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Apprenticeships remain an important source of skilled workers in Australia; however, commencements have not kept pace with the skills demand projected in the previous decade. During that same decade, undergraduate university enrolments grew considerably. In investigating whether young people who would previously have chosen an apprenticeship have instead entered university, this study uses data from the Longitudinal Surveys of Youth (LSAY) to compare the characteristics of apprenticeship-bound young people with those who are university-bound and to determine whether the characteristics have changed between 2007 and 2019.

The Cambodian government has been successful in raising girls' enrolment and academic achievement in recent years, with girls' enrolment reaching 100% in 2021 and girls outperforming boys in reading, writing and mathematics at Grade 5 level. While this is hugely encouraging for girls' education in the country, these statistics alone do not help us to understand the extent to which gender imbalances are being addressed and improved within the classroom. Therefore, together with their research partner, the Cambodia Development Resource Institute (CDRI), Education Development Trust (EDT) has undertaken research to explore this issue. Specifically, EDT wished to interrogate the question of whether schools and classrooms in Cambodia are simply gender-sensitive and aware of gender discrimination, or whether they are moving to actively root out and address gender imbalances--demonstrating aspects of gender-responsiveness. Key findings demonstrated that although progress has been made in moving towards gender-responsive approaches, more work remains to be done to move beyond practices that simply ensure parity but instead address underlying biases and safeguarding issues. School leaders, middle-tier officials, and ministry personnel also need to support school- and classroom-level level efforts to understand and implement gender-responsive pedagogy. This in turn highlights a clear need for improved teacher training and standardised, clearly defined policy and programming language.

The Sun's global inertial modes are very sensitive to the solar differential rotation and to properties of the deep solar convection zone which are currently poorly constrained. These properties include the superadiabatic temperature gradient, the latitudinal entropy gradient, and the turbulent viscosity. The inertial modes also play a key role in controlling the Sun's large-scale structure and dynamics, in particular the solar differential rotation. This paper summarizes recent observations and advances in the (linear and nonlinear) modeling of the solar inertial modes., Comment: Submitted on 05-Mar-2024 for publication in procs. IAU Symp 365, Eds. A. Getling and L. Kitchatinov

PSR J1618-3921 is one of five known millisecond pulsars (MSPs) in eccentric orbits (eMPSs) located in the Galactic plane, whose formation is poorly understood. Earlier studies of these objects revealed significant discrepancies between observation and predictions from standard binary evolution scenarios of pulsar-Helium white dwarf binaries. We conducted observations with the L-band receiver of the MeerKAT radio telescope and the UWL receiver of the Parkes Murriyang radio telescope between 2019 and 2021. These data were added to archival observations. We perform an analysis of this joint 23-year-dataset. We use the recent observations to give a brief account of the emission properties of J1618-3921, including a Rotating Vector model fit of the linear polarisation position angle of the pulsar. The long timing baseline allowed for a highly significant measurement of the rate of advance of periastron of $\dot{\omega}$. We can only report a low significance detection of the orthometric Shapiro delay parameters $h_3$ and $\varsigma$, leading to mass estimates of the total and individual binary masses. We detect an unexpected change in the orbital period of, which is an order of magnitude larger and carries an opposite sign to what is expected from Galactic acceleration and the Shklovskii effect. We also detect a significant second derivative of the spin frequency. Furthermore, we report an unexpected, abrupt change of the mean pulse profile in June 2021 with unknown origin. We propose that the anomalous $\dot{P_b}$ and $\ddot{f}$ indicate an additional varying acceleration due to a nearby mass, i.e., the J1618-3921 binary system is likely part of a hierarchical triple. This finding suggests that at least some eMSPs might have formed in triple star systems. Although the uncertainties are large, the binary companion mass is consistent with the $P_b$ - $M_{WD}$ relation., Comment: 17 pages, 11 figures, 3 tables

K2-24 is a planetary system composed of two transiting low-density Neptunians locked in an almost perfect 2:1 resonance and showing large TTVs, i.e., an excellent laboratory to search for signatures of planetary migration. Previous studies performed with K2, Spitzer and RV data tentatively claimed a significant non-zero eccentricity for one or both planets, possibly high enough to challenge the scenario of pure disk migration through resonant capture. With 13 new CHEOPS light curves (seven of planet -b, six of planet -c), we carried out a global photometric and dynamical (RV+TTV) re-analysis by including all the available literature data as well. We got the most accurate set of planetary parameters to date for the K2-24 system, including radii and masses at 1% and 5% precision (now essentially limited by the uncertainty on stellar parameters) and non-zero eccentricities $e_b=0.0498_{-0.0018}^{+0.0011}$, $e_c=0.0282_{-0.0007}^{+0.0003}$ detected at very high significance for both planets. Such relatively large values imply the need for an additional physical mechanism of eccentricity excitation during or after the migration stage. Also, while the accuracy of the previous TTV model had drifted by up to 0.5 days at the current time, we constrained the orbital solution firmly enough to predict the forthcoming transits for the next ~15 years, thus enabling an efficient follow-up with top-level facilities such as JWST or ESPRESSO., Comment: 19 pages, 11 figures, 8 tables. Accepted for publication in A&A on September 4, 2024

The Wide Angle Search for Planets (WASP) survey used transit photometry to discover nearly 200 gas-giant exoplanets and derive their planetary and stellar parameters. Reliable determination of the planetary density depends on accurate measurement of the planet's radius, obtained from the transit depth and photodynamical determination of the stellar radius. The stellar density, and hence the stellar radius are typically determined in a model-independent way from the star's reflex orbital acceleration and the transit profile. Additional flux coming from the system due to a bright, undetected stellar binary companion can, however, potentially dilute the transit curve and radial velocity signal, leading to under-estimation of the planet's mass and radius, and to overestimation of the planet's density. In this study, we cross-check the published radii of all the WASP planet host stars, determined from their transit profiles and radial-velocity curves, against radiometric measurements of stellar radii derived from their angular diameters (via the Infrared Flux method) and trigonometric parallaxes. We identify eight systems showing radiometric stellar radii significantly greater than their published photodynamical values: WASPs 20, 85, 86, 103, 105, 129, 144 and 171. We investigate these systems in more detail to establish plausible ranges of angular and radial-velocity separations within which such "stealth binaries" could evade detection, and deduce their likely orbital periods, mass ratios, and flux ratios. After accounting for the dilution of transit depth and radial velocity amplitude, we find that on average, the planetary densities for the identified stealth binary systems should be reduced by a factor of 1.3.

$\require{mediawiki-texvc}$Cosmic Reionisation commenced when ultraviolet (UV) radiation produced in the first galaxies began illuminating the cold, neutral gas that filled the primordial Universe. Recent James Webb Space Telescope (JWST) observations have shown that surprisingly UV-bright galaxies were in place beyond redshift $z = 14$, when the Universe was less than 300 Myr old. Smooth turnovers of their UV continua have been interpreted as damping-wing absorption of Lyman-$\alpha$ (Ly$\alpha$), the principal hydrogen transition. However, spectral signatures encoding crucial properties of these sources, such as their emergent radiation field, largely remain elusive. Here we report spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES) of a galaxy at redshift $z = 13.0$ that reveal a singular, bright emission line unambiguously identified as Ly$\alpha$, in addition to a smooth turnover. We observe an equivalent width of $\text{EW}_\mathrm{Ly\alpha} > 40 \, \AA$ (rest frame), previously only seen at $z < 9$ where the intervening intergalactic medium (IGM) becomes increasingly ionised. Together with a very blue UV continuum, the Ly$\alpha$ line indicates the galaxy is a prolific producer of ionising photons, a significant fraction of which may escape. This suggests it resides in an early reionised region preventing complete extinction of Ly$\alpha$, thus shedding new light on the nature of the earliest galaxies and the onset of Reionisation only 330 Myr after the Big Bang., Comment: 19 pages, 9 figures, 3 tables. Comments welcome

Point-source transient events (PSTEs) - optical events that are both extremely fast and extremely small - pose several challenges to an imaging system. Due to their speed, accurately characterizing such events often requires detectors with very high frame rates. Due to their size, accurately detecting such events requires maintaining coverage over an extended field-of-view, often through the use of imaging focal plane arrays (FPA) with a global shutter readout. Traditional imaging systems that meet these requirements are costly in terms of price, size, weight, power consumption, and data bandwidth, and there is a need for cheaper solutions with adequate temporal and spatial coverage. To address these issues, we develop a novel compressed sensing algorithm adapted to the rolling shutter readout of an imaging system. This approach enables reconstruction of a PSTE signature at the sampling rate of the rolling shutter, offering a 1-2 order of magnitude temporal speedup and a proportional reduction in data bandwidth. We present empirical results demonstrating accurate recovery of PSTEs using measurements that are spatially undersampled by a factor of 25, and our simulations show that, relative to other compressed sensing algorithms, our algorithm is both faster and yields higher quality reconstructions. We also present theoretical results characterizing our algorithm and corroborating simulations. The potential impact of our work includes the development of much faster, cheaper sensor solutions for PSTE detection and characterization., Comment: 20 pages, 11 figures

Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have been applied to build interatomic potential from ab initio results. Here, We report an energy-free machine-learning calculator that combines three individually trained neural networks to predict the energy and atomic forces of particulate matter. Three structures were investigated: a monometallic nanoparticle, a bimetallic nanoalloy, and a supported metal crystallites. Atomic energies were predicted via a graph neural network, leading to a mean absolute error (MAE) within 0.004 eV from Density Functional Theory (DFT) calculations. The task of predicting atomic forces was split over two feedforward networks, one predicting the force's norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/A against DFT results. The interpretability of the graph neural network predictions was demonstrated by underlying the physics of the monometallic particle in the form of cohesion energy.

Current observational evidence reveals that fast radio bursts (FRBs) exhibit bandwidths ranging from a few dozen MHz to several GHz. Traditional FRB searches primarily employ matched filter methods on time series collapsed across the entire observational bandwidth. However, with modern ultra-wideband receivers featuring GHz-scale observational bandwidths, this approach may overlook a significant number of events. We investigate the efficacy of sub-banded searches for FRBs, a technique seeking bursts within limited portions of the bandwidth. These searches aim to enhance the significance of FRB detections by mitigating the impact of noise outside the targeted frequency range, thereby improving signal-to-noise ratios. We conducted a series of Monte Carlo simulations, for the $400$-MHz bandwidth Parkes 21-cm multi-beam (PMB) receiver system and the Parkes Ultra-Wideband Low (UWL) receiver, simulating bursts down to frequency widths of about $100$\,MHz. Additionally, we performed a complete reprocessing of the high-latitude segment of the High Time Resolution Universe South survey (HTRU-S) of the Parkes-Murriyang telescope using sub-banded search techniques. Simulations reveal that a sub-banded search can enhance the burst search efficiency by $67_{-42}^{+133}$ % for the PMB system and $1433_{-126}^{+143}$ % for the UWL receiver. Furthermore, the reprocessing of HTRU led to the confident detection of eighteen new bursts, nearly tripling the count of FRBs found in this survey. These results underscore the importance of employing sub-banded search methodologies to effectively address the often modest spectral occupancy of these signals., Comment: Accepted for publication (A&A)

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. We sharpen prior work, proving that accurate recovery in the Wasserstein distance is possible with more noise than previously known. As a main application, our result yields a simple "linear query" algorithm for constructing a differentially private synthetic data distribution with Wasserstein-1 error $\tilde{O}(1/n)$ based on a dataset of $n$ points in $[-1,1]$. This bound is optimal up to log factors and matches a recent breakthrough of Boedihardjo, Strohmer, and Vershynin [Probab. Theory. Rel., 2024], which uses a more complex "superregular random walk" method to beat an $O(1/\sqrt{n})$ accuracy barrier inherent to earlier approaches. We illustrate a second application of our new moment-based recovery bound in numerical linear algebra: by improving an approach of Braverman, Krishnan, and Musco [STOC 2022], our result yields a faster algorithm for estimating the spectral density of a symmetric matrix up to small error in the Wasserstein distance.

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don't fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.

In order to tackle the problem of sampling from heavy tailed, high dimensional distributions via Markov Chain Monte Carlo (MCMC) methods, Yang, Latuszy\'nski, and Roberts (2022) (arXiv:2205.12112) introduces the stereographic projection as a tool to compactify $\mathbb{R}^d$ and transform the problem into sampling from a density on the unit sphere $\mathbb{S}^d$. However, the improvement in algorithmic efficiency, as well as the computational cost of the implementation, are still significantly impacted by the parameters used in this transformation. To address this, we introduce adaptive versions of the Stereographic Random Walk (SRW), the Stereographic Slice Sampler (SSS), and the Stereographic Bouncy Particle Sampler (SBPS), which automatically update the parameters of the algorithms as the run progresses. The adaptive setup allows us to better exploit the power of the stereographic projection, even when the target distribution is neither centered nor homogeneous. We present a simulation study showing each algorithm's robustness to starting far from the mean in heavy tailed, high dimensional settings, as opposed to Hamiltonian Monte Carlo (HMC). We establish a novel framework for proving convergence of adaptive MCMC algorithms over collections of simultaneously uniformly ergodic Markov operators, including continuous time processes. This framework allows us to prove LLNs and a CLT for our adaptive Stereographic algorithms., Comment: 49 pages, 11 figures

This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein.

Single-shot quantum error correction has the potential to speed up quantum computations by removing the need for multiple rounds of syndrome extraction in order to be fault-tolerant. Using Quantinuum's H2 trapped-ion quantum computer, we implement the [[33,1,4]] 4D surface code and perform the first experimental demonstration of single-shot quantum error correction with bare ancilla qubits. We conduct memory experiments comparing the 2D and 4D surface codes and find that despite differences in qubit use and syndrome extraction circuit depth, the 4D surface code matches or outperforms the 2D surface code in both the fault-tolerant and single-shot regimes.

Let $\lambda(n)$ be the Liouville function. We study the distribution of \[ \frac{1}{x^{1/2}}\sum_{x\leq n\leq 2x}\lambda(f(n)) \] over random polynomials $f$ of fixed degree $d$ and coefficients bounded in magnitude by $H$. In particular we prove that the first $d+1$ moments are Gaussian., Comment: Comments welcome

The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles, which can be programmed to mitigate stop-and-go traffic through intelligent speed commands and, thus, reduce emissions. But would such dynamic eco-driving move the needle on climate change? A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. We address this challenge with large-scale scenario modeling efforts and by using multi-task deep reinforcement learning with a carefully designed network decomposition strategy. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities, simulating a million traffic scenarios. Overall, we find that vehicle trajectories optimized for emissions can cut city-wide intersection carbon emissions by 11-22%, without harming throughput or safety, and with reasonable assumptions, equivalent to the national emissions of Israel and Nigeria, respectively. We find that 10% eco-driving adoption yields 25%-50% of the total reduction, and nearly 70% of the benefits come from 20% of intersections, suggesting near-term implementation pathways. However, the composition of this high-impact subset of intersections varies considerably across different adoption levels, with minimal overlap, calling for careful strategic planning for eco-driving deployments. Moreover, the impact of eco-driving, when considered jointly with projections of vehicle electrification and hybrid vehicle adoption remains significant. More broadly, this work paves the way for large-scale analysis of traffic externalities, such as time, safety, and air quality, and the potential impact of solution strategies., Comment: In review

The advent of diffraction limited sources and developments in detector technology opens up new possibilities for the study of materials in situ and operando. Coherent X-ray diffraction techniques such as coherent X-ray diffractive imaging (CXDI) and X-ray photon correlation spectroscopy (XPCS) are capable for this purpose and provide complimentary information, although due to signal-to-noise requirements, their simultaneous demonstration has been limited. Here, we demonstrate a strategy for the simultaneous use of CXDI and XPCS to study in situ the Brownian motion of colloidal gold nanoparticles of 200 nm diameter suspended in a glycerol-water mixture. We visualise the process of agglomeration, examine the spatiotemporal space accessible with the combination of techniques, and demonstrate CXDI with 22 ms temporal resolution., Comment: 29 pages, 12 figures, 40 references

A graph $G$ is $k$-vertex-critical if $\chi(G)=k$ but $\chi(G-v)

Let $G$ be a group. Associate a graph $\mathcal{E}_G$ (called the co-Engel graph of $G$) with $G$ whose vertex set is $G$ and two distinct vertices $x$ and $y$ are adjacent if $[x, {}_k y] \neq 1$ and $[y, {}_k x] \neq 1$ for all positive integer $k$. This graph, under the name ``Engel graph'', was introduced by Abdollahi. Let $L(G)$ be the set of all left Engel elements of $G$. In this paper, we realize the induced subgraph of co-Engel graphs of certain finite non-Engel groups $G$ induced by $G \setminus L(G)$. We write $\mathcal{E}^-(G)$ to denote the subgraph of $\mathcal{E}_G$ induced by $G \setminus L(G)$. We also compute genus, various spectra, energies and Zagreb indices of $\mathcal{E}^-(G)$ for those groups. As a consequence, we determine (up to isomorphism) all finite non-Engel group $G$ such that the clique number is at most $4$ and $\mathcal{E}^-$ is toroidal or projective. Further, we show that $\coeng{G}$ is super integral and satisfies the E-LE conjecture and the Hansen--Vuki{\v{c}}evi{\'c} conjecture for the groups considered in this paper.

Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The problem of bias and fairness in ML algorithms is becoming an increasingly greater source of concern. In concurrence, existing literature has also indicated that mental health conditions can manifest differently across genders and cultures. We hypothesise that the representation of features (acoustic, textual, and visual) and their inter-modal relations would vary among subjects from different cultures and genders, thus impacting the performance and fairness of various ML models. We present the very first evaluation of multimodal gender fairness in depression manifestation by undertaking a study on two different datasets from the USA and China. We undertake thorough statistical and ML experimentation and repeat the experiments for several different algorithms to ensure that the results are not algorithm-dependent. Our findings indicate that though there are differences between both datasets, it is not conclusive whether this is due to the difference in depression manifestation as hypothesised or other external factors such as differences in data collection methodology. Our findings further motivate a call for a more consistent and culturally aware data collection process in order to address the problem of ML bias in depression detection and to promote the development of fairer agents and robots for wellbeing., Comment: 9 Pages, 7 Tables. To be published and indexed in the IEEE Xplore Digital Library under the ACII 2024 Workshop Proceedings

We prove that every unitary invertible quantum field theory satisfies a generalization of the famous spin statistics theorem. To formulate this extension, we define a `higher spin' action of the stable orthogonal group $O$ on appropriate spacetime manifolds, which extends both the reflection involution and spin flip. On the algebraic side, we define a `higher statistics' action of $O$ on the universal target for invertible field theories, $I\mathbb{Z}$, which extends both complex conjugation and fermion parity $(-1)^F$. We prove that every unitary invertible quantum field theory intertwines these actions., Comment: 6 pages, no figures

We study, from both a theoretical and observational perspective, the physical origin and spectroscopic impact of extreme nebular emission in high-redshift galaxies. The nebular continuum, which can appear during extreme starbursts, is of particular importance as it tends to redden UV slopes and has a significant contribution to the UV luminosities of galaxies. Furthermore, its shape can be used to infer the gas density and temperature of the ISM. First, we provide a theoretical background, showing how different stellar populations (SPS models, IMFs, and stellar temperatures) and nebular conditions impact observed galaxy spectra. We demonstrate that, for systems with strong nebular continuum emission, 1) UV fluxes can increase by up to 0.7~magnitudes (or more in the case of hot/massive stars) above the stellar continuum, which may help reconcile the surprising abundance of bright high-redshift galaxies and the elevated UV luminosity density at $z>10$, 2) at high gas densities, UV slopes can redden from $\beta\lesssim-2.5$ to $\beta\sim-1$, 3) observational measurements of $\xi_{ion}$ are grossly underestimated, and 4) UV downturns from two-photon emission can masquerade as DLAs. Second, we present a dataset of 58 galaxies observed with NIRSpec on JWST at $2.510$ galaxies, finding that UV slopes and UV downturns are in some cases redder or steeper than expected from SPS models, which may hint at more exotic (e.g. hotter/more massive stars or AGN) ionizing sources., Comment: 26 pages, 18 figures, submitted to The Open Journal of Astrophysics

The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median $\text{T}_1$ by $\sim22\%$ ($p=0.002$), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch prior to niobium deposition also show $\sim33\%$ lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials' differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS.

In this study, we investigate in situ etching of \b{eta}-Ga2O3 in a metal-organic chemical vapor deposition (MOCVD) system using tert-Butyl chloride (TBCl). We report the successful etching of both heteroepitaxial (-201)-oriented and homoepitaxial (010)-oriented \b{eta}-Ga2O3 films over a wide range of substrate temperature, TBCl molar flows, and reactor pressures. We identify that the likely etchant is HCl (g) formed by the pyrolysis of TBCl in the hydrodynamic boundary layer above the substrate. The temperature dependence of the etch rate reveals two distinct regimes characterized by markedly different apparent activation energies. The extracted apparent activation energies suggest that at temperatures below ~800 {\deg}C the etch rate is likely limited by desorption of etch products, while at higher substrate temperatures chemisorption of HCl limits the etch rate. The relative etch rates of heteroepitaxial (-201) and homoepitaxial (010) \b{eta}-Ga2O3 were observed to scale by the ratio of the surface energies indicating an anisotropic etch. For (010) homoepitaxial films, relatively smooth post-etch surface morphology was achieved by tuning the etching parameters., Comment: 12 pages, 8 figures

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

It is shown that every $2$-shifted Poisson structure on a finitely generated semi-free commutative differential graded algebra $A$ defines a very explicit infinitesimal $2$-braiding on the homotopy $2$-category of the symmetric monoidal dg-category of finitely generated semi-free $A$-dg-modules. This provides a concrete realization, to first order in the deformation parameter $\hbar$, of the abstract deformation quantization results in derived algebraic geometry due to Calaque, Pantev, To\"en, Vaqui\'e and Vezzosi. Of particular interest is the case when $A$ is the Chevalley-Eilenberg algebra of a higher Lie algebra, where the braided monoidal deformations developed in this paper may be interpreted as candidates for representation categories of `higher quantum groups'., Comment: 36 pages

Let A be a graph type and B an equivalence relation on a group $G$. Let $[g]$ be the equivalence class of $g$ with respect to the equivalence relation B. The B superA graph of $G$ is an undirected graph whose vertex set is $G$ and two distinct vertices $g, h \in G$ are adjacent if $[g] = [h]$ or there exist $x \in [g]$ and $y \in [h]$ such that $x$ and $y$ are adjacent in the A graph of $G$. In this paper, we compute spectrum of equality/conjugacy supercommuting graphs of dihedral/dicyclic groups and show that these graphs are not integral.

Community resilience refers to the ability to prepare for, absorb, recover from, and adapt to disruptive events, but specific definitions and measures for resilience can vary widely from researcher to researcher or from discipline to discipline. Community resilience is often measured using a set of indicators based on census, socioeconomic, and community organizational data, but these metrics and measures for community resilience provide little guidance for policymakers to determine how best to increase the community resilience. This article proposes to measure community resilience based on value focused thinking. We propose an objectives hierarchy that begins with a community decision makers' fundamental objective for resilience. Six high level objectives for community resilience, including social resilience, economic resilience, infrastructure resilience, environmental resilience, availability of resources, and functionality of critical services, are broken down into measurable attributes that focus on specific outcomes that a decision maker would like to achieve if a disruption occurs. This new way of assessing resilience is applied to measure the resilience of an illustrative community to an improvised explosive device, a cyberattack, a tornado, a flood, and a winter storm. Keywords: Community Resilience, Resiliency, Risk Analysis

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery., Comment: 18 pages, 11 figures, AAAI/ACM AIES24

This undergraduate thesis is focused on introducing the reader to concepts related to the search for topological obstructions to the existence of compact gradient shrinking Ricci soliton metrics in dimension four. It contains a discussion of the relevant background material for this subject. Furthermore, it introduces the problem of extending the Hitchin-Thorpe inequality to gradient shrinking Ricci soliton metrics and explores the limitations of current results in that direction. At last, the topic of compact Kaehler gradient shrinking Ricci solitons is introduced and the classification of these spaces is outlined in literature-study fashion., Comment: 18 pages, Undergraduate Thesis

We report the spectroscopic confirmation and fundamental properties of TOI-757 b, a mini-Neptune on a 17.5-day orbit transiting a bright star ($V = 9.7$ mag) discovered by the TESS mission. We acquired high-precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space-borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground-based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI-757 b has a radius of $R_{\mathrm{p}} = 2.5 \pm 0.1 R_{\oplus}$ and a mass of $M_{\mathrm{p}} = 10.5^{+2.2}_{-2.1} M_{\oplus}$, implying a bulk density of $\rho_{\text{p}} = 3.6 \pm 0.8$ g cm$^{-3}$. Our internal composition modeling was unable to constrain the composition of TOI-757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with $e$ = 0.39$^{+0.08}_{-0.07}$, making it one of the very few highly eccentric planets among precisely characterized mini-Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI-757 b's formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star-star interactions during the earlier epoch of the Galactic disk formation, given the low metallicity and older age of TOI-757., Comment: Accepted for publication in MNRAS; 26 pages, 14 figures, 6 tables

We show that following the standard mantra of quantum chemistry and diagonalizing the Born-Oppenheimer (BO) Hamiltonian $\hat H_{\rm BO}(\bm R)$ is not the optimal means to construct potential energy surfaces. A better approach is to diagonalize a phase-space electronic Hamiltonian, $\hat H_{\rm PS}(\bm R,\bm P)$, which is parameterized by both nuclear position $\bm R$ and nuclear momentum $\bm P$. The foundation of such a non-perturbative phase-space electronic Hamiltonian can be made rigorous using a partial Wigner transform and the method has exactly the same cost as BO for a semiclassical calculation (and only a slight increase in cost for a quantum nuclear calculation). For a three-particle system, with two heavy particles and one light particle, numerical results show that a phase space electronic Hamiltonian produces not only meaningful electronic momenta (which are completely ignored by BO theory) but also far better vibrational energies. As such, for high level results and/or systems with degeneracies and spin degrees of freedom, we anticipate that future electronic structure and quantum chemistry packages will need to take as input not just the positions of the nuclei but also their momenta.

This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS., Comment: 2-page spread version. The online version is available at https://www.usparticlephysics.org/2023-p5-report/ and the graphics at https://usparticlephysics.org/media-assets-library

Superior computational power promised by quantum computers utilises the fundamental quantum mechanical principle of entanglement. However, achieving entanglement and verifying that the generated state does not follow the principle of local causality has proven difficult for spin qubits in gate-defined quantum dots, as it requires simultaneously high concurrence values and readout fidelities to break the classical bound imposed by Bell's inequality. Here we employ heralded initialization and calibration via gate set tomography (GST), to reduce all relevant errors and push the fidelities of the full 2-qubit gate set above 99 %, including state preparation and measurement (SPAM). We demonstrate a 97.17 % Bell state fidelity without correcting for readout errors and violate Bell's inequality with a Bell signal of S = 2.731 close to the theoretical maximum of $2\sqrt{2}$. Our measurements exceed the classical limit even at elevated temperatures of 1.1 K or entanglement lifetimes of 100 $\mu s$., Comment: 19 pages, 5 main figures, 9 extended data figures

Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and characterize their structures and atomic sites that are correlated with high catalytic activity. Identification of these ''active sites'' has relied heavily on extrapolation to supported NP systems from investigation of idealized surfaces, experimentally using single crystals or supported NPs which are always modelled computationally using slab or regular polyhedra models. However, the ability of these methods to predict catalytic activity remains qualitative at best, as the structure of metal NPs in reactive environments has only been speculated from indirect experimental observations, or otherwise remains wholly unknown. Here, by circumventing these limitations for highly accurate simulation methods, we provide direct atomistic insight into the dynamic restructuring of metal NPs by combining in situ spectroscopy with molecular dynamics simulations powered by a machine learned force field. We find that in reactive environments, NP surfaces evolve to a state with poorly defined atomic order, while the core of the NP may remain bulk-like. These insights prove that long-standing conceptual models based on idealized faceting for small metal NP systems are not representative of real systems under exposure to reactive environments. We show that the resultant structure can be elucidated by combining advanced spectroscopy and computational tools. This discovery exemplifies that to enable faithful quantitative predictions of catalyst function and stability, we must move beyond idealized-facet experimental and theoretical models and instead employ systems which include realistic surface structures that respond to relevant physical and chemical conditions., Comment: 18 pages, 11 figures (4 main, 7 SI)

Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-component systems have been shown to result in higher product yields, improved selectivities, and greater stability through catalytic cycling, the structural dynamics of these materials in response to various stimuli (e.g. temperature, adsorbates, etc.) are not understood with atomistic resolution. Here, we present a highly accurate equivariant machine-learned force field (MLFF), constructed from ab initio training data collected using Bayesian active learning, that is able to reliably simulate PdAu surfaces and NPs in response to thermal treatment as well as exposure to reactive H$_2$ atmospheres. We thus provide a single model that is able to reliably describe the full space of geometric and chemical complexity for such a heterogeneous catalytic system across single crystals, gas-phase interactions, and NPs reacting with H$_2$, including catalyst degradation and explicit reactivity. Ultimately, we provide direct atomistic evidence that verifies existing experimental hypotheses for bimetallic catalyst deactivation under reaction conditions, namely that Pd preferentially segregates into the Au bulk through aggressive catalytic cycling and that this degradation is site-selective, as well as the reactivity for hydrogen exchange as a function of Pd ensemble size. We demonstrate that understanding of the atomistic evolution of these active sites is of the utmost importance, as it allows for design and control of material structure and corresponding performance, which can be vetted in silico., Comment: 19 pages, 8 figures (main + SI)

Emerging active regions are associated with convective flows on the spatial scale and lifetimes of supergranules. To understand how these flows are involved in the formation of active regions, we aim to identify where active regions emerge in the supergranulation flow pattern. We computed supergranulation scale flow maps at the surface for all active regions in the Solar Dynamics Observatory Helioseismic Emerging Active Region Survey. We classified each of the active regions into four bins, based on the amplitude of their average surface flow divergence at emergence. We then averaged the flow divergence over the active regions in each bin as a function of time. We also considered a corresponding set of control regions. We found that, on average, the flow divergence increases during the day prior to emergence at a rate independent of the amount of flux that emerges. By subtracting the averaged flow divergence of the control regions, we found that active region emergence is associated with a remaining converging flow at 0.5-1 days prior to emergence. This remnant flow, $\Delta \, \mathrm{div} \, \mathbf{v_h} = (-4.9 \pm 1.7) \times 10^{-6}$ 1/s, corresponds to a flow speed of 10-20 m/s (an order of magnitude less than supergranulation flows) out to a radius of about 10 Mm. We show that these observational results are qualitatively supported by simulations of a small bipole emerging through the near-surface convective layers of the Sun. The question remains whether these flows are driving the emergence, or are caused by the emergence., Comment: 22 pages, 27 figures, accepted to be published in MNRAS

Quantum systems in excited states are attracting significant interest with the advent of noisy intermediate scale quantum (NISQ) devices. While ground states of small molecular systems are typically explored using hybrid variational algorithms like the variational quantum eigensolver (VQE), the study of excited states has received much less attention, partly due to the absence of efficient algorithms. In this work, we introduce the {\em subspace search quantum imaginary time evolution} (SSQITE) method, which calculates excited states using quantum devices by integrating key elements of the subspace search variational quantum eigensolver (SSVQE) and the quantum imaginary time evolution (QITE) method. The effectiveness of SSQITE is demonstrated through calculations of low-lying excited states of benchmark model systems, including $\text{H}_2$ and $\text{LiH}$ molecules. With its robustness in avoiding local minima, SSQITE shows promise for advancing quantum computations of excited states across a wide range of applications., Comment: 15 pages, 5 Figures

Everyday AI detection requires differentiating between people and AI in informal, online conversations. In many cases, people will not interact directly with AI systems but instead read conversations between AI systems and other people. We measured how well people and large language models can discriminate using two modified versions of the Turing test: inverted and displaced. GPT-3.5, GPT-4, and displaced human adjudicators judged whether an agent was human or AI on the basis of a Turing test transcript. We found that both AI and displaced human judges were less accurate than interactive interrogators, with below chance accuracy overall. Moreover, all three judged the best-performing GPT-4 witness to be human more often than human witnesses. This suggests that both humans and current LLMs struggle to distinguish between the two when they are not actively interrogating the person, underscoring an urgent need for more accurate tools to detect AI in conversations.

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/$c^2$, as well as upper limits on dark photon kinetic mixing and axion-like particle axioelectric coupling for masses between 1.2 and 23.3 eV/$c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross-section sensitivity was achieved., Comment: 7 pages + title and references, 4 figures, and 1 table

Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can an agent learn such a state representation, and how can it detect when it has found one? We introduce a metric that can accomplish both objectives, without requiring access to--or knowledge of--an underlying, unobservable state space. Our metric, the $\lambda$-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD($\lambda$) with a different value of $\lambda$. Since TD($\lambda$=0) makes an implicit Markov assumption and TD($\lambda$=1) does not, a discrepancy between these estimates is a potential indicator of a non-Markovian state representation. Indeed, we prove that the $\lambda$-discrepancy is exactly zero for all Markov decision processes and almost always non-zero for a broad class of partially observable environments. We also demonstrate empirically that, once detected, minimizing the $\lambda$-discrepancy can help with learning a memory function to mitigate the corresponding partial observability. We then train a reinforcement learning agent that simultaneously constructs two recurrent value networks with different $\lambda$ parameters and minimizes the difference between them as an auxiliary loss. The approach scales to challenging partially observable domains, where the resulting agent frequently performs significantly better (and never performs worse) than a baseline recurrent agent with only a single value network., Comment: GitHub URL: https://github.com/brownirl/lambda_discrepancy; Videos: https://lambda-discrepancy.github.io/

Superconducting quantum processors are one of the leading platforms for realizing scalable fault-tolerant quantum computation (FTQC). The recent demonstration of post-fabrication tuning of Josephson junctions using alternating-bias assisted annealing (ABAA) technique and a reduction in junction loss after ABAA illuminates a promising path towards precision tuning of qubit frequency while maintaining high coherence. Here, we demonstrate precision tuning of the maximum $|0\rangle\rightarrow |1\rangle$ transition frequency ($f_{01}^{\rm max}$) of tunable transmon qubits by performing ABAA at room temperature using commercially available test equipment. We characterize the impact of junction relaxation and aging on resistance spread after tuning, and demonstrate a frequency equivalent tuning precision of 7.7 MHz ($0.17\%$) based on targeted resistance tuning on hundreds of qubits, with a resistance tuning range up to $18.5\%$. Cryogenic measurements on tuned and untuned qubits show evidence of improved coherence after ABAA with no significant impact on tunability. Despite a small global offset, we show an empirical $f_{01}^{\rm max}$ tuning precision of 18.4 MHz by tuning a set of multi-qubit processors targeting their designed Hamiltonians. We experimentally characterize high-fidelity parametric resonance iSWAP gates on two ABAA-tuned 9-qubit processors with fidelity as high as $99.51\pm 0.20\%$. On the best-performing device, we measured across the device a median fidelity of $99.22\%$ and an average fidelity of $99.13\pm 0.12 \%$. Yield modeling analysis predicts high detuning-edge-yield using ABAA beyond the 1000-qubit scale. These results demonstrate the cutting-edge capability of frequency targeting using ABAA and open up a new avenue to systematically improving Hamiltonian targeting and optimization for scaling high-performance superconducting quantum processors.