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We present the first interferometric imaging of molecular line emission from the Ring Nebula, NGC~6720, in the form of Submillimeter Array (SMA) observations of CO $J=2\rightarrow 1$ emission. The SMA $^{12}$CO(2--1) mapping data, with $\sim$3$''$ spatial resolution and 2 km s$^{-1}$ velocity resolution, provide an unprecedentedly detailed, 3D view of the Ring's clumpy molecular envelope. The morphology of the velocity-integrated SMA $^{12}$CO(2--1) image closely resembles those of near-IR H$_2$ and PAH emission in JWST/NIRCam imaging of NGC~6720, with the molecular gas forming a geometrically thin layer surrounding the ionized gas imaged by HST and JWST. A simple, geometrical model of the $^{12}$CO(2--1) data shows that the intrinsic structure of NGC~6720's molecular envelope closely resembles a truncated, triaxial ellipsoid that is viewed close to pole-on, and that the dynamical age of the molecular envelope is $\sim$6000 yr. The SMA $^{12}$CO(2--1) data furthermore reveal that filamentary features seen projected in the Ring's interior in JWST imaging are in fact fast-moving polar knots or bullets with radial velocities of $\pm$45--50 km s$^{-1}$ relative to systemic, and that the hot progenitor star remnant is positioned at the precise geometric center of the clumpy, ellipsoidal molecular shell. We assert that the Ring's molecular envelope was formed via a relatively sudden, AGB-terminating mass ejection event $\sim$6000 yr ago, and that this ellipsoidal envelope was then punctured by fast, collimated polar outflows resulting from interactions between the progenitor and one or more companion stars. Such an evolutionary scenario may describe most molecule-rich, ``Ring-like'' planetary nebulae., Comment: 17 pages, 10 figures; to appear in The Astrophysical Journal (accepted 20 Jan. 2025)

A Calabi-Yau pair of index one and complexity zero is toric. Furthermore, a Calabi-Yau pair of index one and complexity one is of cluster type. In this article, we study Calabi-Yau pairs of index one and complexity two. For each such pair, we either prove that its alteration complexity is zero or we associate to it a del Pezzo fibration over a toric variety. In the latter case, we show that the initial pair is of cluster type if and only if the del Pezzo fibration is of cluster type. We give criteria for del Pezzo fibrations over toric bases to be of cluster type. Along the way, we prove a statement about the invariance of the coregularity in families of Calabi-Yau pairs over toric bases., Comment: 25 pages

Let $X$ be a $n$-dimensional smooth projective variety and $L$ be an ample Cartier divisor on $X$. We conjecture that a very general element of the linear system $|K_X+(3n+1)L|$ is a hyperbolic algebraic variety. This conjecture holds for some classical varieties: surfaces, products of projective spaces, and Grassmannians. In this article, we investigate the conjecture for $X$ a toric variety. We give a positive answer to the conjecture for smooth projective toric varieties. For a Gorenstein toric threefold $X$, we show that $|K_X+9L|$ is a hyperbolic linear system for any ample Cartier divisor $L$., Comment: 15 pages, comments welcome

Let $B\subset \mathbb{P}^3$ be an slc quartic surface. The existence of an embedding $\mathbb{G}_m^3\hookrightarrow \mathbb{P}^3\setminus B$ implies that $B$ has coregularity zero. In this article, we initiate the classification of coregularity zero slc quartic surfaces $B\subset \mathbb{P}^3$ for which $\mathbb{P}^3\setminus B$ contains an algebraic torus $\mathbb{G}_m^3$. Equivalently, the classification of cluster type pairs $(\mathbb{P}^3,B)$. Along the way, we give criteria for a log Calabi--Yau pair $(X,B)$ over a toric variety $T$ to be of cluster type., Comment: 24 pages

Let $X\rightarrow S$ be a fibration of relative dimension at most two and let $(X,\Delta)$ be a klt pair for which $K_X+\Delta \equiv_S 0$. We show that there are only finitely many Mori chambers and Mori faces in the movable effective cone $\mathcal{M}^e(X/S)$ up to the action of relative pseudo-automorphisms of $X/S$ preserving $\Delta$., Comment: 35 pages

Understanding the spread of infectious diseases such as COVID-19 is crucial for informed decision-making and resource allocation. A critical component of disease behavior is the velocity with which disease spreads, defined as the rate of change between time and space. In this paper, we propose a spatio-temporal modeling approach to determine the velocities of infectious disease spread. Our approach assumes that the locations and times of people infected can be considered as a spatio-temporal point pattern that arises as a realization of a spatio-temporal log-Gaussian Cox process. The intensity of this process is estimated using fast Bayesian inference by employing the integrated nested Laplace approximation (INLA) and the Stochastic Partial Differential Equations (SPDE) approaches. The velocity is then calculated using finite differences that approximate the derivatives of the intensity function. Finally, the directions and magnitudes of the velocities can be mapped at specific times to examine better the spread of the disease throughout the region. We demonstrate our method by analyzing COVID-19 spread in Cali, Colombia, during the 2020-2021 pandemic., Comment: 29 pages, 46 figures, Journal American Statistican, submitted on 3rd of September

Rationality is not a constructible property in families. In this article, we consider stronger notions of rationality and study their behavior in families of Fano varieties. We first show that being toric is a constructible property in families of Fano varieties. The second main result of this article concerns an intermediate notion that lies between toric and rational varieties, namely cluster type varieties. A cluster type $\mathbb Q$-factorial Fano variety contains an open dense algebraic torus, but the variety does not need to be endowed with a torus action. We prove that, in families of $\mathbb Q$-factorial terminal Fano varieties, being of cluster type is a constructible condition. As a consequence, we show that there are finitely many smooth families parametrizing $n$-dimensional smooth cluster type Fano varieties., Comment: 26 pages, comments welcome

Our method extends the application of random spanning trees to cases where the response variable belongs to the exponential family, making it suitable for a wide range of real-world scenarios, including non-Gaussian likelihoods. The proposed model addresses the limitations of previous spatial clustering methods by allowing all within-cluster model parameters to be cluster-specific, thus offering greater flexibility. Additionally, we propose a Bayesian inference algorithm that overcomes the computational challenges associated with the reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm by employing composition sampling and the integrated nested Laplace approximation (INLA) to compute the marginal distribution necessary for the acceptance probability. This enhancement improves the mixing and feasibility of Bayesian inference for complex models. We demonstrate the effectiveness of our approach through simulation studies and apply it to real-world disease mapping applications: COVID-19 in the United States of America, and dengue fever in the states of Minas Gerais and S\~ao Paulo, Brazil. Our results highlight the model's capability to uncover meaningful spatial patterns and temporal dynamics in disease outbreaks, providing valuable insights for public health decision-making and resource allocation.

We prove that a general rational smooth Fano threefold admits a toric model. More precisely, for a general rational smooth Fano threefold $X$, we show the existence of a boundary divisor $D$ for which $(X,D)\simeq_{\rm cbir} (\mathbb{P}^3,H_0+H_1+H_2+H_3)$, where the $H_i$'s are the coordinate hyperplanes. In particular, a general rational smooth Fano threefold has birational complexity zero. We argue that the three conditions: rationality, generality, and smoothness are indeed necessary for the theorem., Comment: 23 pages. arXiv admin note: text overlap with arXiv:2309.16784; text overlap with arXiv:0806.2107 by other authors

Let $(X,\Delta)$ be a dlt log Calabi-Yau pair admitting a polarized endomorphism. We show that $(X,\Delta)$ is a finite quotient of a toric log Calabi-Yau fibration over an abelian variety. We provide an example which shows that the previous statement does not hold if we drop the dlt condition of $(X,\Delta)$ even if $X$ is a smooth variety. Given a klt type variety $X$ and a log Calabi-Yau pair $(X,\Delta)$ admitting a polarized endomorphism, we show that a suitable birational modification of $(X,\Delta)$ is a finite quotient of a toric log Calabi-Yau fibration over an abelian variety., Comment: 24 pages

Operator learning problems arise in many key areas of scientific computing where Partial Differential Equations (PDEs) are used to model physical systems. In such scenarios, the operators map between Banach or Hilbert spaces. In this work, we tackle the problem of learning operators between Banach spaces, in contrast to the vast majority of past works considering only Hilbert spaces. We focus on learning holomorphic operators - an important class of problems with many applications. We combine arbitrary approximate encoders and decoders with standard feedforward Deep Neural Network (DNN) architectures - specifically, those with constant width exceeding the depth - under standard $\ell^2$-loss minimization. We first identify a family of DNNs such that the resulting Deep Learning (DL) procedure achieves optimal generalization bounds for such operators. For standard fully-connected architectures, we then show that there are uncountably many minimizers of the training problem that yield equivalent optimal performance. The DNN architectures we consider are `problem agnostic', with width and depth only depending on the amount of training data $m$ and not on regularity assumptions of the target operator. Next, we show that DL is optimal for this problem: no recovery procedure can surpass these generalization bounds up to log terms. Finally, we present numerical results demonstrating the practical performance on challenging problems including the parametric diffusion, Navier-Stokes-Brinkman and Boussinesq PDEs.

In this position paper we address the Software Sustainability from the IN perspective, so that the Software Engineering (SE) community is aware of the need to contribute towards sustainable software companies, which need to adopt a holistic approach to sustainability considering all its dimensions (human, economic and environmental). A series of important challenges to be considered in the coming years are presented, in order that advances in involved SE communities on the subject can be harmonised and used to contribute more effectively to this field of great interest and impact on society.

We study the birational complexity of log Calabi-Yau $3$-folds. For such a pair $(X,B)$ of index one and coregularity zero, we show that $c_{\rm bir}(X,B)\in \{0,2,3\}$. Further, we prove that $(X,B)$ has a log Calabi-Yau crepant birational model that admits a crepant contraction to $(\mathbb{P}^{3-c},H_0+\dots+H_{3-c})$, where $c=c_{\rm bir}(X,B)$. To prove this, we give a geometric characterization of standard $\mathbb{P}^1$-links., Comment: 18 pages

Spatial modelling often uses Gaussian random fields to capture the stochastic nature of studied phenomena. However, this approach incurs significant computational burdens (O(n3)), primarily due to covariance matrix computations. In this study, we propose to use a low-rank approximation of a thin plate spline as a spatial random effect in Bayesian spatial models. We compare its statistical performance and computational efficiency with the approximated Gaussian random field (by the SPDE method). In this case, the dense matrix of the thin plate spline is approximated using a truncated spectral decomposition, resulting in computational complexity of O(kn2) operations, where k is the number of knots. Bayesian inference is conducted via the Hamiltonian Monte Carlo algorithm of the probabilistic software Stan, which allows us to evaluate performance and diagnostics for the proposed models. A simulation study reveals that both models accurately recover the parameters used to simulate data. However, models using a thin plate spline demonstrate superior execution time to achieve the convergence of chains compared to the models utilizing an approximated Gaussian random field. Furthermore, thin plate spline models exhibited better computational efficiency for simulated data coming from different spatial locations. In a real application, models using a thin plate spline as spatial random effect produced similar results in estimating a relative index of abundance for a benthic marine species when compared to models incorporating an approximated Gaussian random field. Although they were not the more computational efficient models, their simplicity in parametrization, execution time and predictive performance make them a valid alternative for spatial modelling under Bayesian inference., Comment: Preliminary results of this analysis were presented in CMStatistics, 2023 (Berlin)

In this article, we study the geometry of log Calabi-Yau pairs $(X,B)$ of index one and birational complexity zero. Firstly, we propose a conjecture that characterizes such pairs $(X,B)$ in terms of their dual complex and the rationality of their log canonical places. Secondly, we show that for these pairs the open set $X\setminus B$ is divisorially covered by open affine subvarieties which are isomorphic to open subvarieties of algebraic tori. We introduce and study invariants that measure the geometry and the number of these open subvarieties of algebraic tori. Thirdly, we study boundedness properties of log Calabi-Yau pairs of index one and birational complexity zero. For instance, in dimension $2$ we prove that such pairs are affinely bounded., Comment: 27 pages

Learning approximations to smooth target functions of many variables from finite sets of pointwise samples is an important task in scientific computing and its many applications in computational science and engineering. Despite well over half a century of research on high-dimensional approximation, this remains a challenging problem. Yet, significant advances have been made in the last decade towards efficient methods for doing this, commencing with so-called sparse polynomial approximation methods and continuing most recently with methods based on Deep Neural Networks (DNNs). In tandem, there have been substantial advances in the relevant approximation theory and analysis of these techniques. In this work, we survey this recent progress. We describe the contemporary motivations for this problem, which stem from parametric models and computational uncertainty quantification; the relevant function classes, namely, classes of infinite-dimensional, Banach-valued, holomorphic functions; fundamental limits of learnability from finite data for these classes; and finally, sparse polynomial and DNN methods for efficiently learning such functions from finite data. For the latter, there is currently a significant gap between the approximation theory of DNNs and the practical performance of deep learning. Aiming to narrow this gap, we develop the topic of practical existence theory, which asserts the existence of dimension-independent DNN architectures and training strategies that achieve provably near-optimal generalization errors in terms of the amount of training data.

Let $(X,B)$ be a log Calabi--Yau pair of dimension $n$, index one, and birational complexity $c$. We show that $(X,B)$ has a crepant birational model that admits a tower of Mori fiber spaces of which at least $n-c$ are conic fibrations. Motivated by the proof of the previous statement, we introduce new measures of the complexity of a log Calabi--Yau pair; the alteration complexity and the conic complexity. We characterize when these invariants are zero. Finally, we give applications of the tools of the main theorem to birational superrigidity, Fano hypersurfaces, dual complexes, Weil indices of Fano varieties, and klt singularities., Comment: 46 pages

Hidden Markov models (HMMs) are probabilistic methods in which observations are seen as realizations of a latent Markov process with discrete states that switch over time. Moving beyond standard statistical tests, HMMs offer a statistical environment to optimally exploit the information present in multivariate time series, uncovering the latent dynamics that rule them. Here, we extend the Poisson HMM to the multilevel framework, accommodating variability between individuals with continuously distributed individual random effects following a lognormal distribution, and describe how to estimate the model in a fully parametric Bayesian framework. The proposed multilevel HMM enables probabilistic decoding of hidden state sequences from multivariate count time-series based on individual-specific parameters, and offers a framework to quantificate between-individual variability formally. Through a Monte Carlo study we show that the multilevel HMM outperforms the HMM for scenarios involving heterogeneity between individuals, demonstrating improved decoding accuracy and estimation performance of parameters of the emission distribution, and performs equally well when not between heterogeneity is present. Finally, we illustrate how to use our model to explore the latent dynamics governing complex multivariate count data in an empirical application concerning pilot whale diving behaviour in the wild, and how to identify neural states from multi-electrode recordings of motor neural cortex activity in a macaque monkey in an experimental set up. We make the multilevel HMM introduced in this study publicly available in the R-package mHMMbayes in CRAN.

We present early results from our program of ALMA Band 6 (1.3mm) molecular line mapping of a sample of nearby, well-studied examples of high-excitation, bipolar/pinched-waist and molecule-rich planetary nebulae (Hubble 5 and NGC 2440, 2818, 2899, 6302, and 6445). We have mapped these planetary nebulae (PNe) in isotopologues of CO as well as various molecular line tracers of high-energy irradiation, such as HCN, CN, HNC, and HCO+, with the complementary goals of establishing nebular kinematics as well as the zones of UV-heated and X-ray-ionized molecular gas within each nebula. The resulting high-resolution ALMA molecular emission-line maps reveal the regions of high-excitation bipolar PNe in which molecular gas, presumably ejected during asymptotic giant branch stages of the PN progenitor stars, survives and evolves chemically. We present a summary of molecular species detected to date in the sample nebulae, and we use example results for one PN (NGC 6455) to demonstrate the power of the ALMA data in revealing the structures, kinematics, and compositions of the equatorial molecular tori that are a common feature of the sample objects., Comment: 6 pages, 4 figures, accepted for IAU Proceedings Series (IAUS 384)

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

The action of a finite group $G$ on a compact Riemann surface $X$ naturally induces another action of $G$ on its Jacobian variety $\operatorname{J}(X)$. In many cases, each component of the group algebra decomposition of $\operatorname{J}(X)$ is isogenous to a Prym varieties of an intermediate covering of the Galois covering $\pi_G\colon X \to X/G$; in such a case, we say that the group algebra decomposition is affordable by Prym varieties. In this article, we present an infinite family of groups that act on Riemann surfaces in a manner that the group algebra decomposition of $\operatorname{J}(X)$ is not affordable by Prym varieties; namely, affine groups $\operatorname{Aff}(\mathbb{F}_q)$ with some exceptions: $q = 2$, $q = 9$, $q$ a Fermat prime, $q = 2^n$ with $2^n-1$ a Mersenne prime and some particular cases when $X/G$ has genus $0$ or $1$. In each one of this exceptional cases, we give the group algebra decomposition of $\operatorname{J}(X)$ by Prym varieties., Comment: 14 pages

We present Submillimeter Array (SMA) mapping of $^{12}$CO $J=2\rightarrow 1$, $^{13}$CO $J=2\rightarrow 1$, and CN $N=2\rightarrow 1$ emission from the Ring-like planetary nebula (PN) NGC 3132, one of the subjects of JWST Early Release Observation (ERO) near-infrared imaging. The $\sim$5$''$ resolution SMA data demonstrate that the Southern Ring's main, bright, molecule-rich ring is indeed an expanding ring, as opposed to a limb-brightened shell, in terms of its intrinsic (physical) structure. This suggests that NGC 3132 is a bipolar nebula viewed more or less pole-on (inclination $\sim$15--30$^\circ$). The SMA data furthermore reveal that the nebula harbors a second expanding molecular ring that is aligned almost orthogonally to the main, bright molecular ring. We propose that this two-ring structure is the remnant of an ellipsoidal molecular envelope of ejecta that terminated the progenitor star's asymptotic giant branch evolution and was subsequently disrupted by a series of misaligned fast, collimated outflows or jets resulting from interactions between the progenitor and one or more companions., Comment: 20 pages, 12 figures; accepted by The Astrophysical Journal

We introduce the notion of birational complexity of a log Calabi-Yau pair. This invariant measures how far the log Calabi-Yau pair is to being birational to a toric pair. We study fundamental properties of the new invariant, with a particular focus on the geometry of dual complexes., Comment: 37 pages. Minor changes

The total energy cost of computing activities is steadily increasing and projections indicate that it will be one of the dominant global energy consumers in the coming decades. However, perhaps due to its relative youth, the video game sector has not yet developed the same level of environmental awareness as other computing technologies despite the estimated three billion regular video game players in the world. This work evaluates the energy consumption of the most widely used industry-scale video game engines: Unity and Unreal Engine. Specifically, our work uses three scenarios representing relevant aspects of video games (Physics, Statics Meshes, and Dynamic Meshes) to compare the energy consumption of the engines. The aim is to determine the influence of using each of the two engines on energy consumption. Our research has confirmed significant differences in the energy consumption of video game engines: 351% in Physics in favor of Unity, 17% in Statics Meshes in favor of Unity, and 26% in Dynamic Meshes in favor of Unreal Engine. These results represent an opportunity for worldwide potential savings of at least 51 TWh per year, equivalent to the annual consumption of nearly 13 million European households, that might encourage a new branch of research on energy-efficient video game engines.

Let $X$ be a Fano type variety and $(X,\Delta)$ be a log Calabi-Yau pair with $\Delta$ a Weil divisor. If $(X,\Delta)$ admits a polarized endomorphism, then we show that $(X,\Delta)$ is a finite quotient of a toric pair. Along the way, we prove that a klt Calabi-Yau pair $(X,\Delta)$ with standard coefficients that admits a polarized endomorphism is the quotient of an abelian variety., Comment: 19 pages. v2: Section 5 and examples revised

In this article, we study the orbifold fundamental group $\pi_1^{\rm orb}(X,\Delta)$ of a Calabi--Yau pair $(X,\Delta)$ with log canonical singularities. We conjecture that the orbifold fundamental group $\pi_1^{\rm orb}(X,\Delta)$ of a $n$-dimensional log Calabi--Yau pair admits a normal solvable subgroup of rank at most $2n$ and index at most $c(n)$. We prove this conjecture in the case that $n=2$. More precisely, for a log Calabi--Yau surface pair $(X,\Delta)$ we show that $\pi_1^{\rm orb}(X,\Delta)$ is the extension of a nilpotent group of length at most $2$ and rank at most $4$ by a finite group of order at most $7200$. We also show that the bounds on the nilpotency length, rank, and order of the finite group quotient in this result are sharp. Finally, we provide some necessary criteria for a log Calabi--Yau surface $(X,\Delta)$ to have an infinite, or a non virtually abelian orbifold fundamental group., Comment: v2: Theorems D and E are new, Theorem F is now stronger. We improved the introduction, the overall structure of the paper and of the proof of the main theorem

Infinite-dimensional, holomorphic functions have been studied in detail over the last several decades, due to their relevance to parametric differential equations and computational uncertainty quantification. The approximation of such functions from finitely many samples is of particular interest, due to the practical importance of constructing surrogate models to complex mathematical models of physical processes. In a previous work, [5] we studied the approximation of so-called Banach-valued, $(\boldsymbol{b},\varepsilon)$-holomorphic functions on the infinite-dimensional hypercube $[-1,1]^{\mathbb{N}}$ from $m$ (potentially adaptive) samples. In particular, we derived lower bounds for the adaptive $m$-widths for classes of such functions, which showed that certain algebraic rates of the form $m^{1/2-1/p}$ are the best possible regardless of the sampling-recovery pair. In this work, we continue this investigation by focusing on the practical case where the samples are pointwise evaluations drawn identically and independently from a probability measure. Specifically, for Hilbert-valued $(\boldsymbol{b},\varepsilon)$-holomorphic functions, we show that the same rates can be achieved (up to a small polylogarithmic or algebraic factor) for essentially arbitrary tensor-product Jacobi (ultraspherical) measures. Our reconstruction maps are based on least squares and compressed sensing procedures using the corresponding orthonormal Jacobi polynomials. In doing so, we strengthen and generalize past work that has derived weaker nonuniform guarantees for the uniform and Chebyshev measures (and corresponding polynomials) only. We also extend various best $s$-term polynomial approximation error bounds to arbitrary Jacobi polynomial expansions. Overall, we demonstrate that i.i.d.\ pointwise samples are near-optimal for the recovery of infinite-dimensional, holomorphic functions.

At the high temperatures present inside recently formed neutron stars ($T\gtrsim 5\times 10^{8}\, \text{K}$), the particles in their cores are in the "strong-coupling" regime, in which collisional forces make them behave as a single, stably stratified, and thus non-barotropic fluid. In this regime, axially symmetric hydromagnetic quasi-equilibrium states are possible, which are only constrained to have a vanishing azimuthal Lorentz force. In such equilibria, the particle species are not in chemical ($\beta$) equilibrium, so $\beta$ decays (Urca reactions) tend to restore the chemical equilibrium, inducing fluid motions that change the magnetic field configuration. If the stars remained hot for a sufficiently long time, this evolution would eventually lead to a chemical equilibrium state, in which the fluid is barotropic and the magnetic field, if axially-symmetric, satisfies the non-linear Grad-Shafranov equation. In this work, we present a numerical scheme that decouples the magnetic and thermal evolution, enabling to efficiently perform, for the first time, long-term magneto-thermal simulations in this regime for different magnetic field strengths and geometries. Our results demonstrate that, even for magnetar-strength fields $\gtrsim 10^{16} \, \mathrm{G}$, the feedback from the magnetic evolution on the thermal evolution is negligible. Thus, as the core passively cools, the Urca reactions quickly become inefficient at restoring chemical equilibrium, so the magnetic field evolves very little, and the Grad-Shafranov equilibrium is not attained in this regime. Therefore, any substantial evolution of the core magnetic field must occur later, in the cooler "weak-coupling" regime ($T\lesssim 5\times 10^8 \, \mathrm{K}$), in which Urca reactions are effectively frozen and ambipolar diffusion becomes relevant., Comment: 19 pages, 12 figures, submitted to MNRAS

In the analysis of spatial point patterns on linear networks, a critical statistical objective is estimating the first-order intensity function, representing the expected number of points within specific subsets of the network. Typically, non-parametric approaches employing heating kernels are used for this estimation. However, a significant challenge arises in selecting appropriate bandwidths before conducting the estimation. We study an intensity estimation mechanism that overcomes this limitation using adaptive estimators, where bandwidths adapt to the data points in the pattern. While adaptive estimators have been explored in other contexts, their application in linear networks remains underexplored. We investigate the adaptive intensity estimator within the linear network context and extend a partitioning technique based on bandwidth quantiles to expedite the estimation process significantly. Through simulations, we demonstrate the efficacy of this technique, showing that the partition estimator closely approximates the direct estimator while drastically reducing computation time. As a practical application, we employ our method to estimate the intensity of traffic accidents in a neighbourhood in Medellin, Colombia, showcasing its real-world relevance and efficiency., Comment: 19 pages, 7 figures

Spatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field process. This model can be used, for example, to predict air pollution levels by combining point data from monitoring stations and areal data from satellite imagery. However, if the data presents preferential sampling, that is, if the observed point locations are not independent of the underlying spatial process, the inference obtained from models that ignore such a dependence structure might not be valid. In this paper, we present a Bayesian spatial model for the fusion of point and areal data that takes into account preferential sampling. The model combines the Bayesian melding specification and a model for the stochastically dependent sampling and underlying spatial processes. Fast Bayesian inference is performed using the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches. The performance of the model is assessed using simulated data in a range of scenarios and sampling strategies that can appear in real settings. The model is also applied to predict air pollution in the USA., Comment: 21 pages

We study Fano varieties endowed with a faithful action of a symmetric group, as well as analogous results for Calabi--Yau varieties, and log terminal singularities. We show the existence of a constant $m(n)$, so that every symmetric group $S_k$ acting on an $n$-dimensional Fano variety satisfies $k \leq m(n)$. We prove that $m(n)> n+\sqrt{2n}$ for every $n$. On the other hand, we show that $\lim_{n\to \infty} m(n)/(n+1)^2 \leq 1$. However, this asymptotic upper bound is not expected to be sharp. We obtain sharp bounds for certain classes of varieties. For toric varieties, we show that $m(n)=n+2$ for $n\geq 4$. For Fano quasismooth weighted complete intersections, we prove the asymptotic equality $\lim_{n\to \infty} m(n)/(n+1)=1$. Among the Fano weighted complete intersections, we study the maximally symmetric ones and show that they are closely related to the Fano--Fermat varieties, i.e., Fano complete intersections in $\mathbb P^N$ cut out by Fermat hypersurfaces. Finally, we draw a connection between maximally symmetric Fano varieties and boundedness of Fano varieties. For instance, we show that the class of $S_8$-equivariant Fano $4$-folds forms a bounded family. In contrast, the $S_7$-equivariant Fano $4$-folds are unbounded., Comment: 32 pages, 3 tables

The tumour microenvironment plays a fundamental role in understanding the development and progression of cancer. This paper proposes a novel spatial point process model that accounts for inhomogeneity and interaction to flexibly model a complex database of cells in the tumour immune microenvironments of a cohort of patients with non-small-cell lung cancer whose samples have been processed using digital pathology techniques. Specifically, an inhomogeneous multitype Gibbs point process model with an associated Fiksel-type interaction function is proposed. Estimation and inference procedures are conducted through maximum pseudolikelihood, considering replicated multitype point patterns.

Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumour microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyse, for example, the co-varying location and functions of multiple different types of immune cells. Complex spatial relationships between different immune cells have been shown to correlate with patient outcomes and may reveal new pathways for targeted immunotherapy treatments. This tutorial reviews methods and procedures relating to spatial point patterns for complex data analysis. We consider tissue cells as a realisation of a spatial point process for each patient. We focus on proper functional descriptors for each observation and techniques that allow us to obtain information about inter-patient variation. Ovarian cancer is the deadliest gynaecological malignancy and can resist chemotherapy treatment effective in cancers. We use a dataset of high-grade serous ovarian cancer samples from 51 patients. We examine the immune cell composition (T cells, B cells, macrophages) within tumours and additional information such as cell classification (tumour or stroma) and other patient clinical characteristics. Our analyses, supported by reproducible software, apply to other digital pathology datasets.