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Recent advances in one-step generative models typically follow a two-stage process: first training a teacher diffusion model and then distilling it into a one-step student model. This distillation process traditionally relies on both the teacher model's score function to compute the distillation loss and its weights for student initialization. In this paper, we explore whether one-step generative models can be trained directly without this distillation process. First, we show that the teacher's score function is not essential and propose a family of distillation methods that achieve competitive results without relying on score estimation. Next, we demonstrate that initialization from teacher weights is indispensable in successful training. Surprisingly, we find that this benefit is not due to improved ``input-output" mapping but rather the learned feature representations, which dominate distillation quality. Our findings provide a better understanding of the role of initialization in one-step model training and its impact on distillation quality., Comment: 13 pages, Technical Report

In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an upper bound on the model class risk (the lowest possible risk that can be attained by any model in the class). However, it is also of interest to establish lower bounds on the model class risk, for instance so that we can determine whether our fitted model is at least approximately optimal within the class, or, so that we can decide whether the model class is unsuitable for the particular task at hand. Particularly in the setting of interpolation learning where machine learning models are trained to reach zero error on the training data, we might ask if, at the very least, a positive lower bound on the model class risk is possible -- or are we unable to detect that "all models are wrong"? In this work, we answer these questions in a distribution-free setting by establishing a model-agnostic, fundamental hardness result for the problem of constructing a lower bound on the best test error achievable over a model class, and examine its implications on specific model classes such as tree-based methods and linear regression.

Measuring the properties of planets younger than about 50 Myr helps to test different planetary formation and evolution models. NASA's Transiting Exoplanet Survey Satellite (TESS) has observed nearly the entire sky, including a wide range of star-forming regions and young stellar clusters, expanding our census of the newborn planet population. In this work, we present the discovery of the TIC 88785435 planetary system located in the Upper-Centaurus Lupus (UCL) region of the Scorpius-Centaurus OB association (Sco-Cen) and a preliminary survey of the planet population within Sco-Cen. TIC 88785435 is a pre-main sequence, K7V dwarf ($M_\star = 0.72M_\odot$, $R_\star = 0.91R_\odot$, $T_\mathrm{eff}$ = 3998K, V = 11.7 mag) located within the bounds of UCL. We investigate the distribution of rotation periods measured from the TESS long-cadence data and the Halpha and Li abundances from the spectra of TIC 88785435. TESS long-candence data reveal that TIC 88785435 hosts a transiting super-Neptune ($R_b = 5.03R_\oplus$, P = 10.51 days), TIC 88785435 b. Ground-based follow-up validates the planetary nature of TIC 88785435 b. Using the TESS data, we perform a preliminary survey to investigate how TIC 88785435 b compares to the population of newly born planets located within Sco-Cen., Comment: 15 pages, 9 Figures, submitted to AJ

The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation., Comment: 9 pages, 11 figures, 2 tables

We propose a test of the conditional independence of random variables $X$ and $Y$ given $Z$ under the additional assumption that $X$ is stochastically increasing in $Z$. The well-documented hardness of testing conditional independence means that some further restriction on the null hypothesis parameter space is required, but in contrast to existing approaches based on parametric models, smoothness assumptions, or approximations to the conditional distribution of $X$ given $Z$ and/or $Y$ given $Z$, our test requires only the stochastic monotonicity assumption. Our procedure, called PairSwap-ICI, determines the significance of a statistic by randomly swapping the $X$ values within ordered pairs of $Z$ values. The matched pairs and the test statistic may depend on both $Y$ and $Z$, providing the analyst with significant flexibility in constructing a powerful test. Our test offers finite-sample Type I error control, and provably achieves high power against a large class of alternatives that are not too close to the null. We validate our theoretical findings through a series of simulations and real data experiments., Comment: 69 pages, 5 figures

Stasis is a unique cosmological phenomenon in which the abundances of different energy components in the universe (such as matter, radiation, and vacuum energy) each remain fixed even though they scale differently under cosmological expansion. Moreover, extended epochs exhibiting stasis are generally cosmological attractors in many BSM settings and thus arise naturally and without fine-tuning. To date, stasis has been found within a number of very different BSM cosmologies. In some cases, stasis emerges from theories that contain large towers of decaying states (such as theories in extra dimensions or string theory). By contrast, in other cases, no towers of states are needed, and stasis instead emerges due to thermal effects involving particle annihilation rather than decay. In this paper, we study the dynamics of the energy flows in all of these theories during stasis, and find that these theories all share a common energy-flow structure which in some sense lies between particle decay and particle annihilation. This structure has been hidden until now but ultimately lies at the root of the stasis phenomenon, with all of the previous stases appearing as different manifestations of this common underlying structure. This insight not only allows us to understand the emergence of stasis in each of these different scenarios, but also provides an important guide for the potential future discovery of stasis in additional cosmological systems., Comment: 23 pages, LaTeX, 6 figures

Astronomers have found more than a dozen planets transiting 10-40 million year old stars, but even younger transiting planets have remained elusive. A possible reason for the lack of such discoveries is that newly formed planets are not yet in a configuration that would be recognized as a transiting planet or cannot exhibit transits because our view is blocked by a protoplanetary disk. However, we now know that many outer disks are warped; provided the inner disk is depleted, transiting planets may thus be visible. Here we report the observations of the transiting planet IRAS 04125+2902 b orbiting a 3 Myr, 0.7 M$_\odot$, pre-main sequence star in the Taurus Molecular Cloud. IRAS 04125+2902 hosts a nearly face-on (i $\sim$ 30$^\circ$) transitional disk and a wide binary companion. The planet has a period of 8.83 days, a radius of 10.9 R$_\oplus$ (0.97R$_J$), and a 95%-confidence upper limit on its mass of 90M$_\oplus$ (0.3M$_J$) from radial velocity measurements, making it a possible precursor of the super-Earths and sub-Neptunes that are commonly found around main-sequence stars. The rotational broadening of the star and the orbit of the wide (4", 635 AU) companion are both consistent with edge-on orientations. Thus, all components of the system appear to be aligned except the outer disk; the origin of this misalignment is unclear. Given the rare set of circumstances required to detect a transiting planet at ages when the disk is still present, IRAS 04125+2902 b likely provides a unique window into sub-Neptunes immediately following formation., Comment: Initial version submitted to Nature. Stellar, and hence planetary, parameters slightly differ from final version. Published version available at https://www.nature.com/articles/s41586-024-08123-3

This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty quantification guarantees for machine learning systems. Much of the current interest in conformal prediction is due to its ability to integrate into complex machine learning workflows, solving the problem of forming prediction sets without any assumptions on the form of the data generating distribution. Since contemporary machine learning algorithms have generally proven difficult to analyze directly, conformal prediction's main appeal is its ability to provide formal, finite-sample guarantees when paired with such methods. The goal of this book is to teach the reader about the fundamental technical arguments that arise when researching conformal prediction and related questions in distribution-free inference. Many of these proof strategies, especially the more recent ones, are scattered among research papers, making it difficult for researchers to understand where to look, which results are important, and how exactly the proofs work. We hope to bridge this gap by curating what we believe to be some of the most important results in the literature and presenting their proofs in a unified language, with illustrations, and with an eye towards pedagogy., Comment: This material will be published by Cambridge University Press as Theoretical Foundations of Conformal Prediction by Anastasios N. Angelopoulos, Rina Foygel Barber and Stephen Bates. This prepublication version is free to view/download for personal use only. Not for redistribution/resale/use in derivative works. Copyright Anastasios N. Angelopoulos, Rina Foygel Barber and Stephen Bates, 2024

Laser-driven (LD) ion acceleration has been explored in a newly constructed short focal length beamline at the BELLA petawatt facility (interaction point 2, iP2). For applications utilizing such LD ion beams, a beam transport system is required, which for reasons of compactness be ideally contained within 3 m. While they are generated from a micron-scale source, large divergence and energy spread of LD ion beams present a unique challenge to transporting them compared to beams from conventional accelerators. This study gives an overview of proposed compact transport designs using permanent magnets satisfying different requirements depending on the application for the iP2 proton beamline such as radiation biology, material science, and high energy density science. These designs are optimized for different parameters such as energy spread and peak proton density according to the application's need. The various designs consist solely of permanent magnet elements, which can provide high magnetic field gradients on a small footprint. While the field strengths are fixed, we have shown that the beam size is able to be tuned effectively by varying the placement of the magnets. The performance of each design was evaluated based on high order particle tracking simulations of typical LD proton beams. We also examine the ability of certain configurations to tune and select beam energies, critical for specific applications. A more detailed investigation was carried out for a design to deliver 10 MeV LD accelerated ions for radiation biology applications. With these transport system designs, the iP2 beamline is ready to house various application experiments., Comment: 12 pages, 9 figures, to be submitted to Physical Review Accelerators and Beams

We investigate black hole-star binaries formed in $N$-body simulations of massive, dense star clusters. We simulate 32 clusters with varying initial masses ($10^{4}~\rm M_{\odot}$ to $10^{6}~\rm M_{\odot}$), densities ($1200~\rm M_{\odot}~pc^{-3}$ to $10^{5}~\rm M_{\odot}~pc^{-3}$), and metallicities $(Z = 0.01,~0.001,~0.0001)$. Our results reveal that star clusters produce a diverse range of BH-star binaries, with dynamical interactions leading to extreme systems characterised by large orbital separations and high black hole masses. Of the ejected BH-main sequence (BH-MS) binaries, $20\%$ form dynamically, while the rest originate from the primordial binary population initially present in the cluster. Ejected BH-MS binaries that are dynamically formed have more massive black holes, lower-mass stellar companions, and over half are in a hierarchical triple system. All unbound BH-giant star (BH-GS) binaries were ejected as BH-MS binaries and evolved into the BH-GS phase outside the cluster. Due to their lower-mass companions, most dynamically formed binaries do not evolve into BH-GS systems within a Hubble time. Consequently, only 2 of the 35 ejected BH-GS binaries are dynamically formed. We explore the formation pathways of Gaia-like systems, identifying two Gaia BH1-like binaries that formed through dynamical interactions, and two Gaia BH2-like systems with a primordial origin. We did not find any system resembling Gaia BH3, which may however be attributed to the limited sample size of our simulations., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the reverse Kullback-Leibler (KL) divergence due to its tractability. However, the mode-seeking behavior of reverse KL hinders effective approximation of multi-modal target distributions. To address this, we propose to minimize the reverse KL along diffusion trajectories of both model and target densities. We refer to this objective as the reverse diffusive KL divergence, which allows the model to capture multiple modes. Leveraging this objective, we train neural samplers that can efficiently generate samples from the target distribution in one step. We demonstrate that our method enhances sampling performance across various Boltzmann distributions, including both synthetic multi-modal densities and n-body particle systems., Comment: 23 pages, 6 figures, 3 tables, 1 algorithm

Understanding binary black hole (BBH) dynamics in dense star clusters is key to interpreting the gravitational wave detections by LIGO and Virgo. Here, we perform $N$-body simulations of star clusters, focusing on BBH formation mechanisms, dynamical evolution and merging properties. We explore a wide parameter space of initial conditions, with cluster masses ranging from $10^{4}$ to $10^{6}~\mathrm{M_{\odot}}$, densities from $10^{3}$ to $10^{5}~\rm M_{\odot}pc^{-3}$, and up to $100\%$ of massive stars in binaries. We show that most BBH mergers originate from the primordial binary population rather than being dynamically assembled, and that the evolution towards merger for most of these binaries is not significantly altered by dynamical encounters. As a result, the overall number of BBH mergers from the $N$-body simulations is nearly identical to that obtained when the same stellar population is evolved in isolation. Contrary to theoretical expectations, nearly all dynamically formed BBH mergers occur when the binary is still bound to its host cluster, with $\simeq 90\%$ of all dynamical mergers occurring within the cluster core region. In about half of these mergers the binary is part of a stable black hole-triple system. In one model, stellar mergers lead to the formation of a $\simeq 200\,\mathrm{M_\odot}$ black hole, which then grows to $\simeq 300\,\mathrm{M_\odot}$ through black hole mergers. Our study highlights the importance of detailed $N$-body simulations in capturing the evolution of black hole populations in dense clusters and challenges conclusions based on semi-analytical and Monte Carlo methods., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition or caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide information on what a successful output should be and common occurrences of algorithm failures for various processing pipelines. Our method reduces the time spent on QC by a factor of over 20 when compared to naively opening outputs in an image viewer and demonstrate how it can facilitate aggregation and sharing of QC results within a team. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient., Comment: 22 pages, 12 figures, 1 table, 6 supplemental figures

The characterization of young planets (< 300 Myr) is pivotal for understanding planet formation and evolution. We present the 3-5$\mu$m transmission spectrum of the 17 Myr, Jupiter-size ($R$ $\sim$10$R_{\oplus}$) planet, HIP 67522 b, observed with JWST/NIRSpec/G395H. To check for spot contamination, we obtain a simultaneous $g$-band transit with SOAR. The spectrum exhibits absorption features 30-50% deeper than the overall depth, far larger than expected from an equivalent mature planet, and suggests that HIP 67522 b's mass is $<$20 $M_{\oplus}$ irrespective of cloud cover and stellar contamination. A Bayesian retrieval analysis returns a mass constraint of $13.8\pm1.0M_{\oplus}$. This challenges the previous classification of HIP 67522 b as a hot Jupiter and instead, positions it as a precursor to the more common sub-Neptunes. With a density of $<$0.10g/cm$^{3}$, HIP 67522 b is one of the lowest density planets known. We find strong absorption from H$_{2}$O and CO$_{2}$ ($\ge7\sigma$), a modest detection of CO (3.5$\sigma$), and weak detections of H$_2$S and SO$_2$ ($\simeq2\sigma$). Comparisons with radiative-convective equilibrium models suggest supersolar atmospheric metallicities and solar-to-subsolar C/O ratios, with photochemistry further constraining the inferred atmospheric metallicity to 3$\times$10 Solar due to the amplitude of the SO$_2$ feature. These results point to the formation of HIP 67522 b beyond the water snowline, where its envelope was polluted by icy pebbles and planetesimals. The planet is likely experiencing substantial mass loss (0.01-0.03 M$_{\oplus}$ Myr$^{-1}$), sufficient for envelope destruction within a Gyr. This highlights the dramatic evolution occurring within the first 100 Myr of its existence., Comment: Accepted for publication in The Astronomical Journal; 32 pages, 18 figures, 7 tables

We examine bicoset digraphs and their natural properties from the point of view of symmetry. We then consider connected bicoset digraphs that are $X$-joins with collections of empty graphs, and show that their automorphism groups can be obtained from their natural irreducible quotients. We then show that such digraphs can be recognized from their connection sets., Comment: The work of the second author is supported in part by the Slovenian Research Agency (research program P1-0285 and research projects N1-0140, N1-0160, J1-2451, N1-0208, J1-3001, J1-3003, J1-4008, and J1-50000), while the work of the third author is supported in part by the Slovenian Research Agency (research program P1-0285 and Young Researchers Grant)

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

It has recently been shown that extended cosmological epochs can exist during which the abundances associated with different energy components remain constant despite cosmological expansion. Indeed, this "stasis" behavior has been found to arise generically in many BSM theories containing large towers of states, and even serves as a cosmological attractor. However, all previous studies of stasis took place within non-thermal environments, or more specifically within environments in which thermal effects played no essential role in realizing or sustaining the stasis. In this paper, we demonstrate that stasis can emerge and serve as an attractor even within thermal environments, with thermal effects playing a critical role in the stasis dynamics. Moreover, within such environments, we find that no towers of states are needed -- a single state experiencing two-body annihilations will suffice. This work thus extends the stasis phenomenon into the thermal domain and demonstrates that thermal effects can also generally give rise to an extended stasis epoch, even when only a single matter species is involved., Comment: 16 pages, LaTeX, 7 figures

Wind power generation plays a crucial role in transitioning away from fossil fuel-dependent energy sources, contributing significantly to the mitigation of climate change. Monitoring and evaluating the aerodynamics of large wind turbine rotors is crucial to enable more wind energy deployment. This is necessary to achieve the European climate goal of a reduction in net greenhouse gas emissions by at least 55% by 2030, compared to 1990 levels. This paper presents a comparison between two measurement systems for evaluating the aerodynamic performance of wind turbine rotor blades on a full-scale wind tunnel test. One system uses an array of ten commercial compact ultra-low power micro-electromechanical systems (MEMS) pressure sensors placed on the blade surface, while the other employs high-accuracy lab-based pressure scanners embedded in the airfoil. The tests are conducted at a Reynolds number of 3.5 x 10^6, which represents typical operating conditions for wind turbines. MEMS sensors are of particular interest, as they can enable real-time monitoring which would be impossible with the ground truth system. This work provides an accurate quantification of the impact of the MEMS system on the blade aerodynamics and its measurement accuracy. Our results indicate that MEMS sensors, with a total sensing power below 1.6 mW, can measure key aerodynamic parameters like Angle of Attack (AoA) and flow separation with a precision of 1{\deg}. Although there are minor differences in measurements due to sensor encapsulation, the MEMS system does not significantly compromise blade aerodynamics, with a maximum shift in the angle of attack for flow separation of only 1{\deg}. These findings indicate that surface and low-power MEMS sensor systems are a promising approach for efficient and sustainable wind turbine monitoring using self-sustaining Internet of Things devices and wireless sensor networks.

Given a family of pretrained models and a hold-out set, how can we construct a valid conformal prediction set while selecting a model that minimizes the width of the set? If we use the same hold-out data set both to select a model (the model that yields the smallest conformal prediction sets) and then to construct a conformal prediction set based on that selected model, we suffer a loss of coverage due to selection bias. Alternatively, we could further splitting the data to perform selection and calibration separately, but this comes at a steep cost if the size of the dataset is limited. In this paper, we address the challenge of constructing a valid prediction set after efficiency-oriented model selection. Our novel methods can be implemented efficiently and admit finite-sample validity guarantees without invoking additional sample-splitting. We show that our methods yield prediction sets with asymptotically optimal size under certain notion of continuity for the model class. The improved efficiency of the prediction sets constructed by our methods are further demonstrated through applications to synthetic datasets in various settings and a real data example.

Laser-driven (LD) ion acceleration has been explored in a newly constructed short focal length beamline at the BELLA petawatt facility (interaction point 2, iP2). For applications utilizing such LD ion beams, a beam transport system is required, which for reasons of compactness be ideally contained within 3 m. The large divergence and energy spread of LD ion beams present a unique challenge to transporting them compared to beams from conventional accelerators. This work gives an overview of proposed compact transport designs that can satisfy different requirements depending on the application for the iP2 proton beamline such as radiation biology, material science, and high energy density science. These designs are optimized for different parameters such as energy spread and peak proton density according to an application's need. The various designs consist solely of permanent magnet elements, which can provide high magnetic field gradients on a small footprint. While the field strengths are fixed, we have shown that the beam size and energy can be tuned effectively by varying the placement of the magnets. The performance of each design was evaluated based on high order particle tracking simulations of typical LD proton beams. A more detailed investigation was carried out for a design to deliver 10 MeV LD accelerated ions for radiation biology applications. With these transport system designs, the iP2 beamline is ready to house various application experiments., Comment: 6 pages, 3 figures, accepted for publication in the proceedings of the 2022 IEEE Advanced Accelerator Concepts Workshop (AAC). J.T. De Chant is also affiliated with Michigan State University

Observing and characterizing young planetary systems can aid in unveiling the evolutionary mechanisms that sculpt the mature exoplanet population. As an all-sky survey, NASA's Transiting Exoplanet Survey Satellite (TESS) has expanded the known young planet population as it has observed young comoving stellar populations. This work presents the discovery of a multiplanet system orbiting the 61 Myr old G4V star TIC 434398831 (M = 0.99 Msun, R = 0.91 Rsun, Teff = 5638 K, Tmag = 11.31) located in the Theia 116 comoving population. We estimate the population's age based on rotation periods measured from the TESS light curves, isochrone fitting, and measurements of lithium equivalent widths in the spectra of Theia 116 members. The TESS FFI light curves reveal a mini-Neptune (Rb = 3.51 Rearth, Pb = 3.69 days) and super-Neptune (Rc = 5.63 Rearth, Pc = 6.21 days) with an orbital period ratio slightly larger than 5:3. Follow-up observations from CHEOPS and ground-based telescopes confirm the transits of TIC 434398831 b and c, and constrain their transit times. We explore the potential mass-loss histories of the two planets in order to probe possible initial conditions of the planets immediately after formation., Comment: 14 pages, 13 figures, 5 tables. Submitted to MNRAS

Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting but have struggled with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from physics-free machine learning (ML) methods and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists' judgment but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here, we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High-Resolution Rapid Refresh (HRRR) model, the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. For grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with a median CSI of 0.04 for HRRR. However, despite hydrologically relevant improvements in point-by-point forecasts from NowcastNet, caveats include the overestimation of spatially aggregated precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists, and river managers suggests the possibility of improved flood emergency response and hydropower management.

Statistical learning under distribution shift is challenging when neither prior knowledge nor fully accessible data from the target distribution is available. Distributionally robust learning (DRL) aims to control the worst-case statistical performance within an uncertainty set of candidate distributions, but how to properly specify the set remains challenging. To enable distributional robustness without being overly conservative, in this paper, we propose a shape-constrained approach to DRL, which incorporates prior information about the way in which the unknown target distribution differs from its estimate. More specifically, we assume the unknown density ratio between the target distribution and its estimate is isotonic with respect to some partial order. At the population level, we provide a solution to the shape-constrained optimization problem that does not involve the isotonic constraint. At the sample level, we provide consistency results for an empirical estimator of the target in a range of different settings. Empirical studies on both synthetic and real data examples demonstrate the improved accuracy of the proposed shape-constrained approach.

Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not well understood and they come with a number of drawbacks, most notably their complex initialisation and normalisation schemes. In this work, we address some of these issues by proposing RotRNN -- a linear recurrent model which utilises the convenient properties of rotation matrices. We show that RotRNN provides a simple and efficient model with a robust normalisation procedure, and a practical implementation that remains faithful to its theoretical derivation. RotRNN also achieves competitive performance to state-of-the-art linear recurrent models on several long sequence modelling datasets., Comment: Next Generation of Sequence Modeling Architectures Workshop at ICML 2024

The youngest ($<$50 Myr) planets are vital to understand planet formation and early evolution. The 17 Myr system HIP 67522 is already known to host a giant ($\simeq$10$R_\oplus$) planet on a tight orbit. In the discovery paper, Rizzuto et al. 2020 reported a tentative single transit detection of an additional planet in the system using TESS. Here, we report the discovery of HIP 67522 c which matches with that single transit event. We confirm the signal with ground-based multi-wavelength photometry from Sinistro and MuSCAT4. At a period of 14.33 days, planet c is close to a 2:1 mean motion resonance with b (6.96 days or 2.06:1). The light curve shows distortions during many of the transits, which are consistent with spot crossing events and/or flares. Fewer stellar activity events are seen in the transits of planet b, suggesting that planet c is crossing a more active latitude. Such distortions, combined with systematics in the TESS light curve extraction, likely explain why planet c was previously missed., Comment: Accepted for publication in ApJ Letters

Single-step Coulomb excitation of $^{46,48,49,50}$Ti is presented. A complete set of $E2$ matrix elements for the quintuplet of states in $^{49}$Ti, centered on the $2^+$ core excitation, was measured for the first time. A total of nine $E2$ matrix elements are reported, four of which were previously unknown. $^{49}_{22}$Ti$_{27}$ shows a $20\%$ quenching in electric quadrupole transition strength as compared to its semi-magic $^{50}_{22}$Ti$_{28}$ neighbour. This $20\%$ quenching, while empirically unprecedented, can be explained with a remarkably simple two-state mixing model, which is also consistent with other ground-state properties such as the magnetic dipole moment and electric quadrupole moment. A connection to nucleon transfer data and the quenching of single-particle strength is also demonstrated. The simplicity of the $^{49}$Ti-$^{50}$Ti pair (i.e., approximate single-$j$ $0f_{7/2}$ valence space and isolation of yrast states from non-yrast states) provides a unique opportunity to disentangle otherwise competing effects in the ground-state properties of atomic nuclei, the emergence of collectivity, and the role of proton-neutron interactions., Comment: 8 pages, 5 figures, accepted in Physics Letters B

It has long been known that a vertex-transitive graph $\Gamma$ is isomorphic to a double coset graph $\text{Cos}(G,H,S)$ of a transitive group $G\le\text{Aut}(\Gamma)$, a vertex stabilizer $H\le G$, and some subset $S\subseteq G$. We show that the automorphism group of the Cayley graph $\text{Cay}(G,S)$ with connection set $S$ can be obtained from the automorphism group of $\text{Cos}(G,H,S)$ and vice versa. We also show that the isomorphism problem for double coset graphs is equivalent to the isomorphism problem for Cayley graphs provided one knows all groups $G$ for which a fixed Cayley graph is a Cayley graph of $G$. Our main tool is a "recognition theorem", which recognizes when a Cayley graph of a group $G$ is a wreath product of two graphs based upon its connection set., Comment: This work is supported in part by the Slovenian Research Agency (research program P1-0285 and research projects N1-0140, N1-0160, J1-2451, N1-0208, J1-3001, J1-3003, J1-4008, and J1-50000)

Error correction allows a quantum computer to preserve states long beyond the decoherence time of its physical qubits. Key to any scheme of error correction is the decoding algorithm, which estimates the error state of qubits from the results of syndrome measurements. The leading proposal for quantum error correction, the surface code, has fast and accurate decoders, but several recently proposed quantum low-density parity check (qLDPC) codes allow more logical information to be encoded in significantly fewer physical qubits. The state-of-the-art decoder for general qLDPC codes, BP-OSD, has a cheap Belief Propagation stage, followed by linear algebra and search stages which can each be slow in practice. We introduce the Ambiguity Clustering decoder (AC) which, after the Belief Propagation stage, divides the measurement data into clusters that can be decoded independently. We benchmark AC on the recently proposed bivariate bicycle qLDPC codes and find that, with 0.3% circuit-level depolarising noise, AC is up to 27x faster than BP-OSD with matched accuracy. Our implementation of AC decodes the 144-qubit Gross code in 135us per round of syndrome extraction on an M2 CPU, already fast enough to keep up with neutral atom and trapped ion systems., Comment: v2 corrects an error in the configuration of BP-OSD which inflated its run times. Independently, we improved the configuration of AC. The net result is a narrowing of the time gap between the decoders in our matched accuracy comparison. The paper has also been restructured, and additional data and discussion have been added in appendices

High entropy oxides (HEO)s have garnered much interest due to their available high degree of tunability. Here, we study the local structure of (MgNiCuCoZn)0.167(MnCr)0.083O, a composition based on the parent HEO (MgNiCuCoZn)0.2O.We synthesized a series of thin films via pulsed laser deposition at incremental oxygen partial pressures. X-ray diffraction shows lattice parameter to decrease with increased pO2 pressures until the onset of phase separation. X-ray absorption fine structure shows that specific atomic species in the composition dictate the global structure of the material as Cr, Co, and Mn shift to energetically favorable coordination with increasing pressure. Transmission electron microscopy analysis on a lower-pressure sample exhibits a rock salt structure, but the higher-pressure sample reveals reflections reminiscent of the spinel structure. In all, these findings give a more complete picture on how (MgNiCuCoZn)0.167(MnCr)0.083O forms with varying initial conditions and advances fundamental knowledge of cation behavior in high entropy oxides.

The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covariances. In this paper, we leverage the recently proposed covariance moment matching technique and introduce a novel method for learning the diagonal covariance. Unlike traditional data-driven diagonal covariance approximation approaches, our method involves directly regressing the optimal diagonal analytic covariance using a new, unbiased objective named Optimal Covariance Matching (OCM). This approach can significantly reduce the approximation error in covariance prediction. We demonstrate how our method can substantially enhance the sampling efficiency, recall rate and likelihood of commonly used diffusion models.