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The challenge of understanding the Universe's dynamics, particularly the Hubble tension, requires precise measurements of the Hubble constant. Building upon the existing spectral-siren method, which capitalizes on population information from gravitational-wave sources, this paper explores an alternative way to analyze the population data to obtain the cosmological parameters in $\Lambda$CDM. We demonstrated how non-parametric methods, which are flexible models that can be used to agnostically reconstruct arbitrary probability densities, can be incorporated into this framework and leverage the detector-frame mass distribution to infer the cosmological parameters. We tested our method with mock data and applied it to $70$ binary black hole mergers from the third gravitational-wave transient catalog of the LIGO-Virgo-KAGRA Collaboration.

Tests of general relativity (GR) can be systematically biased when our waveform models are inaccurate. We here study systematic biases in tests of general relativity induced by neglecting lensing effects for millilensed gravitational-wave signals, where the lens mass is typically in the $10^3M_\odot$--$10^5M_\odot$ range. In particular, we use a nested-sampling Bayesian parameter estimation and model selection analysis of a millilensed signal with an unlensed parameterized post-Einsteinian (ppE) recovery model. We find that the ppE model is significantly biased toward a detection of a deviation from general relativity at signal-to-noise ratios of 30 and higher, especially when the source is aligned with the lens mass (the lensing effect is pronounced) and when its total mass is low (the signal duration is long). We use a toy model and the linear signal and Laplace approximations to provide a semi-analytic explanation for the trends in the systematic errors found in the nested sampling analysis. Moreover, a Bayes factor analysis reveals that the (unlensed) ppE model is weakly favored over the (unlensed) GR model, and a fitting factor study shows there is a significant loss of signal-to-noise ratio when using the (unlensed) ppE model. This implies that although a parameter estimation study may incorrectly infer a deviation from general relativity, a residual signal-to-noise ratio test would reveal that the ppE model is not a good fit to the data. Thus, with current detectors, millilensing-induced systematic biases are unlikely to result in false positive detections of GR deviations., Comment: 25 pages, 14 figures

Intels efforts to build a practical quantum computer are focused on developing a scalable spin-qubit platform leveraging industrial high-volume semiconductor manufacturing expertise and 300 mm fabrication infrastructure. Here, we provide an overview of the design, fabrication, and demonstration of a new customized quantum test chip, which contains 12-quantum-dot spin-qubit linear arrays, code named Tunnel Falls. These devices are fabricated using immersion and extreme ultraviolet lithography (EUV), along with other standard high-volume manufacturing (HVM) processes, as well as production-level process control. We present key device features and fabrication details, as well as qubit characterization results confirming device functionality. These results corroborate our fabrication methods and are a crucial step towards scaling of extensible 2D qubit array schemes.

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model's flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model's ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market.

In this note, we connect two seemingly unrelated objects: On the one hand is a two-dimensional drift-diffusion process $X$ with divergence-free and time-independent drift $b$. The drift is given by a stationary Gaussian ensemble, and we focus on the critical case where a small-scale cut-off is necessary for well-posedness and the large-scale cancellations lead to a borderline super-diffusive behavior. On the other hand is the natural diffusion $F$ on the Lie group $\textbf{SL}(2)$ of matrices of determinant one. As a consequence of this connection, the strongly non-Gaussian character of $F$ transmits to how $X$ depends on its starting point.

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics., Comment: 22 pages, 13 figures, 10 tables

Major players in e-commerce process dynamically incoming orders in real-time and already use advanced anticipation techniques, like AI, to predict characteristics of future orders. However, at the warehousing level, there are still no unambiguous recommendations on integrating anticipation with intelligent online optimization algorithms, nor an unbiased benchmark to assess the improvement potential of advanced anticipation over myopic techniques, as optimal online solutions are usually unavailable. In this paper, we compute and analyze Complete-Information Optimal policy Solutions (CIOSs), of an exact perfect anticipation algorithm with full knowledge of future customer orders' arrival times and items, for picking operations in picker-to-parts warehouses. We provide analytical properties and leverage CIOSs to uncover decision patterns that enhance simpler algorithms, improving both makespan (costs) and order turnover (delivery speed). Using a metric similar to the gap to optimality, we quantify the gains from optimization elements and the improvements remaining for advanced anticipation mechanisms. To compute CIOSs, we design the first exact algorithm for the Order Batching, Sequencing, and Routing Problem with Release Times (OBSRP-R), based on dynamic programming. Our analysis advises on the largely overlooked intervention - the dynamic adjustment of started batches, and the strategic relocation of an idle picker towards future picking locations. The former affected over 60% of CIOS orders and triggered consistent improvements across warehousing policies. The latter occurred before 39%-62% CIOS orders and could decrease a myopic policy's gap to optimal turnover by 4.3% on average (up to 14%). Notably, we challenge the debated concept of strategic waiting, revealing why the latter resembles an "all-in" gamble and harms both makespan and order turnover when intervention is allowed.

The wolensing Python package offers a solution for gravitational wave lensing computations within the full wave-optics regime. This tool is primarily designed to calculate the gravitational lensing amplification factor including diffractive effects, an essential component for generating accurate lensed gravitational wave waveforms. These waveforms are integral to astrophysical and cosmological studies related to gravitational-wave lensing. Integrating with lensingGW (Pagano, Hannuksela, and Li 2020), wolensing provides solutions for image positions in the high-frequency regime where wave and geometrical optics converge. This functionality allows the amplification factor to be applicable across a wider frequency range. Another key feature of wolensing is its ability to plot time delay contours on the lens plane, offering researchers a visual tool to better understand the relationship between the lens system and the amplification factor. wolensing is compatible with various lens models in lenstronomy (Birrer et al. 2021). There are also built-in lens models including point mass, singular isothermal sphere (SIS),and nonsingular isothermal ellipsoid (NIE) with jax (Bradbury et al. 2018) supporting GPU computation. Users can accommodate different lens models in the code with jax. wolensing is available as an open-source package on PyPI and can be installed via pip.

We present a detailed study of a scalar differential equation with threshold state-dependent delayed feedback. This equation arises as a simplification of a gene regulatory model. There are two monotone nonlinearities in the model: one describes the dependence of delay on state, and the other is the feedback nonlinearity. Both increasing and decreasing nonlinearities are considered. Our analysis is exhaustive both analytically and numerically as we examine the bifurcations of the system for various combinations of increasing and decreasing nonlinearities. We identify rich bifurcation patterns including Bautin, Bogdanov-Takens, cusp, fold, homoclinic, and Hopf bifurcations whose existence depend on the derivative signs of nonlinearities. Our analysis confirms many of these patterns in the limit where the nonlinearities are switch-like and change their value abruptly at a threshold. Perhaps one of the most surprising findings is the existence of a Hopf bifurcation to a periodic solution when the nonlinearity is monotone increasing and the time delay is a decreasing function of the state variable--something that is impossible in the case of a constant delay.

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders., Comment: 38 pages, 14 figures

We present the results of an asteroseismic study of HD 118660 (TIC 171729860), being a chemically peculiar (mild Am) star exhibiting $\delta$ Scuti ($\delta$ Sct) pulsations. It is based on the analysis of two sectors of time-series photometry from the space mission TESS and seismic modelling. It yielded the detection of 15 and 16 frequencies for TESS sectors 23 and 50, respectively. The identified pulsation modes include four radial ($\ell=0$) and five dipolar ($\ell=1$) ones. The radial modes are overtones with order $n$ ranging from $3$ and $6$. Such high values of $n$ are theoretically not expected for stars with the effective temperature of HD 118660 ($\rm T_{\rm eff}\approx 7550 \rm K$ ) located near the red edge of the $\delta$ Sct instability strip. To estimate the asteroseismic parameters, we have generated a grid of stellar models assuming a solar metallicity ($Z=0.014$) and different values for the convective overshooting parameter ($0.1\leq \alpha_{\rm ov}\leq 0.3$). We conclude that the analysis of the radial modes is insufficient to constrain $\alpha_{\rm ov}$ and $Z$ for $\delta$ Sct stars. The value for the equatorial velocity of HD 118660 derived from the seismic radius and the rotational frequency is consistent with values found in the literature., Comment: 10 pages, 7 figures, Accepted for publication in MNRAS

Periodic Leaky-Wave Antennas (P-LWA) offer highly directive and space-scanning radiation. Unfortunately, they have been plagued by the ``broadside issue'', characterized by a degradation in gain when the antenna's main beam is steered across broadside. While this issue has been addressed by circuit and network approaches, a related fundamental and general electromagnetic theory has been lacking. This paper fills this gap. We first show that a P-LWA is a $\mathcal{PT}$-symmetric system, whose even- and odd-mode coupling in the complex space of temporal frequencies leads to the characteristic pair of two-sheet Riemann surfaces. We observe that the branch cuts of these surfaces, which form the well-known $\mathcal{PT}$-symmetric double pitchfork spectrum, correspond to the ``balanced frequency'' condition, while the branch point at the junction of the pitchforks, is an exceptional point that corresponds to the ``$Q$-balanced'' condition, two conditions that where previously shown to be the conditions for eliminating the broadside issue. In order to acquire an independent and rigorous interpretation of this spectrum, we further transform the coupled complex temporal eigenfrequencies into complex spatial eigenfrequencies. We identify the resulting spatial frequencies as coupled forward-backward modes, with frequency-independent imaginary parts (leakage factors), a condition for P-LWA equalization across broadside. Finally, we derive the scattering parameters of the P-LWA and show that matching is achieved only at one of the two ends of the P-LWA structure. This work both provides a solid foundation to the theory of P-LWAs and represents an original contribution to the field of $\mathcal{PT}$-symmetry.

We study the complexity of S-adic sequences corresponding to a family of 216 multi-dimensional continued fractions maps, called Triangle Partition maps (TRIP maps), with an emphasis on those with low upper bounds on complexity. Our main result is to prove that the complexity of S-adic sequences corresponding the triangle map (called the (e,e,e)-TRIP map in this paper) has upper bound at most 3n. Our second main result is to prove an upper bound of 2n+1 on complexity for another TRIP map. We discuss a dynamical phenomenon, which we call ``hidden $R^2$ behavior,'' that occurs in this map and its relationship to complexity. Combining this with previously known results and a list of counter-examples, we provide a complete list of the TRIP maps which have upper bounds on complexity of at most 3n, except for one remaining case for which we conjecture such an upper bound to hold.

The development of low-loss reconfigurable integrated optical devices enables further research into technologies including photonic signal processing, analogue quantum computing, and optical neural networks. Here, we introduce digital patterning of coupled waveguide arrays as a platform capable of implementing unitary matrix operations. Determining the required device geometry for a specific optical output is computationally challenging and requires a robust and versatile inverse design protocol. In this work we present an approach using high speed neural network surrogate based gradient optimization, capable of predicting patterns of refractive index perturbations based on switching of the ultra-low loss chalcogenide phase change material, antimony tri-selinide ($\text{Sb}_{2}\text{Se}_{3}$). Results for a $3 \times 3$ silicon waveguide array are presented, demonstrating control of both amplitude and phase for each transmission matrix element. Network performance is studied using neural network optimization tools such as dataset augmentation and supplementation with random noise, resulting in an average fidelity of 0.94 for unitary matrix targets. Our results show that coupled waveguide arrays with perturbation patterns offer new routes for achieving programmable integrated photonics with a reduced footprint compared to conventional interferometer-mesh technology., Comment: 22 pages, 16 figures

Let $G$ be a smooth algebraic group over the field of rational functions of an excellent Dedekind scheme $S$ of equal characteristic $p>0.$ A N\'eron lft-model of $G$ is a smooth separated model $\mathscr{G} \to S$ of $G$ satisfying a universal property. Predicting whether a given $G$ admits such a model is a very delicate (and, in general, open) question if $S$ has infinitely many closed points, which is the subject of Conjecture I due to Bosch-L\"utkebohmert-Raynaud. This conjecture was recently proven by T. Suzuki and the author if the residue fields of $S$ at closed points are perfect, but refuted in general. The aim of the present paper is two-fold: firstly, we give a new construction of counterexamples which is more general and provides a conceptual explanation for the only counterexamples known previously, as well as providing many new counterexamples. Secondly, we shall give a new and elementary proof of Conjecture I in the case of perfect residue fields. Both parts make use of the concept of weakly permawound unipotent groups recently introduced by Rosengarten., Comment: 25 pages

Macroscopic fundamental diagrams (MFDs) and related network traffic dynamics models have received both theoretical support and empirical validation with the emergence of new data collection technologies. However, the existence of well-defined MFD curves can only be expected for traffic networks with specific topologies and is subject to various disturbances, most importantly hysteresis phenomena. This study aims to improve the understanding of hysteresis in Macroscopic Fundamental Diagrams and Network Exit Functions (NEFs) during rush hour conditions. We apply the LWR theory to a highway corridor featuring a location-dependent downstream bottleneck to identify a figure-eight hysteresis pattern, clockwise on the top and counter-clockwise on the bottom. We discuss why this general pattern is rare in practical scenarios, where a single clockwise loop is more typical. The paper discusses the impact of the road topology and demand parameters on the formation and intensity of hysteresis loops analytically. To substantiate these findings, we employ numerical simulations using the Cell Transmission Model (CTM). Our simulations show that even a slight reduction in the capacity of the homogeneous section can significantly decrease MFD hysteresis while maintaining outflow at the corridor's downstream end. These reductions can be achieved with minimal intervention through standard traffic control measures, such as dynamic speed limits or ramp metering.

E-commerce operations are essentially online, with customer orders arriving dynamically. However, very little is known about the performance of online policies for warehousing with respect to optimality, particularly for order picking and batching operations, which constitute a substantial portion of the total operating costs in warehouses. We aim to close this gap for one of the most prominent dynamic algorithms, namely reoptimization (Reopt), which reoptimizes the current solution each time when a new order arrives. We examine Reopt in the Online Order Batching, Sequencing, and Routing Problem (OOBSRP), in both cases when the picker uses either a manual pushcart or a robotic cart. Moreover, we examine the noninterventionist Reopt in the case of a manual pushcart, wherein picking instructions are provided exclusively at the depot. We establish analytical performance bounds employing worst-case and probabilistic analysis. We demonstrate that, under generic stochastic assumptions, Reopt is almost surely asymptotically optimal and, notably, we validate its near-optimal performance in computational experiments across a broad range of warehouse settings. These results underscore Reopt's relevance as a method for online warehousing applications.

High-performance programmable silicon photonic circuits are considered to be a critical part of next generation architectures for optical processing, photonic quantum circuits and neural networks. Low-loss optical phase change materials (PCMs) offer a promising route towards non-volatile free-form control of light. Here, we exploit direct-write digital patterning of waveguides using layers of the PCM Sb$_2$Se$_3$ with a thickness of up to 100 nm, demonstrating the ability to strongly increase the effect per pixel compared to previous implementations where much thinner PCM layers were used. We exploit the excellent refractive index matching between Sb$_2$Se$_3$ and silicon to achieve a low-loss hybrid platform for programmable photonics. A five-fold reduction in modulation length of a Mach-Zehnder interferometer is achieved compared to previous work using thin-film Sb$_2$Se$_3$ devices, decreased to 5 $\mu$m in this work. Application of the thicker PCM layers in direct-write digital programming of a multimode interferometer (MMI) shows a three-fold reduction of the number of programmed pixels to below 10 pixels per device. The demonstrated scaling of performance with PCM layer thickness is important for establishing the optimum working range for hybrid silicon-PCM devices and holds promise for achieving ultracompact programmable photonic circuits., Comment: 24 pages, 5 figures

Modelling longitudinal data is an important yet challenging task. These datasets can be high-dimensional, contain non-linear effects and time-varying covariates. Gaussian process (GP) prior-based variational autoencoders (VAEs) have emerged as a promising approach due to their ability to model time-series data. However, they are costly to train and struggle to fully exploit the rich covariates characteristic of longitudinal data, making them difficult for practitioners to use effectively. In this work, we leverage linear mixed models (LMMs) and amortized variational inference to provide conditional priors for VAEs, and propose LMM-VAE, a scalable, interpretable and identifiable model. We highlight theoretical connections between it and GP-based techniques, providing a unified framework for this class of methods. Our proposal performs competitively compared to existing approaches across simulated and real-world datasets., Comment: Under review

We have investigated the magnetic diffuse scattering of isostructural $S$ = 5/2 fcc antiferromagnets Ba$_2$MnTeO$_6$ and Ba$_2$MnWO$_6$ using polarized neutrons. Both materials display short-range correlated magnetism above their respective magnetic ordering temperatures of 20 K and 8 K. The spin correlations were analysed using a Reverse Monte Carlo approach. For Ba$_2$MnTeO$_6$, we find antiferromagnetic nearest-neighbor correlations along with ferromagnetic next-nearest neighbor correlations directly linked to the Type I order below $T_N$. For Ba$_2$MnWO$_6$, both the nearest-neighbor and next-nearest-neighbor spin correlations are antiferromagnetic in the paramagnetic state. The short-range spin correlations persist up to $T$ = 7$T_N$. The magnetic diffuse scattering was also fitted using Onsager reaction-field theory allowing us to evaluate the magnetic interactions in these materials. We obtained $J_1$ = -3.25(3) K and $J_2$ = 0.41(2) K for Ba$_2$MnTeO$_6$ and $J_1$ = -1.08(1) K and $J_2$ = -0.88(1) K for Ba$_2$MnWO$_6$. These interactions are comparable to previous results from inelastic neutron scattering experiments below $T_N$, which highlights the potential of the Onsager approach for the analysis of magnetic interactions., Comment: 21+4 pages, 5+3 figures

Twisted bilayer graphene (tBLG) near the magic angle is an interesting platform to study correlated electronic phases. These phases are gate-tunable and are closely related to the presence of flat electronic bands, isolated by single-particle band gaps. This allows electrostatically controlled confinement of charge carriers in the flat bands to explore the interplay between confinement, band renormalisation, electron-electron interactions and the moir\'e superlattice, potentially revealing key mechanisms underlying these electronic phases. Here, we show gate-controlled flat-band charge carrier confinement in near-magic-angle tBLG, resulting in well-tunable Coulomb blockade resonances arising from the charging of electrostatically defined islands in tBLG. Coulomb resonance measurements allow to study magnetic field-induced quantum oscillations in the density of states of the source-drain reservoirs, providing insight into the gate-tunable Fermi surfaces of tBLG. Comparison with tight-binding calculations emphasises the importance of displacement-field-induced band renormalisation, which is crucial for future advanced gate-tunable quantum devices and circuits in tBLG., Comment: 25 pages, 14 figures

On the 29th of May 2023, the LIGO-Virgo-KAGRA Collaboration observed a compact binary coalescence event consistent with a neutron star-black hole merger, though the heavier object of mass 2.5-4.5 $M_\odot$ would fall into the purported lower mass gap. An alternative explanation for apparent observations of events in this mass range has been suggested as strongly gravitationally lensed binary neutron stars. In this scenario, magnification would lead to the source appearing closer and heavier than it really is. Here, we investigate the chances and possible consequences for the GW230529 event to be gravitationally lensed. We find this would require high magnifications and we obtain low rates for observing such an event, with a relative fraction of lensed versus unlensed observed events of $2 \times 10^{-3}$ at most. When comparing the lensed and unlensed hypotheses accounting for the latest rates and population model, we find a 1/58 chance of lensing, disfavoring this option. Moreover, when the magnification is assumed to be strong enough to bring the mass of the heavier binary component below the standard limits on neutron star masses, we find high probability for the lighter object to have a sub-solar mass, making the binary even more exotic than a mass-gap neutron star-black hole system. Even when the secondary is not sub-solar, its tidal deformability would likely be measurable, which is not the case for GW230529. Finally, we do not find evidence for extra lensing signatures such as the arrival of additional lensed images, type-II image dephasing, or microlensing. Therefore, we conclude it is unlikely for GW230529 to be a strongly gravitationally lensed binary neutron star signal., Comment: 15 pages, 11 figures

Here we propose an extension of the (deterministic and the nondeterministic) finite automaton with translucent letters (DFAwtl and NFAwtl), which lies between these automata and their non-returning variants (that is, the nr-DFAwtl and the nr-NFAwtl). This new model works like a DFAwtl or an NFAwtl, but on seeing the end-of-tape marker, it may change its internal state and continue with its computation instead of just ending it, accepting or rejecting. This new type of automaton is called a repetitive deterministic or nondeterministic finite automaton with translucent letters (RDFAwtl or RNFAwtl). In the deterministic case, the new model is strictly more expressive than the DFAwtl, but less expressive than the nr-DFAwtl, while in the nondeterministic case, the new model is equivalent to the NFAwtl., Comment: In Proceedings NCMA 2024, arXiv:2409.06120

Random coalescent processes and branching processes are two fundamental constructs in the field of stochastic processes, each with a rich history and a wide range of applications. Though developed within distinct contexts, in this note we present a novel connection between a multi-type (vector) multiplicative coalescent process and a multi-type branching process with Poisson offspring distributions. More specifically, we show that the equations that govern the phenomenon of gelation in the vector multiplicative coalescent process are equivalent to the set of equations that yield the extinction probabilities of the corresponding multi-type Poisson branching process. We then leverage this connection with two applications, one in each direction. The first is a new quick proof of gelation in the vector multiplicative coalescent process using a well known result of branching processes, and the second is a new series expression for the extinction probabilities of the multi-type Poisson branching process using results derived from the theory of vector multiplicative coalescence. While the correspondence is fairly straightforward, it illuminates a deep connection between these two paradigms which we hope will continue to reveal new insights and potential for cross-disciplinary research., Comment: 9 pages, 1 figure

gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library and a back-end with highly optimized CUDA kernels. gsplat offers numerous features that enhance the optimization of Gaussian Splatting models, which include optimization improvements for speed, memory, and convergence times. Experimental results demonstrate that gsplat achieves up to 10% less training time and 4x less memory than the original implementation. Utilized in several research projects, gsplat is actively maintained on GitHub. Source code is available at https://github.com/nerfstudio-project/gsplat under Apache License 2.0. We welcome contributions from the open-source community., Comment: 17 pages, 2 figures, JMLR MLOSS

We give a complete classification, up to birational equivalence, of all fibrations by plane projective rational quartic curves in characteristic two., Comment: 31 pages. Comments welcome at any time!

We propose a simple estimator that allows to calculate the absolute value of a system's partition function from a finite sampling of its canonical ensemble. The estimator utilizes a volume correction term to compensate the effect that the finite sampling cannot cover the whole configuration space. As a proof of concept, the estimator is applied to calculate the partition function for several model systems, and the results are compared with the numerically exact solutions. Excellent agreement is found, demonstrating that a solution for an efficient calculation of partition functions is possible.

Objective: To explore and compare the performance of ChatGPT and other state-of-the-art LLMs on domain-specific NER tasks covering different entity types and domains in TCM against COVID-19 literature. Methods: We established a dataset of 389 articles on TCM against COVID-19, and manually annotated 48 of them with 6 types of entities belonging to 3 domains as the ground truth, against which the NER performance of LLMs can be assessed. We then performed NER tasks for the 6 entity types using ChatGPT (GPT-3.5 and GPT-4) and 4 state-of-the-art BERT-based question-answering (QA) models (RoBERTa, MiniLM, PubMedBERT and SciBERT) without prior training on the specific task. A domain fine-tuned model (GSAP-NER) was also applied for a comprehensive comparison. Results: The overall performance of LLMs varied significantly in exact match and fuzzy match. In the fuzzy match, ChatGPT surpassed BERT-based QA models in 5 out of 6 tasks, while in exact match, BERT-based QA models outperformed ChatGPT in 5 out of 6 tasks but with a smaller F-1 difference. GPT-4 showed a significant advantage over other models in fuzzy match, especially on the entity type of TCM formula and the Chinese patent drug (TFD) and ingredient (IG). Although GPT-4 outperformed BERT-based models on entity type of herb, target, and research method, none of the F-1 scores exceeded 0.5. GSAP-NER, outperformed GPT-4 in terms of F-1 by a slight margin on RM. ChatGPT achieved considerably higher recalls than precisions, particularly in the fuzzy match. Conclusions: The NER performance of LLMs is highly dependent on the entity type, and their performance varies across application scenarios. ChatGPT could be a good choice for scenarios where high recall is favored. However, for knowledge acquisition in rigorous scenarios, neither ChatGPT nor BERT-based QA models are off-the-shelf tools for professional practitioners., Comment: 22 pages with 2 figures

The dense and dynamic environments within active galactic nuclei (AGN) accretion disks may serve as prolific birthplaces for binary black holes (BBHs) and one possible origin for some of the BBHs detected by gravitational-wave (GW) observatories. We show that a considerable fraction of the BBH in AGN disks will be strongly lensed by the central supermassive black hole (SMBH). Thus, the non-observation of lensed GW signals can be used to constrain the fraction of BBH binaries residing in AGN disks. The non-detection of lensing with current ${\cal O}(100)$ detections will be sufficient to start placing constraints on the fraction of BBHs living within accretion disks near the SMBH. In the next-generation detectors era, with ${\cal O}(10^5)$ BBH observations and no lensed events, we will be able to rule out most migration traps as dominant birthplaces of BBH mergers; moreover, we will be able to constrain the minimum size of the accretion disk. On the other hand, should AGNs constitute a major formation channel, lensed events from AGNs will become prominent in the future., Comment: 8 pages, 4 figures

Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.

Modern TTS systems are capable of creating highly realistic and natural-sounding speech. Despite these developments, the process of customizing TTS voices remains a complex task, mostly requiring the expertise of specialists within the field. One reason for this is the utilization of deep learning models, which are characterized by their expansive, non-interpretable parameter spaces, restricting the feasibility of manual customization. In this paper, we present a novel human-in-the-loop paradigm based on an evolutionary algorithm for directly interacting with the parameter space of a neural TTS model. We integrated our approach into a user-friendly graphical user interface that allows users to efficiently create original voices. Those voices can then be used with the backbone TTS model, for which we provide a Python API. Further, we present the results of a user study exploring the capabilities of VoiceX. We show that VoiceX is an appropriate tool for creating individual, custom voices.

We study the collection and delivery problem of biomedical specimens (CDSP) with multiple trips, time windows, a homogeneous fleet, and the objective of minimizing total completion time of delivery requests. This is a prominent problem in healthcare logistics, where specimens (blood, plasma, urin etc.) collected from patients in doctor's offices and hospitals are transported to a central laboratory for advanced analysis. To the best of our knowledge, available exact solution approaches for CDSP have been able to solve only small instances with up to 9 delivery requests. In this paper, we propose a two-index mixed-integer programming formulation that, when used with an off-the-shelf solver, results in a fast exact solution approach. Computational experiments on a benchmark data set confirm that the proposed formulation outperforms both the state-of-the-art model and the state-of-the-art metaheuristic from the literature, solving 80 out of 168 benchmark instances to optimality, including a significant number of instances with 100 delivery requests., Comment: This work has been submitted for possible publication

Two-dimensional materials (2DMs) have been widely investigated because of their potential for heterogeneous integration with modern electronics. However, several major challenges remain, such as the deposition of high-quality dielectrics on 2DMs and the tuning of the 2DM doping levels. Here, we report a scalable plasma-enhanced atomic layer deposition (PEALD) process for direct deposition of a nonstoichiometric aluminum oxide (AlOX) dielectric, overcoming the damage issues associated with conventional methods. Furthermore, we control the thickness of the dielectric layer to systematically tune the doping level of 2DMs. The experimental results demonstrate successful deposition without detectable damage, as confirmed by Raman spectroscopy and electrical measurements. Our method enables tuning of the Dirac and threshold voltages of back-gated graphene and MoS${_2}$ field-effect transistors (FETs), respectively, while also increasing the charge carrier mobility in both device types. We further demonstrate the method in top-gated MoS${_2}$ FETs with double-stack dielectric layers (AlOX+Al${_2}$O${_3}$), achieving critical breakdown field strengths of 7 MV/cm and improved mobility compared with the back gate configuration. In summary, we present a PEALD process that offers a scalable and low-damage solution for dielectric deposition on 2DMs, opening new possibilities for precise tuning of device characteristics in heterogeneous electronic circuits., Comment: 28 pages

Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700$\,$attoseconds and peak powers up to 200$\,$GW at $\sim$ 1$\,$keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.

We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.

In this paper we propose a computational approach to proving the Birkhoff conjecture on the restricted three-body problem, which asserts the existence of a disk-like global surface of section. Birkhoff had conjectured this surface of section as a tool to prove existence of a direct periodic orbit. Using techniques from validated numerics we prove the existence of an approximately circular direct orbit for a wide range of mass parameters and Jacobi energies. We also provide methods to rigorously compute the Conley-Zehnder index of periodic Hamiltonian orbits using computational tools, thus giving some initial steps for developing computational Floer homology and providing the means to prove the Birkhoff conjecture via symplectic topology. We apply this method to various symmetric orbits in the restricted three-body problem.

Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyradiomics and MRCradiomics libraries. Feature selection was performed using the maximum relevance minimum redundancy (MRMR) technique. We aimed to enhance clinical decision support through multimodal learning and feature fusion. Results: Our SVM model, utilizing combined features from Pyradiomics and MRCradiomics, achieved an AUC of 0.74 on the Multi-Improd dataset (Siemens scanner) but decreased to 0.60 on the Philips test set. The RF model showed similar trends, with notable robustness for models using Pyradiomics features alone (AUC of 0.78 on Philips). Conclusions: These findings demonstrate the potential of multimodal feature integration to improve the robustness and generalizability of machine-learning models for clinical decision support in prostate cancer detection. This study marks a significant step towards developing reliable AI-driven diagnostic tools that maintain efficacy across various imaging platforms.

In this paper, we introduce a novel model for random hypergraphs based on weighted random connection models. In accordance with the standard theory for hypergraphs, this model is constructed from a bipartite graph. In our stochastic model, both vertex sets of this bipartite graph form marked Poisson point processes and the connection radius is inversely proportional to a product of suitable powers of the marks. Hence, our model is a common generalization of weighted random connection models and AB random geometric graphs. For this hypergraph model, we investigate the limit theory of a variety of graph-theoretic and topological characteristics such as higher-order degree distribution, Betti numbers of the associated Dowker complex as well as simplex counts. In particular, for the latter quantity we identify regimes of convergence to normal and to stable distribution depending on the heavy-tailedness of the weight distribution. We conclude our investigation by a simulation study and an application to the collaboration network extracted from the arXiv dataset., Comment: 29 pages. 11 figures

In situ imageomics leverages machine learning techniques to infer biological traits from images collected in the field, or in situ, to study individuals organisms, groups of wildlife, and whole ecosystems. Such datasets provide real-time social and environmental context to inferred biological traits, which can enable new, data-driven conservation and ecosystem management. The development of machine learning techniques to extract biological traits from images are impeded by the volume and quality data required to train these models. Autonomous, unmanned aerial vehicles (UAVs), are well suited to collect in situ imageomics data as they can traverse remote terrain quickly to collect large volumes of data with greater consistency and reliability compared to manually piloted UAV missions. However, little guidance exists on optimizing autonomous UAV missions for the purposes of remote sensing for conservation and biodiversity monitoring. The UAV video dataset curated by KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos required three weeks to collect, a time-consuming and expensive endeavor. Our analysis of KABR revealed that a third of the videos gathered were unusable for the purposes of inferring wildlife behavior. We analyzed the flight telemetry data from portions of UAV videos that were usable for inferring wildlife behavior, and demonstrate how these insights can be integrated into an autonomous remote sensing system to track wildlife in real time. Our autonomous remote sensing system optimizes the UAV's actions to increase the yield of usable data, and matches the flight path of an expert pilot with an 87% accuracy rate, representing an 18.2% improvement in accuracy over previously proposed methods.

The integration of van der Waals (vdW) materials into photonic devices has laid out a foundation for many new quantum and optoelectronic applications. Despite tremendous progress in the nanofabrication of photonic building blocks from vdW crystals, there are still limitations, specifically with large-area devices and masking. Here, we focus on hexagonal boron nitride (hBN) as a vdW material and present a double etch method that overcomes problems associated with methods that employ metallic films and resist-based films for masking. Efficacy of the developed protocol is demonstrated by designing and fabricating a set of functional photonic components including waveguides, ring resonators and photonic crystal cavities. The functionality of the fabricated structures is demonstrated through optical characterization over several key spectral ranges. These include the near-infrared and blue ranges, where the hBN boron vacancy (VB-) spin defects and the coherent B center quantum emitters emit, respectively. The double etch method enables fabrication of high-quality factor optical cavities and constitutes a promising pathway toward on-chip integration of vdW materials., Comment: 10 pages, 4 figures

SARS-CoV-2 usually evolves at a relatively constant rate over time. Occasionally, however, lineages arise with higher-than-expected numbers of mutations given the date of sampling. Such lineages can arise for a variety of reasons, including selection pressures imposed by evolution during a chronic infection or exposure to mutation-inducing drugs like molnupiravir. We have developed an open-source web-based application (SMDP: SARS-CoV-2 Mutation Distribution Profiler; https://eringill.shinyapps.io/covid_mutation_distributions) that compares a list of user-submitted lineage-defining mutations with established mutation distributions including those observed during (1) the first nine months of the pandemic, (2) during the global transmission of Omicron, (3) during the chronic infection of immunocompromised patients, and (4) during zoonotic spillover from humans to deer. The application calculates the most likely distribution for the user's mutation list and displays log likelihoods for all distributions. In addition, the transition:transversion ratio of the user's list is calculated to determine whether there is evidence of exposure to a mutation-inducing drug such as molnupiravir and indicates whether the list contains mutations in the proofreading domain of nsp14 which could lead to a higher-than-expected mutation rate in the lineage. This tool will be useful for public health and researchers seeking to rapidly infer evolutionary histories of SARS-CoV-2 variants, which can aid risk assessment and public health responses.

'$ler$' is a statistics-based Python package specifically designed for computing detectable rates of both lensed and unlensed GW events, catering to the requirements of the LIGO-Virgo-KAGRA Scientific Collaboration and astrophysics research scholars. The core functionality of '$ler$' intricately hinges upon the interplay of various components which include sampling the properties of compact-binary sources, lens galaxies characteristics, solving lens equations to derive properties of resultant images, and computing detectable GW rates. This comprehensive functionality builds on the leveraging of array operations and linear algebra from the $numpy$ library, enhanced by interpolation methods from $scipy$ and Python's $multiprocessing$ capabilities. Efficiency is further boosted by the $numba$ library's Just-In-Time ($njit$) compilation, optimizing extensive numerical computations and employing the inverse transform sampling method to replace more cumbersome rejection sampling. The modular design of '$ler$' not only optimizes speed and functionality but also ensures adaptability and upgradability, supporting the integration of additional statistics as research evolves. Currently, '$ler$' is an important tool in generating simulated GW events, both lensed and unlensed, and provides astrophysically accurate distributions of event-related parameters for both detectable and non-detectable events. This functionality aids in event validation and enhances the forecasting of detection capabilities across various GW detectors to study such events. The architecture of the '$ler$' API facilitates seamless compatibility with other software packages, allowing researchers to integrate and utilize its functionalities based on specific scientific requirements., Comment: 5 pages, 1 Logo in each of the pages, this is for the JOSS publication

We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals. Within a single model, we demonstrate 3D faces generated from artistic sketches, 2D face landmarks, Canny edges, FLAME face model parameters, portrait photos, or text prompts. Our approach is based on a diffusion process that generates 3D geometry in a 2D parameterized UV domain. Geometry generation passes each conditioning signal through a set of cross-attention layers (IP-Adapter), one set for each user-defined conditioning signal. The result is an easy-to-use 3D face generation tool that produces high resolution geometry with fine-grain user control.

The paper provides a detailed proof that complicated motion exists in Shilnikov's scenario of a smooth vectorfield $V$ on $mathbb{R}^3$ with $V(0)=0$ so that the equation $x'=V(x)$ has a homoclinic solution $h$ with $\lim_{|t|\to\infty}h(t)=0$, and $DV(0)$ has eigenvalues $u>0$ and $\sigma\pm\mu$, $\sigma<0<\mu$, with $0<\sigma+u$., Comment: 33 pages, 8 figures

We have previously presented a version of the Weak Equivalence Principle for a quantum particle as an exact analog of the classical case, based on the Heisenberg picture analysis of free particle motion. Here, we take that to a full formalism of quantum mechanics in a generic curved space(time). Our basic perspective is to take seriously the noncommutative symplectic geometry corresponding to the quantum observable algebra. Particle position coordinate transformations and a nontrivial metric assigning an invariant inner product to vectors, and covectors, are implemented accordingly. That allows an analog to the classical picture of the phase space as the cotangent bundle. The mass-independent quantum geodesic equations as equations of free particle motion under a generic metric as a quantum observable are obtained from an invariant Hamiltonian. Hermiticity of momentum observables is to be taken as reference frame dependent. Our results have a big contrast to the alternative obtained based on the Schr\"odinger wavefunction representation. Hence, the work points to a very different approach to quantum gravity., Comment: 24 pages in RevTex, no figure

The phenomenology of hadronic states at high energy is well described in the framework of Quantum Chromodynamics. The theory, well established by now, cannot be applied to the description of quark-antiquark states at low energy unless their degrees of freedom are treated as effective ones, a condition resulting from confinement. Own to the similarities with other quantum many-body systems we have adopted a non-perturbative scheme where quarks are treated as quasiparticles, which further interact between them, allowing meson-like states to be described as collective superposition of quasiparticle-pairs. The results of the calculations, performed in the context of the random phase approximation method, show that this scheme is a suitable one to describe meson states up to energies of the order of couple of GeV.

A rather clear problem has remained in black hole physics: localizing black holes. One of the recent theoretical ways proposed to identify black hole mergers' hosts is through multi-messenger gravitational lensing: matching the properties of a lensed galactic host with those of a lensed gravitational wave. This paper reviews the most recent literature and introduces some of the ongoing work on the localization of binary black holes and their host galaxies through lensing of gravitational waves and their electromagnetically-bright hosts., Comment: 17 pages, 4 figures. Proceedings from the Royal Society Meeting for Multi-messenger Lensing (2024). Submitted to Phil. Trans. A