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For a set $P$ of $n$ points in general position in the plane, the flip graph $F(P)$ has a vertex for each non-crossing spanning tree on $P$ and an edge between any two spanning trees that can be transformed into each other by one edge flip. The diameter ${\rm diam}(F(P))$ of this graph is subject of intensive study. For points in general position, it is between $3n/2-5$ and $2n-4$, with no improvement for 25 years. For points in convex position, it lies between $3n/2 - 5$ and $\approx1.95n$, where the lower bound was conjectured to be tight up to an additive constant and the upper bound is a recent breakthrough improvement over several bounds of the form $2n-o(n)$. In this work, we provide new upper and lower bounds on ${\rm diam}(F(P))$, mainly focusing on points in convex position. We show $14n/9 - O(1) \le {\rm diam}(F(P)) \le 5n/3 - 3$, by this disproving the conjectured upper bound of $3n/2$ for convex position, and relevantly improving both the long-standing lower bound for general position and the recent new upper bound for convex position. We complement these by showing that if one of $T,T'$ has at most two boundary edges, then ${\rm dist}(T,T') \le 2d/2 < 3n/2$, where $d = |T-T'|$ is the number of edges in one tree that are not in the other. To prove both the upper and the lower bound, we introduce a new powerful tool. Specifically, we convert the flip distance problem for given $T,T'$ to the problem of a largest acyclic subset in an associated conflict graph $H(T,T')$. In fact, this method is powerful enough to give an equivalent formulation of the diameter of $F(P)$ for points $P$ in convex position up to lower-order terms. As such, conflict graphs are likely the key to a complete resolution of this and possibly also other reconfiguration problems., Comment: an extended abstract appears at SODA 2025

Forecasting of armed conflicts is an important area of research that has the potential to save lives and prevent suffering. However, most existing forecasting models provide only point predictions without any individual-level uncertainty estimates. In this paper, we introduce a novel extension to conformal prediction algorithm which we call bin-conditional conformal prediction. This method allows users to obtain individual-level prediction intervals for any arbitrary prediction model while maintaining a specific level of coverage across user-defined ranges of values. We apply the bin-conditional conformal prediction algorithm to forecast fatalities from armed conflict. Our results demonstrate that the method provides well-calibrated uncertainty estimates for the predicted number of fatalities. Compared to standard conformal prediction, the bin-conditional method outperforms offers improved calibration of coverage rates across different values of the outcome, but at the cost of wider prediction intervals.

Event datasets including those provided by Uppsala Conflict Data Program (UCDP) are based on reports from the media and international organizations, and are likely to suffer from reporting bias. Since the UCDP has strict inclusion criteria, they most likely under-estimate conflict-related deaths, but we do not know by how much. Here, we provide a generalizable, cross-national measure of uncertainty around UCDP reported fatalities that is more robust and realistic than UCDP's documented low and high estimates, and make available a dataset and R package accounting for the measurement uncertainty. We use a structured expert elicitation combined with statistical modelling to derive a distribution of plausible number of fatalities given the number of battle-related deaths and the type of violence documented by the UCDP. The results can help scholars understand the extent of bias affecting their empirical analyses of organized violence and contribute to improve the accuracy of conflict forecasting systems.

We consider Non Autonomous Conformal Iterative Function Systems (NACIFS) and their limit set. Our main concern is harmonic measure and its dimensions : Hausdorff and Packing. We prove that this two dimensions are continuous under perturbations and that they verify Bowen's and Manning's type formulas. In order to do so we prove general results about measures, and more generally about positive functionals, defined on a symbolic space, developing tools from thermodynamical formalism in a non-autonomous setting.

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

Automatic estimation of cardiac ultrasound image quality can be beneficial for guiding operators and ensuring the accuracy of clinical measurements. Previous work often fails to distinguish the view correctness of the echocardiogram from the image quality. Additionally, previous studies only provide a global image quality value, which limits their practical utility. In this work, we developed and compared three methods to estimate image quality: 1) classic pixel-based metrics like the generalized contrast-to-noise ratio (gCNR) on myocardial segments as region of interest and left ventricle lumen as background, obtained using a U-Net segmentation 2) local image coherence derived from a U-Net model that predicts coherence from B-Mode images 3) a deep convolutional network that predicts the quality of each region directly in an end-to-end fashion. We evaluate each method against manual regional image quality annotations by three experienced cardiologists. The results indicate poor performance of the gCNR metric, with Spearman correlation to the annotations of rho = 0.24. The end-to-end learning model obtains the best result, rho = 0.69, comparable to the inter-observer correlation, rho = 0.63. Finally, the coherence-based method, with rho = 0.58, outperformed the classical metrics and is more generic than the end-to-end approach. The image quality prediction tool is available as an open source Python library at https://github.com/GillesVanDeVyver/arqee.

This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023., Comment: Forecasting competition, conflict forecasting, forecasting with uncertainty

The integrated nested Laplace approximation (INLA) method has become a popular approach for computationally efficient approximate Bayesian computation. In particular, by leveraging sparsity in random effect precision matrices, INLA is commonly used in spatial and spatio-temporal applications. However, the speed of INLA comes at the cost of restricting the user to the family of latent Gaussian models and the likelihoods currently implemented in {INLA}, the main software implementation of the INLA methodology. {inlabru} is a software package that extends the types of models that can be fitted using INLA by allowing the latent predictor to be non-linear in its parameters, moving beyond the additive linear predictor framework to allow more complex functional relationships. For inference it uses an approximate iterative method based on the first-order Taylor expansion of the non-linear predictor, fitting the model using INLA for each linearised model configuration. {inlabru} automates much of the workflow required to fit models using {R-INLA}, simplifying the process for users to specify, fit and predict from models. There is additional support for fitting joint likelihood models by building each likelihood individually. {inlabru} also supports the direct use of spatial data structures, such as those implemented in the {sf} and {terra} packages. In this paper we outline the statistical theory, model structure and basic syntax required for users to understand and develop their own models using {inlabru}. We evaluate the approximate inference method using a Bayesian method checking approach. We provide three examples modelling simulated spatial data that demonstrate the benefits of the additional flexibility provided by {inlabru}.

Tissue tracking in echocardiography is challenging due to the complex cardiac motion and the inherent nature of ultrasound acquisitions. Although optical flow methods are considered state-of-the-art (SOTA), they struggle with long-range tracking, noise occlusions, and drift throughout the cardiac cycle. Recently, novel learning-based point tracking techniques have been introduced to tackle some of these issues. In this paper, we build upon these techniques and introduce EchoTracker, a two-fold coarse-to-fine model that facilitates the tracking of queried points on a tissue surface across ultrasound image sequences. The architecture contains a preliminary coarse initialization of the trajectories, followed by reinforcement iterations based on fine-grained appearance changes. It is efficient, light, and can run on mid-range GPUs. Experiments demonstrate that the model outperforms SOTA methods, with an average position accuracy of 67% and a median trajectory error of 2.86 pixels. Furthermore, we show a relative improvement of 25% when using our model to calculate the global longitudinal strain (GLS) in a clinical test-retest dataset compared to other methods. This implies that learning-based point tracking can potentially improve performance and yield a higher diagnostic and prognostic value for clinical measurements than current techniques. Our source code is available at: https://github.com/riponazad/echotracker/., Comment: Submitted version that got provisionally (early) accepted (top 11%) to MICCAI2024

Anomalous Microwave Emission (AME) is a major component of Galactic emission in the frequency band 10 to 60 GHz and is commonly modelled as rapidly rotating spinning dust grains. The photodissociation region (PDR) at the boundary of the $\lambda$-Orionis Hii region has been identified by several recent analyses as one of the brightest spinning dust emitting sources in the sky. We investigate the Barnard 30 dark cloud, a dark cloud embedded within the $\lambda$-Orionis PDR. We use total-power observations of Barnard 30 from the CO Mapping Array Project (COMAP) pathfinder instrument at 26 to 34GHz with a resolution of 4.5 arcminutes alongside existing data from Planck, WISE, IRAS, ACT, and the 1.447GHz GALFACTS survey. We use aperture photometry and template fitting to measure the spectral energy distribution of Barnard 30. We find that the spinning dust is the dominant emission component in the 26 to 34GHz range at the $7 \sigma$ level ($S_{30GHz} = 2.85\pm0.43$Jy). We find no evidence that polycyclic aromatic hydrocarbons are the preferred carrier for the spinning dust emission, suggesting that the spinning dust carriers are due to a mixed population of very small grains. Finally, we find evidence for variations in spinning dust emissivity and peak frequency within Barnard 30, and that these variations are possibly driven by changes in dust grain population and the total radiation field. Confirming the origin of the variations in the spinning dust spectrum will require both future COMAP observations at 15GHz combined with spectroscopic mid-infrared data of Barnard 30., Comment: 21 pages, 13 figures. Submitted to MNRAS

This study compares the design practices and performance of ChatGPT 4.0, a large language model (LLM), against graduate engineering students in a 48-hour prototyping hackathon, based on a dataset comprising more than 100 prototypes. The LLM participated by instructing two participants who executed its instructions and provided objective feedback, generated ideas autonomously and made all design decisions without human intervention. The LLM exhibited similar prototyping practices to human participants and finished second among six teams, successfully designing and providing building instructions for functional prototypes. The LLM's concept generation capabilities were particularly strong. However, the LLM prematurely abandoned promising concepts when facing minor difficulties, added unnecessary complexity to designs, and experienced design fixation. Communication between the LLM and participants was challenging due to vague or unclear descriptions, and the LLM had difficulty maintaining continuity and relevance in answers. Based on these findings, six recommendations for implementing an LLM like ChatGPT in the design process are proposed, including leveraging it for ideation, ensuring human oversight for key decisions, implementing iterative feedback loops, prompting it to consider alternatives, and assigning specific and manageable tasks at a subsystem level.

Modern methods for quantifying and predicting species distribution play a crucial part in biodiversity conservation. Occupancy models are a popular choice for analyzing species occurrence data as they allow to separate the observational error induced by imperfect detection, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio-temporal models is too high. In this work, we demonstrate how INLA may be used to fit complex occupancy models and how the R-INLA package can provide a user-friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process, can be framed as latent Gaussian models and benefit from the powerful INLA machinery. A large selection of complex modelling features, and random effect modelshave already been implemented in R-INLA. These become available for occupancy models, providing the user with an efficient and flexible toolbox. We illustrate how INLA provides a computationally efficient framework for developing and fitting complex occupancy models using two case studies. Through these, we show how different spatio-temporal models that include spatial-varying trends, smooth terms, and spatio-temporal random effects can be fitted. At the cost of limiting the complexity of the detection model, INLA can incorporate a range of complex structures in the process. INLA-based occupancy models provide an alternative framework to fit complex spatiotemporal occupancy models. The need for new and more flexible computationally approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements., Comment: To be published in IEEE Journal of Biomedical and Health Informatics. 10 pages, 4 figures

Recently, $p$-presentation distances for $p\in [1,\infty]$ were introduced for merge trees and multiparameter persistence modules as more sensitive variations of the respective interleaving distances ($p=\infty$). It is well-known that computing the interleaving distance is NP-hard in both cases. We extend this result by showing that computing the $p$-presentation distance is NP-hard for all $p\in [1,\infty)$ for both merge trees and $t$-parameter persistence modules for any $t\geq 2$. Though the details differ, both proofs follow the same novel strategy, suggesting that our approach can be adapted to proving the NP-hardness of other distances based on sums or $p$-norms., Comment: 28 pages, 7 figures

This paper introduces the R package INLAjoint, designed as a toolbox for fitting a diverse range of regression models addressing both longitudinal and survival outcomes. INLAjoint relies on the computational efficiency of the integrated nested Laplace approximations methodology, an efficient alternative to Markov chain Monte Carlo for Bayesian inference, ensuring both speed and accuracy in parameter estimation and uncertainty quantification. The package facilitates the construction of complex joint models by treating individual regression models as building blocks, which can be assembled to address specific research questions. Joint models are relevant in biomedical studies where the collection of longitudinal markers alongside censored survival times is common. They have gained significant interest in recent literature, demonstrating the ability to rectify biases present in separate modeling approaches such as informative censoring by a survival event or confusion bias due to population heterogeneity. We provide a comprehensive overview of the joint modeling framework embedded in INLAjoint with illustrative examples. Through these examples, we demonstrate the practical utility of INLAjoint in handling complex data scenarios encountered in biomedical research.

Plasmoid-mediated reconnection plays a fundamental role in different solar atmospheric phenomena. Numerical reproduction of this process is therefore essential for developing robust solar models. Our goal is to assess plasmoid-mediated reconnection across various numerical resistivity models in order to investigate how plasmoid numbers and reconnection rates depend on the Lundquist number. We used the Bifrost code to drive magnetic reconnection in a 2D coronal fan-spine topology, carrying out a parametric study of several experiments with different numerical resolution and resistivity models. We employed three anomalous resistivity models: (1) the original hyper-diffusion from Bifrost, (2) a resistivity proportional to current density, and (3) a resistivity quadratically proportional to electron drift velocity. For comparisons, experiments with uniform resistivity were also run. Plasmoid-mediated reconnection is obtained in most of the experiments. With uniform resistivity, increasing the resolution reveals higher plasmoid frequency with weaker scaling to the Lundquist number, obtaining 7.9-12 plasmoids per minute for $S_L\in[1.8 \times 10^4, 2.6\times 10^5]$ with a scaling of $S_L^{0.210}$ in the highest-resolution resistivity cases, transcending into Petschek reconnection in the high-$S_L$ limit and Sweet-Parker reconnection in the low-$S_L$ limit. Anomalous resistivity leads to similar results even with lower resolution. The drift-velocity-dependent resistivity excellently reproduces Petschek reconnection for any Lundquist number, and similar results are seen with resistivity proportional to current-density. Among the different resistivity models applied on the given numerical resolution, the hyper-diffusion model reproduced plasmoid characteristics in closest resemblance to those obtained with uniform resistivity at a significantly higher resolution.

In this work, we present a new approach for constructing models for correlation matrices with a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. We suggest an automatic approach to define a prior using a natural sequence of simpler models within the Penalized Complexity framework for the unknown parameters in these models. We illustrate this approach with three applications: a multivariate linear regression of four biomarkers, a multivariate disease mapping, and a multivariate longitudinal joint modelling. Each application underscores our method's intuitive appeal, signifying a substantial advancement toward a more cohesive and enlightening model that facilitates a meaningful interpretation of correlation matrices.

Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure, Comment: This work has been submitted to the IEEE for possible publication

Performing a Bayesian inference on large spatio-temporal models requires extracting inverse elements of large sparse precision matrices for marginal variances. Although direct matrix factorizations can be used for the inversion, such methods fail to scale well for distributed problems when run on large computing clusters. On the contrary, Krylov subspace methods for the selected inversion have been gaining traction. We propose a parallel hybrid approach based on domain decomposition, which extends the Rao-Blackwellized Monte Carlo estimator for distributed precision matrices. Our approach exploits the strength of Krylov subspace methods as global solvers and efficiency of direct factorizations as base case solvers to compute the marginal variances using a divide-and-conquer strategy. By introducing subdomain overlaps, one can achieve a greater accuracy at an increased computational effort with little to no additional communication. We demonstrate the speed improvements on both simulated models and a massive US daily temperature data., Comment: 17 pages, 7 figures

Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian models (LGMs) pose unique challenges as it is difficult to check assumptions about the distribution of the latent parameters. Discrepancy measures are often used to quantify the degree to which a model fit deviates from the observed data. We construct discrepancy measures by (a) defining an alternative model with relaxed assumptions and (b) deriving the discrepancy measure most sensitive to discrepancies induced by this alternative model. We also promote a workflow for model criticism that combines model checking with subsequent robustness analysis. As a result, we obtain a general recipe to check assumptions in LGMs and the impact of these assumptions on the results. We demonstrate the ideas by assessing the latent Gaussianity assumption, a crucial but often overlooked assumption in LGMs. We illustrate the methods via examples utilising Stan and provide functions for easy usage of the methods for general models fitted through R-INLA., Comment: 40 pages, 21 figures

For $A\subseteq \mathbb{R}$, let $A+A=\{a+b: a,b\in A\}$ and $AA=\{ab: a,b\in A\}$. For $k\in \mathbb{N}$, let $SP(k)$ denote the minimum value of $\max\{|A+A|, |AA|\}$ over all $A\subseteq \mathbb{N}$ with $|A|=k$. Here we establish $SP(k)=3k-3$ for $2\leq k \leq 7$, the $k=7$ case achieved for example by $\{1,2,3,4,6,8,12\}$, while $SP(k)=3k-2$ for $k=8,9$, the $k=9$ case achieved for example by $\{1,2,3,4,6,8,9,12,16\}$. For $4\leq k \leq 7$, we provide two proofs using different applications of Freiman's $3k-4$ theorem; one of the proofs includes extensive case analysis on the product sets of $k$-element subsets of $(2k-3)$-term arithmetic progressions. For $k=8,9$, we apply Freiman's $3k-3$ theorem for product sets, and investigate the sumset of the union of two geometric progressions with the same common ratio $r>1$, with separate treatments of the overlapping cases $r\neq 2$ and $r\geq 2$., Comment: 11 pages, 1 table, 4 figures, references added, Lemma 3.5 proof reorganized, some Section 3 results extended to negative common ratios, other referee suggestions incorporated; to appear in Involve

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior for the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package 'fbesag' equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary.

Over the past decade, interval arithmetic (IA) has been utilized to determine tolerance bounds of phased array beampatterns. IA only requires that the errors of the array elements are bounded, and can provide reliable beampattern bounds even when a statistical model is missing. However, previous research has not explored the use of IA to find the error realizations responsible for achieving specific bounds. In this study, the capabilities of IA are extended by introducing the concept of ``backtracking'', which provides a direct way of addressing how specific bounds can be attained. Backtracking allows for the recovery of both the specific error realization and the corresponding beampattern, enabling the study and verification of which errors result in the worst-case array performance in terms of the peak sidelobe level. Moreover, IA is made applicable to a wider range of arrays by adding support for arbitrary array geometries with directive elements and mutual coupling, in addition to element amplitude, phase, and positioning errors. Lastly, a simple formula for approximate bounds of uniformly bounded errors is derived and numerically verified. This formula gives insights into how array size and apodization cannot reduce the worst-case peak sidelobe level beyond a certain limit., Comment: This article may be downloaded for personal use only. Any other use requires author and AIP Publishing prior permission. This article appears in The Journal of the Acoustical Society of America and may be found at https://doi.org/10.1121/10.0019715. The current e-print was typeset by the authors and can differ in, e.g., pagination, reference numbering, and typographic detail

Magnetic reconnection is a fundamental mechanism in astrophysics. A common challenge in mimicking this process numerically in particular for the Sun is that the solar electrical resistivity is small compared to the diffusive effects caused by the discrete nature of codes. We aim to study different anomalous resistivity models and their respective effects on simulations related to magnetic reconnection in the Sun. We used the Bifrost code to perform a 2D numerical reconnection experiment in the corona that is driven by converging opposite polarities at the solar surface. This experiment was run with three different commonly used resistivity models: 1) the hyper-diffusion model originally implemented in Bifrost, 2) a resistivity proportional to the current density, and 3) a resistivity proportional to the square of the electron drift velocity. The study was complemented with a 1D experiment of a Harris current sheet with the same resistivity models. The 2D experiment shows that the three resistivity models are capable of producing results in satisfactory agreement with each other in terms of the current sheet length, inflow velocity, and Poynting influx. Even though Petschek-like reconnection occurred with the current density-proportional resistivity while the other two cases mainly followed plasmoid-mediated reconnection, the large-scale evolution of thermodynamical quantities such as temperature and density are quite similar between the three cases. For the 1D experiment, some recalibration of the diffusion parameters is needed to obtain comparable results. Specifically the hyper-diffusion and the drift velocity-dependent resistivity model needed only minor adjustments, while the current density-proportional model needed a rescaling of several orders of magnitude., Comment: 15 pages, 8 figures, 1 movie

While decomposition of one-parameter persistence modules behaves nicely, as demonstrated by the algebraic stability theorem, decomposition of multiparameter modules is well known to be unstable in a certain precise sense. Until now, it has not been clear that there is any way to get around this and build a meaningful stability theory for multiparameter module decomposition, as naive attempts to do this tend to fail. We introduce tools and definitions, in particular $\epsilon$-refinements and the $\epsilon$-erosion neighborhood of a module, to make sense of the question of how to build such a theory. Having built this foundation, we define the $\epsilon$-pruning of a module, which is a new invariant acting like a ``refined barcode'' that shows great promise to extract features from a module by approximately decomposing it. The main theorem of the paper can be interpreted as a generalization of the algebraic stability theorem to multiparameter modules up to a factor of $2r$, where $r$ is the maximal pointwise dimension of one of the modules. Another interpretation is that the pruning of a module $M$ detects all summands appearing in modules in a neighborhood of $M$. Furthermore, we show that the factor $2r$ appearing in the theorem is close to optimal. We also define a distance that we conjecture to be a stable bottleneck-type distance, which has long been a missing piece in multipersistence theory. Finally, we discuss whether the main theorem can be improved for modules that decompose into pointwise low-dimensional summands, and pose a conjecture phrased purely in terms of basic linear algebra and graph theory that seems to capture the difficulty of doing this. We also show that this conjecture is relevant for other areas of multipersistence, like the complexity of approximating the interleaving distance, and recent applications of homological algebra to multipersistence., Comment: 40 pages, 7 figures

We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discovering the presence of hidden information within stego-images using trained neural networks as steganalyzers that are applied to stego-images. Our results show that such steganalyzers are less effective when SA-CNN is employed during the generation of a stego-image. We also explore the advantages and disadvantages of representing all the possible outputs of our SA-CNN within a smaller, discrete space, rather than a continuous space. Our SA-CNN enables certain classes of parametric steganographic algorithms to be customized based on characteristics of the cover media in which information is to be embedded. Thus, SA-CNN is adaptive in the sense that it enables the core steganographic algorithm to be especially configured for each particular instance of cover media. Experimental results are provided that employ a recent steganographic technique, S-UNIWARD, both with and without the use of SA-CNN. We then apply both sets of stego-images, those produced with and without SA-CNN, to an exmaple steganalyzer, Yedroudj-Net, and we compare the results. We believe that this approach for the integration of neural networks with hand-crafted algorithms increases the reliability and adaptability of steganographic algorithms., Comment: 6 pages, 6 figures

We present a new upper limit on the cosmic molecular gas density at $z=2.4-3.4$ obtained using the first year of observations from the CO Mapping Array Project (COMAP). COMAP data cubes are stacked on the 3D positions of 243 quasars selected from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalog, yielding a 95% upper limit for flux from CO(1-0) line emission of 0.129 Jy km/s. Depending on the balance of the emission between the quasar host and its environment, this value can be interpreted as an average CO line luminosity $L'_\mathrm{CO}$ of eBOSS quasars of $\leq 1.26\times10^{11}$ K km pc$^2$ s$^{-1}$, or an average molecular gas density $\rho_\mathrm{H_2}$ in regions of the universe containing a quasar of $\leq 1.52\times10^8$ M$_\odot$ cMpc$^{-3}$. The $L'_\mathrm{CO}$ upper limit falls among CO line luminosities obtained from individually-targeted quasars in the COMAP redshift range, and the $\rho_\mathrm{H_2}$ value is comparable to upper limits obtained from other Line Intensity Mapping (LIM) surveys and their joint analyses. Further, we forecast the values obtainable with the COMAP/eBOSS stack after the full 5-year COMAP Pathfinder survey. We predict that a detection is probable with this method, depending on the CO properties of the quasar sample. Based on the achieved sensitivity, we believe that this technique of stacking LIM data on the positions of traditional galaxy or quasar catalogs is extremely promising, both as a technique for investigating large galaxy catalogs efficiently at high redshift and as a technique for bolstering the sensitivity of LIM experiments, even with a fraction of their total expected survey data., Comment: 26 pages, 22 figures; Version 2 is as accepted by ApJ

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive Bayesian hierarchical models. In combination with the stochastic partial differential equations (SPDE) approach it gives rise to an efficient method for spatial-temporal modeling. In this work we build on the INLA-SPDE approach, by putting forward a performant distributed memory variant, INLA-DIST, for large-scale applications. To perform the arising computational kernel operations, consisting of Cholesky factorizations, solving linear systems, and selected matrix inversions, we present two numerical solver options, a sparse CPU-based library and a novel blocked GPU-accelerated approach which we propose. We leverage the recurring nonzero block structure in the arising precision (inverse covariance) matrices, which allows us to employ dense subroutines within a sparse setting. Both versions of INLA-DIST are highly scalable, capable of performing inference on models with millions of latent parameters. We demonstrate their accuracy and performance on synthetic as well as real-world climate dataset applications., Comment: 22 pages, 14 figures

For oceanographic applications, probabilistic forecasts typically have to deal with i) high-dimensional complex models, and ii) very sparse spatial observations. In search-and-rescue operations at sea, for instance, the short-term predictions of drift trajectories are essential to efficiently define search areas, but in-situ buoy observations provide only very sparse point measurements, while the mission is ongoing. Statistically optimal forecasts, including consistent uncertainty statements, rely on Bayesian methods for data assimilation to make the best out of both the complex mathematical modeling and the sparse spatial data. To identify suitable approaches for data assimilation in this context, we discuss localisation strategies and compare two state-of-the-art ensemble-based methods for applications with spatially sparse observations. The first method is a version of the ensemble-transform Kalman filter, where we tailor a localisation scheme for sparse point data. The second method is the implicit equal-weights particle filter which has recently been tested for related oceanographic applications. First, we study a linear spatio-temporal model for contaminant advection and diffusion, where the analytical Kalman filter provides a reference. Next, we consider a simplified ocean model for sea currents, where we conduct state estimation and predict drift. Insight is gained by comparing ensemble-based methods on a number of skill scores including prediction bias and accuracy, distribution coverage, rank histograms, spatial connectivity and drift trajectory forecasts.

In recent work, Darmon, Pozzi and Vonk explicitly construct a modular form whose spectral coefficients are $p$-adic logarithms of Gross-Stark units and Stark-Heegner points. Here we describe how this construction gives rise to a practical algorithm for explicitly computing these logarithms to specified precision, and how to recover the exact values of the Gross-Stark units and Stark-Heegner points from them. Key tools are overconvergent modular forms, reduction theory of quadratic forms and Newton polygons. As an application, we tabulate Brumer-Stark units in narrow Hilbert class fields of real quadratic fields with discriminants up to $10000$, for primes less than $20$, as well as Stark-Heegner points on elliptic curves., Comment: 23 pages, 4 tables, 2 figures

Bayesian methods and software for spatial data analysis are generally now well established in the scientific community. Despite the wide application of spatial models, the analysis of multivariate spatial data using R-INLA has not been widely described in the existing literature. Therefore, the main objective of this article is to demonstrate that R-INLA is a convenient toolbox to analyse different types of multivariate spatial datasets. Additionally, this will be illustrated by analysing three datasets which are publicly available. Furthermore, the details and the R code of these analyses are provided to exemplify how to adjust multivariate spatial datasets with R-INLA., Comment: Submitted to the RJournal (19 pages and 6 figures)

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS" (Alvares et al., 2021). In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.