Search

This paper presents a control variate-based Markov chain Monte Carlo algorithm for efficient sampling from the probability simplex, with a focus on applications in large-scale Bayesian models such as latent Dirichlet allocation. Standard Markov chain Monte Carlo methods, particularly those based on Langevin diffusions, suffer from significant discretization errors near the boundaries of the simplex, which are exacerbated in sparse data settings. To address this issue, we propose an improved approach based on the stochastic Cox--Ingersoll--Ross process, which eliminates discretization errors and enables exact transition densities. Our key contribution is the integration of control variates, which significantly reduces the variance of the stochastic gradient estimator in the Cox--Ingersoll--Ross process, thereby enhancing the accuracy and computational efficiency of the algorithm. We provide a theoretical analysis showing the variance reduction achieved by the control variates approach and demonstrate the practical advantages of our method in data subsampling settings. Empirical results on large datasets show that the proposed method outperforms existing approaches in both accuracy and scalability.

Human longevity leaders with remarkably long lifespan play a crucial role in the advancement of longevity research. In this paper, we propose a stochastic model to describe the evolution of the age of the oldest person in the world by a Markov process, in which we assume that the births of the individuals follow a Poisson process with increasing intensity, lifespans of individuals are independent and can be characterized by a gamma-Gompertz distribution with time-dependent parameters. We utilize a dataset of the world's oldest person title holders since 1955, and we compute the maximum likelihood estimate for the parameters iteratively by numerical integration. Based on our preliminary estimates, the model provides a good fit to the data and shows that the age of the oldest person alive increases over time in the future. The estimated parameters enable us to describe the distribution of the age of the record holder process at a future time point., Comment: 14 pages, 5 figures

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.

In recent years, the number and importance of autonomous racing leagues, and consequently the number of studies on them, has been growing. The seamless integration between different series has gained attention due to the scene's diversity. However, the high cost of full scale racing makes it a more accessible development model, to research at smaller form factors and scale up the achieved results. This paper presents a scalable architecture designed for autonomous racing that emphasizes modularity, adaptability to diverse configurations, and the ability to supervise parallel execution of pipelines that allows the use of different dynamic strategies. The system showcased consistent racing performance across different environments, demonstrated through successful participation in two relevant competitions. The results confirm the architecture's scalability and versatility, providing a robust foundation for the development of competitive autonomous racing systems. The successful application in real-world scenarios validates its practical effectiveness and highlights its potential for future advancements in autonomous racing technology.

We give a method to determine an explicit solution to a system of two inhomogeneous linear recursive sequences of higher order. Our approach can be used efficiently in solving certain combinatorial problems. We finish the paper by considering a tiling problem with black and white dominoes, and we use the method as a demonstration to find the solution., Comment: 11 pages, 3 figures

We compare the effective action of the scale factor obtained from lattice quantum gravity (in the form of Causal Dynamical Triangulations (CDT)) to the corresponding effective action obtained from the simplest Functional Renormalization Group (FRG) calculation. In this way, we can identify the generic infinite four-volume limit of the lattice theory in the so-called de Sitter phase with the Gaussian fixed-point limit or the IR fixed-point limit obtained by FRG. We also show how to identify a putative UV lattice gravity fixed point. Our Monte Carlo simulations of CDT allow for the existence of such a UV fixed point, although the data precision does not yet provide a proof of its existence. The concept of a correlation length relevant for the lattice gravity fixed points is argued to be different from the concept of correlation lengths encountered in field theories in a fixed spacetime background.

In response to global concerns regarding air quality and the environmental impact of greenhouse gas emissions, detecting and quantifying sources of emissions has become critical. To understand this impact and target mitigations effectively, methods for accurate quantification of greenhouse gas emissions are required. In this paper, we focus on the inversion of concentration measurements to estimate source location and emission rate. In practice, such methods often rely on atmospheric stability class-based Gaussian plume dispersion models. However, incorrectly identifying the atmospheric stability class can lead to significant bias in estimates of source characteristics. We present a robust approach that reduces this bias by jointly estimating the horizontal and vertical dispersion parameters of the Gaussian plume model, together with source location and emission rate, atmospheric background concentration, and sensor measurement error variance. Uncertainty in parameter estimation is quantified through probabilistic inversion using gradient-based MCMC methods. A simulation study is performed to assess the inversion methodology. We then focus on inference for the published Chilbolton dataset which contains controlled methane releases and demonstrates the practical benefits of estimating dispersion parameters in source inversion problems., Comment: 21 pages, 9 figures

In this paper, we generalise an interesting geometry problem from the 1995 edition of the International Mathematical Olympiad (IMO) using analytic geometry tools., Comment: 9 pages, 5 figures

The Metropolis-Hastings (MH) algorithm is one of the most widely used Markov Chain Monte Carlo schemes for generating samples from Bayesian posterior distributions. The algorithm is asymptotically exact, flexible and easy to implement. However, in the context of Bayesian inference for large datasets, evaluating the likelihood on the full data for thousands of iterations until convergence can be prohibitively expensive. This paper introduces a new subsample MH algorithm that satisfies detailed balance with respect to the target posterior and utilises control variates to enable exact, efficient Bayesian inference on datasets with large numbers of observations. Through theoretical results, simulation experiments and real-world applications on certain generalised linear models, we demonstrate that our method requires substantially smaller subsamples and is computationally more efficient than the standard MH algorithm and other exact subsample MH algorithms., Comment: 44 pages, 10 tables, 7 tables

This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI., Comment: Preprint of upcoming book published by Cambridge University Press. Comments and feedback are welcome

Hot subdwarfs (sdO/B) are the stripped helium cores of red giants formed by binary interactions. Close hot subdwarf binaries with massive white dwarf companions have been proposed as possible progenitors of thermonuclear supernovae type Ia (SN Ia). If the supernova is triggered by stable mass transfer from the helium star, the companion should survive the explosion and should be accelerated to high velocities. The hypervelocity star US 708 is regarded as the prototype for such an ejected companion. To find more of those objects we conducted an extensive spectroscopic survey. Candidates for such fast stars have been selected from the spectroscopic database of the Sloan Digital Sky Survey (SDSS) and several ground-based proper motion surveys. Follow-up spectroscopy has been obtained with several 4m- to 10m-class telescopes. Combining the results from quantitative spectroscopic analyses with space-based astrometry from \textit{Gaia} Early Data Release 3 (EDR3) we determined the atmospheric and kinematic parameters of 53 fast hot subdwarf stars. None of these stars is unbound to the Galaxy, although some have Galactic restframe velocities close to the Galactic escape velocity. 21 stars are apparently single objects, which crossed the Galactic disc within their lifetimes in the sdO/B stage and could be regarded as potential candidates for the SN Ia ejection scenario. However, the properties of the full sample are more consistent with a pure old Galactic halo population. We therefore conclude that the fast sdO/B stars we found are likely to be extreme halo stars., Comment: 22 pages, A&A accepted

Based on the combinatorial interpretation of the ordered Bell numbers, which count all the ordered partitions of the set $[n]=\{1,2,\dots,n\}$, we introduce the Fibonacci partition as a Fibonacci permutation of its blocks. Then we define the Fibonacci-Fubini numbers that count the total number of Fibonacci partitions of $[n]$. We study the classical properties of this sequence (generating function, explicit and Dobi\'nski-like formula, etc.), we give combinatorial interpretation, and we extensively examine the Fibonacci-Fubini arithmetic triangle. We give some associate linear recurrence sequences, where in some sequences the Stirling numbers of the first and second kinds appear as well., Comment: 18 pages, 3 figures, 3 tables

Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversible continuous-time processes endowed with momentum, and hence can mix better than standard reversible MCMC samplers. Furthermore, they can incorporate exact sub-sampling schemes which only require access to a single (randomly selected) data point at each iteration, yet without introducing bias to the algorithm's stationary distribution. However, the range of models for which PDMPs can be used, particularly with sub-sampling, is limited. We propose approximate simulation of PDMPs with sub-sampling for scalable sampling from posterior distributions. The approximation takes the form of an Euler approximation to the true PDMP dynamics, and involves using an estimate of the gradient of the log-posterior based on a data sub-sample. We thus call this class of algorithms stochastic-gradient PDMPs. Importantly, the trajectories of stochastic-gradient PDMPs are continuous and can leverage recent ideas for sampling from measures with continuous and atomic components. We show these methods are easy to implement, present results on their approximation error and demonstrate numerically that this class of algorithms has similar efficiency to, but is more robust than, stochastic gradient Langevin dynamics.

Coherent light sources emitting in the terahertz range are highly sought after for fundamental research and applications. THz lasers rely on achieving population inversion. We demonstrate the generation of THz radiation using nitrogen-vacancy (NV) centers in a diamond single crystal. Population inversion is achieved through the Zeeman splitting of the $S=1$ state in $15\ \text{T}$, resulting in a splitting of $0.42\ \text{THz}$, where the middle $S_z=0$ sublevel is selectively pumped by visible light. To detect the THz radiation, we utilize a phase-sensitive THz setup, optimized for electron spin resonance measurements (ESR). We determine the spin-lattice relaxation time up to $15\ \text{T}$ using the light-induced ESR measurement, which shows the dominance of phonon-mediated relaxation and the high efficacy of the population inversion. The THz radiation is tunable by the magnetic field, thus these findings may lead to the next generation of tunable coherent THz sources., Comment: 45 pages, 14 figures

Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this merging without imposing distributional assumptions on the posteriors. We propose using diffusion generative modelling to fit density approximations to the subposterior distributions. This approach outperforms existing methods on challenging merging problems, while its computational cost scales more efficiently to high dimensional problems than existing density estimation approaches., Comment: 16 pages, 5 figures

This paper presents necessary, sufficient, and equivalent conditions for the spherical convexity of non-homogeneous quadratic functions. In addition to motivating this study and identifying useful criteria for determining whether such functions are spherically convex, we discovered surprising properties that distinguish spherically convex quadratic functions from their geodesically convex counterparts in both hyperbolic and Euclidean spaces. Since spherically convex functions over the entire sphere are constant, we restricted our focus to proper spherically convex subsets of the sphere. Although most of our results pertain to non-homogeneous quadratic functions on the spherically convex set of unit vectors with positive coordinates, we also present findings for more general spherically convex sets. Beyond the general non-homogeneous quadratic functions, we consider explicit special cases where the matrix in the function's definition is of a specific type, such as positive, diagonal, and Z-matrix.

This paper describes how to propagate wavefields for arbitrary numbers of traditional time steps in a single step, called a superstep. We show how to construct operators that accomplish this task for finite-difference time domain schemes, including temporal first-order schemes in isotropic, anisotropic and elastic media, as well as temporal second-order schemes for acoustic media. This task is achieved by implementing a computational tradeoff differing from traditional single step wavefield propagators by precomputing propagator matrices for each model location for k timesteps (a superstep) and using these propagator matrices to advance the wavefield k time steps at once. This tradeoff separates the physics of the propagator matrix computation from the computer science of wavefield propagation and allows each discipline to provide their optimal modular solutions.

In recent years, interest in gradient-based optimization over Riemannian manifolds has surged. However, a significant challenge lies in the reliance on hyperparameters, especially the learning rate, which requires meticulous tuning by practitioners to ensure convergence at a suitable rate. In this work, we introduce innovative learning-rate-free algorithms for stochastic optimization over Riemannian manifolds, eliminating the need for hand-tuning and providing a more robust and user-friendly approach. We establish high probability convergence guarantees that are optimal, up to logarithmic factors, compared to the best-known optimally tuned rate in the deterministic setting. Our approach is validated through numerical experiments, demonstrating competitive performance against learning-rate-dependent algorithms., Comment: ICML 2024

Recent results in the literature indicate that artificial neural networks (ANNs) can outperform the dynamic factor model (DFM) in terms of the accuracy of GDP nowcasts. Compared to the DFM, the performance advantage of these highly flexible, nonlinear estimators is particularly evident in periods of recessions and structural breaks. From the perspective of policy-makers, however, nowcasts are the most useful when they are conveyed with uncertainty attached to them. While the DFM and other classical time series approaches analytically derive the predictive (conditional) distribution for GDP growth, ANNs can only produce point nowcasts based on their default training procedure (backpropagation). To fill this gap, first in the literature, we adapt two different deep learning algorithms that enable ANNs to generate density nowcasts for U.S. GDP growth: Bayes by Backprop and Monte Carlo dropout. The accuracy of point nowcasts, defined as the mean of the empirical predictive distribution, is evaluated relative to a naive constant growth model for GDP and a benchmark DFM specification. Using a 1D CNN as the underlying ANN architecture, both algorithms outperform those benchmarks during the evaluation period (2012:Q1 -- 2022:Q4). Furthermore, both algorithms are able to dynamically adjust the location (mean), scale (variance), and shape (skew) of the empirical predictive distribution. The results indicate that both Bayes by Backprop and Monte Carlo dropout can effectively augment the scope and functionality of ANNs, rendering them a fully compatible and competitive alternative for classical time series approaches.

Continuous normalizing flows (CNFs) learn the probability path between a reference distribution and a target distribution by modeling the vector field generating said path using neural networks. Recently, Lipman et al. (2022) introduced a simple and inexpensive method for training CNFs in generative modeling, termed flow matching (FM). In this paper, we repurpose this method for probabilistic inference by incorporating Markovian sampling methods in evaluating the FM objective, and using the learned CNF to improve Monte Carlo sampling. Specifically, we propose an adaptive Markov chain Monte Carlo (MCMC) algorithm, which combines a local Markov transition kernel with a non-local, flow-informed transition kernel, defined using a CNF. This CNF is adapted on-the-fly using samples from the Markov chain, which are used to specify the probability path for the FM objective. Our method also includes an adaptive tempering mechanism that allows the discovery of multiple modes in the target distribution. Under mild assumptions, we establish convergence of our method to a local optimum of the FM objective. We then benchmark our approach on several synthetic and real-world examples, achieving similar performance to other state-of-the-art methods, but often at a significantly lower computational cost.

The stochastic dynamics of a rigid inclusion constrained to move on a curved surface has many applications in biological and soft matter physics, ranging from the diffusion of passive or active membrane proteins to the motion of phoretic particles on liquid-liquid interfaces. Here we construct intrinsic Langevin equations for an oriented rigid inclusion on a curved surface using Cartan's method of moving frames. We first derive the Hamiltonian equations of motion for the translational and rotational momenta in the body frame. Surprisingly, surface curvature couples the linear and angular momenta of the inclusion. We then add to the Hamiltonian equations linear friction, white noise and arbitrary configuration-dependent forces and torques to obtain intrinsic Langevin equations of motion in phase space. We provide the integrability conditions, made non-trivial by surface curvature, for the forces and torques to admit a potential, thus distinguishing between passive and active stochastic motion. We derive the corresponding Fokker-Planck equation in geometric form and obtain fluctuation-dissipation relations that ensure Gibbsian equilibrium. We extract the overdamped equations of motion by adiabatically eliminating the momenta from the Fokker-Planck equation, showing how a peculiar cancellation leads to the naively expected Smoluchowski limit. The overdamped equations can be used for accurate and efficient intrinsic Brownian dynamics simulations of passive, driven and active diffusion processes on curved surfaces. Our work generalises to the collective dynamics of many inclusions on curved surfaces., Comment: 17 pages, 2 figures

We present the list of variable stars we found in the \kep\ superstamp data covering approximately nine arcminutes from the central region of NGC6819. This is a continuation of our work presented by Sanjayan et al.(2022a). We classified the variable stars based on the variability type and we established their cluster membership based on the available Gaia Data Release 3 astrometry. Our search revealed 385 variable stars but only 128 were found to be cluster members. In the case of eclipsing and contact binaries we calculated the mid-times of eclipses and derived ephemerides. We searched for eclipse timing variation using the observed minus calculated diagrams. Only five objects show significant orbital period variation. We used isochrones calculated within the MESA Isochrones and Stellar Tracks project and derived the average age (2.54 Gyr), average distance (2.3 kpc) and iron content [Fe/H] = -0.01(2), of NGC6819. We confirm this distance by the one derived from Gaia astrometry of the cluster members with membership probabilities greater than 0.9.

Although supernovae is a well-known endpoint of an accreting white dwarf, alternative theoretical possibilities has been discussing broadly, such as the accretion-induced collapse (AIC) event as the endpoint of oxygen-neon (ONe) white dwarfs, either accreting up to or merging to excess the Chandrasekhar limit (the maximum mass of a stable white dwarf). AIC is an important channel to form neutron stars, especially for those unusual systems, which are hardly produced by core-collapse supernovae. However, the observational evidences for this theoretical predicted event and its progenitor are all very limited. In all of the known progenitors, white dwarfs increase in mass by accretion. Here, we report the discovery of an intriguing binary system Lan 11, consisted of a stripped core-helium-burning hot subdwarf and an unseen compact object of 1.08 to 1.35 $M_{\odot}$. Our binary population synthesis calculations, along with the absence of detection from the deep radio observations of the Five-hundred-meter Aperture Spherical Radio Telescope, strongly suggest that the latter is an ONe white dwarf. The total mass of this binary is 1.67 to 1.92 $M_{\odot}$}, significantly excessing the Chandrasekhar limit. The reproduction of its evolutionary history indicates that the unique system has undergone two phases of common envelope ejections, implying a born nature of this massive ONe white dwarf rather than an accretion growth from its companion. These results, together with short orbital period of this binary (3.65 hours), suggest that this system will merge in 500-540 Myr, largely triggering an AIC event, although the possibility of type Ia supernova cannot be fully ruled out. This finding greatly provides valuable constraints on our understanding of stellar endpoints, whatever leading to an AIC or a supernova., Comment: 25 pages, 14 figures

Is artificial consciousness theoretically possible? Is it plausible? If so, is it technically feasible? To make progress on these questions, it is necessary to lay some groundwork clarifying the logical and empirical conditions for artificial consciousness to arise and the meaning of relevant terms involved. Consciousness is a polysemic word: researchers from different fields, including neuroscience, Artificial Intelligence, robotics, and philosophy, among others, sometimes use different terms in order to refer to the same phenomena or the same terms to refer to different phenomena. In fact, if we want to pursue artificial consciousness, a proper definition of the key concepts is required. Here, after some logical and conceptual preliminaries, we argue for the necessity of using dimensions and profiles of consciousness for a balanced discussion about their possible instantiation or realisation in artificial systems. Our primary goal in this paper is to review the main theoretical questions that arise in the domain of artificial consciousness. On the basis of this review, we propose to assess the issue of artificial consciousness within a multidimensional account. The theoretical possibility of artificial consciousness is already presumed within some theoretical frameworks; however, empirical possibility cannot simply be deduced from these frameworks but needs independent empirical validation. We break down the complexity of consciousness by identifying constituents, components, and dimensions, and reflect pragmatically about the general challenges confronting the creation of artificial consciousness. Despite these challenges, we outline a research strategy for showing how "awareness" as we propose to understand it could plausibly be realised in artificial systems.

One of the most popular and studied recursive series is the Fibonacci sequence. It is challenging to see how Fibonacci numbers can be used to generate other recursive sequences. In our article, we describe some families of integer recurrence sequences as rational polynomial linear combinations of Fibonacci numbers., Comment: 9 pages

Several articles deal with tilings with various shapes, and also a very frequent type of combinatorics is to examine the walks on graphs or on grids. We combine these two things and give the numbers of the shortest walks crossing the tiled $(1\times n)$ and $(2\times n)$ square grids by covering them with squares and dominoes. We describe these numbers not only recursively, but also as rational polynomial linear combinations of Fibonacci numbers., Comment: 16 pages, 11 figures

Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatial phenomenon of interest. It is common for predictors to also be measured on different spatial supports than the response variables. Although there is no standard way to work with spatial data with different supports, a prevalent approach used by practitioners has been to use downscaling or interpolation to project all the variables of analysis towards a common support, and then using standard spatial models. The main disadvantage with this approach is that simple interpolation can introduce biases and, more importantly, the uncertainty associated with the change of support is not taken into account in parameter estimation. In this article, we propose a Bayesian spatial latent Gaussian model that can handle data with different rectilinear supports in both the response variable and predictors. Our approach allows to handle changes of support more naturally according to the properties of the spatial stochastic process being used, and to take into account the uncertainty from the change of support in parameter estimation and prediction. We use spatial stochastic processes as linear combinations of basis functions where Gaussian Markov random fields define the weights. Our hierarchical modelling approach can be described by the following steps: (i) define a latent model where response variables and predictors are considered as latent stochastic processes with continuous support, (ii) link the continuous-index set stochastic processes with its projection to the support of the observed data, (iii) link the projected process with the observed data. We show the applicability of our approach by simulation studies and modelling land suitability for improved grassland in Rhondda Cynon Taf, a county borough in Wales., Comment: 45 pages, 16 figures

We present results of a continuation of our Transiting Exoplanet Survey Satellite (TESS) search for short-period pulsations in compact stellar objects observed during Years 2 and 4 of the TESS mission that targeted the northern ecliptic hemisphere. For many of the targets, we exploit unpublished spectroscopic data to confirm or determine the object's spectral classification. From the TESS photometry, we identify 50 short-period hot-subdwarf pulsators, including 35 sdB and 15 sdOB stars. The sample contains 26 pulsators not known before the TESS mission. Nine stars show signals at both low and high frequencies, and are therefore ``hybrid'' pulsators. For each pulsator, we report the list of prewhitened frequencies and we show amplitude spectra calculated from the TESS data. We attempt to identify possible multiplets caused by stellar rotation, and we report five candidates with rotation periods between 11 and 46d. Having the search for p-mode pulsating hot subdwarfs in TESS Sectors 1 - 60 done, we discuss the completeness of the study, as well as instability strip and the evolutionary status of the stars we found. We also compare the distribution of pulsation periods as a function of effective temperature and surface gravity with theoretical predictions., Comment: accepted

Approximately 150 low-mass white dwarfs, with masses below 0.4Msun, have been discovered. The majority of these low-mass WDs are observed in binary systems as they cannot be formed through single-star evolution within the Hubble time. In this study, we present a comprehensive analysis of the double low-mass WD eclipsing binary system J2102-4145. Our investigation involved an extensive observational campaign, resulting in the acquisition of approximately 28 hours of high-speed photometric data across multiple nights using NTT/ULTRACAM, SOAR/Goodman, and SMARTS-1m telescopes. These observations have provided critical insights into the orbital characteristics of this system, including parameters such as inclination and orbital period. To disentangle the binary components of J2102-4145, we employed the XT GRID spectral fitting method with GMOS/Gemini-South and X-Shooter data. Additionally, we used the PHOEBE package for light curve analysis on NTT/ULTRACAM high-speed time-series photometry data to constrain the binary star properties. Our analysis reveals remarkable similarities between the two components of this binary system. For the primary star, we determined Teff1 = 13688 +- 65 K, log g1 = 7.36 +- 0.01, R1 = 0.0211 +- 0.0002 Rsun, and M1 = 0.375 +- 0.003 Msun, while the secondary star is characterized by Teff2 = 12952 +- 53 K, log g2 = 7.32 +- 0.01, R2 = 0.0203 +- 0.0002 Rsun, and M2 = 0.31 +- 0.003 Msun. Furthermore, we observe a notable discrepancy between Teff and R of the less massive WD compared to evolutionary sequences for WDs from the literature, which has significant implications for our understanding of WD evolution. We discuss a potential formation scenario for this system that might explain this discrepancy and explore its future evolution. We predict that this system will merge in about 800 Myr, evolving into a helium-rich hot subdwarf star and later into a hybrid He/CO WD.

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential., Comment: Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Hot subdwarf B (sdB) stars are evolved, subluminous, helium-burning stars, most likely formed when red-giant stars lose their hydrogen envelope via interactions with close companions. They play an important role in our understanding of binary evolution, stellar atmospheres, and interiors. Within the sdB population, only a small fraction are known to exhibit pulsations. Pulsating sdBs have typically been discovered serendipitously in various photometric surveys, lacking specific selection criteria for the sample. Consequently, while individual properties of these stars are well-known, a comprehensive understanding of the entire population and many related questions remain unanswered. The introduction of Gaia has presented an exceptional chance to create an unbiased sample by employing precise criteria and ensuring a high degree of completeness. The progression of high-precision and high-duty cycle photometric monitoring facilitated by space missions such as Kepler/K2 and the Transiting Exoplanet Survey Satellite (TESS) has yielded an unparalleled wealth of data for pulsating sdBs. In this work, we created a dataset of confirmed pulsating sdB stars by combining information from various ground- and space-based photometric surveys. Utilizing this dataset, we present a thorough approach to search for pulsating sdB stars based on the current Gaia DR3 sample. Using TESS photometry, we discovered 61 new pulsating sdB stars and 20 variable sdBs whose source of variability remains to be determined through future spectroscopic follow-up observations., Comment: 22 pages, 9 Figures - Accepted for publication in A&A

We aim to understand the influence of evolutionary modified surface abundances on the strength of the stellar wind in the helium-dominated subdwarfs CD-46 8926 and CD-51 11879. We used our own optical spectroscopy combined with archival ultraviolet spectroscopy and photometry to derive basic parameters and surface abundances of selected stars. The resulting parameterst served as input for the METUJE stellar wind code, which predicts the wind structure of these stars. We compared the derived wind parameters with the predictions derived for solar abundances. The optical analysis showed that both subdwarfs have effective temperatures in excess of 60 kK and a strong overabundance of carbon in the case of CD-46 8926 and nitrogen in the case of CD-51 11879. We interpret the abundance patterns as being a result of enrichment by the products of nuclear reactions. The modified abundances reduce the wind mass-loss rate by tens of percent. The reduction improves the predicted wind line profiles in comparison to observations. The change in helium abundance does not have a strong effect on the wind parameters. As a result of a lower estimated bolometric luminosity and mass-loss rate and a larger distance, the expected X-ray luminosities become lower and agree with observationas. The nucleosynthesis does not significantly alter the strength of the wind of hot subdwarfs, but the inclusion of proper stellar parameters improves the agreement with observational wind characteristics. Our analysis indicates that subdwarfs overabundant in helium are typically able to launch wind. This conclusion is supported by data gathered for thousands of subdwarfs from the literature, which shows that subdwarfs overabundant in helium avoid the region in the Kiel diagram where the winds are predicted to be absent. This can be interpreted in terms of the gravitational settling of helium, which is suppressed by the winds., Comment: 10 pages, accepted for publication in Astronomy & Astrophysics

Binary evolution theory predicts that the second common envelope (CE) ejection can produce low-mass (0.32-0.36 Msun) subdwarf B (sdB) stars inside ultrashort-orbital-period binary systems, as their helium cores are ignited under nondegenerate conditions. With the orbital decay driven by gravitational-wave (GW) radiation, the minimum orbital periods of detached sdB binaries could be as short as ~20 minutes. However, only four sdB binaries with orbital periods below an hour have been reported so far, while none of them has an orbital period approaching the above theoretical limit. Here we report the discovery of a 20.5-minute-orbital-period ellipsoidal binary, TMTS J052610.43+593445.1, in which the visible star is being tidally deformed by an invisible carbon-oxygen white dwarf (WD) companion. The visible component is inferred to be an sdB star with a mass of ~0.33 Msun, approaching that of helium-ignition limit, although a He-core WD cannot be completely ruled out. In particular, the radius of this low-mass sdB star is only 0.066 Rsun, about seven Earth radii, possibly representing the most compact nondegenerate star ever known. Such a system provides a key clue to map the binary evolution scheme from the second CE ejection to the formation of AM CVn stars having a helium-star donor, and it will also serve as a crucial verification binary of space-borne GW detectors in the future., Comment: 24 pages, 11 figures, 1 table, published on Nature Astronomy, URL: https://www.nature.com/articles/s41550-023-02188-2

Quantum signal processing (QSP) is a highly successful algorithmic primitive in quantum computing which leads to conceptually simple and efficient quantum algorithms using the block-encoding framework of quantum linear algebra. Multivariate variants of quantum signal processing (MQSP) could be a valuable tool in extending earlier results via implementing multivariate (matrix) polynomials. However, MQSP remains much less understood than its single-variate version lacking a clear characterization of "achievable" multivariate polynomials. We show that Haah's characterization of general univariate QSP can be extended to homogeneous bivariate (commuting) quantum signal processing. We also show a similar result for an alternative inhomogeneous variant when the degree in one of the variables is at most 1, but construct a counterexample where both variables have degree 2, which in turn refutes an earlier characterization proposed / conjectured by Rossi and Chuang for a related restricted class of MQSP. Finally, we describe homogeneous multivariate (non-commuting) QSP variants that break away from the earlier two-dimensional treatment limited by its reliance on Jordan-like decompositions, and might ultimately lead to the development of novel quantum algorithms., Comment: 17 pages