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Ultra-thin two-dimensional (2D) materials have gained significant attention for making next-generation optoelectronic devices. Here, we report a large-area heterojunction photodetector fabricated using a liquid metal-printed 2D $\text{SnO}_2$ layer transferred onto CdTe thin films. The resulting device demonstrates efficient broadband light sensing from visible to near-infrared wavelengths, with enhanced detectivity and faster photo response than bare CdTe photodetectors. Significantly, the device shows a nearly $10^5$-fold increase in current than the dark current level when illuminated with a 780 nm laser and achieves a specific detectivity of around $10^{12} \, \text{Jones}$, nearly two orders of magnitude higher than a device with pure CdTe thin film. Additionally, temperature-dependent optoelectronic testing shows that the device maintains a stable response up to $140^\circ \text{C}$ and generates distinctive photocurrent at temperatures up to $80^\circ \text{C}$, demonstrating its thermal stability. Using band structure analysis, density functional theory (DFT) calculations, and photocurrent mapping, the formation of a $p$-$n$ junction is indicated, contributing to the enhanced photo response attributed to the efficient carrier separation by the built-in potential in the hetero-junction and the superior electron mobility of 2D $\text{SnO}_2$. Our results highlight the effectiveness of integrating liquid metal-exfoliated 2D materials for enhanced photodetector performance.

The HALHF collaboration has discussed a new baseline for the project, taking into account comments from the accelerator community on various aspects of the original design. In particular, these concerned the practicality of the dual-purpose linac to accelerate both colliding positron bunches and the drive beams required for the plasma linac. In addition, many other aspects of the project were also considered; the discussion and conclusions are documented in this paper. Finally, a new baseline is outlined that has been optimised and addresses several weaknesses in the original design, has higher luminosity, reduced centre-of-mass energy boost and additional features such as positron polarization as well as electron polarization. Although HALHF has become longer and more expensive, it remains significantly smaller and cheaper than other mature Higgs factory designs currently under discussion., Comment: 32 pages, 13 figures, 2 tables

We demonstrate electrically tunable, spin-dependent, directional coupling of single photons by embedding quantum dots (QDs) in a waveguide-coupled nanocavity. The directional behavior arises from direction-dependent interference between two cavity modes when coupled to the device waveguides. The small mode volume cavity enables simultaneous Purcell enhancement (${10.8\pm0.7}$) and peak directional contrast (${88\pm1\%}$), exceeding current state-of-the-art waveguide-only systems. We also present a scattering matrix model for the transmission through this structure, alongside a quantum trajectory-based model for predicting the system's directionality, which we use to explain the observed asymmetry in directional contrast seen in QD devices. Furthermore, the nanocavity enables wide-range electrical tuning of the emitter's directional contrast. We present results showing precise tuning of a QD emission line from a directional contrast of ${2\%}$ to ${96\%}$. In combination, these characteristics make this cavity-waveguide approach promising for use as a building block in directional nanophotonic circuits.

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates., Comment: MNRAS accepted

The stochastic gravitational wave background (SGWB) can be observed in the nanohertz band using a pulsar timing array (PTA). Here a computationally efficient state-space framework is developed for analysing SGWB data, in which the stochastic gravitational wave strain at Earth is tracked with a non-linear Kalman filter and separated simultaneously from intrinsic, achromatic pulsar spin wandering. The filter is combined with a nested sampler to estimate the parameters of the model, and to calculate a Bayes factor for selecting between models with and without a SGWB. The procedure extends previous state-space formulations of PTA data analysis applied to individually resolvable binary black hole sources. The performance of the new algorithm is tested on synthetic data from the first International PTA Mock Data Challenge. It is shown that the algorithm distinguishes a SGWB from pure noise for $A_{\rm gw} \geq 3 \times 10^{-14}$, where $A_{\rm gw}$ denotes the standard normalization factor for a power spectral density with power-law exponent $-13/3$. Additional, systematic validation tests are also performed with synthetic data generated independently by adjusting the injected parameters to cover astrophysically plausible ranges. Full posterior distributions are recovered and tested for accuracy. The state-space procedure is memory-light and evaluates the likelihood for a standard-sized PTA dataset in $\lesssim 10^{-1}$ s without optimization on a standard central processing unit., Comment: 10 pages, 4 figures + appendices. Accepted for publication in MNRAS

Josephson junctions containing ferromagnetic materials are under consideration for applications in digital superconducting logic and memory. Some memory applications rely on the ability to reverse the magnetization direction of a "soft" magnetic layer within the junction using a small local magnetic field generated on the chip. It is crucial, therefore, to find a suitable soft magnetic material with a low switching field and low switching energy. A popular magnetic material for such applications is Ni$_{80}$Fe$_{20}$, also known as Permalloy, however Permalloy has a rather large magnetization, leading to large magnetic switching energies. In this work we explore Cr-doped Permalloy, specifically Ni$_{73}$Fe$_{18}$Cr$_{9}$, which has a saturation magnetization just under two-thirds that of Permalloy. Josephson junctions containing this NiFeCr alloy undergo a 0-$\pi$ transition at a NiFeCr thickness of 2.3 nm, and the critical supercurrent decays in the alloy over a short characteristic length of 0.36 nm. Switching fields of a few millitesla are promising, but the short decay length and overall small values of the critical current in the Josephson junctions may preclude the use of NiFeCr in current cryogenic memory technologies., Comment: 7 pages, 4 figures

The ability to engineer pairs of entangled photons is essential to quantum information science, and generating these states using spontaneous parametric down-conversion (SPDC) in nano- and micrometer-scale materials offers numerous advantages. To properly engineer such sources, a reliable model describing nano- and micrometer-scale SPDC is necessary; however, such a theoretical description remains a challenge. Here, we propose a simplified model to describe SPDC in resonant structures, which considers the generation of photon pairs and the resonant enhancement of spectral bands to be separate processes, even though they actually occur simultaneously. We compare our simplified model to both the rigorous theory of SPDC in an etalon - a simple example of a resonant structure - and our experiments on SPDC in etalons and find agreement for low-gain SPDC. By simplifying the calculations required to generate photon pairs, our model promises to make designing complex resonant structures easier, and it promises to hasten the iteration of designs across the field of quantum state engineering., Comment: 9 pages, 7 figures

We provide a rounding error analysis of a mixed-precision preconditioned Jacobi algorithm, which uses low precision to compute the preconditioner, applies it in high precision (amounting to two matrix-matrix multiplications) and solves the eigenproblem using the Jacobi algorithm in working precision. Our analysis yields meaningfully smaller relative forward error bounds for the computed eigenvalues compared with those of the standard Jacobi algorithm. We further prove that, after preconditioning, if the off-diagonal entries of the preconditioned matrix are sufficiently small relative to its smallest diagonal entry, the relative forward error bound is independent of the condition number of the original matrix. We present two constructions for the preconditioner that exploit low precision, along with their error analyses. Our numerical experiments confirm our theoretical results and compare the relative forward error of the proposed algorithm with the standard Jacobi algorithm, a preconditioned Jacobi algorithm, and MATLAB's $\texttt{eig}$ function., Comment: 24 pages

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

The focus of this work is on a class of solutions of the defocusing Ablowitz-Ladik lattice on an arbitrarily large background which are discrete analogs of the Kuznetsov-Ma (KM) breathers of the focusing nonlinear Schrodinger equation. One such solution was obtained in 2019 as a byproduct of the Inverse Scattering Transform, and it was observed that the solution could be regular for certain choices of the soliton parameters, but its regularity was not analyzed in detail. This work provides a systematic investigation of the conditions on the background and on the spectral parameters that guarantee the KM solution to be non-singular on the lattice for all times. Furthermore, a novel KM-type breather solution is presented which is also regular on the lattice under the same conditions. We also employ Darboux transformations to obtain a multi-KM breather solution, and show that parameters choices exist for which a double KM breather solution is regular on the lattice. We analyze the features of these solutions, including their frequency which, when tending to 0, renders them proximal to rogue waveforms. Finally, numerical results on the stability and spatio-temporal dynamics of the single KM breathers are presented, showcasing the potential destabilization of the obtained states due to the modulational instability of their background.

Let $R$ be either the ring of Lipschitz quaternions, or the ring of Hurwitz quaternions. Then, $R$ is a subring of the division ring $\mathbb{D}$ of rational quaternions. For $S \subseteq R$, we study the collection $\rm{Int}(S,R) = \{f \in \mathbb{D}[x] \mid f(S) \subseteq R\}$ of polynomials that are integer-valued on $S$. The set $\rm{Int}(S,R)$ is always a left $R$-submodule of $\mathbb{D}[x]$, but need not be a subring of $\mathbb{D}[x]$. We say that $S$ is a ringset of $R$ if $\rm{Int}(S,R)$ is a subring of $\mathbb{D}[x]$. In this paper, we give a complete classification of the finite subsets of $R$ that are ringsets.

Quantum computing promises solutions to classically difficult and new-found problems through controlling the subtleties of quantum computing. The Quantum Approximate Optimisation Algorithm (QAOA) is a recently proposed quantum algorithm designed to tackle difficult combinatorial optimisation problems utilising both quantum and classical computation. The hybrid nature, generality and typically low gate-depth make it a strong candidate for near-term implementation in quantum computing. Finding the practical limits of the algorithm is currently an open problem. Until now, no tools to facilitate the design and validation of probabilistic quantum optimisation algorithms such as the QAOA on a non-trivial scale exist. Graph similarity is a long standing classically difficult problem withstanding decades of research from academia and industry. Determining the maximal edge overlap between all possible node label permutations is an NP-Complete task and provides an apt measure of graph similarity. We introduce a novel quantum optimisation simulation package facilitating investigation of all constituent components of the QAOA from desktop to cluster scale using graph similarity as an example. Our simulation provides flexibility and performance. We investigate eight classical optimisation methods each at six levels of decomposition. Moreover an encoding for permutation based problems such as graph similarity through edge overlap to the QAOA allows for significant quantum memory savings at the cost of additional operations. This compromise extends into the classical portion of the algorithm as the inclusion of infeasible solutions creates a challenging cost-function landscape. We present performance analysis of our simulation and of the QAOA setting a precedent for investigating and validating numerous other difficult problems to the QAOA as we move towards realising practical quantum computation., Comment: 82 pages, 19 figures, 1 table. Honors thesis originally completed in 2018 at the University of Western Australia. While the research reflects my knowledge and understanding at the time, it may no longer represent the current state of the field

We consider the quantum query complexity of local search as a function of graph geometry. Given a graph $G = (V,E)$ with $n$ vertices and black box access to a function $f : V \to \mathbb{R}$, the goal is find a vertex $v$ that is a local minimum, i.e. with $f(v) \leq f(u)$ for all $(u,v) \in E$, using as few oracle queries as possible. We show that the quantum query complexity of local search on $G$ is $\Omega\bigl( \frac{n^{\frac{3}{4}}}{\sqrt{g}} \bigr)$, where $g$ is the vertex congestion of the graph. For a $\beta$-expander with maximum degree $\Delta$, this implies a lower bound of $ \Omega\bigl(\frac{\sqrt{\beta} \; n^{\frac{1}{4}}}{\sqrt{\Delta} \; \log{n}} \bigr)$. We obtain these bounds by applying the strong weighted adversary method to a construction by Br\^anzei, Choo, and Recker (2024). As a corollary, on constant degree expanders, we derive a lower bound of $\Omega\bigl(\frac{n^{\frac{1}{4}}}{ \sqrt{\log{n}}} \bigr)$. This improves upon the best prior quantum lower bound of $\Omega\bigl( \frac{n^{\frac{1}{8}}}{\log{n}}\bigr) $ by Santha and Szegedy (2004). In contrast to the classical setting, a gap remains in the quantum case between our lower bound and the best-known upper bound of $O\bigl( n^{\frac{1}{3}} \bigr)$ for such graphs.

Spatial modes are a promising candidate for encoding information for classical and quantum optical communication due to their potential high information capacity. Unfortunately, compensation of the wavefront upon propagation through the atmosphere is necessary to benefit from advantages spatial modes offer. In this work, we leverage the success of convolutional networks in denoising and classifying images to improve information transfer of spatial modes. Hermite-Gauss, Laguerre-Gauss, and Ince-Gauss modes are experimentally generated using single photons and imaged. A denoising autoencoder corrects for turbulence effects on the wavefront, followed by a convolutional neural network to classify mode orders. The model achieves a 99.2% classification accuracy across all modes, and Hermite-Gauss modes exhibited the highest individual mode accuracy. As the convolutional networks rely solely on intensity, they offer an efficient and cost-effective tool for optical communication systems in the single photon limit.

Spiking Neural Networks (SNNs) promise efficient spatio-temporal data processing owing to their dynamic nature. This paper addresses a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, by reformulating it as a time-series segmentation task inherently suited for SNN execution. Automated RFI detection systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram-to-spike encoding methods and network parameters, applying first-order leaky integrate-and-fire SNNs to tackle RFI detection. To enhance the contrast between RFI and background information, we introduce a divisive normalisation-inspired pre-processing step, which improves detection performance across multiple encoding strategies. Our approach achieves competitive performance on a synthetic dataset and compelling results on real data from the Low-Frequency Array (LOFAR) instrument. To our knowledge, this work is the first to train SNNs on real radio astronomy data successfully. These findings highlight the potential of SNNs for performing complex time-series tasks, paving the way for efficient, real-time processing in radio astronomy and other data-intensive fields., Comment: 27 pages, 5 figures, 5 tables. In-Review

We present an application of our new theoretical formulation of quantum dynamics, moment propagation theory (MPT) (Boyer et al., J. Chem. Phys. 160, 064113 (2024)), for employing machine-learning techniques to simulate the quantum dynamics of electrons. In particular, we use real-time time-dependent density functional theory (RT-TDDFT) simulation in the gauge of the maximally localized Wannier functions (MLWFs) for training the MPT equation of motion. Spatially-localized time-dependent MLWFs provide a concise representation that is particularly convenient for the MPT expressed in terms of increasing orders of moments. The equation of motion for these moments can be integrated in time while the analytical expressions are quite involved. In this work, machine-learning techniques were used to train the the second-order time derivatives of the moments using first-principles data from the RT-TDDFT simulation, and this MPT enabled us to perform electron dynamics efficiently. The application to computing optical absorption spectrum for various systems was demonstrated as a proof-of-principles example of this approach. In addition to isolated molecules (water, benzene, and ethene), condensed matter systems (liquid water and crystalline silicon) were studied, and we also explored how the principle of the nearsightedness of electrons can be employed in this context.

The Gibbs ensemble of the truncated KdV (TKdV) equation has been shown to accurately describe the anomalous wave statistics observed in laboratory experiments, in particular the emergence of extreme events. Here, we introduce a novel proposal distribution that facilitates efficient rejection sampling of the TKdV Gibbs measure. Within parameter regimes accessible to laboratory experiments and capable of producing extreme events, the proposal distribution generates 1--6 orders of magnitude more accepted samples than does a naive, uniform distribution. When equipped with the new proposal distribution, a simple rejection algorithm enjoys key advantages over a Markov chain Monte Carlo algorithm, include better parallelization properties and generation of uncorrelated samples.

The heat transfer of fluid can be greatly enhanced by natural convection, leading to the famous Nusselt-Rayleigh number scaling that has been a focus of modern fluid dynamics. Our work explores natural convection in an annular domain, where the annular geometry reinforces the large-scale circulation. To understand the heat transfer and flow pattern in this novel geometry, we derive a reduced model from the Navier-Stokes-Boussinesq equations where the equations of flow and heat are transformed to a system of low-order partial differential equations, whose solution preserves the same boundary layer structures seen in the direct numerical simulation. By matching the solutions inside and outside the boundary layer, we recover all the scaling laws observed in the direct numerical simulation, further demonstrating the accuracy of this reduced model. Our results also provide a systematic way of analyzing thermal convection in an annular domain, which brings us one step closer to understanding the origin of large-scale circulation and the mechanism of convective heat transfer.

Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs of restrictions. We conduct a cost-effectiveness analysis of NPI policies enacted at the state level in the United States in 2020. Although school closures reduced viral transmission, their social impact in terms of student learning loss was too costly, depriving the nation of \$2 trillion (USD2020), conservatively, in future GDP. Moreover, this marginal trade-off between school closure and COVID deaths was not inescapable: a combination of other measures would have been enough to maintain similar or lower mortality rates without incurring such profound learning loss. Optimal policies involve consistent implementation of mask mandates, public test availability, contact tracing, social distancing orders, and reactive workplace closures, with no closure of schools beyond the usual 16 weeks of break per year. Their use would have reduced the gross impact of the pandemic in the US in 2020 from \$4.6 trillion to \$1.9 trillion and, with high probability, saved over 100,000 lives. Our results also highlight the need to address the substantial global learning deficit incurred during the pandemic.

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

We present the first numerical calculation of the virtual photon polarization and assess the feasibility of measuring the polarization via the anisotropic decay $\gamma^{*} \rightarrow \mu\mu$ using the LHC-ALICE detector. In presence of intense magnetic fields generated in high-energy non-central heavy-ion collisions that exceed the critical magnetic field intensity of quantum electrodynamics (QED), prompt virtual photons are predicted to decay anisotropically into lepton pairs, which we call virtual photon polarization. Using a relativistic resistive magnetohydrodynamics model, we computed the time evolution of the magnetic field and used these results to estimate the averaged polarization by calculating the vacuum polarization under the influence of the magnetic field at specific times. The estimated polarization deviates from zero with a statistical significance of $0.07\sigma$ with the data statistics collected from 2010 to 2011 and $0.15\sigma$ with the one from 2015 to 2018. It is understandable that the magnetic field could not be detected through polarization due to low statistical significance. With the data collecting the ongoing ALICE run from 2023 to 2026, the statistics dramatically increase by the upgraded LHC and the new data processing system. Thereby we expect that the statistical significance could reach $\sim 1.7\sigma$, resulting in a promising probe for detecting the intense magnetic fields., Comment: 7 pages, 4 figures

We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+-0.02)(3 deg-|b|)] and an event rate of {\Gamma} = [16.08+-0.28]*10^-6exp[(0.44+-0.02)(3 deg-|b|)] star^-1 year^-1 using a sample consisting of 3525 microlensing events, with Einstein radius crossing times of tE < 760 days and source star magnitude of IsWe confirm our results are consistent with the latest measurements from OGLE-IV 8 year dataset (Mr\'oz et al. 2019). We find our result is inconsistent with a prediction based on Galactic models, especially in the central region with |b|<3 deg. These results can be used to improve the Galactic bulge model, and more central regions can be further elucidated by future microlensing experiments, such as The PRime-focus Infrared Microlensing Experiment (PRIME) and Nancy Grace Roman Space Telescope.

The relationship between magnetic activity and Rossby number is one way through which stellar dynamos can be understood. Using measured rotation rates and X-ray to bolometric luminosity ratios of an ensemble of stars, we derive empirical convective turnover times based on recent observations and re-evaluate the X-ray activity-Rossby number relationship. In doing so, we find a sharp rise in the convective turnover time for stars in the mass range of $0.35-0.4\ \rm M_{\odot}$, associated with the onset of a fully convective internal stellar structure. Using $\texttt{MESA}$ stellar evolution models, we infer the location of dynamo action implied by the empirical convective turnover time. The empirical convective turnover time is found to be indicative of dynamo action deep within the convective envelope in stars with masses $0.1-1.2\ \rm M_{\odot}$, crossing the fully convective boundary. Our results corroborate past works suggesting that partially and fully convective stars follow the same activity-Rossby relation, possibly owing to similar dynamo mechanisms. Our stellar models also give insight into the dynamo mechanism. We find that empirically determined convective turnover times correlate with properties of the deep stellar interior. These findings are in agreement with global dynamo models that see a reservoir of magnetic flux accumulate deep in the convection zone before buoyantly rising to the surface., Comment: 9 Figures and 2 Tables

Pulsar timing arrays can detect continuous nanohertz gravitational waves emitted by individual supermassive black hole binaries. The data analysis procedure can be formulated within a time-domain, state-space framework, in which the radio timing observations are related to a temporal sequence of latent states, namely the intrinsic pulsar spin frequency. The achromatic wandering of the pulsar spin frequency is tracked using a Kalman filter concurrently with the pulse frequency modulation induced by a gravitational wave from a single source. The modulation is the sum of terms proportional to the gravitational wave strain at the Earth and at every pulsar in the array. Here we generalize previous state-space formulations of the pulsar timing array problem to include the pulsar terms; that is, we copy the pulsar terms from traditional, non-state-space analyses over to the state-space framework. The performance of the generalized Kalman filter is tested using astrophysically representative software injections in Gaussian measurement noise. It is shown that including the pulsar terms corrects for previously identified biases in the parameter estimates (especially the sky position of the source) which also arise in traditional matched-filter analyses that exclude the pulsar terms. Additionally, including the pulsar terms decreases the minimum detectable strain by $14\%$. Overall, the study verifies that the pulsar terms do not raise any special extra impediments for the state-space framework, beyond those studied in traditional analyses. The inspiral-driven evolution of the wave frequency at the Earth and at the retarded time at every pulsar in the array is also investigated., Comment: 24 pages, 13 figures. Accepted for publication in MNRAS. arXiv admin note: text overlap with arXiv:2409.14613

Late-stage Ca-sulfate-filled fractures are common on Mars. Notably, the Shenandoah formation in the western edge of Jezero crater preserves a variety of Ca-sulfate minerals in the fine-grained siliciclastic rocks explored by the Perseverance rover. However, the depositional environment and timing of the formation of these sulfates is unknown. To address this outstanding problem, we developed a new technique to map the crystal textures of these sulfates in situ at two stratigraphically similar locations in the Shenandoah formation, allowing us to constrain the burial depth and paleoenvironment at the time of their deposition. Our results suggest that some Ca-sulfate analyzed was formed at a burial depth greater than 80m, whereas Ca-sulfates present at another outcrop likely precipitated in a shallow-subsurface environment. These results indicate that samples collected for potential return to Earth at the two studied locations capture two different times and distinct chemical conditions in the depositional history of the Shenandoah formation providing multiple opportunities to evaluate surface and subsurface habitability.

Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge. Sound examples can be found at https://presto-music.github.io/web/.

If Com is the reduced commutative operad, the category of Com-algebras in spectra is the category of nounital commutative ring spectra. The theme of this survey is that many important constructions on Com-algebras are given by taking the derived circle product with well chosen right Com-modules. Examples of constructions arising this way include the tensor product of a based space K with such an algebra I, and TQ(I), the Topological Andre-Quillen homology spectrum of I. We then show how filtrations of right Com-modules can be used to filter such constructions. A natural decreasing filtration on right Com-modules, when specialized to the Com-bimodule Com, defines the augmentation ideal tower of I, built out of the extended powers of TQ(I). A natural increasing filtration on right Com-modules, when specialized to the right Com-module used to define TQ(I), defines a filtration on TQ(I) built out of I and the spaces in the Lie cooperad. There are two versions of this in the literature -- by the author and by Behrens and Rezk -- and our setting here makes it easy to prove that these agree. Much of this applies with Com replaced by a more general reduced operad, and we make a few remarks about this., Comment: 18 pages

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI. Unlike existing applications such as Be My AI and Seeing AI, NaviGPT combines image recognition and contextual navigation guidance into a single system, offering continuous feedback on the user's surroundings without the need for app-switching. Meanwhile, NaviGPT compensates for the response delays of LLM by using location and sensor data, aiming to provide practical and efficient navigation support for PVI in dynamic environments., Comment: 7 pages, 3 figures, this work has been accepted by the 2025 ACM International Conference on Supporting Group Work (GROUP '25)

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

We introduce a compact, two-camera laparoscope that combines active stereo depth estimation and speckle-illumination spatial frequency domain imaging (si-SFDI) to map profile-corrected, pixel-level absorption and reduced scattering optical properties in tissues with complex geometries. Our approach uses multimode fiber-coupled laser illumination to generate high-contrast speckle patterns, requiring only two images for optical property estimation. We demonstrate 3D profilometry using active stereo from low-coherence RGB laser flood illumination, which corrects for measured intensity variations caused by object height and surface angle differences. Validation against conventional SFDI in phantoms and an in-vivo human finger study showed good agreement, with profile-correction improving accuracy for complex geometries. This stereo-laparoscopic implementation of si-SFDI provides a simple method to obtain accurate optical property maps through a laparoscope, potentially offering quantitative endogenous contrast for minimally invasive surgical guidance.

In a previous paper, one of us interpreted mod 2 Dyer-Lashof operations as explicit A-module extensions between Brown-Gitler modules, and showed these A-modules can be topologically realized by finite spectra occurring as fibers of maps between 2-local dual Brown-Gitler spectra. Starting from these constructions, in this paper, we show that infinite families of these finite spectra are of chromatic type 2, with mod 2 cohomology that is free over A(1). Applications include classifying the dual Brown-Gitler spectra after localization with respect to K-theory., Comment: 26 pages. v2: minor revisions, with a much improved formulation of the main theorem

Continuous nanohertz gravitational waves from individual supermassive black hole binaries may be detectable with pulsar timing arrays. A novel search strategy is developed, wherein intrinsic achromatic spin wandering is tracked simultaneously with the modulation induced by a single gravitational wave source in the pulse times of arrival. A two-step inference procedure is applied within a state-space framework, such that the modulation is tracked with a Kalman filter, which then provides a likelihood for nested sampling. The procedure estimates the static parameters in the problem, such as the sky position of the source, without fitting for ensemble-averaged statistics such as the power spectral density of the timing noise, and therefore complements traditional parameter estimation methods. It also returns the Bayes factor relating a model with a single gravitational wave source to one without, complementing traditional detection methods. It is shown via astrophysically representative software injections in Gaussian measurement noise that the procedure distinguishes a gravitational wave from pure noise down to a characteristic wave strain of $h_0 \approx 2 \times 10^{-15}$. Full posterior distributions of model parameters are recovered and tested for accuracy. There is a bias of $\approx 0.3$ rad in the marginalised one-dimensional posterior for the orbital inclination $\iota$, introduced by dropping the so-called `pulsar terms'. Smaller biases $\lesssim 10 \%$ are also observed in other static parameters., Comment: 26 pages, 11 figures. Accepted for publication in MNRAS

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

Objective: Head impact information including impact directions, speeds and force are important to study traumatic brain injury, design and evaluate protective gears. This study presents a deep learning model developed to accurately predict head impact information, including location, speed, orientation, and force, based on head kinematics during helmeted impacts. Methods: Leveraging a dataset of 16,000 simulated helmeted head impacts using the Riddell helmet finite element model, we implemented a Long Short-Term Memory (LSTM) network to process the head kinematics: tri-axial linear accelerations and angular velocities. Results: The models accurately predict the impact parameters describing impact location, direction, speed, and the impact force profile with R2 exceeding 70% for all tasks. Further validation was conducted using an on-field dataset recorded by instrumented mouthguards and videos, consisting of 79 head impacts in which the impact location can be clearly identified. The deep learning model significantly outperformed existing methods, achieving a 79.7% accuracy in identifying impact locations, compared to lower accuracies with traditional methods (the highest accuracy of existing methods is 49.4%). Conclusion: The precision underscores the model's potential in enhancing helmet design and safety in sports by providing more accurate impact data. Future studies should test the models across various helmets and sports on large in vivo datasets to validate the accuracy of the models, employing techniques like transfer learning to broaden its effectiveness.

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPSxSR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one (MPSR1) is to use the time derivative of MPS, and another (MPSR2) is to use the first eigenvalue of the strain rate tensor. Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we conducted a comparison of these two MPSR methodologies across nine in-vivo and in-silico head impact datasets and found that 95MPSR1 was 5.87% larger than 95MPSR2, and 95MPSxSR1 was 2.55% larger than 95MPSxSR2. Across every element in all head impacts, MPSR1 was 8.28% smaller than MPSR2, and MPSxSR1 was 8.11% smaller than MPSxSR2. Furthermore, logistic regression models were trained to predict TBI based on the MPSR (or MPSxSR), and no significant difference was observed in the predictability across different variables. The consequence of misuse of MPSR and MPSxSR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1%. The evidence suggested that these two methodologies were not significantly different in detecting TBI.

Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads. The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention. However, current UAV simulation environments lack semantic context suited to this hybrid approach. To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning. HAMERITT includes scenario generation capabilities that offer mission-relevant contextual symbolic information in addition to raw sensor data. Scenarios include symbolic descriptions for entities of interest and their relations to scene elements, as well as spatial-temporal constraints in the form of time-bounded areas of interest with prior probabilities and restricted zones within those areas. HAMERITT also features support for training distinct algorithm threads for maneuver vs. perception within an end-to-end mission run. Future work includes improving scenario realism and scaling symbolic context generation through automated workflow., Comment: 6 pages, 5 figures, 1 table

Stars with initial masses larger than 8 solar masses undergo substantial mass loss through mechanisms that remain elusive. Unraveling the origins of this mass loss is important for comprehending the evolutionary path of these stars, the type of supernova explosion and whether they become neutron stars or black hole remnants. In 2022 December, RW Cep experienced the Great Dimming in its visible brightness, presenting a unique opportunity to understand mass loss mechanisms. Our previous observations of RW Cep from the CHARA Array, taken during the dimming phase, show a compelling asymmetry in the star images, with a darker zone on the west side of the star indicating presence of dust in front of the star in our line of sight. Here, we present multi-epoch observations from CHARA while the star re-brightened in 2023. We created images using three image reconstruction methods and an analytical model fit. Comparisons of images acquired during the dimming and re-brightening phases reveal remarkable differences. Specifically, the west side of RW Cep, initially obscured during the dimming phase, reappeared during the subsequent re-brightening phase and the measured angular diameter became larger by 8%. We also observed image changes from epoch to epoch while the star is brightening indicating the time evolution of dust in front of the star. We suggest that the dimming of RW Cep was a result from a recent surface mass ejection event, generating a dust cloud that partially obstructed the stellar photosphere., Comment: Accepted by The Astrophysical Journal Letters (ApJL)

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the $\omega$B97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization., Comment: 7 pages, 2 figues

We will construct post-quantum encryption algorithms based on three-variable polynomial Beal-Schur congruence. After giving a proof of Beal's conjecture and citing some applications of it to selected cases where the discrete logarithm and some of its generalizations are unsolvable problems, we will investigate the formulation and validity of an appropriate version of the Beal's conjecture on finite fields of integers. In contrast to the infinite case, we will show that the corresponding Beal-Schur congruence equation $x^{p}+y^{q}\equiv z^{r} (mod \mathcal{N})$ has non-trivial solutions into the finite field $\mathbb{Z}_{\mathcal{N}} $, for all sufficiently large primes $\mathcal{N}$ that do not divide the product $xyz$, under certain mutual divisibility conditions of the exponents $p$, $q$ and $r$. We will apply this result to generate the so-called BS cryptosystems, i.e., simple and secure post-quantum encryption algorithms based on the Beal-Schur congruence equation, as well as new cryptographic key generation methods, whose post-quantum algorithmic encryption security relies on having an infinite number of options for the parameters $p$, $q$, $r$, $\mathcal{N}$., Comment: 36 pages