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Biphoton frequency combs are promising resources for quantum networking due in large part to their compatibility with the telecommunication infrastructure. In this work, we propose a method to periodically compress broadband frequency-entangled photons into biphoton frequency combs by utilizing time-varying linear cavities. Our approach hinges on rapid modulation of the input cavity coupling, yielding high spectral purity in each output comb line similar to that achieved with narrowband filters, but without the associated loss in flux. We examine the dependence of spectral purity and compression on coupling strength, cavity loss, and switching speed, finding realistic regimes supporting purities in excess of 0.999 and peak enhancement factors of 100 and beyond., Comment: 5 pages, 4 figures

Large language models (LLMs) have recently shown significant potential in various biological tasks such as protein engineering and molecule design. These tasks typically involve black-box discrete sequence optimization, where the challenge lies in generating sequences that are not only biologically feasible but also adhere to hard fine-grained constraints. However, LLMs often struggle with such constraints, especially in biological contexts where verifying candidate solutions is costly and time-consuming. In this study, we explore the possibility of employing LLMs as highly-constrained bilevel optimizers through a methodology we refer to as Language Model Optimization with Margin Expectation (LLOME). This approach combines both offline and online optimization, utilizing limited oracle evaluations to iteratively enhance the sequences generated by the LLM. We additionally propose a novel training objective -- Margin-Aligned Expectation (MargE) -- that trains the LLM to smoothly interpolate between the reward and reference distributions. Lastly, we introduce a synthetic test suite that bears strong geometric similarity to real biophysical problems and enables rapid evaluation of LLM optimizers without time-consuming lab validation. Our findings reveal that, in comparison to genetic algorithm baselines, LLMs achieve significantly lower regret solutions while requiring fewer test function evaluations. However, we also observe that LLMs exhibit moderate miscalibration, are susceptible to generator collapse, and have difficulty finding the optimal solution when no explicit ground truth rewards are available., Comment: Supercedes arXiv:2407.00236v1

The advent of wurtzite ferroelectrics is enabling a new generation of ferroelectric devices for computer memory that has the potential to bypass the von Neumann bottleneck, due to their robust polarization and silicon compatibility. However, the microscopic switching mechanism of wurtzites is still undetermined due to the limitations of density functional theory simulation size and experimental temporal and spatial resolution. Thus, physics-informed materials engineering to reduce coercive field and breakdown in these devices has been limited. Here, the atomistic mechanism of domain wall migration and domain growth in wurtzites is uncovered using molecular dynamics and Monte Carlo simulations of aluminum nitride. We reveal the anomalous switching mechanism of fast 1D single columns of atoms propagating from a slow-moving 2D fractal-like domain wall. We find that the critical nucleus in wurtzites is a single aluminum ion that breaks its bond with one nitrogen and bonds to another nitrogen; this creates a cascade that only flips atoms directly in the same column, due to the extreme locality (sharpness) of the domain walls in wurtzites. We further show how the fractal shape of the domain wall in the 2D plane breaks assumptions in the KAI model and leads to the anomalously fast switching in wurtzite structured ferroelectrics.

Scanning objects with a more tightly focused beam (for example of photons or electrons) can provide higher-resolution images. However the stronger localisation of energy deposition can damage tissues in organic samples or may rearrange the chemical structure or physical properties of inorganic materials. Scanning an object with a broad beam can deliver an equivalent probe energy but spreads it over a much wider footprint. Sharp images can be reconstructed from the diffuse implanted signal when a decoding step can recover a delta-like impulse response. Huffman sequences, by design, have the optimal delta-like autocorrelation for aperiodic (non-cyclic) convolution and are well-conditioned. Here we adapt 1D Huffman sequences to design 2D Huffman-like discrete arrays that have spatially broad, relatively thin and uniform intensity profiles that retain excellent aperiodic autocorrelation metrics. Examples of broad shaped diffuse beams were developed for the case of x-ray imaging. A variety of masks were fabricated by the deposition of finely structured layers of tantalum on a silicon oxide wafer. The layers form a pattern of discrete pixels that modify the shape of an incident uniform beam of low energy x-rays as it passes through the mask. The intensity profiles of the x-ray beams after transmission through these masks were validated, first by acquiring direct-detector x-ray images of the masks, and second by raster scanning a pinhole over each mask pattern, pixel-by-pixel, collecting "bucket" signals as applied in traditional ghost imaging. The masks were then used to raster scan the shaped x-ray beam over several simple binary and "gray" test objects, again producing bucket signals, from which sharp reconstructed object images were obtained by deconvolving their bucket images.

Though Cliffords and matchgates are both examples of classically simulable circuits, they are considered simulable for different reasons. While the simulability of Clifford conjugated matchgate circuits for single qubit outputs has been briefly considered, the simulability of Clifford and matchgate hybrid circuits has not been generalized up to this point. In this paper we resolve this, studying simulability of marginals as well as Pauli expectation values of Clifford and matchgate hybrid circuits. We describe a hierarchy of Clifford circuits, and find that as we consider more general Cliffords, we lose some amount of simulability of bitstring outputs. Most importantly, we show that the known simulability of Pauli expectation values of Clifford circuits acting on product states can be generalized to Clifford circuits acting after any matchgate circuit. We conclude with some general discussion about the relationship between Cliffords and matchgates, and argue that we can understand stabilizer states as the vacuum states of particular fermion-to-qubit encodings., Comment: 24 pages, 4 figures

Self-interaction error (SIE), arising from the imperfect cancellation of the spurious classical Coulomb interaction between an electron and itself, is a persistent challenge in modern density functional approximations. This issue is illustrated using the prototypical one-electron system $H_2^+$. While significant efforts have been made to eliminate SIE through the development of computationally expensive nonlocal density functionals, it is equally important to explore whether SIE can be mitigated within the framework of more efficient semilocal density functionals. In this study, we present a non-empirical meta-generalized gradient approximation (meta-GGA) that incorporates the Laplacian of the electron density. Our results demonstrate that the meta-GGA significantly reduces SIE, yielding a binding energy curve for $H_2^+$ that matches the exact solution at equilibrium and improves across a broad range of bond lengths over those of the Perdew-Burke-Ernzerhof (PBE) and strongly-constrained and appropriately-normed (SCAN) semilocal density functionals. This advancement paves the way for further development within the realm of semilocal approximations.

This study presents a comprehensive analysis of the infrared (IR) luminosity functions (LF) of star-forming (SF) galaxies and active galactic nuclei (AGN) using data from the ZFOURGE survey. We employ CIGALE to decompose the spectral energy distribution (SED) of galaxies into SF and AGN components to investigate the co-evolution of these processes at higher redshifts and fainter luminosities. Our CIGALE-derived SF and AGN LFs are generally consistent with previous studies, with an enhancement at the faint end of the AGN LFs. We attribute this to CIGALE's capability to recover low-luminosity AGN more accurately, which may be underrepresented in other works. As anticipated, the CIGALE SF LFs are best fit with a Schechter function, whereas the AGN LFs align more closely with a Saunders function. We find evidence for a significant evolutionary epoch for AGN activity at $z \approx 1$, comparable to the peak of cosmic star formation at $z \approx 2$, which we also recover well. Based on our results, the gas supply in the early universe favoured the formation of brighter star-forming galaxies until $z=2$, below which the gas for SF becomes increasingly exhausted. Conversely, AGN activity peaked earlier and declined more slowly until $z \approx 1$, suggesting a possible feedback scenario in which $2.5-3$ Gyrs offset the evolution of SF and AGN activity., Comment: 16 pages, 7 figures, 8 tables. Submitted for publication in PASA

The isotopic compositions of samples returned from Cb-type asteroid Ryugu and Ivuna-type (CI) chondrites are distinct from other carbonaceous chondrites, which has led to the suggestion that Ryugu and CI chondrites formed in a different region of the accretion disk, possibly around the orbits of Uranus and Neptune. We show that, like for Fe, Ryugu and CI chondrites also have indistinguishable Ni isotope anomalies, which differ from those of other carbonaceous chondrites. We propose that this unique Fe and Ni isotopic composition reflects different accretion efficiencies of small FeNi metal grains among the carbonaceous chondrite parent bodies. The CI chondrites incorporated these grains more efficiently, possibly because they formed at the end of the disk's lifetime, when planetesimal formation was also triggered by photoevaporation of the disk. Isotopic variations among carbonaceous chondrites may thus reflect fractionation of distinct dust components from a common reservoir, implying CI chondrites and Ryugu may have formed in the same region of the accretion disk as other carbonaceous chondrites., Comment: Published open access in Science Advances

The Jordan--Wigner transformation permits one to convert spin $1/2$ operators into spinless fermion ones, or vice versa. In some cases, it transforms an interacting spin Hamiltonian into a noninteracting fermionic one which is exactly solved at the mean-field level. Even when the resulting fermionic Hamiltonian is interacting, its mean-field solution can provide surprisingly accurate energies and correlation functions. Jordan--Wigner is, however, only one possible means of interconverting spin and fermionic degrees of freedom. Here, we apply several such techniques to the XXZ and $J_1\text{--}J_2$ Heisenberg models, as well as to the pairing or reduced BCS Hamiltonian, with the aim of discovering which of these mappings is most useful in applying fermionic mean-field theory to the study of spin Hamiltonians., Comment: Submittted to J Chem Phys

Polycyclic aromatic hydrocarbons (PAHs) are among the most ubiquitous compounds in the universe, accounting for up to ~25% of all interstellar carbon. Since most unsubstituted PAHs do not possess permanent dipole moments, they are invisible to radio astronomy. Constraining their abundances relies on the detection of polar chemical proxies, such as aromatic nitriles. We report the detection of 2- and 4-cyanopyrene, isomers of the recently detected 1-cyanopyrene. We find that these isomers are present in an abundance ratio of ~2:1:2, which mirrors the number of equivalent sites available for CN addition. We conclude that there is evidence that the cyanopyrene isomers formed by direct CN addition to pyrene under kinetic control in hydrogen-rich gas at 10 K and discuss constraints on the H/CN ratio for PAHs in TMC-1., Comment: Submitted version to comply with licensing agreements

Polycyclic aromatic hydrocarbons (PAHs) are expected to be the most abundant class of organic molecules in space. Their interstellar lifecycle is not well understood, and progress is hampered by difficulties detecting individual PAH molecules. Here, we present the discovery of CN-functionalized pyrene, a 4-ring PAH, in the dense cloud TMC-1 using the 100-m Green Bank Telescope. We derive an abundance of 1-cyanopyrene of ~1.52 x $10^{12}$ cm$^{-2}$, and from this estimate that the un-substituted pyrene accounts for up to ~0.03-0.3% of the carbon budget in the dense interstellar medium which trace the birth sites of stars and planets. The presence of pyrene in this cold (~10 K) molecular cloud agrees with its recent measurement in asteroid Ryugu where isotopic clumping suggest a cold, interstellar origin. The direct link to the birth site of our solar system is strengthened when we consider the solid state pyrene content in the pre-stellar materials compared to comets, which represent the most pristine material in the solar system. We estimate that solid state pyrene can account for 1% of the carbon within comets carried by this one single organic molecule. The abundance indicates pyrene is an "island of stability" in interstellar PAH chemistry and suggests a potential cold molecular cloud origin of the carbon carried by PAHs that is supplied to forming planetary systems, including habitable worlds such as our own., Comment: Version of manuscript revised to comply with licensing requirements

Non-invasive imaging of microwave (MW) magnetic fields with microscale lateral resolution is pivotal for various applications, such as MW technologies and integrated circuit failure analysis. Diamond nitrogen-vacancy (NV) center magnetometry has emerged as an ideal tool, offering $\mu$m-scale resolution, millimeter-scale field of view, high sensitivity, and non-invasive imaging compatible with diverse samples. However, up until now, it has been predominantly used for imaging of static or low-frequency magnetic fields or, concerning MW field imaging, to directly characterize the same microwave device used to drive the NV spin transitions. In this work we leverage an NV center ensemble in diamond for wide-field imaging of MW magnetic fields generated by a test device employing a differential measurement protocol. The microscope is equipped with a MW loop to induce Rabi oscillations between NV spin states, and the MW field from the device-under-test is measured through local deviations in the Rabi frequency. This differential protocol yields magnetic field maps of a 2.57 GHz MW field with a sensitivity of $\sim$ 9 $\mu$T Hz$^{-1/2}$ for a total measurement duration of $T = 357$ s, covering a $340\times340$ $\mu$m$^2$ field of view with a $\mu$m-scale spatial resolution and a DUT input power dynamic range of 30 dB. This work demonstrates a novel NV magnetometry protocol, based on differential Rabi frequency measurement, that extends NV wide-field imaging capabilities to imaging of weak MW magnetic fields that would be difficult to measure directly through standard NV Rabi magnetometry.

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.

A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures which aim to learn specialized modules dedicated to structures in a task that can be composed to solve novel problems with similar structures. While the compositionality of these architectures is guaranteed by design, the modules specializing is not. Here we theoretically study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization. To this end we introduce a minimal space of datasets motivated by practical systematic generalization benchmarks. From this space of datasets we present a mathematical definition of systematicity and study the learning dynamics of linear neural modules when solving components of the task. Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity. Finally, we confirm that the theoretical results in our tractable setting generalize to more complex datasets and non-linear architectures., Comment: The Eleventh International Conference on Learning Representations 2023

Tight-binding models provide great insight and are a low-cost alternative to \emph{ab initio} methods for calculation of a material's electronic structure. These models are used to calculate optical responses, including nonlinear optical effects such as the shift current bulk photovoltaic effect. The validity of tight-binding models is often evaluated by comparing their band structures to those calculated with Density Functional Theory. However, we find that band structure agreement is a necessary but not sufficient condition for accurate optical response calculations. In this Letter, we compute the shift current response and dielectric tensor using a variety of tight-binding models of {MoS$_2$}, including both Slater-Koster and Wannier tight-binding models that treat the Mo $4d$ orbitals and/or S $3p$ orbitals. We also truncate hoppings in the Wannier function models to next-nearest neighbor, as is common in tight-binding methods, in order to gauge the effect on optical response. By examining discrepancies in energies and optical matrix elements, we determine the interpolation quality of the different tight-binding models and establish that agreement in both band structure and wavefunctions is required to accurately model optical response

Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models relies on their ability to build task specific representation, a process influenced by interactions among datasets, architectures, initialization strategies, and optimization algorithms. Prior studies highlight that different initializations can place networks in either a lazy regime, where representations remain static, or a rich/feature learning regime, where representations evolve dynamically. Here, we examine how initialization influences learning dynamics in deep linear neural networks, deriving exact solutions for lambda-balanced initializations-defined by the relative scale of weights across layers. These solutions capture the evolution of representations and the Neural Tangent Kernel across the spectrum from the rich to the lazy regimes. Our findings deepen the theoretical understanding of the impact of weight initialization on learning regimes, with implications for continual learning, reversal learning, and transfer learning, relevant to both neuroscience and practical applications., Comment: 10 pages, 8 figures

We demonstrate spectral shaping of entangled photons with a six-channel microring-resonator-based silicon photonic pulse shaper. Through precise calibration of thermal phase shifters in a microresonator-based pulse shaper, we demonstrate line-by-line phase control on a 3~GHz grid for two frequency-bin-entangled qudits, corresponding to Hilbert spaces of up to $6\times 6$ ($3\times 3$) dimensions for shared (independent) signal-idler filters. The pulse shaper's fine spectral resolution enables control of nanosecond-scale temporal features, which are observed by direct coincidence detection of biphoton correlation functions that show excellent agreement with theory. This work marks, to our knowledge, the first demonstration of biphoton pulse shaping using an integrated spectral shaper and holds significant promise for applications in quantum information processing.

As audio machine learning outcomes are deployed in societally impactful applications, it is important to have a sense of the quality and origins of the data used. Noticing that being explicit about this sense is not trivially rewarded in academic publishing in applied machine learning domains, and neither is included in typical applied machine learning curricula, we present a study into dataset usage connected to the top-5 cited papers at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). In this, we conduct thorough depth-first analyses towards origins of used datasets, often leading to searches that had to go beyond what was reported in official papers, and ending into unclear or entangled origins. Especially in the current pull towards larger, and possibly generative AI models, awareness of the need for accountability on data provenance is increasing. With this, we call on the community to not only focus on engineering larger models, but create more room and reward for explicitizing the foundations on which such models should be built., Comment: in Proceedings of the 21st International Conference on Content-based Multimedia Indexing, September 18-20 2024, Reykjavik, Iceland

In Canada's northern regions, linear disturbances such as roads, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population (Rangifer tarandus). To address the critical need for management of these disturbances, there is a strong emphasis on developing mapping approaches that accurately identify forest habitat fragmentation. The traditional approach is manually generating maps, which is time-consuming and lacks the capability for frequent updates. Instead, applying deep learning methods to multispectral satellite imagery offers a cost-effective solution for automated and regularly updated map production. Deep learning models have shown promise in extracting paved roads in urban environments when paired with high-resolution (<0.5m) imagery, but their effectiveness for general linear feature extraction in forested areas from lower resolution imagery remains underexplored. This research employs a deep convolutional neural network model based on the VGGNet16 architecture for semantic segmentation of lower resolution (10m) Sentinel-2 satellite imagery, creating precise multi-class linear disturbance maps. The model is trained using ground-truth label maps sourced from the freely available Alberta Institute of Biodiversity Monitoring Human Footprint dataset, specifically targeting the Boreal and Taiga Plains ecozones in Alberta, Canada. Despite challenges in segmenting lower resolution imagery, particularly for thin linear disturbances like seismic exploration lines that can exhibit a width of 1-3 pixels in Sentinel-2 imagery, our results demonstrate the effectiveness of the VGGNet model for accurate linear disturbance retrieval. By leveraging the freely available Sentinel-2 imagery, this work advances cost-effective automated mapping techniques for identifying and monitoring linear disturbance fragmentation.

The motion of domain walls is crucial for ferroelectric switching. Conventionally, the switching dynamics is believed to be determined by the motion of one or a few low-energy domain wall types of dominant population. Here, we challenge this conventional idea in multiple-order-parameter ferroelectrics. Using hafnia as example, we show that multiple-order-parameter nature not only provides various mobile domain walls and defect-like immobile domain walls, but also enables the domain wall reactions. In analogy with chemical reactions where substances react to form new substances, domain walls could also react to form other domain walls during switching. We identify several elementary domain wall reaction types including synthesis, decomposition, and exchange reactions. Domain walls are continually changed by these reactions during switching so that the switching behavior reflects the statistical average of many domain wall types with distinct mobility and stability. These reactions also lead to phenomenon like remanent nuclei and defect site nucleation that facilitate switching and lower coercive field. Finally, the concept of domain wall reaction is not limited to hafnia but can be generalized to any multiple-order-parameter ferroelectric. This work conceptualizes domain wall reaction, expands theory of ferroelectric switching, and suggests a practical way for defect engineering to control switching behavior.

We present the first parameterized analysis of a standard (1+1) Evolutionary Algorithm on a distribution of vertex cover problems. We show that if the planted cover is at most logarithmic, restarting the (1+1) EA every $O(n \log n)$ steps will find a cover at least as small as the planted cover in polynomial time for sufficiently dense random graphs $p > 0.71$. For superlogarithmic planted covers, we prove that the (1+1) EA finds a solution in fixed-parameter tractable time in expectation. We complement these theoretical investigations with a number of computational experiments that highlight the interplay between planted cover size, graph density and runtime.

We report on spectroscopy of the low-lying electronic transitions in $^{224}$Ra$^+$. The ion's low charge to mass ratio and convenient wavelengths make $^{224}$Ra$^+$ a promising optical clock candidate. We measured the frequencies of the the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{P}_{1/2}$ cooling transition, the $^2{S}_{1/2}\ $$\leftrightarrow$$\ ^2{D}_{5/2}$ clock transition, the $^2{D}_{3/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ electric dipole transition, and the $^2{D}_{5/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ cleanout transition. From these measurements we calculate the frequencies of the $^2{D}_{3/2}\ $$\leftrightarrow$$\ ^2{P}_{1/2}$ repump transition, the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{D}_{3/2}$ electric quadrupole transition, and the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ electric dipole transition., Comment: 5 pages, 8 figures

The filtering distribution captures the statistics of the state of a dynamical system from partial and noisy observations. Classical particle filters provably approximate this distribution in quite general settings; however they behave poorly for high dimensional problems, suffering weight collapse. This issue is circumvented by the ensemble Kalman filter which is an equal-weight interacting particle system. However, this finite particle system is only proven to approximate the true filter in the linear Gaussian case. In practice, however, it is applied in much broader settings; as a result, establishing its approximation properties more generally is important. There has been recent progress in the theoretical analysis of the algorithm, establishing stability and error estimates in non-Gaussian settings, but the assumptions on the dynamics and observation models rule out the unbounded vector fields that arise in practice and the analysis applies only to the mean field limit of the ensemble Kalman filter. The present work establishes error bounds between the filtering distribution and the finite particle ensemble Kalman filter when the model exhibits linear growth.

Blanco 1 is an $\approx 130\,\mathrm{Myr}$ open cluster located 240 pc from the Sun below the Galactic plane. Recent studies have reported the existence of diffuse tidal tails extending 50-60 pc from the cluster center based on the positions and velocities measured by Gaia. To independently assess the reality and extent of this structure, we used light curves generated from TESS full-frame images to search for photometric rotation periods of stars in and around Blanco 1. We detected rotation periods down to a stellar effective temperature of $\approx 3100\,\mathrm{K}$ in 347 of the 603 cluster member candidates for which we have light curves. For cluster members in the core and candidate members in the tidal tails, both within a temperature range of 4400 to 6200 K, 74% and 72% of the rotation periods are consistent with the single-star gyrochronological sequence, respectively. In contrast, a comparison sample of field stars yielded gyrochrone-consistent rotation periods for only 8.5% of stars. The tidal tail candidates' overall conformance to the core members' gyrochrone sequence implies that their contamination ratio is consistent with zero and < 0.33 at the $2\sigma$ level. This result confirms the existence of Blanco 1 tidal tails and doubles the number of Blanco 1 members for which there are both spatio-kinematic and rotation-based cluster membership verification. Extending the strategy of using TESS light curves for gyrochronology to other nearby young open clusters and stellar associations may provide a viable strategy for mapping out their dissolution and broadening the search for young exoplanets., Comment: 20 pages, 10 figures, 4 tables. Accepted by ApJ. Tables available as ancillary files in Astropy ECSV format

Many compact objects (black holes and neutron stars) exist in binaries. These binaries are normally discovered through their interactions, either from accretion as an X-ray binary or collisions as a gravitational wave source. However, the majority of compact objects in binaries should be non-interacting. Recently proposed discoveries have used radial velocities of a bright star (main sequence or evolved) that are indicative of a massive but dark companion, which is inferred to be a compact object. Unfortunately, this burgeoning new field has been hindered by false positives, including the ``Unicorn'' (V723 Mon) which was initially believed to be a red giant/black hole binary before being refuted. In this work, we investigate the evolution of stellar binary populations over time, using the binary evolution code COSMIC to simulate binary populations and determine the probability of a candidate object being either a ``true Unicorn'' (actual compact objects in binaries) or a false positive. We find that main sequence stars have a higher true Unicorn probability than red giants or naked helium stars (an exposed core of an evolved star), particularly if the companion is more massive and is >3 times less luminous than the MS star. We also find that a top-heavy initial mass function raises the true Unicorn probability further, that super-solar metallicity reduces the probability, and that most true Unicorns are found at periods <100 days. Finally, we find that a significant fraction of true Unicorns do not evolve into x-ray binaries during the age of the universe., Comment: 16 pages, 6 figures, submitted to MNRAS

Indoor photovoltaics (IPVs) provide an increasingly promising solution for powering Internet-of-Things smart devices, which has led to a surge in IPV research and the development of new IPV technologies. However, the diverse lighting scenarios adopted in IPV research pose unique challenges in characterization, reporting, and benchmarking, which may obscure genuine performance improvements and result in inaccurate conclusions due to characterization errors. Spectral variations among artificial light sources further complicate benchmarking. This study provides a comprehensive, quantitative analysis of these challenges, investigating them through experimental characterization of IPVs covering a broad performance parameter space, including c-Si, a-Si:H, perovskite, and organic devices. We reveal that many of these challenges can lead to unacceptable error levels. A particularly critical issue is the angular interplay among the light source, measuring device, and IPV device, which compromises accuracy under diffuse illumination. To address these challenges, we evaluate practical protocols to overcome angular issues and enable benchmarking against standardized spectral conditions. To facilitate the implementation of our findings, we provide comprehensive checklists for accurate IPV characterization, reporting, and benchmarking. We anticipate that our analyses and guidelines will stimulate further advancements in IPV technologies by ensuring reliable performance evaluation, thereby facilitating the realization of IPVs' full potential.

Most music widely consumed in Western Countries contains song lyrics, with U.S. samples reporting almost all of their song libraries contain lyrics. In parallel, social science theory suggests that personal values - the abstract goals that guide our decisions and behaviors - play an important role in communication: we share what is important to us to coordinate efforts, solve problems and meet challenges. Thus, the values communicated in song lyrics may be similar or different to those of the listener, and by extension affect the listener's reaction to the song. This suggests that working towards automated estimation of values in lyrics may assist in downstream MIR tasks, in particular, personalization. However, as highly subjective text, song lyrics present a challenge in terms of sampling songs to be annotated, annotation methods, and in choosing a method for aggregation. In this project, we take a perspectivist approach, guided by social science theory, to gathering annotations, estimating their quality, and aggregating them. We then compare aggregated ratings to estimates based on pre-trained sentence/word embedding models by employing a validated value dictionary. We discuss conceptually 'fuzzy' solutions to sampling and annotation challenges, promising initial results in annotation quality and in automated estimations, and future directions.

Introducing structural and/or chemical heterogeneity into otherwise ordered crystals can dramatically alter material properties. Lead-based relaxor ferroelectrics are a prototypical example, with decades of investigation having connected chemical and structural heterogeneity to their unique properties. While theory has pointed to the formation of an ensemble of ``slush''-like polar domains, the lack of direct, spatially resolved volumetric data comparable to simulations presents a significant challenge in measuring the spatial distribution and correlation of local chemistry and structure with the physics underlying relaxor behavior. Here, we address this challenge through three-dimensional volumetric characterization of the prototypical relaxor ferroelectric \ce{0.68Pb(Mg$_{1/3}$Nb$_{2/3}$)O3-0.32PbTiO$_3$} using multislice electron ptychography. Direct comparison with molecular dynamics simulations reveals the intimate relationship between the polar structure and unit-cell level charge imbalance induced by chemical disorder. Further, polar nanodomains are maintained through local correlations arising from residual short-range chemical order. Acting in concert with the chemical heterogeneities, it is also shown that compressive strain enhances out-of-plane correlations and ferroelectric-like order without affecting the in-plane relaxor-like structure. Broadly, these findings provide a pathway to enable detailed atomic scale understanding for hierarchical control of polar domains in relaxor ferroelectric materials and devices, and also present significant opportunities to tackle other heterogeneous systems using complementary theoretical and experimental studies.

Training Large Language Models (LLMs) incurs substantial data-related costs, motivating the development of data-efficient training methods through optimised data ordering and selection. Human-inspired learning strategies, such as curriculum learning, offer possibilities for efficient training by organising data according to common human learning practices. Despite evidence that fine-tuning with curriculum learning improves the performance of LLMs for natural language understanding tasks, its effectiveness is typically assessed using a single model. In this work, we extend previous research by evaluating both curriculum-based and non-curriculum-based learning strategies across multiple LLMs, using human-defined and automated data labels for medical question answering. Our results indicate a moderate impact of using human-inspired learning strategies for fine-tuning LLMs, with maximum accuracy gains of 1.77% per model and 1.81% per dataset. Crucially, we demonstrate that the effectiveness of these strategies varies significantly across different model-dataset combinations, emphasising that the benefits of a specific human-inspired strategy for fine-tuning LLMs do not generalise. Additionally, we find evidence that curriculum learning using LLM-defined question difficulty outperforms human-defined difficulty, highlighting the potential of using model-generated measures for optimal curriculum design.

Supervised operator learning centers on the use of training data, in the form of input-output pairs, to estimate maps between infinite-dimensional spaces. It is emerging as a powerful tool to complement traditional scientific computing, which may often be framed in terms of operators mapping between spaces of functions. Building on the classical random features methodology for scalar regression, this paper introduces the function-valued random features method. This leads to a supervised operator learning architecture that is practical for nonlinear problems yet is structured enough to facilitate efficient training through the optimization of a convex, quadratic cost. Due to the quadratic structure, the trained model is equipped with convergence guarantees and error and complexity bounds, properties that are not readily available for most other operator learning architectures. At its core, the proposed approach builds a linear combination of random operators. This turns out to be a low-rank approximation of an operator-valued kernel ridge regression algorithm, and hence the method also has strong connections to Gaussian process regression. The paper designs function-valued random features that are tailored to the structure of two nonlinear operator learning benchmark problems arising from parametric partial differential equations. Numerical results demonstrate the scalability, discretization invariance, and transferability of the function-valued random features method., Comment: 36 pages, 1 table, 9 figures. SIGEST version of SIAM J. Sci. Comput. Vol. 43 No. 5 (2021) pp. A3212-A3243, hence text overlap with arXiv:2005.10224

This research study uses hierarchical logistic regression to identify predictors of first-class STEM degree outcomes at a research-intensive Russell Group university in the UK between 2012 and 2022. By building a multivariate binary logistic model with random intercepts for different STEM degree subjects, we find that prior academic attainment, ethnicity, gender, socioeconomic status, age, and course duration are statistically significant predictors of achieving a first-class degree. By determining the odds ratios and average marginal effects of socio-demographic predictors, we find evidence for the existence of age, ethnicity, gender, and socioeconomic awarding gaps. The largest awarding gap exists between Black and White students, with Black students having 0.45 (95\% CI: 0.30-0.68) times the odds, and a 14\% lower probability, of achieving a first-class degree compared to White students, holding all other variables constant. Students who graduate from 4-year degrees are found to have, on average, a 27\% higher probability of achieving a first-class degree than students on 3-year degrees. Despite raw data suggesting that male students outperform female students, the multivariate hierarchical analysis revealed higher odds for female students after controlling for other factors and accounting for nested data structures. Analysis using year-specific average marginal effects indicates that awarding gaps have not significantly changed between 2012 and 2022. This research study provides a robust analytical framework for use by other departments and institutions aiming to identify and address awarding gaps., Comment: 20 pages

Autoencoders have found widespread application, in both their original deterministic form and in their variational formulation (VAEs). In scientific applications it is often of interest to consider data that are comprised of functions; the same perspective is useful in image processing. In practice, discretisation (of differential equations arising in the sciences) or pixellation (of images) renders problems finite dimensional, but conceiving first of algorithms that operate on functions, and only then discretising or pixellating, leads to better algorithms that smoothly operate between different levels of discretisation or pixellation. In this paper function-space versions of the autoencoder (FAE) and variational autoencoder (FVAE) are introduced, analysed, and deployed. Well-definedness of the objective function governing VAEs is a subtle issue, even in finite dimension, and more so on function space. The FVAE objective is well defined whenever the data distribution is compatible with the chosen generative model; this happens, for example, when the data arise from a stochastic differential equation. The FAE objective is valid much more broadly, and can be straightforwardly applied to data governed by differential equations. Pairing these objectives with neural operator architectures, which can thus be evaluated on any mesh, enables new applications of autoencoders to inpainting, superresolution, and generative modelling of scientific data., Comment: 56 pages, 25 figures

Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures., Comment: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective that directly optimizes the utility of the patient representation over the subsequent clinical outcome prediction. This meta-objective directly targets the usefulness of a representation generated from unlabeled clinical measurement forecast for later supervised tasks. The meta-learned can then be directly used in target risk prediction, and the limited available samples can be used for further fine-tuning the model performance. The effectiveness of our approach is tested on a real open source patient EHR dataset MIMIC-III. We are able to demonstrate that our attention-based patient state representation approach can achieve much better performance for predicting target risk with low resources comparing with both direct supervised learning and pretraining with all-observation trajectory forecast., Comment: NeurIPS 2020

This paper presents bounds, estimators, and signal design strategies for exploiting both known pilot resources and unknown data payload resources in time-of-arrival (TOA)-based positioning systems with orthogonal frequency-division multiplexing (OFDM) signals. It is the first to derive the Ziv-Zakai bound (ZZB) on TOA estimation for OFDM signals containing both known pilot and unknown data resources. In comparison to the Cramer-Rao bounds (CRBs) derived in prior work, this ZZB captures the low-signal-to-noise ratio (SNR) thresholding effects in TOA estimation and accounts for an unknown carrier phase. The derived ZZB is evaluated against CRBs and empirical TOA error variances. It is then evaluated on signals with resource allocations optimized for pilot-only TOA estimation, quantifying the performance gain over the best-case pilot-only signal designs. Finally, the positioning accuracy of maximum-likelihood and decision-directed estimators is evaluated on simulated low-Earth-orbit non-terrestrial-network channels and compared against their respective ZZBs.

Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of nanoparticle surface dynamics is challenging since it requires both high spatial and temporal resolution. Though ultrafast transmission electron microscopy (TEM) can achieve picosecond temporal resolution, it is limited to nanometer spatial resolution. On the other hand, with the high readout rate of new electron detectors, conventional TEM has the potential to visualize atomic structure with millisecond time resolutions. However, the need to limit electron dose rates to reduce beam damage yields millisecond images that are dominated by noise, obscuring structural details. Here we show that a newly developed unsupervised denoising framework based on artificial intelligence enables observations of metal nanoparticle surfaces with time resolutions down to 10 ms at moderate electron dose. On this timescale, we find that many nanoparticle surfaces continuously transition between ordered and disordered configurations. The associated stress fields can penetrate below the surface leading to defect formation and destabilization making the entire nanoparticle fluxional. Combining this unsupervised denoiser with electron microscopy greatly improves spatio-temporal characterization capabilities, opening a new window for future exploration of atomic-level structural dynamics in materials.

We demonstrate nonlocal modulation of entangled photons with truly distributed RF clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains sub-picosecond synchronization -- namely, drift less than $\sim$0.5 ps in a 5.5 km fiber over 30 min (fractionally only $\sim$2$\times$10$^{-8}$ of the total fiber delay) -- and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

In this article we establish two fundamental results for the sublevel set persistent homology for stationary processes indexed by the positive integers. The first is a strong law of large numbers for the persistence diagram (treated as a measure "above the diagonal" in the extended plane) evaluated on a large class of sets and functions$\unicode{x2014}$more than just continuous functions with compact support. We prove this result subject to only minor conditions that the sequence is ergodic and the tails of the marginals are not too heavy. The second result is a central limit theorem for the persistence diagram evaluated on the class of all step functions; this result holds as long as a $\rho$-mixing criterion is satisfied and the distributions of the partial maxima do not decay too slowly. Our results greatly expand those extant in the literature to allow for more fruitful use in statistical applications, beyond idealized settings. Examples of distributions and functions for which the limit theory holds are provided throughout., Comment: 30 pages, 1 figure. Versions 1 and 2 uploaded to arXiv should be disregarded as they contained an erroneous proof of the CLT, which has been corrected in Version 3

Relativistic exact two-component coupled-cluster calculations of molecular sensitivity factors for nuclear Schiff moments (NSMs) are reported. We focus on molecules containing heavy nuclei, especially octupole-deformed nuclei. Analytic relativistic coupled-cluster gradient techniques are used and serve as useful tools for identifying candidate molecules that sensitively probe for physics beyond the Standard Model in the hadronic sector. Notably, these tools enable straightforward ``black-box'' calculations. Two competing chemical mechanisms that contribute to the NSM are analyzed, illuminating the physics of ligand effects on NSM sensitivity factors.

White dwarf atmospheres are frequently polluted by material from their own planetary systems. Absorption features from Ca, Mg, Fe and other elements can provide unique insights into the provenance of this exoplanetary material, with their relative abundances being used to infer accretion of material with core- or mantle-like composition. Across the population of white dwarfs, the distribution of compositions reveals the prevalence of geological and collisional processing across exoplanetary systems. By predicting the distribution of compositions in three evolutionary scenarios, this work assesses whether they can explain current observations. We consider evolution in an asteroid belt analog, in which collisions between planetary bodies that formed an iron core lead to core- or mantle-rich fragments. We also consider layer-by-layer accretion of individual bodies, such that the apparent composition of atmospheric pollution changes during the accretion of a single body. Finally, we consider that compositional spread is due to random noise. We find that the distribution of Ca, Fe and Mg in a sample of 202 cool DZs is consistent with the random noise scenario, although 7 individual systems show strong evidence of core-mantle differentiation from additional elements and/or low noise levels. Future surveys which detect multiple elements in each of a few hundred white dwarfs, with well understood biases, have the potential to confidently distinguish between the three models., Comment: 20 pages, 13 figures. Accepted for publication in MNRAS

In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesian inference for large-scale inverse problems in science and engineering applications. The computational challenges we address with the proposed methodology are: (i) the need for repeated evaluations of expensive forward models; (ii) the potential existence of multiple modes; and (iii) the fact that gradient of, or adjoint solver for, the forward model might not be feasible. While existing Bayesian inference methods meet some of these challenges individually, we propose a framework that tackles all three systematically. Our approach builds upon the Fisher-Rao gradient flow in probability space, yielding a dynamical system for probability densities that converges towards the target distribution at a uniform exponential rate. This rapid convergence is advantageous for the computational burden outlined in (i). We apply Gaussian mixture approximations with operator splitting techniques to simulate the flow numerically; the resulting approximation can capture multiple modes thus addressing (ii). Furthermore, we employ the Kalman methodology to facilitate a derivative-free update of these Gaussian components and their respective weights, addressing the issue in (iii). The proposed methodology results in an efficient derivative-free sampler flexible enough to handle multi-modal distributions: Gaussian Mixture Kalman Inversion (GMKI). The effectiveness of GMKI is demonstrated both theoretically and numerically in several experiments with multimodal target distributions, including proof-of-concept and two-dimensional examples, as well as a large-scale application: recovering the Navier-Stokes initial condition from solution data at positive times., Comment: 42 pages, 10 figures

The robustness of the macroscopic quantum nature of a superconductor can be characterized by the superfluid stiffness, $\rho_s$, a quantity that describes the energy required to vary the phase of the macroscopic quantum wave function. In unconventional superconductors, such as cuprates, the low-temperature behavior of $\rho_s$ drastically differs from that of conventional superconductors due to quasiparticle excitations from gapless points (nodes) in momentum space. Intensive research on the recently discovered magic-angle twisted graphene family has revealed, in addition to superconducting states, strongly correlated electronic states associated with spontaneously broken symmetries, inviting the study of $\rho_s$ to uncover the potentially unconventional nature of its superconductivity. Here we report the measurement of $\rho_s$ in magic-angle twisted trilayer graphene (TTG), revealing unconventional nodal-gap superconductivity. Utilizing radio-frequency reflectometry techniques to measure the kinetic inductive response of superconducting TTG coupled to a microwave resonator, we find a linear temperature dependence of $\rho_s$ at low temperatures and nonlinear Meissner effects in the current bias dependence, both indicating nodal structures in the superconducting order parameter. Furthermore, the doping dependence shows a linear correlation between the zero temperature $\rho_s$ and the superconducting transition temperature $T_c$, reminiscent of Uemura's relation in cuprates, suggesting phase-coherence-limited superconductivity. Our results provide strong evidence for nodal superconductivity in TTG and put strong constraints on the mechanisms of these graphene-based superconductors.

We present radio observations of the galaxy cluster Abell S1136 at 888 MHz, using the Australian Square Kilometre Array Pathfinder radio telescope, as part of the Evolutionary Map of the Universe Early Science program. We compare these findings with data from the Murchison Widefield Array, XMM-Newton, the Wide-field Infrared Survey Explorer, the Digitised Sky Survey, and the Australia Telescope Compact Array. Our analysis shows the X-ray and radio emission in Abell S1136 are closely aligned and centered on the BCG, while the X-ray temperature profile shows a relaxed cluster with no evidence of a cool core. We find that the diffuse radio emission in the centre of the cluster shows more structure than seen in previous low-resolution observations of this source, which appeared formerly as an amorphous radio blob, similar in appearance to a radio halo; our observations show the diffuse emission in the Abell S1136 galaxy cluster contains three narrow filamentary structures visible at 888 MHz, between$\sim 80$ and 140 kpc in length; however the properties of the diffuse emission do not fully match that of a radio (mini-)halo or (fossil) tailed radio source., Comment: To appear in PASA

Transformers, and the attention mechanism in particular, have become ubiquitous in machine learning. Their success in modeling nonlocal, long-range correlations has led to their widespread adoption in natural language processing, computer vision, and time-series problems. Neural operators, which map spaces of functions into spaces of functions, are necessarily both nonlinear and nonlocal if they are universal; it is thus natural to ask whether the attention mechanism can be used in the design of neural operators. Motivated by this, we study transformers in the function space setting. We formulate attention as a map between infinite dimensional function spaces and prove that the attention mechanism as implemented in practice is a Monte Carlo or finite difference approximation of this operator. The function space formulation allows for the design of transformer neural operators, a class of architectures designed to learn mappings between function spaces, for which we prove a universal approximation result. The prohibitive cost of applying the attention operator to functions defined on multi-dimensional domains leads to the need for more efficient attention-based architectures. For this reason we also introduce a function space generalization of the patching strategy from computer vision, and introduce a class of associated neural operators. Numerical results, on an array of operator learning problems, demonstrate the promise of our approaches to function space formulations of attention and their use in neural operators.

In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in large language models. Using challenging Linguistic Olympiad puzzles, we evaluate (i) capabilities for in-context identification and generalisation of linguistic patterns in very low-resource or extinct languages, and (ii) abilities to follow complex task instructions. The LingOly benchmark covers more than 90 mostly low-resource languages, minimising issues of data contamination, and contains 1,133 problems across 6 formats and 5 levels of human difficulty. We assess performance with both direct accuracy and comparison to a no-context baseline to penalise memorisation. Scores from 11 state-of-the-art LLMs demonstrate the benchmark to be challenging, and models perform poorly on the higher difficulty problems. On harder problems, even the top model only achieved 38.7% accuracy, a 24.7% improvement over the no-context baseline. Large closed models typically outperform open models, and in general, the higher resource the language, the better the scores. These results indicate, in absence of memorisation, true multi-step out-of-domain reasoning remains a challenge for current language models., Comment: Oral presentation at NeurIPS 2024 Datasets and Benchmarks Track. 10 pages, 5 figures, 22 pages supplemental materials

Quantum entanglement is an essential feature of many-body systems that impacts both quantum information processing and fundamental physics. The growth of entanglement is a major challenge for classical simulation methods. In this work, we investigate the relationship between quantum entanglement and quantum simulation, showing that product-formula approximations can perform better for entangled systems. We establish a tighter upper bound for algorithmic error in terms of entanglement entropy and develop an adaptive simulation algorithm incorporating measurement gadgets to estimate the algorithmic error. This shows that entanglement is not only an obstacle to classical simulation, but also a feature that can accelerate quantum algorithms., Comment: 31 pages, 6 figures

This paper introduces a novel reconfigurable and power-efficient FPGA (Field-Programmable Gate Array) implementation of an operator splitting algorithm for Non-Terrestial Network's (NTN) relay satellites model predictive orientation control (MPC). Our approach ensures system stability and introduces an innovative reconfigurable bit-width FPGA-based optimization solver. To demonstrate its efficacy, we employ a real FPGA-In-the-Loop hardware setup to control simulated satellite dynamics. Furthermore, we conduct an in-depth comparative analysis, examining various fixed-point configurations to evaluate the combined system's closed-loop performance and power efficiency, providing a holistic understanding of the proposed implementation's advantages.