schooling was required in spring 2021 and spring 2024 to return to spring 2019 levels? 3) How do these patterns differ by race/ethnicity?

This brief continues ongoing research by NWEA® examining the degree to which the COVID-19 pandemic, and its associated school closures, influenced student learning. Prior research focused on math and reading and found that the pandemic's negative impact steadily accumulated during the 2020-2021 school year, with significant disparities in achievement and growth compared to prepandemic trends (Lewis, Kuhfeld, Ruzek, & McEachin, 2021). Although there were modest rebounds in reading and math during the 2021-2022 school year, progress toward pandemic recovery largely stalled in 2022-2023 and 2023-2024, with achievement and growth trends still trailing behind prepandemic levels (Kuhfeld & Lewis, 2022; Lewis & Kuhfeld, 2023; Lewis & Kuhfeld, 2024). Building on previous research series focused on math and reading achievement, this brief explores how the pandemic influenced student achievement in science. It examines science test scores across seven school years to understand how COVID-19 influenced science achievement.

Getting it Right from the Start (GIRFTS) is a research study that aims to implement a response to intervention (RTI) framework in early primary school (Foundation and Grade 1) to improve oral language and reading. RTI is a multi-tiered conceptual framework that supports student learning through delivery of high-quality classroom instruction, early identification of students with additional learning needs and early intervention. This research snapshot highlights some real-life examples from the GIRFTS study and outlines strategies schools can use to increase their readiness for change. School staff (teachers and leaders) and implementation support partners (ISPs) provided examples of strategies that increased school readiness for change during focus groups and interviews conducted 12 to 15 months after the GIRFTS project commenced in their school communities. This snapshot focuses on 3 key strategies that school staff and ISPs reported focusing on to increase readiness during the initial stages of implementation of GIRFTS. They are: (1) ensuring buy-in from the school community and external stakeholders; (2) investing in training programs and support systems; and (3) accepting that change takes time, and that readiness for change is dynamic. [This report was prepared by the Centre for Community Child Health, a research group of Murdoch Children's Research Institute (MCRI) with funding contributions from Eureka Benevolent Fund.]

The background galaxies in Euclid Early Release Observations images of the Perseus cluster make up a remarkable sample in its combination of 0.57 deg$^2$ area, 25.3 and 23.2 AB mag depth, as well as 0.1" and 0.3" angular resolutions, in optical and near-IR bands, respectively. Towards characterising the history of the Hubble sequence, we perform a preliminary morphological analysis of 2445 and 12,786 galaxies with $I_E < 21$ and $I_E < 23$, respectively. We use single-S\'ersic profiles and the sums of a S\'ersic bulge and an exponential disc to model these galaxies with SourceXtractor++ and analyse their parameters in order to assess their consistencies and biases. The fitted galaxies to $I_E < 21$ span the various Hubble types with ubiquitous bulge and disc components, and a bulge-to-total light ratio B/T taking all values from 0 to 1. The median effective radius of the single-S\'ersic profile is a biased estimate of galaxy size, intermediate between the bulge and disc effective radii, depending on B/T. The axis ratio of the single-S\'ersic profile overestimates that of the disc, increasingly so with B/T. The model impacts the photometry with -0.08 to 0.01 mag median systematic $I_E$ offsets between single-S\'ersic and bulge+disc total magnitudes, and a 0.05 to 0.15 mag dispersion, from low to high B/T. We measure a median 0.4 mag bulge-disc colour difference in $I_E - J_E$ that originates from the disc-dominated galaxies, whereas bulge-dominated galaxies have similar median colours of their components. Remarkably, we also measure redder-inside disc colour gradients, based on 5 to 10% systematic variations of disc effective radii between the optical and near-IR bands. This analysis demonstrates the usefulness and limits of single-S\'ersic profile modelling, and the power of bulge-disc decomposition for reliably characterising the morphology of lenticulars and spirals in Euclid images., Comment: Submitted to A&A, comments welcome. 27 pages, 19 figures, 6 tables (+ appendix of 6 pages, 18 figures - exemples gallery, 1 table)

Categorical response data are ubiquitous in complex survey applications, yet few methods model the dependence across different outcome categories when the response is ordinal. Likewise, few methods exist for the common combination of a longitudinal design and categorical data. By modeling individual survey responses at the unit-level, it is possible to capture both ordering information in ordinal responses and any longitudinal correlation. However, accounting for a complex survey design becomes more challenging in the unit-level setting. We propose a Bayesian hierarchical, unit-level, model-based approach for categorical data that is able to capture ordering among response categories, can incorporate longitudinal dependence, and accounts for the survey design. To handle computational scalability, we develop efficient Gibbs samplers with appropriate data augmentation as well as variational Bayes algorithms. Using public-use microdata from the Household Pulse Survey, we provide an analysis of an ordinal response that asks about the frequency of anxiety symptoms at the beginning of the COVID-19 pandemic. We compare both design-based and model-based estimators and demonstrate superior performance for the proposed approaches., Comment: 38 pages, 7 figures

The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse and logistics management. Existing solutions often address box size variations, but overlook their intrinsic and physical properties, such as density and rigidity, which are crucial for real-world applications. We use reinforcement learning (RL) to solve this problem by employing action space masking to direct the RL policy toward valid actions. Unlike previous methods that rely on heuristic stability assessments which are difficult to assess in physical scenarios, our framework utilizes online learning to dynamically train the action space mask, eliminating the need for manual heuristic design. Extensive experiments demonstrate that our proposed method outperforms existing state-of-the-arts. Furthermore, we deploy our learned task planner in a real-world robotic palletizer, validating its practical applicability in operational settings., Comment: Accepted by ICRA 2025

Query-driven recommendation with unknown items poses a challenge for users to understand why certain items are appropriate for their needs. Query-driven Contrastive Summarization (QCS) is a methodology designed to address this issue by leveraging language-based item descriptions to clarify contrasts between them. However, existing state-of-the-art contrastive summarization methods such as STRUM-LLM fall short of this goal. To overcome these limitations, we introduce Q-STRUM Debate, a novel extension of STRUM-LLM that employs debate-style prompting to generate focused and contrastive summarizations of item aspects relevant to a query. Leveraging modern large language models (LLMs) as powerful tools for generating debates, Q-STRUM Debate provides enhanced contrastive summaries. Experiments across three datasets demonstrate that Q-STRUM Debate yields significant performance improvements over existing methods on key contrastive summarization criteria, thus introducing a novel and performant debate prompting methodology for QCS.

Kelvin probe force microscopy (KPFM) is a powerful tool for studying contact electrification, using an tiny tip to image voltages caused by transferred charge. It has been used in stationary studies focused on finding patterns (e.g. heterogeneity) in transferred charge, but also in dynamic studies aimed at understanding how charge escapes. Here, we show that the charge dynamics in all but the very best insulators are too fast for patterns found in stationary studies to be meaningful. Using a custom-built system, we are able to quickly (~30 s) transfer samples from our contact-charging apparatus to the atomic force microscopy (AFM). For materials at the lower end of `good insulators', we see potential decay that is shorter than the timescale of a typical KPFM scan (~10 minutes). We develop a minimal model to explain this decay based on bulk conductivity, and show that the measured timescale increases with the prediction from this model. To rule out surface conductivity, we perform additional experiments with the macroscopic method of scanning Kelvin probe microscopy. Our results highlight an important but widely overlooked consideration for dynamic vs. stationary studies in contact-electrified surfaces, and suggest that some patterns observed in the latter, e.g. charge heterogeneity, are not as widespread as previously thought.

This work develops a framework to create meshes with user-specified homology from potentially dirty geometry by coupling background grids, persistent homology, and a generalization of volume fractions. For a mesh with fixed grid size, the topology of the output mesh changes predictably and monotonically as its volume-fraction threshold decreases. Topological anti-aliasing methods are introduced to resolve pinch points and disconnected regions that are artifacts of user choice of grid size and orientation, making the output meshes suitable for downstream processes including analysis. The methodology is demonstrated on geographical, mechanical, and graphics models in 2D and 3D using a custom-made software called Tusqh. The work demonstrates that the proposed framework is viable for generating meshes on topologically invalid geometries and for automatic defeaturing of small geometric artifacts. Finally, the work shows that although subdividing the background grid frequently improves the topological and geometrical fidelity of the output mesh, there are simple 2D examples for which the topology does not converge under refinement for volume-fraction codes.

In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially given the fine temporal granularity of the data. In this study, we focus on a dataset of home activity records from people living with Dementia. We propose a two-stage self-supervised learning approach. The first stage involves converting time-series activities into text strings, which are then encoded by a fine-tuned language model. In the second stage, these time-series vectors are bi-dimensionalized for applying PageRank method, to analyze latent state transitions to quantitatively assess participants behavioral patterns and identify activity biases. These insights, combined with diagnostic data, aim to support personalized care interventions., Comment: NeurIPS 2024 workshop Time Series in the Age of Large Models. arXiv admin note: substantial text overlap with arXiv:2502.09173

Avatar is a critical medium for identity representation in social virtual reality (VR). However, options for disability expression are highly limited on current avatar interfaces. Improperly designed disability features may even perpetuate misconceptions about people with disabilities (PWD). As more PWD use social VR, there is an emerging need for comprehensive design standards that guide developers and designers to create inclusive avatars. Our work aim to advance the avatar design practices by delivering a set of centralized, comprehensive, and validated design guidelines that are easy to adopt, disseminate, and update. Through a systematic literature review and interview with 60 participants with various disabilities, we derived 20 initial design guidelines that cover diverse disability expression methods through five aspects, including avatar appearance, body dynamics, assistive technology design, peripherals around avatars, and customization control. We further evaluated the guidelines via a heuristic evaluation study with 10 VR practitioners, validating the guideline coverage, applicability, and actionability. Our evaluation resulted in a final set of 17 design guidelines with recommendation levels.

In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data. This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach tailored to uncover low-rank structures. The first stage converts time-series activities into text sequences encoded by a pre-trained language model, providing a rich, high-dimensional latent state space using a PageRank-based method. This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form that enhances interpretability. This low-rank representation not only enhances model interpretability but also facilitates clustering and transition analysis, revealing key behavioral patterns correlated with clinicalmetrics such as MMSE and ADAS-COG scores. Our findings demonstrate the framework's potential in supporting cognitive status prediction, personalized care interventions, and large-scale health monitoring., Comment: AAAI 2025 Workshop on Large Language Models and Generative AI for Health

This paper describes the new Globular Clusters GMRT Pulsar Search (GCGPS) survey. This survey aims to find MSPs in the globular clusters (GCs) of the Milky Way using uGMRT. The observations use the uGMRT's Band-4 (550$-$750 MHz) and Band-3 (300$-$500 MHz) receivers, which are well suited for steep-spectral-index radio sources like MSPs; the survey will eventually cover the GCs accessible to the uGMRT sky (i.e. $\delta\:>\:\sim\:-\:53^\circ$), and that is South of $\delta = -17^\circ$ (FAST sky limit) and have not been targeted with the sensitivity of this survey. The observations started in May 2023, having so far resulted in seven new discoveries. In this paper, we present the discovery and follow-up study of the first pulsar from this survey, J1617$-$2258A, a 4.32 ms binary MSP that is also the first to be discovered in the globular cluster NGC 6093. We localised this MSP with arc-sec precision from imaging and obtained the unique timing solution from more than one year of timing observations with the Band-4 (550$-$750 MHz) receivers of the uGMRT. This revealed an unusual binary MSP, with a $\sim$ 19-hour, highly eccentric (e $\sim$ 0.54) orbit having a low-mass companion. This orbital eccentricity allowed the measurement of the rate of advance of periastron for this system, which led to the derivation of its total mass, $1.67 \, \pm \, 0.06 \, \rm M_{\odot}$; this together with the system's mass function implies, for the pulsar and the companion, $M_\mathrm{p} < 1.60 \, \rm M_{\odot}$ and $M_\mathrm{c} > 0.072 \, \rm M_{\odot}$. The system is likely a perturbed MSP-Helium WD system seen at a low orbital inclination., Comment: 19 pages, 11 figures, and 4 tables; Submitted to Astrophysical Journal (ApJ)

We report the results of deep H-alpha and [O III] images of the bright WN7/WC4 Wolf-Rayet star WR 8 (HD 62910). These data show considerably more surrounding nebulosity than seen in prior imaging. The brighter portions of the nebula span 6' in diameter and exhibit considerable fine-scale structure including numerous emission clumps and bright head-tail like features presumably due to the effects of the WR star's stellar winds. Due to the overlap of a relatively bright band of unrelated foreground diffuse interstellar H-alpha emission, WR 8's nebula is best viewed via its [O III] emission. A faint 9' x 13' diffuse outer nebulosity is detected surrounding the nebula's main ring of emission. The nebula's optical structure is substantially different from that of its thermal continuum dust emission seen in WISE 22 micron infrared images which show a smaller and sharply defined emission shell., Comment: 8 pages, 6 figures, 1 table

Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class., Comment: 32 pages, 5 figures, code is available at https://github.com/rwl93/bnp4ood

HL Tau is one of the most well-studied Class I young stellar objects, including frequent observations at near- and mid-infrared, (sub-) millimeter, and X-ray wavelengths. We present the results of an X-ray variability monitoring campaign with XMM-Newton in 2020 and X-ray gratings spectroscopy from Chandra/HETGS in 2018. We find that the X-ray spectrum of HL Tau is consistently hot (with characteristic plasma temperatures $T \gtrsim 30$ MK) over 31 epochs spanning 20 years, which is consistent in temperature with most Class I YSOs. The high-resolution HETG spectrum indicates the presence of some cooler plasma. We characterize the variability of the star across the 31 observations and find a subset of observations with significant variability on a $\sim$21-day timescale in the observed count rate and flux. We discuss the possible origins of this variability, and identify further observations that would better constrain the nature of the changes., Comment: 16 pages, nine figures. Accepted to the Astronomical Journal

We study the effectiveness of the time-localised principal resolvent forcing mode at actuating the near wall cycle of turbulence. The mode is restricted to a wavelet pulse and computed from an SVD of the windowed wavelet-based resolvent operator so that it produces the largest amplification via the linearised Navier-Stokes equations. We then inject this time-localised mode into the turbulent minimal flow unit at different intensities, and measure the instantaneous deviation of the system's response from the optimal resolvent response mode. This is possible under the new formulation, which enables the modes to represent transient trajectories. For the most energetic spatial wave numbers in the minimal flow unit -- constant in the streamwise direction and once-periodic in the spanwise direction -- the forcing mode takes the shape of streamwise rolls and produces a response mode in the form of streamwise streaks that transiently grow and decay. For initial times and close to the wall, the DNS response matches the principal response mode well, but due to nonlinearities, the response across all forcing intensities decays prematurely, and a higher forcing intensity leads to faster energy decay. The principal forcing mode still leads to significant energy amplification and is more effective than a randomly-generated forcing structure and the second suboptimal resolvent forcing mode at amplifying the near-wall streaks. We compute the nonlinear energy transfer to secondary modes and observe that the breakdown of the actuated mode proceeds similarly across all forcing intensities: in the near-wall region, the induced streak forks into a structure twice periodic in the spanwise direction; in the outer region, the streak breaks up into a structure that is once periodic in the streamwise direction. In both regions, spanwise gradients account for the dominant share of nonlinear energy transfer., Comment: arXiv admin note: text overlap with arXiv:2312.15465

Cosmic birefringence is an effect where the plane of polarisation of the cosmic microwave background (CMB) is rotated by an angle $\beta$ through coupling to a hypothetical parity-violating field. We analyse the Planck Public Release 4 (PR4 or NPIPE) data using a map-space analysis method and find $\beta=0.46^\circ\pm 0.04^\circ(\mathrm{stat.})\pm0.28^\circ(\mathrm{syst.})$ for SEVEM CMB maps and $\beta=0.48^\circ\pm 0.04^\circ(\mathrm{stat.})\pm 0.28^\circ(\mathrm{syst.})$ for Commander CMB maps. These values are slightly higher than previously published results, which may be explained by the fact that we have not attempted to remove any potential bias from miscalibration of the Planck polarimeters. The uncertainty in this miscalibration dominates the systematic uncertainty, which also means that our results are consistent with no parity violation. An advantage of the map-space analysis is that it is easy to investigate any variations on the sky, for example caused by foreground contamination. Our results for isotropic birefringence are fairly robust against different spatial data cuts, but there may be hints of a foreground systematic (north versus south hemispheres) or uncontrolled miscalibration effect (T peaks versus E peaks) that should be followed up in future studies. We additionally find no evidence of a cosmic birefringence dipole (anisotropic birefringence)., Comment: 26 pages, 17 figures, 2 tables

The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans., Comment: 11 pages, 5 figures, Conference Paper

Benjamini, Kalifa and Tzalik recently proved that there is an absolute constant $c>0$ such that any graph with at most $c\cdot2^d/d$ edges and no isolated vertices is a minor of the $d$-dimensional hypercube $Q_d$, while there is an absolute constant $K > 0$ such that $Q_d$ is not $(K\cdot2^d/\sqrt{d})$-minor-universal. We show that $Q_d$ does not contain 3-uniform expander graphs with $C\cdot2^d/d$ edges as minors. This matches the lower bound up to a constant factor and answers one of their questions., Comment: 7 pages, 1 figure

Accreting supermassive black hole binaries are powerful multimessenger sources emitting both gravitational and electromagnetic (EM) radiation. Understanding the accretion dynamics of these systems and predicting their distinctive EM signals is crucial to informing and guiding upcoming efforts aimed at detecting gravitational waves produced by these binaries. To this end, accurate numerical modeling is required to describe both the spacetime and the magnetized gas around the black holes. In this paper, we present two key advances in this field of research. First, we have developed a novel 3D general relativistic magnetohydrodynamics (GRMHD) framework that combines multiple numerical codes to simulate the inspiral and merger of supermassive black hole binaries starting from realistic initial data and running all the way through merger. Throughout the evolution, we adopt a simple but functional prescription to account for gas cooling through the emission of photons. Next, we have applied our new computational method to follow the time evolution of a circular, equal-mass, non-spinning black hole binary for ${\sim\!200}$ orbits starting from a separation of ${20\,r_g}$ and reaching the post-merger evolutionary stage of the system. We have identified how and when the minidisks dissolve as the binary compresses. We also show that even when the binary ``decouples'' from its surrounding disk, its luminosity decreases by only a factor of a few and abruptly increases by ${\sim\!50\%}$ at the time of merger, accompanied by an equally abrupt change in spectrum. Finally, the magnetic flux brought to the spin-parameter ${\sim\!0.68}$ merger remnant is able to drive a relativistic, Poynting-flux-dominated jet., Comment: 36 pages, 22 figures. Submitted to Physical Review D

Given a positive definite even lattice and a commutative ring, there is a standard construction of a lattice vertex algebra over the commutative ring, and this is promoted to a vertex operator algebra when the determinant of the lattice is invertible. We describe the groups of automorphisms of these vertex algebras and vertex operator algebras as affine group schemes, showing in particular that each is an extension of an explicitly described split reductive group of ADE type by the outer automorphism group of the lattice., Comment: 20 pages

Millions of new pieces of malicious software (i.e., malware) are introduced each year. This poses significant challenges for antivirus vendors, who use machine learning to detect and analyze malware, and must keep up with changes in the distribution while retaining knowledge of older variants. Continual learning (CL) holds the potential to address this challenge by reducing the storage and computational costs of regularly retraining over all the collected data. Prior work, however, shows that CL techniques, which are designed primarily for computer vision tasks, fare poorly when applied to malware classification. To address these issues, we begin with an exploratory analysis of a typical malware dataset, which reveals that malware families are diverse and difficult to characterize, requiring a wide variety of samples to learn a robust representation. Based on these findings, we propose $\underline{M}$alware $\underline{A}$nalysis with $\underline{D}$iversity-$\underline{A}$ware $\underline{R}$eplay (MADAR), a CL framework that accounts for the unique properties and challenges of the malware data distribution. Through extensive evaluation on large-scale Windows and Android malware datasets, we show that MADAR significantly outperforms prior work. This highlights the importance of understanding domain characteristics when designing CL techniques and demonstrates a path forward for the malware classification domain., Comment: 13 pages, 12 figures, 7 tables

Laser frequency combs (LFCs) are an important component of Doppler radial velocity (RV) spectroscopy that pushes fractional precision to the $10^{-10}$ level, as required to identify and characterize Earth-like exoplanets. However, large intensity variations across the LFC spectrum that arise in nonlinear broadening limit the range of comb lines that can be used for optimal wavelength calibration with sufficient signal-to-noise ratio. Furthermore, temporal spectral-intensity fluctuations of the LFC, that are coupled to flux-dependent detector defects, alter the instrumental point spread function (PSF) and result in spurious RV shifts. To address these issues and improve calibration precision, spectral flattening is crucial for LFCs to maintain a constant photon flux per comb mode. In this work, we demonstrate a dynamic spectral shaping setup using a spatial light modulator (SLM) over the wavelength range of 800nm to 1300nm. The custom shaping compensates for amplitude fluctuations in real time and can also correct for wavelength-dependent spectrograph transmission, achieving a spectral profile that delivers the constant readout necessary for maximizing precision. Importantly, we characterize the out-of-loop properties of the spectral flattener to verify a twofold improvement in spectral stability. This technique, combined with our approach of pumping the waveguide spectral broadener out-of-band at 1550 nm, reduces the required dynamic range. While this spectral region is tailored for the LFC employed at the Habitable-zone Planet Finder (HPF) spectrograph, the method is broadly applicable to any LFC used for astronomical spectrograph calibration., Comment: 15 pages, 11 figures

We introduce Quantized Language-Image Pretraining (QLIP), a visual tokenization method that combines state-of-the-art reconstruction quality with state-of-the-art zero-shot image understanding. QLIP trains a binary-spherical-quantization-based autoencoder with reconstruction and language-image alignment objectives. We are the first to show that the two objectives do not need to be at odds. We balance the two loss terms dynamically during training and show that a two-stage training pipeline effectively mixes the large-batch requirements of image-language pre-training with the memory bottleneck imposed by the reconstruction objective. We validate the effectiveness of QLIP for multimodal understanding and text-conditioned image generation with a single model. Specifically, QLIP serves as a drop-in replacement for the visual encoder for LLaVA and the image tokenizer for LlamaGen with comparable or even better performance. Finally, we demonstrate that QLIP enables a unified mixed-modality auto-regressive model for understanding and generation., Comment: Tech report. Project page: https://nvlabs.github.io/QLIP/

A distinct power-law break is apparent m_AB approximately 21 in the deep Near-Infrared PEARLS-JWST galaxy counts. The break becomes more pronounced at longer wavelengths, with the counts slope flattening smoothly with apparent magnitude in the shortest band used at 0.9 microns, trending towards an increasingly broken slope by the longest wavelength passband of JWST NIRCam, 4.4 microns. This behaviour is remarkably well predicted by the GALFORM semi-analytical model of galaxy formation. We use the model to diagnose the origin of this behaviour. We find that the features that are responsible for the break are: 1) the inherent break in the luminosity function; 2) the change in the volume element with redshift and 3) the redshift-dependent nature of the k-correction. We study the contribution to these effects by early and late-type galaxies, using as a proxy for morphology the bulge-to-total stellar mass ratio. We find that the way in which ellipticals populate the bright end of the luminosity function while spirals dominate the faint end is preserved in the galaxy number counts, with a characteristic stellar mass at the break of approximately 10^10 M_sun. We also find that the shape of the number counts is mainly driven by galaxies with relatively low redshift (z < 2) for the PEARLS observational limit of m_AB < 28. We give a comprehensive description of why the galaxy number counts in the near-infrared PEARLS-JWST observation look the way they do and which population of galaxies is dominant at each apparent magnitude.

Following the Dirac Notation in Quantum Mechanics (QM), we propose the Bracket Notation (PBN) by defining a probability-bra (P-bra), P-ket, P-bracket, P-identity, etc. Using the PBN, many formulae, such as normalizations and expectations in systems of one or more random variables, can now be written in abstract basis-independent expressions, which are easy to expand by inserting a proper P-identity. The time evolution of homogeneous Markov processes can also be formatted in such a way. Our system P-kets are identified with probability vectors, and our system P-bra is comparable to the Doi state function or the Peliti standard bra. In the Heisenberg picture of the PBN, a random variable becomes a stochastic process, and the Chapman-Kolmogorov equations are obtained by inserting a time-dependent P-identity. Also, some QM expressions in the Dirac notation are naturally transformed into probability expressions in PBN by a special Wick rotation. Potential applications show the usefulness of the PBN beyond the constrained domain and range of Hermitian operators on Hilbert Spaces in QM all the way to IT., Comment: 20 pages

The mean apparent magnitude of Starlink Mini Direct-To-Cell (DTC) satellites observed in brightness mitigation mode is 5.16, while the mean of magnitudes adjusted to a uniform distance of 1,000 km is 6.47. The DTCs have faded since early in 2024 because SpaceX subsequently adjusted the spacecraft attitudes to dim them. A physical model for satellite brightness that fits the observations is described.

Isotopically layered amorphous solid water films were used to measure the diffusivity of deeply supercooled liquid water near the glass transition. The films, composed of separate layers of oxygen 16 and oxygen 18 labeled water, were grown by vapor deposition at low temperature and then heated to observe the intermixing of the isotopic layers. Very slow heating rates (as low as 0.0001 K/s) were used to decouple the diffusion and crystallization processes to ensure that the observed intermixing occurred at temperatures that were well-separated from the onset of crystallization. Numerical simulations of the desorption spectra were used to extract the translational diffusivities. The diffusivities obtained in this paper are consistent with translational liquid-like motion at temperatures near and above the proposed Tg of 136 K. These findings support the idea that the melt of amorphous water, above its glass transition temperature is thermodynamically continuous with normal supercooled liquid.

The last decade has seen an explosive growth of interest in exploiting developments in machine learning to accelerate lattice QCD calculations. On the sampling side, generative models are a promising approach to mitigating critical slowing down and topological freezing. Meanwhile, signal-to-noise problems have been shown to be improvable by the use of optimized improved observables. Both techniques can be made free of bias, resulting in trustworthy but reduced statistical errors. This talk reviews recent developments in this field., Comment: 12 pages, 7 figures; contribution to the 41st International Symposium on Lattice Field Theory (Lattice 2024), 28 July - 3 August 2024, Liverpool, UK; added reference

The optimal bit-width for achieving the best trade-off between quantized model size and accuracy has been a subject of ongoing debate. While some advocate for 4-bit quantization, others propose that 1.58-bit offers superior results. However, the lack of a cohesive framework for different bits has left such conclusions relatively tenuous. We present ParetoQ, the first unified framework that facilitates rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. Our findings reveal a notable learning transition between 2 and 3 bits: For 3-bits and above, the fine-tuned models stay close to their original pre-trained distributions, whereas for learning 2-bit networks or below, the representations change drastically. By optimizing training schemes and refining quantization functions, ParetoQ surpasses all previous methods tailored to specific bit widths. Remarkably, our ParetoQ ternary 600M-parameter model even outperforms the previous SoTA ternary 3B-parameter model in accuracy, using only one-fifth of the parameters. Extensive experimentation shows that ternary, 2-bit, and 3-bit quantization maintains comparable performance in the size-accuracy trade-off and generally exceeds 4-bit and binary quantization. Considering hardware constraints, 2-bit quantization offers promising potential for memory reduction and speedup.

Humans strive to design safe AI systems that align with our goals and remain under our control. However, as AI capabilities advance, we face a new challenge: the emergence of deeper, more persistent relationships between humans and AI systems. We explore how increasingly capable AI agents may generate the perception of deeper relationships with users, especially as AI becomes more personalised and agentic. This shift, from transactional interaction to ongoing sustained social engagement with AI, necessitates a new focus on socioaffective alignment-how an AI system behaves within the social and psychological ecosystem co-created with its user, where preferences and perceptions evolve through mutual influence. Addressing these dynamics involves resolving key intrapersonal dilemmas, including balancing immediate versus long-term well-being, protecting autonomy, and managing AI companionship alongside the desire to preserve human social bonds. By framing these challenges through a notion of basic psychological needs, we seek AI systems that support, rather than exploit, our fundamental nature as social and emotional beings.

This study explores computational approaches for measuring moral foundations (MFs) in non-English corpora. Since most resources are developed primarily for English, cross-linguistic applications of moral foundation theory remain limited. Using Chinese as a case study, this paper evaluates the effectiveness of applying English resources to machine translated text, local language lexicons, multilingual language models, and large language models (LLMs) in measuring MFs in non-English texts. The results indicate that machine translation and local lexicon approaches are insufficient for complex moral assessments, frequently resulting in a substantial loss of cultural information. In contrast, multilingual models and LLMs demonstrate reliable cross-language performance with transfer learning, with LLMs excelling in terms of data efficiency. Importantly, this study also underscores the need for human-in-the-loop validation of automated MF assessment, as the most advanced models may overlook cultural nuances in cross-language measurements. The findings highlight the potential of LLMs for cross-language MF measurements and other complex multilingual deductive coding tasks., Comment: 12 pages, 2 figures, 6 tables

Numerical simulation of incompressible fluid flows has been an active topic of research in Scientific Computing for many years, with many contributions to both discretizations and linear and nonlinear solvers. In this work, we propose an improved relaxation scheme for higher-order Taylor-Hood discretizations of the incompressible Stokes and Navier-Stokes equations, demonstrating its efficiency within monolithic multigrid preconditioners for the linear(ized) equations. The key to this improvement is an improved patch construction for Vanka-style relaxation introducing, for the first time, overlap in the pressure degrees of freedom within the patches. Numerical results demonstrate significant improvement in both multigrid iterations and time-to-solution for the linear Stokes case, on both triangular and quadrilateral meshes. For the nonlinear Navier-Stokes case, we show similar improvements, including in the number of nonlinear iterations needed in an inexact Newton method.

A quantum-mechanical system comes naturally equipped with a convex space: each (Hermitian) operator has a (real) expectation value, and the expectation value of the square any Hermitian operator must be non-negative. This space is of exponential (e.g.~in volume) dimension, but low-dimensional projections can be efficiently explored by standard algorithms. Such approaches have been used to precisely constrain critical exponents of conformal field theories ("conformal bootstrap") and, more recently, to constrain the ground state physics of various quantum-mechanical systems, including lattice field theories. In this talk we discuss related approaches to systematically constraining the real-time dynamics of quantum systems, which are otherwise obstructed from study by sign problems and the ill-posed nature of analytic continuation., Comment: 7 pages, 2 figures; proceedings for LATTICE 2024

Previous work in aesthetic categorization and explainability utilizes manual labeling and classification to explain aesthetic scores. These methods require a complex labeling process and are limited in size. Our proposed approach attempts to explain aesthetic assessment models through visualizing dataset trends and automatic categorization of visual aesthetic features through training neural networks on different versions of the same dataset. By evaluating the models adapted to each specific modality using existing and novel metrics, we can capture and visualize aesthetic features and trends.

The Keck Planet Imager and Characterizer (KPIC) is a series of upgrades for the Keck II Adaptive Optics (AO) system and the NIRSPEC spectrograph to enable diffraction limited, high resolution (R>30000) spectroscopy of exoplanets and low mass companions in the K and L bands. Phase I consisted of single mode fiber injection/extraction units (FIU/FEU) used in conjunction with a H band pyramid wavefront sensor. The use of single mode fibers provides a gain in stellar rejection, a substantial reduction in sky background, and an extremely stable line spread function in the spectrograph. Phase II, deployed and commissioned in 2022, brought a 1000 actuator deformable mirror, beam shaping optics, a vortex mask, and other upgrades to the FIU/FEU. An additional service mission in 2024 extended operations down to y band, delivered an atmospheric dispersion corrector, and provided access to two laser frequency combs. KPIC phase II brings higher planet throughput, lower stellar leakage and many new observing modes which extend its ability to characterize exoplanets at high spectral resolution, building on the success of phase I. In this paper we present a description of the final phase II version of KPIC, along with results of system level laboratory testing and characterization showing the instrument's phase II throughput, stability, repeatability, and other key performance metrics prior to delivery and during installation at Keck. We outlined the capabilities of the various observing modes enabled by the new modules as well as efforts to compensate for static aberrations and non common path errors at Keck, which were issues that plagued phase I. Finally, we show results from commissioning., Comment: 36 pages, 16 figures

Older adults have increasing difficulty with retrospective memory, hindering their abilities to perform daily activities and posing stress on caregivers to ensure their wellbeing. Recent developments in Artificial Intelligence (AI) and large context-aware multimodal models offer an opportunity to create memory support systems that assist older adults with common issues like object finding. This paper discusses the development of an AI-based, wearable memory assistant, MemPal, that helps older adults with a common problem, finding lost objects at home, and presents results from tests of the system in older adults' own homes. Using visual context from a wearable camera, the multimodal LLM system creates a real-time automated text diary of the person's activities for memory support purposes, offering object retrieval assistance using a voice-based interface. The system is designed to support additional use cases like context-based proactive safety reminders and recall of past actions. We report on a quantitative and qualitative study with N=15 older adults within their own homes that showed improved performance of object finding with audio-based assistance compared to no aid and positive overall user perceptions on the designed system. We discuss further applications of MemPal's design as a multi-purpose memory aid and future design guidelines to adapt memory assistants to older adults' unique needs., Comment: 15 pages

In this paper, we presents a novel method for improving text-to-image generation by combining Large Language Models (LLMs) with diffusion models, a hybrid approach aimed at achieving both higher quality and efficiency in image synthesis from text descriptions. Our approach introduces a new dynamic KL-weighting strategy to optimize the diffusion process, along with incorporating semantic understanding from pre-trained LLMs to guide the generation process. The proposed method significantly improves both the visual quality and alignment of generated images with text descriptions, addressing challenges such as computational inefficiency, instability in training, and robustness to textual variability. We evaluate our method on the COCO dataset and demonstrate its superior performance over traditional GAN-based models, both quantitatively and qualitatively. Extensive experiments, including ablation studies and human evaluations, confirm that our method outperforms existing approaches in terms of image realism, relevance to the input text, and overall aesthetic quality. Our approach also shows promise in scalability to other multimodal tasks, making it a versatile solution for a wide range of generative applications.

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm hyper-parameters.

We prove relative versions of many earlier results about almost invariant sets and splittings of groups. In particular, we prove a relative version of the algebraic torus theorem, and we prove the existence and uniqueness of relative versions of algebraic regular neighbourhoods and of JSJ decompositions.

The next-generation mm/sub-mm/far-IR astronomy will in part be enabled by advanced digital signal processing (DSP) techniques. The Prime-Cam instrument of the Fred Young Submillimeter Telescope (FYST), featuring the largest array of submillimeter detectors to date, utilizes a novel overlap-channel polyphase synthesis filter bank (OC-PSB) for the AC biasing of detectors, implemented on a cutting-edge Xilinx Radio Frequency System on Chip (RFSoC). This design departs from traditional waveform look-up-table(LUT) in memory, allowing real-time, dynamic signal generation, enhancing usable bandwidth and dynamic range, and enabling microwave kinetic inductance detector (MKID) tracking for future readout systems. Results show that the OC-PSB upholds critical performance metrics such as signal-to-noise ratio (SNR) while offering additional benefits such as scalability. This paper will discuss DSP design, RFSoC implementation, and laboratory performance, demonstrating OC-PSB's potential in submillimeter-wave astronomy MKID readout systems., Comment: SPIE Astronomical Telescopes + Instrumentation conference proceedings 2024

MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To address these issues, we introduce an approach that pretrains a model on synthetically generated motion artifacts before applying transfer learning for QC classification. This method not only improves the accuracy in identifying poor-quality scans but also reduces training time and resource requirements compared to training from scratch. By leveraging synthetic data, we provide a more robust and resource-efficient solution for QC automation in MRI, paving the way for broader adoption in diverse research settings., Comment: Accepted at ISBI 2025

Hypervelocity stars (HVSs) are produced by the Hills mechanism when a stellar binary is disrupted by a supermassive black hole (SMBH). The HVS Survey detected 21 unbound B-type main-sequence stars in the Milky Way's outer halo that are consistent with ejection via the Hills mechanism. We revisit the trajectories of these stars in light of proper motions from {\it Gaia} DR3 and modern constraints on the Milky Way -- Large Magellanic Cloud (LMC) orbit. We find that half of the unbound HVSs discovered by the HVS Survey trace back not the Galactic Center, but to the LMC. Motivated by this finding, we construct a forward-model for HVSs ejected from an SMBH in the LMC and observed through the selection function of the HVS Survey. The predicted spatial and kinematic distributions of simulated HVSs are remarkably similar to the observed distributions. In particular, we reproduce the conspicuous angular clustering of HVSs around the constellation Leo. This clustering occurs because HVSs from the LMC are boosted by $\sim300\,{\rm km\,s^{-1}}$ by the orbital motion of the LMC, and stars launched parallel to this motion are preferentially selected as HVS candidates. We find that the birth rate and clustering of LMC HVSs cannot be explained by supernova runaways or dynamical ejection scenarios not involving a SMBH. From the ejection velocities and relative number of Magellanic vs. Galactic HVSs, we constrain the mass of the LMC SMBH to be $10^{5.8^{+0.2}_{-0.4}} M_{\odot}$ ($\simeq 6\times10^5 M_{\odot}$)., Comment: submitted to ApJ; commments welcome