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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: 29 pages, 1 figure

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, 9 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, 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: 9 pages, 5 figures, 16 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.

Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown parameter to an element of a solution space, often the solution space for a differential equation; an observation operator mapping an element of the solution space to the data space; a noise model describing how noise pollutes the observations; and a prior model describing knowledge about the unknown parameter before the data is acquired. This paper is concerned with learning the prior model from data; in particular, learning the prior from multiple realizations of indirect data obtained through the noisy observation process. The prior is represented, using a generative model, as the pushforward of a Gaussian in a latent space; the pushforward map is learned by minimizing an appropriate loss function. A metric that is well-defined under empirical approximation is used to define the loss function for the pushforward map to make an implementable methodology. Furthermore, an efficient residual-based neural operator approximation of the forward model is proposed and it is shown that this may be learned concurrently with the pushforward map, using a bilevel optimization formulation of the problem; this use of neural operator approximation has the potential to make prior learning from indirect data more computationally efficient, especially when the observation process is expensive, non-smooth or not known. The ideas are illustrated with the Darcy flow inverse problem of finding permeability from piezometric head measurements.

The article presents a systematic study of the problem of conditioning a Gaussian random variable $\xi$ on nonlinear observations of the form $F \circ \phi(\xi)$ where $\phi: \mathcal{X} \to \mathbb{R}^N$ is a bounded linear operator and $F$ is nonlinear. Such problems arise in the context of Bayesian inference and recent machine learning-inspired PDE solvers. We give a representer theorem for the conditioned random variable $\xi \mid F\circ \phi(\xi)$, stating that it decomposes as the sum of an infinite-dimensional Gaussian (which is identified analytically) as well as a finite-dimensional non-Gaussian measure. We also introduce a novel notion of the mode of a conditional measure by taking the limit of the natural relaxation of the problem, to which we can apply the existing notion of maximum a posteriori estimators of posterior measures. Finally, we introduce a variant of the Laplace approximation for the efficient simulation of the aforementioned conditioned Gaussian random variables towards uncertainty quantification.

Among the bent tail radio galaxies common in galaxy clusters are some with long, collimated tails (so-called head-tail galaxies) shaped by their interactions with the intracluster medium (ICM). Here we report the discovery of intricate filamentary structure in and beyond the ~28' (570 kpc) long, helical radio tail of the Corkscrew Galaxy (1610-60.5, ESO137-G007), which resides in the X-ray bright cluster Abell 3627 (D = 70 Mpc). Deep radio continuum data were obtained with wide-field Phased Array Feeds on the Australian Square Kilometer Array Pathfinder (ASKAP) at 944 MHz and 1.4 GHz. The Corkscrew Galaxy is located 15' north of the prominent wide-angle tail (WAT) radio galaxy 1610-60.8 (ESO137-G006) near the cluster centre. While the bright (young) part of its radio tail is highly collimated, the faint (old) part shows increasing oscillation amplitudes, break-ups, and filaments. We find a stunning set of arc-shaped radio filaments beyond and mostly orthogonal to the collimated Corkscrew tail end, forming a partial bubble. This may be the first detection of a "proto-lobe" seen in 3D MHD simulations by Nolting et al. (2019), formed by the face-on impact of the Corkscrew Galaxy with a shock front in the cluster outskirts. Interactions of the radio galaxy tail with the ICM are likely responsible for the tail collimation and shear forces within the ICM for its increasingly filamentary structure. We also report the discovery of small (~20-30 kpc) ram-pressure stripped radio tails in four Abell 3627 cluster galaxies., Comment: 11 pages, 7 figures, MNRAS, submitted

We report the ASKAP discovery of a new Galactic supernova remnant (SNR) candidate G308.73+1.38, which we name Raspberry. This new SNR candidate has an angular size of 20.7 arcmin $\times$ 16.7 arcmin, and we measure a total integrated flux of 407$\pm$50 mJy. We estimate Raspberry's most likely diameter of 10$-$30 pc which would place it at a distance of 3$-$5 kpc, on the near side of the Milky Way's Scutum$-$Centaurus Arm. We also find a Stokes$-$V point source close to the centre of Raspberry with a $\sim$5$\sigma$ significance. This point source may be the remaining compact source, a neutron star, or possibly a pulsar, formed during the initial supernova event., Comment: 3 pages, 1 figure

Operator learning is a variant of machine learning that is designed to approximate maps between function spaces from data. The Fourier Neural Operator (FNO) is a common model architecture used for operator learning. The FNO combines pointwise linear and nonlinear operations in physical space with pointwise linear operations in Fourier space, leading to a parameterized map acting between function spaces. Although FNOs formally involve convolutions of functions on a continuum, in practice the computations are performed on a discretized grid, allowing efficient implementation via the FFT. In this paper, the aliasing error that results from such a discretization is quantified and algebraic rates of convergence in terms of the grid resolution are obtained as a function of the regularity of the input. Numerical experiments that validate the theory and describe model stability are performed.

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes which affects code performance and ease of physical realization. For device architectures restricted to 2D local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate bicycle qLDPC codes and show that they are well suited for a parallel syndrome measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes bivariate bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits., Comment: 16 pages, 10 figures

Ferroelectric hafnia exhibits promising robust polarization and silicon compatibility for ferroelectric devices. Unfortunately, it suffers from difficult polarization switching. Methods to enable easier polarization switching are needed, and the underlying reason for this switching difficulty is not understood. Here, we investigated the 180$^\circ$ domain walls of hafnia and their motion through nucleation. We found that the domains of multiple-order parameter hafnia possess complicated three-dimensional dipole patterns and lead to domain walls of different symmetry. The most common domain wall type is a complex domain wall involving reversal of both polarization and tetragonality order parameters. This domain wall symmetry ensures a good matching of the dipoles perpendicular to the domain wall, which leads to low domain wall energy. However, this ensures a sharp, high energy, charged domain wall on the edges of nuclei that results in difficult nucleation. Thus, this domain wall is too stable to move, which explains the switching difficulty of hafnia. By contrast, another simple domain wall, involving only polarization reversal, has a poor matching of dipoles perpendicular to the domain wall. This leads to higher domain wall energy and ensures a diffusive and low energy charged domain wall that enables easier nucleation. This simple domain wall is thus not too stable and easier to move. Our theory advances domain wall nucleation theory from the field of conventional single-order parameter to multiple-order parameters. We propose controlling the populations of different domain wall types in hafnia as a way to enable fast polarization switching and lower coercive fields.

Vibrational control (VC) of photochemistry through the optical stimulation of structural dynamics is a nascent concept only recently demonstrated for model molecules in solution. Extending VC to state-of-the-art materials may lead to new applications and improved performance for optoelectronic devices. Metal halide perovskites are promising targets for VC due to their mechanical softness and the rich array of vibrational motions of both their inorganic and organic sublattices. Here, we demonstrate the ultrafast VC of FAPbBr3 perovskite solar cells via intramolecular vibrations of the formamidinium cation using spectroscopic techniques based on vibrationally promoted electronic resonance. The observed short (~300 fs) time window of VC highlights the fast dynamics of coupling between the cation and inorganic sublattice. First-principles modelling reveals that this coupling is mediated by hydrogen bonds that modulate both lead halide lattice and electronic states. Cation dynamics modulating this coupling may suppress non-radiative recombination in perovskites, leading to photovoltaics with reduced voltage losses.

The diversity optimization is the class of optimization problems, in which we aim at finding a diverse set of good solutions. One of the frequently used approaches to solve such problems is to use evolutionary algorithms which evolve a desired diverse population. This approach is called evolutionary diversity optimization (EDO). In this paper, we analyse EDO on a 3-objective function LOTZ$_k$, which is a modification of the 2-objective benchmark function (LeadingOnes, TrailingZeros). We prove that the GSEMO computes a set of all Pareto-optimal solutions in $O(kn^3)$ expected iterations. We also analyze the runtime of the GSEMO$_D$ (a modification of the GSEMO for diversity optimization) until it finds a population with the best possible diversity for two different diversity measures, the total imbalance and the sorted imbalances vector. For the first measure we show that the GSEMO$_D$ optimizes it asymptotically faster than it finds a Pareto-optimal population, in $O(kn^2\log(n))$ expected iterations, and for the second measure we show an upper bound of $O(k^2n^3\log(n))$ expected iterations. We complement our theoretical analysis with an empirical study, which shows a very similar behavior for both diversity measures that is close to the theory predictions.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community., Comment: 14 pages, 1 figure

We present the discovery of large radio shells around a massive pair of interacting galaxies and extended diffuse X-ray emission within the shells. The radio data were obtained with the Australian Square Kilometer Array Pathfinder (ASKAP) in two frequency bands centred at 944 MHz and 1.4 GHz, respectively, while the X-ray data are from the XMM-Newton observatory. The host galaxy pair, which consists of the early-type galaxies ESO 184-G042 and LEDA 418116, is part of a loose group at a distance of only 75 Mpc (redshift z = 0.017). The observed outer radio shells (diameter ~ 145 kpc) and ridge-like central emission of the system, ASKAP J1914-5433 (Physalis), are likely associated with merger shocks during the formation of the central galaxy (ESO 184-G042) and resemble the new class of odd radio circles (ORCs). This is supported by the brightest X-ray emission found offset from the centre of the Physalis system, instead centered at the less massive galaxy, LEDA 418116. The host galaxy pair is embedded in an irregular envelope of diffuse light, highlighting on-going interactions. We complement our combined radio and X-ray study with high-resolution simulations of the circumgalactic medium (CGM) around galaxy mergers from the Magneticum project to analyse the evolutionary state of the Physalis system. We argue that ORCs / radio shells could be produced by a combination of energy release from the central AGN and subsequent lightening up in radio emission by merger shocks traveling through the CGM of these systems., Comment: 12 pages, 8 figures, submitted to MNRAS

Double perovskites Cs$_2$Au$_2$X$_6$ (X=Cl, Br, I) are prototypical materials that exhibit charge disproportionation of gold into 1+ and 3+ states. It is known that the disproportionation is resolved under high pressures, and this has stimulated many studies into the pressurization of these materials. At present, the phase changes in these materials are still strongly contested. Here, we use density functional theory to study the pressure-dependent behavior of Cs$_2$Au$_2$X$_6$. We find that a tetragonal--cubic transition occurs directly from the ground state $I4/mmm$ structure. Even so, we also found an intermediate tetragonal $P4/mmm$ structure to be very close in energy, suggesting it to be observable. We also find several other competing metastable phases, which explains some of the controversies in the literature. Focusing on one of the metastable phases, we suggest that Cs$_2$Au$_2$X$_6$ can be prepared in a $P4_2/mnm$ structure, analogous to that of KCuF$_3$. The band gap in the $P4_2/mnm$ structure widened as atomic number of the halide was increased, which is the inverse trend compared to the ground state structure. We explain this by the different octahedral distortion ordering in the two structural phases. Furthermore, we show that the conduction band in $P4_2/mnm$ is three dimensionally connected, which is favorable for opto-electronic applications. We submit that this work demonstrates that octahedral distortion ordering is a promising avenue for developing new double perovskites and suggests it to be particular effective in tuning the electronic structure properties., Comment: 25 pages

The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models., Comment: In COLM 2024

Photoluminescence intermittency remains one of the biggest challenges to realizing perovskite quantum dots (QDs) as scalable single photon emitters. We compare CsPbBr3 QDs capped with different ligands, lecithin, and a combination of oleic acid and oleylamine, to elucidate the role of surface chemistry on photoluminescence intermittency. We employ widefield photoluminescence microscopy, sampling the blinking behavior of hundreds of QDs. Using change point analysis, we achieve the robust classification of blinking trajectories, and we analyze representative distributions from large numbers of QDs (Nlecithin = 1308, Noleic acid/oleylamine =1317). We find that lecithin suppresses blinking in CsPbBr3 QDs compared to oleic acid/oleylamine. Under common experimental conditions, lecithin-capped QDs are 7.5 times more likely to be non-blinking and spend 2.5 times longer in their most emissive state, despite both QDs having nearly identical solution photoluminescence quantum yields. We measure photoluminescence as a function of dilution and show that the differences between lecithin and oleic acid/oleylamine capping emerge at low concentrations during preparation for single particle experiments. From experiment and first principles calculations, we attribute the differences in lecithin and oleic acid/oleylamine performance to differences in their ligand binding equilibria. Consistent with our experimental data, density functional theory calculations suggest a stronger binding affinity of lecithin to the QD surface compared to oleic acid/oleylamine, implying a reduced likelihood of ligand desorption during dilution. These results suggest that using more tightly binding ligands is a necessity for surface passivation and consequently, blinking reduction in perovskite QDs used for single particle and quantum light experiments., Comment: 38 pages, 5 figures

In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-context learning -- the induction head (IH), which performs a match-and-copy operation. During training of large transformers on natural language data, IHs emerge around the same time as a notable phase change in the loss. Despite the robust evidence for IHs and this interesting coincidence with the phase change, relatively little is known about the diversity and emergence dynamics of IHs. Why is there more than one IH, and how are they dependent on each other? Why do IHs appear all of a sudden, and what are the subcircuits that enable them to emerge? We answer these questions by studying IH emergence dynamics in a controlled setting by training on synthetic data. In doing so, we develop and share a novel optogenetics-inspired causal framework for modifying activations throughout training. Using this framework, we delineate the diverse and additive nature of IHs. By clamping subsets of activations throughout training, we then identify three underlying subcircuits that interact to drive IH formation, yielding the phase change. Furthermore, these subcircuits shed light on data-dependent properties of formation, such as phase change timing, already showing the promise of this more in-depth understanding of subcircuits that need to "go right" for an induction head., Comment: 26 pages, 18 figures

Efficient spatiotemporal control of optical beams is of paramount importance in diverse technological domains. Conventional systems focusing on quasi-static beam control demand precise phase or wavelength tuning for steering. This work presents a time-efficient solution for dynamic beam steering, emphasizing high-duty-cycle operation with fast scan rates, and eliminating the need for active tuning of the beam direction. We achieve 100%-duty-cycle scans at a rate of $\sim$9.8 GHz within an angular range of $\sim$1$^\circ$. Furthermore, leveraging the dispersion characteristics of a virtually imaged phased array (VIPA), we devise a broadband source array that seamlessly transitions from continuous-angular steering to pulsed discrete-angular operation, unlocking possibilities for high-sensitivity angle-, range-, and time-resolved imaging. We also elucidate the adaptability of integrated photonic designs incorporating wavelength-selective switches and spectral dispersers, for enabling a versatile on-chip realization of the proposed beam steering schemes.

A key challenge towards future quantum internet technology is connecting quantum processors at metropolitan scale. Here, we report on heralded entanglement between two independently operated quantum network nodes separated by 10km. The two nodes hosting diamond spin qubits are linked with a midpoint station via 25km of deployed optical fiber. We minimize the effects of fiber photon loss by quantum frequency conversion of the qubit-native photons to the telecom L-band and by embedding the link in an extensible phase-stabilized architecture enabling the use of the loss-resilient single-photon entangling protocol. By capitalizing on the full heralding capabilities of the network link in combination with real-time feedback logic on the long-lived qubits, we demonstrate the delivery of a predefined entangled state on the nodes irrespective of the heralding detection pattern. Addressing key scaling challenges and being compatible with different qubit systems, our architecture establishes a generic platform for exploring metropolitan-scale quantum networks., Comment: 10 pages, 4 figures, supplementary materials

Accurate approximation of the exchange-correlation (XC) energy in density functional theory (DFT) calculations is essential for reliably modelling electronic systems. Many such approximations are developed from models of the XC hole; accurate reference XC holes for real electronic systems are crucial for evaluating the accuracy of these models however the availability of reliable reference data is limited to a few systems. In this study, we employ the Lieb optimization with a coupled cluster singles and doubles (CCSD) reference to construct accurate coupling-constant averaged XC holes, resolved into individual exchange and correlation components, for five spherically symmetric atoms: He, Li, Be, N, and Ne. Alongside providing a new set of reference data for the construction and evaluation of model XC holes, we compare our data against the exchange and correlation hole models of the established LDA and PBE density functional approximations. Our analysis confirms the established rationalization for the limitations of LDA and the improvement observed with PBE in terms of the hole depth and its long-range decay, demonstrated in real-space for the series of spherically-symmetric atoms.