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JWST provides new opportunities to cross-check the HST Cepheid/SNeIa distance ladder, which yields the most precise local measure of H0. We analyze early JWST subsamples (~1/4 of the HST sample) from the SH0ES and CCHP groups, calibrated by a single anchor (N4258). We find HST Cepheid distances agree well (~1 sigma) with all 8 combinations of methods, samples, and telescopes: JWST Cepheids, TRGB, and JAGB by either group, plus HST TRGB and Miras. The comparisons explicitly include the measurement uncertainty of each method in N4258, an oft-neglected but dominant term. Mean differences are ~0.03 mag, far smaller than the 0.18 mag "Hubble tension." Combining all measures produces the strongest constraint yet on the linearity of HST Cepheid distances, 0.994+-0.010, ruling out distance-dependent bias or offset as the source of the tension at ~7 sigma. Yet, measurements of H0 from current JWST subsamples produce large sampling differences whose size and direction we can directly estimate from the full HST set. We show that Delta(H0)~2.5 km/s/Mpc between the CCHP JWST program and the full HST sample is entirely consistent with differences in sample selection. Combining all JWST samples produces a new, distance-limited set of 16 SNeIa at D<25 Mpc and more closely resembles the full sample thanks to "reversion to the mean" of larger samples. Using JWST Cepheids, JAGB, and TRGB, we find 73.4+-2.1, 72.2+-2.2, and 72.1+-2.2 km/s/Mpc, respectively. Explicitly accounting for SNe in common, the combined-sample three-method result from JWST is H0=72.6+-2.0, similar to H0=72.8 expected from HST Cepheids in the same galaxies. The small JWST sample trivially lowers the Hubble tension significance due to small-sample statistics and is not yet competitive with the HST set (42 SNeIa and 4 anchors), which yields 73.2+-0.9. Still, the joint JWST sample provides important crosschecks which the HST data passes., Comment: submitted to ApJ, comments welcome and appreciated

Quantum sensing based on solid-state spin defects provides a uniquely versatile platform for imaging physical properties at the nanoscale under diverse environmental conditions. Operation of most sensors used to-date is based on projective measurement along a single axis combined with computational extrapolation. Here, we show that the individually addressable carbon-related spin defect in hexagonal boron nitride is a multi-axis spin system for vectorial nanoscale magnetometry. We demonstrate how its low symmetry and strongly spin-selective direct and reverse intersystem crossing dynamics provide sub-$\mu$T/$\sqrt{\text{Hz}}$ magnetic-field sensitivity for both on and off-axis bias magnetic field exceeding 50 mT. Alongside these features, the room-temperature operation and the nanometer-scale proximity enabled by the van der Waals host material further consolidate this system as an exciting quantum sensing platform.

Biomechanics and human movement research often involves measuring multiple kinematic or kinetic variables regularly throughout a movement, yielding data that present as smooth, multivariate, time-varying curves and are naturally amenable to functional data analysis. It is now increasingly common to record the same movement repeatedly for each individual, resulting in curves that are serially correlated and can be viewed as longitudinal functional data. We present a new approach for modelling multivariate multilevel longitudinal functional data, with application to kinematic data from recreational runners collected during a treadmill run. For each stride, the runners' hip, knee and ankle angles are modelled jointly as smooth multivariate functions that depend on subject-specific covariates. Longitudinally varying multivariate functional random effects are used to capture the dependence among adjacent strides and changes in the multivariate functions over the course of the treadmill run. A basis modelling approach is adopted to fit the model -- we represent each observation using a multivariate functional principal components basis and model the basis coefficients using scalar longitudinal mixed effects models. The predicted random effects are used to understand and visualise changes in the multivariate functional data over the course of the treadmill run. In our application, our method quantifies the effects of scalar covariates on the multivariate functional data, revealing a statistically significant effect of running speed at the hip, knee and ankle joints. Analysis of the predicted random effects reveals that individuals' kinematics are generally stable but certain individuals who exhibit strong changes during the run can also be identified. A simulation study is presented to demonstrate the efficacy of the proposed methodology under realistic data-generating scenarios., Comment: Extends/ builds upon the work in arXiv:2408.08200

We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners. The runners' sagittal plane hip and knee angles are modelled jointly as a bivariate function with random effects functions used to account for the dependence among measurements from either side of the body. The model is fitted by first applying multivariate functional principal component analysis (mv-FPCA) and then modelling the mv-FPCA scores using scalar linear mixed effects models. Simulation and bootstrap approaches are introduced to construct simultaneous confidence bands for the fixed effects functions, and covariance functions are reconstructed to summarise the variability structure in the data and thoroughly investigate the suitability of the proposed model. In our scientific application, we observe a statistically significant effect of running speed on both the hip and knee angles. We also observe strong within-subject correlations, reflecting the highly idiosyncratic nature of running technique. Our approach is more generally applicable to modelling multiple streams of smooth kinematic or kinetic data measured repeatedly for multiple subjects in complex experimental designs.

The Santa Cruz Extreme AO Lab (SEAL) testbed is an optical bench meant to design and develop new wavefront control techniques for high-contrast imaging for segmented telescopes. These techniques allow for astronomical efficiency in exoplanet imaging and characterization. SEAL consists of several wavefront sensors (WFS) and deformable mirrors (DM) that are currently performing techniques like predictive control or non-linear reconstruction. In this paper, we present the implementation and characterization of a new coronagraphic branch on SEAL and assess the contrast limitations in the testbed. For our coronagraphic branch, we used a vector vortex coronagraph which has high contrast performance. The W. M. Keck Observatory also uses a vortex coronagraph, allowing us to compare the limitations with our own coronagraph. We relied on the testbed and simulations of the vortex coronagraph to compare performance with expected ones. To create a more reliable simulation, we also injected in our numerical model data collected by a Zernike Wavefront sensor (ZWFS) used to perform fine wavefront sensing on the bench. Now that the coronagraphic branch is aligned on SEAL, we will be able to use contrast as a metric for the performance of wavefront control methods on the bench., Comment: 10 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation: Adaptive Optics Systems IX (Yokohama 2024), Paper 13097-297

Axions are a well-motivated dark matter candidate particle. Haloscopes aim to detect axions in the galactic halo by measuring the photon signal resulting from axions interacting with a strong magnetic field. Existing haloscopes are primarily targeting axion masses which produce microwave-range photons and rely on microwave resonators to enhance the signal power. Only a limited subset of resonator modes are useful for this process, and current cylindrical-style cavities suffer from mode mixing and crowding from other fundamental modes. The majority of these modes can be eliminated by using photonic band gap (PBG) resonators. The band gap behavior of these structures allows for a resonator with mode selectivity based on frequency. We present results from the first tunable PBG resonator, a proof-of-concept design with a footprint compatible with axion haloscopes. We have thoroughly characterized the tuning range of two versions of the structure and report the successful confinement of the operating TM$_{010}$ mode and the elimination of all TE modes within the tuning range., Comment: 7 pages, 8 figures

Recent work shows that LLMs are vulnerable to data poisoning, in which they are trained on partially corrupted or harmful data. Poisoned data is hard to detect, breaks guardrails, and leads to undesirable and harmful behavior. Given the intense efforts by leading labs to train and deploy increasingly larger and more capable LLMs, it is critical to ask if the risk of data poisoning will be naturally mitigated by scale, or if it is an increasing threat. We consider three threat models by which data poisoning can occur: malicious fine-tuning, imperfect data curation, and intentional data contamination. Our experiments evaluate the effects of data poisoning on 23 frontier LLMs ranging from 1.5-72 billion parameters on three datasets which speak to each of our threat models. We find that larger LLMs are increasingly vulnerable, learning harmful behavior -- including sleeper agent behavior -- significantly more quickly than smaller LLMs with even minimal data poisoning. These results underscore the need for robust safeguards against data poisoning in larger LLMs., Comment: 20 pages

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

We extend our previous demonstration of the first on-sky primary mirror segment closed-loop control on Keck using a vector-Zernike wavefront sensor (vZWFS), which improved the Strehl ratio on the NIRC2 science camera by up to 10 percentage points. Segment co-phasing errors contribute to Keck contrast limits and will be necessary to correct for the segmented Extremely Large Telescopes and future space missions. The goal of the post-AO vZWFS on Keck is to monitor and correct segment co-phasing errors in parallel with science observations. The ZWFS is ideal for measuring phase discontinuities and is one of the most sensitive WFSs, but has limited dynamic range. The Keck vZWFS consists of a metasurface mask imposing two different phase shifts to orthogonal polarizations, split into two pupil images, extending its dynamic range. We report on the vZWFS closed-loop co-phasing performance and early work towards understanding the interactions between the AO system and segment phasing. We discuss a comparison of the AO performance when co-phasing by aligning segment edges, as is currently done at Keck, compared with aligning to the average phase over the segments, as is done by the vZWFS., Comment: Proceedings of SPIE, 13097-61, 8 pages, 3 figures, 3 tables

We review the work of Dominic Welsh (1938-2023), tracing his remarkable influence through his theorems, expository writing, students, and interactions. He was particularly adept at bringing different fields together and fostering the development of mathematics and mathematicians. His contributions ranged widely across discrete mathematics over four main career phases: discrete probability, matroids and graphs, computational complexity, and Tutte-Whitney polynomials. We give particular emphasis to his work in matroid theory and Tutte-Whitney polynomials., Comment: 29 pages, one photo, two figures. Revised and expanded version of the guest post of the same title at The Matroid Union, 12 February 2024 (http://matroidunion.org/?p=5304). This version submitted to 2023 MATRIX Annals

Starlight suppression techniques for High-Contrast Imaging (HCI) are crucial to achieving the demanding contrast ratios and inner working angles required for detecting and characterizing exoplanets with a wide range of masses and separations. The advent of photonic technologies provides new opportunities to control the amplitude and phase characteristics of light, with the potential to enhance and control starlight suppression. Here, we present a focal plane optical-fiber-based nulling interferometer working with commercially available components for amplitude and phase modulation. The instrument implements single-mode fiber-coupled elements: a MEMS variable optical attenuator (VOA) matches the on-axis and off-axis starlight amplitude, while a piezoelectric-driven fiber stretcher modifies the optical path difference between the channels to achieve the $\pi$ phase shift condition for destructive interference. We show preliminary lab results using a narrowband light source working at 632 nm and discuss future opportunities for testing on-sky with the Astrophotonics Advancement Platform at Lick Observatory (APALO) at the Shane 3-m Telescope., Comment: submitted to SPIE Astronomical Telescopes and Instrumentation 2024, paper number 13095-95

Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a major systematic that must be overcome in order to detect the 21-cm signal, and simulations that reproduce effects seen in the data can guide strategies for mitigating mutual coupling. In this paper, we analyse 12 nights of data from the Hydrogen Epoch of Reionization Array and compare the data against simulations that include a computationally efficient and physically motivated semi-analytic treatment of mutual coupling. We find that simulated coupling features qualitatively agree with coupling features in the data; however, coupling features in the data are brighter than the simulated features, indicating the presence of additional coupling mechanisms not captured by our model. We explore the use of fringe-rate filters as mutual coupling mitigation tools and use our simulations to investigate the effects of mutual coupling on a simulated cosmological 21-cm power spectrum in a "worst case" scenario where the foregrounds are particularly bright. We find that mutual coupling contaminates a large portion of the "EoR Window", and the contamination is several orders-of-magnitude larger than our simulated cosmic signal across a wide range of cosmological Fourier modes. While our fiducial fringe-rate filtering strategy reduces mutual coupling by roughly a factor of 100 in power, a non-negligible amount of coupling cannot be excised with fringe-rate filters, so more sophisticated mitigation strategies are required., Comment: 19 pages, 12 figures, submitted to MNRAS

Exoplanet direct imaging using adaptive optics (AO) is often limited by non-common path aberrations (NCPAs) and aberrations that are invisible to traditional pupil-plane wavefront sensors (WFSs). This can be remedied by focal-plane (FP) WFSs that characterize aberrations directly from a final science image. Photonic lanterns (PLs) can act as low-order FPWFSs with the ability to direct some light to downstream science instruments. Using a PL on the SEAL (Santa Cruz Extreme AO Laboratory) high-contrast imaging testbed, we demonstrate (1) linear ranges and (2) closed-loop control. Additionally, we simulate the use of the PL in a multi-wavefront sensor AO system, in which multiple WFSs feed back to the same common-path deformable mirror. Building on previous multi-WFS AO demonstrations on SEAL, we simulate a modulated pyramid WFS to sense aberrations of high spatial order and large amplitude, and the PL to sense low order aberrations including NCPAs. We assess adaptive optics performance in this setting using three different PL wavefront reconstruction algorithms. We also provide a new method to experimentally identify the propagation matrix of a PL, making advanced model-based algorithms practical. This work demonstrates the role of photonic technologies and multi-stage wavefront sensing in the context of extreme AO and high contrast imaging., Comment: submitted to SPIE Astronomical Telescopes and Instrumentation 2024, paper number 13097-55

A laser pulse composed of a fundamental and an appropriately phased second harmonic can drive a time-dependent current of photoionized electrons that generates broadband THz radiation. Over the propagation distances relevant to many experiments, dispersion causes the relative phase between the harmonics to evolve. This "dephasing" slows the accumulation of THz energy and results in a multi-cycle THz pulse with significant angular dispersion. Here, we introduce a novel optical configuration that compensates the relative phase evolution, allowing for the formation of a half-cycle THz pulse with almost no angular dispersion. The configuration uses the spherical aberration of an axilens to map a prescribed radial phase variation in the near field to a desired longitudinal phase variation in the far field. Simulations that combine this configuration with an ultrashort flying focus demonstrate the formation of a half-cycle THz pulse with a controlled emission angle and 1/4 the angular divergence of the multi-cycle pulse created by a conventional optical configuration.

Previous studies have shown that eye movement data recorded at 1000 Hz can be used to authenticate individuals. This study explores the effectiveness of eye movement-based biometrics (EMB) by utilizing data from an eye-tracking (ET)-enabled virtual reality (VR) headset (GazeBaseVR) and compares it to the performance using data from a high-end eye tracker (GazeBase) that has been downsampled to 250 Hz. The research also aims to assess the biometric potential of both binocular and monocular eye movement data. GazeBaseVR dataset achieves an equal error rate (EER) of 1.67% and a false rejection rate (FRR) at 10^-4 false acceptance rate (FAR) of 22.73% in a binocular configuration. This study underscores the biometric viability of data obtained from eye-tracking-enabled VR headset., Comment: 9 pages, 6 figures

We present the MOST Hosts survey (Multi-Object Spectroscopy of Transient Hosts). The survey is planned to run throughout the five years of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (PTF/iPTF, SDSS II, ZTF, DECAT, DESIRT). Scientific questions for which the MOST Hosts survey will be useful include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host galaxy properties. Here, we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of these galaxies, 40% do not appear in the DESI primary target list and therefore require a specific program like MOST Hosts. Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity-duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for photometry-only classifiers, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released through the Wiserep platform on a rolling cadence and updated to match the DESI releases. Dates of future releases and updates are available through the https://mosthosts.desi.lbl.gov website., Comment: Submitted to ApJS

We introduce an interpretable-by-design method, optimized model-analog, that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify initial analog states that lead to shadowing target trajectories. The advantage of our method lies in its inherent interpretability, offering insights into initial-error-sensitive regions through estimated weights and the ability to trace the physically-based evolution of the system through analog forecasting. We evaluate our approach using the Community Earth System Model Version 2 Large Ensemble to forecast the El Ni\~no-Southern Oscillation (ENSO) on a seasonal-to-annual time scale. Results show a 10% improvement in forecasting equatorial Pacific sea surface temperature anomalies at 9-12 months leads compared to the original (unweighted) model-analog technique. Furthermore, our model demonstrates improvements in boreal winter and spring initialization when evaluated against a reanalysis dataset. Our approach reveals state-dependent regional sensitivity linked to various seasonally varying physical processes, including the Pacific Meridional Modes, equatorial recharge oscillator, and stochastic wind forcing. Additionally, disparities emerge in the sensitivity associated with El Ni\~no versus La Ni\~na events. El Ni\~no forecasts are more sensitive to initial uncertainty in tropical Pacific sea surface temperatures, while La Ni\~na forecasts are more sensitive to initial uncertainty in tropical Pacific zonal wind stress. This approach has broad implications for forecasting diverse climate phenomena, including regional temperature and precipitation, which are challenging for the original model-analog approach.

This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employed dataset is at least an order-of-magnitude larger than any other dataset from previous related work. Binocular estimates of the optical and visual axes of the eyes and a minimum duration for enrollment and verification are required for our model to achieve a false rejection rate (FRR) of below 3% at a false acceptance rate (FAR) of 1 in 50,000. In terms of identification accuracy which decreases with gallery size, we estimate that our model would fall below chance-level accuracy for gallery sizes of 148,000 or more. Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset., Comment: 31 pages, 20 figures, 5 tables, includes supplementary material

This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquired from publicly available sources. To achieve this, we perform Noisy Student Training after generating pseudo-labels for the unlabeled public data using a strong Conformer RNN-T baseline model. The addition of these pseudo-labeled data results in remarkable improvements in relative Word Error Rate (WER) by 11.5% and 24.3% for our asynchronous and realtime models, respectively. Additionally, the model is more robust to background noise owing to the addition of these data. The results obtained in this study demonstrate that the incorporation of pseudo-labeled publicly available data is a highly effective strategy for improving ASR accuracy and noise robustness.

Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics., Comment: Extended version of SoCS 2024 paper

Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the $h^{\text{FF}}$ heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning., Comment: Extended version of ICAPS 2024 paper

We present an extensive, in-depth analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from 78 participants. In addition to presenting classical signal quality metrics--spatial accuracy, spatial precision and linearity--in ideal settings, we also study the impact of background luminance and headset slippage on device performance. We additionally present a user-centered analysis of eye tracking signal quality, where we highlight the potential differences in user experience as a function of device performance. This work contributes to a growing understanding of eye tracking signal quality in virtual reality headsets, where the performance of applications such as gaze-based interaction, foveated rendering, and social gaze are directly dependent on the quality of eye tracking signal., Comment: 14 pages

What qualities make a feature useful for biometric performance? In prior research, pre-dating the advent of deep learning (DL) approaches to biometric analysis, a strong relationship between temporal persistence, as indexed by the intraclass correlation coefficient (ICC), and biometric performance (Equal Error Rate, EER) was noted. More generally, the claim was made that good biometric performance resulted from a relatively large set of weakly intercorrelated features with high ICC. The present study aimed to determine whether the same relationships are found in a state-of-the-art DL-based eye movement biometric system (``Eye-Know-You-Too''), as applied to two publicly available eye movement datasets. To this end, we manipulate various aspects of eye-tracking signal quality, which produces variation in biometric performance, and relate that performance to the temporal persistence and intercorrelation of the resulting embeddings. Data quality indices were related to EER with either linear or logarithmic fits, and the resulting model R^2 was noted. As a general matter, we found that temporal persistence was an important predictor of DL-based biometric performance, and also that DL-learned embeddings were generally weakly intercorrelated., Comment: 18 pages, 7 Figures

Modern software applications heavily rely on diverse third-party components, libraries, and frameworks sourced from various vendors and open source repositories, presenting a complex challenge for securing the software supply chain. To address this complexity, the adoption of a Software Bill of Materials (SBOM) has emerged as a promising solution, offering a centralized repository that inventories all third-party components and dependencies used in an application. Recent supply chain breaches, exemplified by the SolarWinds attack, underscore the urgent need to enhance software security and mitigate vulnerability risks, with SBOMs playing a pivotal role in this endeavor by revealing potential vulnerabilities, outdated components, and unsupported elements. This research paper conducts an extensive empirical analysis to assess the current landscape of open-source and proprietary tools related to SBOM. We investigate emerging use cases in software supply chain security and identify gaps in SBOM technologies. Our analysis encompasses 84 tools, providing a snapshot of the current market and highlighting areas for improvement.

The rise of large language models (LLMs) has drawn attention to the existence of "jailbreaks" that allow the models to be used maliciously. However, there is no standard benchmark for measuring the severity of a jailbreak, leaving authors of jailbreak papers to create their own. We show that these benchmarks often include vague or unanswerable questions and use grading criteria that are biased towards overestimating the misuse potential of low-quality model responses. Some jailbreak techniques make the problem worse by decreasing the quality of model responses even on benign questions: we show that several jailbreaking techniques substantially reduce the zero-shot performance of GPT-4 on MMLU. Jailbreaks can also make it harder to elicit harmful responses from an "uncensored" open-source model. We present a new benchmark, StrongREJECT, which better discriminates between effective and ineffective jailbreaks by using a higher-quality question set and a more accurate response grading algorithm. We show that our new grading scheme better accords with human judgment of response quality and overall jailbreak effectiveness, especially on the sort of low-quality responses that contribute the most to over-estimation of jailbreak performance on existing benchmarks. We release our code and data at https://github.com/alexandrasouly/strongreject., Comment: Code and data at https://github.com/alexandrasouly/strongreject

Radio interferometers targeting the 21cm brightness temperature fluctuations at high redshift are subject to systematic effects that operate over a range of different timescales. These can be isolated by designing appropriate Fourier filters that operate in fringe-rate (FR) space, the Fourier pair of local sidereal time (LST). Applications of FR filtering include separating effects that are correlated with the rotating sky vs. those relative to the ground, down-weighting emission in the primary beam sidelobes, and suppressing noise. FR filtering causes the noise contributions to the visibility data to become correlated in time however, making interpretation of subsequent averaging and error estimation steps more subtle. In this paper, we describe fringe rate filters that are implemented using discrete prolate spheroidal sequences, and designed for two different purposes -- beam sidelobe/horizon suppression (the `mainlobe' filter), and ground-locked systematics removal (the `notch' filter). We apply these to simulated data, and study how their properties affect visibilities and power spectra generated from the simulations. Included is an introduction to fringe-rate filtering and a demonstration of fringe-rate filters applied to simple situations to aid understanding., Comment: 21 pages, 18 figures, submitted to Monthly Notices of the Royal Astronomical Society

A transduction provides us with a way of using the monadic second-order language of a structure to make statements about a derived structure. Any transduction induces a relation on the set of these structures. This article presents a self-contained presentation of the theory of transductions for the monadic second-order language of matroids. This includes a proof of the matroid version of the Backwards Translation Theorem, which lifts any formula applied to the images of the transduction into a formula which we can apply to the pre-images. Applications include proofs that the class of lattice-path matroids and the class of spike-minors can be defined by sentences in monadic second-order logic.

This paper presents the design and deployment of the Hydrogen Epoch of Reionization Array (HERA) phase II system. HERA is designed as a staged experiment targeting 21 cm emission measurements of the Epoch of Reionization. First results from the phase I array are published as of early 2022, and deployment of the phase II system is nearing completion. We describe the design of the phase II system and discuss progress on commissioning and future upgrades. As HERA is a designated Square Kilometer Array (SKA) pathfinder instrument, we also show a number of "case studies" that investigate systematics seen while commissioning the phase II system, which may be of use in the design and operation of future arrays. Common pathologies are likely to manifest in similar ways across instruments, and many of these sources of contamination can be mitigated once the source is identified.

We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/, Comment: To appear at ICML 2024; Project page: http://sites.research.google/videopoet/

Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application in the other domains remains limited. This paper proposes a Mixup regularization scheme, referred to as UMAP Mixup, designed for ``on-manifold" automated data augmentation for deep learning predictive models. The proposed approach ensures that the Mixup operations result in synthesized samples that lie on the data manifold of the features and labels by utilizing a dimensionality reduction technique known as uniform manifold approximation and projection. Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models., Comment: accepted paper to be published in the proceedings of ICASSP 2024

We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics., Comment: Extended version of AAAI 2024 paper

Detection of the faint 21 cm line emission from the Cosmic Dawn and Epoch of Reionisation will require not only exquisite control over instrumental calibration and systematics to achieve the necessary dynamic range of observations but also validation of analysis techniques to demonstrate their statistical properties and signal loss characteristics. A key ingredient in achieving this is the ability to perform high-fidelity simulations of the kinds of data that are produced by the large, many-element, radio interferometric arrays that have been purpose-built for these studies. The large scale of these arrays presents a computational challenge, as one must simulate a detailed sky and instrumental model across many hundreds of frequency channels, thousands of time samples, and tens of thousands of baselines for arrays with hundreds of antennas. In this paper, we present a fast matrix-based method for simulating radio interferometric measurements (visibilities) at the necessary scale. We achieve this through judicious use of primary beam interpolation, fast approximations for coordinate transforms, and a vectorised outer product to expand per-antenna quantities to per-baseline visibilities, coupled with standard parallelisation techniques. We validate the results of this method, implemented in the publicly-available matvis code, against a high-precision reference simulator, and explore its computational scaling on a variety of problems., Comment: 25 pages, 20 figures, submitted to RAS Techniques and Instruments, matvis is publicly available at https://github.com/HERA-Team/matvis

Observations with radio arrays that target the 21-cm signal originating from the early Universe suffer from a variety of systematic effects. An important class of these are reflections and spurious couplings between antennas. We apply a Hamiltonian Monte Carlo sampler to the modelling and mitigation of these systematics in simulated Hydrogen Epoch of Reionisation Array (HERA) data. This method allows us to form statistical uncertainty estimates for both our models and the recovered visibilities, which is an important ingredient in establishing robust upper limits on the Epoch of Reionisation (EoR) power spectrum. In cases where the noise is large compared to the EoR signal, this approach can constrain the systematics well enough to mitigate them down to the noise level for both systematics studied. Where the noise is smaller than the EoR, our modelling can mitigate the majority of the reflections with there being only a minor level of residual systematics, while cross-coupling sees essentially complete mitigation. Our approach performs similarly to existing filtering/fitting techniques used in the HERA pipeline, but with the added benefit of rigorously propagating uncertainties. In all cases it does not significantly attenuate the underlying signal., Comment: 19 pages, 14 figures, submitted to MNRAS