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Temporal variability in flux and spectral shape is ubiquitous in the X-ray sky and carries crucial information about the nature and emission physics of the sources. The EPIC instrument on board the XMM-Newton observatory is the most powerful tool for studying variability even in faint sources. Each day, it collects a large amount of information about hundreds of new serendipitous sources, but the resulting huge (and growing) dataset is largely unexplored in the time domain. The project called Exploring the X-ray transient and variable sky (EXTraS) systematically extracted all temporal domain information in the XMM-Newton archive. This included a search and characterisation of variability, both periodic and aperiodic, in hundreds of thousands of sources spanning more than eight orders of magnitude in timescale and six orders of magnitude in flux, and a search for fast transients that were missed by standard image analysis. All results, products, and software tools have been released to the community in a public archive. A science gateway has also been implemented to allow users to run the EXTraS analysis remotely on recent XMM datasets. We give details on the new algorithms that were designed and implemented to perform all steps of EPIC data analysis, including data preparation, source and background modelling, generation of time series and power spectra, and search for and characterisation of different types of variabilities. We describe our results and products and give information about their basic statistical properties and advice on their usage. We also describe available online resources. The EXTraS database of results and its ancillary products is a rich resource for any kind of investigation in almost all fields of astrophysics. Algorithms and lessons learnt from our project are also a very useful reference for any current and future experiment in the time domain., Comment: 39 pages; accepted for publication in Astronomy & Astrophysics

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global objective function formed by the addition of partial functions locally known at each node. A number of methods are available for addressing this problem, having different advantages. The goal of this work is to achieve the maximum possible convergence rate. As the first step towards this end, we propose a new method which we show converges faster than other available options. As with most distributed optimization methods, convergence rate depends on a step size parameter. As the second step towards our goal we complement the proposed method with a fully distributed method for estimating the optimal step size that maximizes convergence speed. We provide theoretical guarantees for the convergence of the resulting method in a neighborhood of the solution. Also, for the case in which the global objective function has a single local minimum, we provide a different step size selection criterion together with theoretical guarantees for convergence. We present numerical experiments showing that, when using the same step size, our method converges significantly faster than its rivals. Experiments also show that the distributed step size estimation method achieves an asymptotic convergence rate very close to the theoretical maximum.

Our understanding of the background of the EPIC/pn camera onboard XMM-Newton is incomplete. This affects the study of extended sources and can influence the predictions of the background of future X-ray missions. We provide new results based on the analysis of the largest data set ever used. We focus on the unconcentrated component of the EPIC/pn background - supposedly related to cosmic rays interacting with the telescope. We find that the out-field of view region of the pn detector is actually exposed to the sky. After cleaning from the sky contamination, the unconcentrated background does not show significant spatial variations and its time behaviour is anti-correlated with the solar cycle. We find a very tight, linear correlation between unconcentrated backgrounds detected in the EPIC/pn and MOS2 cameras: this permits the correct evaluation of the pn unconcentrated background of each exposure on the basis of MOS2 data, avoiding the use (as usual) of the contaminated pn regions. We find a tight, linear correlation between the pn unconcentrated background and the proton flux in the 630-970 MeV energy band measured by SOHO/EPHIN. Through this relationship we quantify the contribution of cosmic ray interactions to the pn unconcentrated background and we find a second source which contributes to the pn unconcentrated background for a significant fraction (30%-70%), that does not vary with time and is roughly isotropic. Hard X-ray photons of the CXB satisfy all the known properties of this new component. Our findings provide an important observational confirmation of simulation results on ATHENA., Comment: 21 pages, 14 figures, 2 tables; accepted by the Astrophysical Journal

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of $w_0w_a$CDM$+\sum m_\nu$models between redshift $z=0$ and $z=3$ for spatial scales within the range 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (1) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy and the metric field, (2) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (3) we run over 250 high-resolution simulations with $3000^3$ particles in boxes of 1 (Gpc/$h$)${}^3$ volumes based on paired-and-fixed initial conditions and (4) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter $w_a$ significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors like Halofit, HMCode and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of 1% or better for 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc and $z\leq3$ compared to high-resolution dark matter only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2 ., Comment: 28 pages, 19 figures, submitted to MNRAS

We study a distributed Kalman filtering problem in which a number of nodes cooperate without central coordination to estimate a common state based on local measurements and data received from neighbors. This is typically done by running a local filter at each node using information obtained through some procedure for fusing data across the network. A common problem with existing methods is that the outcome of local filters at each time step depends on the data fused at the previous step. We propose an alternative approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. The main feature of the new approach is that fusion errors introduced at a given time step do not carry over to subsequent steps. This offers advantages in many situations including when a global estimate in only needed at a rate slower than that of measurements or when there are network interruptions. If the global structural data can be fused correctly asymptotically, the stability of local filters is equivalent to that of the centralized Kalman filter. Otherwise, we provide conditions to guarantee stability and bound the resulting estimation error. Numerical experiments are given to show the advantage of our method over other existing alternatives.

Sparse polynomial chaos expansions (PCE) are an efficient and widely used surrogate modeling method in uncertainty quantification for engineering problems with computationally expensive models. To make use of the available information in the most efficient way, several approaches for so-called basis-adaptive sparse PCE have been proposed to determine the set of polynomial regressors ("basis") for PCE adaptively. The goal of this paper is to help practitioners identify the most suitable methods for constructing a surrogate PCE for their model. We describe three state-of-the-art basis-adaptive approaches from the recent sparse PCE literature and conduct an extensive benchmark in terms of global approximation accuracy on a large set of computational models. Investigating the synergies between sparse regression solvers and basis adaptivity schemes, we find that the choice of the proper solver and basis-adaptive scheme is very important, as it can result in more than one order of magnitude difference in performance. No single method significantly outperforms the others, but dividing the analysis into classes (regarding input dimension and experimental design size), we are able to identify specific sparse solver and basis adaptivity combinations for each class that show comparatively good performance. To further improve on these findings, we introduce a novel solver and basis adaptivity selection scheme guided by cross-validation error. We demonstrate that this automatic selection procedure provides close-to-optimal results in terms of accuracy, and significantly more robust solutions, while being more general than the case-by-case recommendations obtained by the benchmark.

The wealth of detections of millisecond pulsars (MSPs) in $\gamma$-rays by {\em Fermi} has spurred searches for these objects among the several unidentified $\gamma$-ray sources. Interesting targets are a sub-class of binary MSPs, dubbed "Black Widows" (BWs) and "Redbacks" (RBs), which are in orbit with low-mass non-degenerate companions fully or partially ablated by irradiation from the MSP wind. These systems can be easily missed in radio pulsar surveys owing to the eclipse of the radio signal by the intra-binary plasma from the ablated companion star photosphere, making them better targets for multi-wavelength observations. We used optical and X-ray data from public databases to carry out a systematic investigation of all the unidentified $\gamma$-ray sources from the Fermi Large Area Telescope (LAT) Third Source Catalog (3FGL), which have been pre-selected as likely MSP candidates according to a machine-learning technique analysis. We tested our procedure by recovering known binary BW/RB identifications and searched for new ones, finding possible candidates. At the same time, we investigated previously proposed BW/RB identifications and we ruled out one of them based upon the updated $\gamma$-ray source coordinates., Comment: 22 pages, 14 figures, 21 pages - This manuscript is based on the MSc Thesis work of C. Braglia, defended on April 2020 at the University of Milan, accepted for publication in MNRAS

Recursive processing in sentence comprehension is considered a hallmark of human linguistic abilities. However, its underlying neural mechanisms remain largely unknown. We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing, namely the storing of grammatical number and gender information in working memory and its use in long-distance agreement (e.g., capturing the correct number agreement between subject and verb when they are separated by other phrases). Although the network, a recurrent architecture with Long Short-Term Memory units, was solely trained to predict the next word in a large corpus, analysis showed the emergence of a very sparse set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number. However, the simulations also showed that this mechanism does not support full recursion and fails with some long-range embedded dependencies. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns, with or without embedding. Human and model error patterns were remarkably similar, showing that the model echoes various effects observed in human data. However, a key difference was that, with embedded long-range dependencies, humans remained above chance level, while the model's systematic errors brought it below chance. Overall, our study shows that exploring the ways in which modern artificial neural networks process sentences leads to precise and testable hypotheses about human linguistic performance.

We report on the results of X-ray observations of 4XMM J111816.0-324910, a transient ultra-luminous X-ray source located in the galaxy NGC 3621. This system is characterised by a transient nature and marked variability with characteristic time-scale of ~3500 s, differently from other ULXs, which in the vast majority show limited intra-observation variability. Such a behaviour is very reminiscent of the so-called heartbeats sometimes observed in the Galactic black hole binary GRS 1915+105, where the variability time-scale is ~10-1000 s. We study the spectral and timing properties of this object and find that overall, once the differences in the variability time-scales are taken into account, they match quite closely those of both GRS 1915+105, and of a number of objects showing heartbeats in their light-curves, including a confirmed neutron star and a super-massive black hole powering an active galactic nucleus. We investigate the nature of the compact object in 4XMM J111816.0-324910 by searching for typical neutron star signatures and by attempting a mass estimate based on different methods and assumptions. Based on the current available data, we are not able to unambiguously determine the nature of the accreting compact object responsible for the observed phenomenology., Comment: Accepted for publication in ApJ. 13 pages, 6 figures

In this paper we propose a new sampling-free approach to solve Bayesian model inversion problems that is an extension of the previously proposed spectral likelihood expansions (SLE) method. Our approach, called stochastic spectral likelihood embedding (SSLE), uses the recently presented stochastic spectral embedding (SSE) method for local spectral expansion refinement to approximate the likelihood function at the core of Bayesian inversion problems. We show that, similar to SLE, this approach results in analytical expressions for key statistics of the Bayesian posterior distribution, such as evidence, posterior moments and posterior marginals, by direct post-processing of the expansion coefficients. Because SSLE and SSE rely on the direct approximation of the likelihood function, they are in a way independent of the computational/mathematical complexity of the forward model. We further enhance the efficiency of SSLE by introducing a likelihood specific adaptive sample enrichment scheme. To showcase the performance of the proposed SSLE, we solve three problems that exhibit different kinds of complexity in the likelihood function: multimodality, high posterior concentration and high nominal dimensionality. We demonstrate how SSLE significantly improves on SLE, and present it as a promising alternative to existing inversion frameworks.

Constructing approximations that can accurately mimic the behavior of complex models at reduced computational costs is an important aspect of uncertainty quantification. Despite their flexibility and efficiency, classical surrogate models such as Kriging or polynomial chaos expansions tend to struggle with highly non-linear, localized or non-stationary computational models. We hereby propose a novel sequential adaptive surrogate modeling method based on recursively embedding locally spectral expansions. It is achieved by means of disjoint recursive partitioning of the input domain, which consists in sequentially splitting the latter into smaller subdomains, and constructing a simpler local spectral expansions in each, exploiting the trade-off complexity vs. locality. The resulting expansion, which we refer to as "stochastic spectral embedding" (SSE), is a piece-wise continuous approximation of the model response that shows promising approximation capabilities, and good scaling with both the problem dimension and the size of the training set. We finally show how the method compares favorably against state-of-the-art sparse polynomial chaos expansions on a set of models with different complexity and input dimension.

This paper focuses on the distributed static estimation problem and a Belief Propagation (BP) based estimation algorithm is proposed. We provide a complete analysis for convergence and accuracy of it. More precisely, we offer conditions under which the proposed distributed estimator is guaranteed to converge and we give concrete characterizations of its accuracy. Our results not only give a new algorithm with good performance but also provide a useful analysis framework to learn the properties of a distributed algorithm. It yields better theoretical understanding of the static distributed state estimator and may generate more applications in the future.

Additive manufacturing (AM) techniques have gained interest in the tissue engineering field thanks to their versatility and unique possibilities of producing constructs with complex macroscopic geometries and defined patterns. Recently, composite materials - namely heterogeneous biomaterials identified as continuous phase (matrix) and reinforcement (filler) - have been proposed as inks that can be processed by AM to obtain scaffolds with improved biomimetic and bioactive properties. Significant efforts have been dedicated to hydroxyapatite (HA)-reinforced composites, especially targeting bone tissue engineering, thanks to the chemical similarities of HA with respect to mineral components of native mineralized tissues. Here we review applications of AM techniques to process HA-reinforced composites and biocomposites for the production of scaffolds with biological matrices, including cellular tissues. The primary outcomes of recent investigations in terms of morphological, structural, and in vitro and in vivo biological properties of the materials are discussed. We classify the approaches based on the nature of the matrices employed to embed the HA reinforcements and produce the tissue substitutes and report a critical discussion on the presented state of the art as well as the future perspectives, to offer a comprehensive picture of the strategies investigated as well as challenges in this emerging field.

We present the first detection of an X-ray flare from an ultracool dwarf of spectral class L. The event was identified in the EXTraS database of XMM-Newton variable sources, and its optical counterpart, J0331-27, was found through a cross-match with the Dark Energy Survey Year 3 release. Next to an earlier four-photon detection of Kelu-1, J0331-27 is only the second L dwarf detected in X-rays, and much more distant than other ultracool dwarfs with X-ray detections (photometric distance of 240 pc). From an optical spectrum with the VIMOS instrument at the VLT, we determine the spectral type of J0331-27 to be L1. The X-ray flare has an energy of E_X,F ~ 2x10^33 erg, placing it in the regime of superflares. No quiescent emission is detected, and from 2.5 Msec of XMM data we derive an upper limit of L_X,qui < 10^27 erg/s. The flare peak luminosity L_X,peak = 6.3x10^29 erg/s, flare duration tau_decay ~ 2400 s, and plasma temperature (~16 MK) are similar to values observed in X-ray flares of M dwarfs. This shows that strong magnetic reconnection events and the ensuing plasma heating are still present even in objects with photospheres as cool as ~2100 K. However, the absence of any other flares above the detection threshold of E_X,F ~2.5x10^32 erg in a total of ~2.5 Ms of X-ray data yields a flare energy number distribution inconsistent with the canonical power law dN/dE ~ E^-2, suggesting that magnetic energy release in J0331-27 -- and possibly in all L dwarfs -- takes place predominantly in the form of giant flares., Comment: 5 pages, 3 figures. Published as a Letter to A&A

In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp.~strictly stealthy) if it produces bounded changes (resp.~no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp.~strictly vulnerable) if it can be destabilized by a stealthy attack (resp.~strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results.

Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful sparse regression solvers to approximate computer models with many input parameters, relying on only few model evaluations. Within the last decade, a large number of algorithms for the computation of sparse PCE have been published in the applied math and engineering literature. We present an extensive review of the existing methods and develop a framework for classifying the algorithms. Furthermore, we conduct a unique benchmark on a selection of methods to identify which approaches work best in practical applications. Comparing their accuracy on several benchmark models of varying dimensionality and complexity, we find that the choice of sparse regression solver and sampling scheme for the computation of a sparse PCE surrogate can make a significant difference, of up to several orders of magnitude in the resulting mean-squared error. Different methods seem to be superior in different regimes of model dimensionality and experimental design size.

We study the problem of detecting an attack on a stochastic cyber-physical system. We aim to treat the problem in its most general form. We start by introducing the notion of asymptotically detectable attacks, as those attacks introducing changes to the system's output statistics which persist asymptotically. We then provide a necessary and sufficient condition for asymptotic detectability. This condition preserves generality as it holds under no restrictive assumption on the system and attacking scheme. To show the importance of this condition, we apply it to detect certain attacking schemes which are undetectable using simple statistics. Our necessary and sufficient condition naturally leads to an algorithm which gives a confidence level for attack detection. We present simulation results to illustrate the performance of this algorithm.

In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations. This work is based on a new set of 10,700 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations., Comment: 28 pages, 8 figures. Submitted to Computational Astrophysics and Cosmology

Ultraluminous X-ray sources (ULXs) are a class of accreting compact objects with X-ray luminosities above 1e39 erg/s. The ULX population counts several hundreds objects but only a minor fraction is well studied. Here we present a detailed analysis of all ULXs hosted in the galaxy NGC 7456. It was observed in X-rays only once in the past (in 2005) by XMM-Newton, but the observation was short and strongly affected by high background. In 2018, we obtained a new, deeper (~90 ks) XMM-Newton observation that allowed us to perform a detailed characterization of the ULXs hosted in the galaxy. ULX-1 and ULX-2, the two brightest objects (Lx~(6-10)e39 erg/s), have spectra that can be described by a two-thermal component model as often found in ULXs. ULX-1 shows also one order of magnitude in flux variability on short-term timescales (hundreds to thousand ks). The other sources (ULX-3 and ULX-4) show flux changes of at least an order of magnitude, and these objects may be candidate transient ULXs although longer X-ray monitoring or further studies are required to ascribe them to the ULX population. In addition, we found a previously undetected source that might be a new candidate ULX (labelled as ULX-5) with a luminosity of ~1e39 erg/s and hard power-law spectral shape, whose nature is still unclear and for which a background Active Galactic Nucleus cannot be excluded. We discuss the properties of all the ULXs in NGC 7456 within the framework of super-Eddington accretion onto stellar mass compact objects. Although no pulsations were detected, we cannot exclude that the sources host neutron stars., Comment: Accepted on ApJ; 10 pages, 5 figures, 3 tables