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Protoplanetary disks are routinely described as finite mass reservoirs left over by the gravitational collapse of the protostar, an assumption that strongly constrains both disk evolution and planet formation models. We propose a different scenario where protoplanetary disks of pre-main sequence stars are assembled primarily by Bondi-Hoyle accretion from the parent gas cloud. We demonstrate that Bondi-Hoyle accretion can supply not only the mass, but also the angular momentum necessary to explain the observed size of protoplanetary disks, and we predict the dependence of the disk specific angular momentum on the stellar mass. Our results are based on an analytical derivation of the scaling of the angular momentum in a turbulent flow, which we also confirm with a numerical simulation of supersonic turbulence. This new scenario for disk formation and evolution may alleviate a number of observational problems as well as compel major revisions of disk and planet formation models., Comment: Submitted to Nature Astronomy

Context. While Class II stars have already accreted most of their mass, the continued inflow of fresh material via Bondi-Hoyle accretion acts as an additional mass reservoir for their circumstellar disks. This may explain the observed accretion rates of pre-main- sequence (PMS) stars, as well as observational inconsistencies in the mass and angular momentum balance of their disks. Aims. Using a new simulation that reproduces the stellar IMF, we want to quantify the role of Bondi-Hoyle accretion in the formation of Class II disks, as well address the prospect of its observational detection with the James Webb Space Telescope (JWST). Methods. We study the mass and angular momentum of the accreting gas using passively-advected tracer particles in the simulation, and we carry out radiative transfer calculations of near-infrared scattering to generate synthetic JWST observations of Bondi-Hoyle trails of PMS stars. Results. Gas accreting on Class II PMS stars approximately 1 Myr after their formation has enough mass and angular momentum to strongly affect the evolution of the preexisting disks. The accreted angular momentum is large enough to also explain the observed size of Class II disks. The orientation of the angular momentum vector can differ significantly from that of the previously accreted gas, which may result in a significant disk warping or misalignment. We also predict that JWST observations of Class II stars will be able to detect Bondi-Hoyle trails with a 90% success rate with only 2 min exposure time, if stars with accretion rates \dot{M} > 5e-10 Msol/yr and luminosity of L > 0.5 Lsol are selected., Comment: 12 pages, 16 figures, submitted to A&A

Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, agents within an ABM follow predetermined 'bounded rational' behavioural rules which can be cumbersome to design and difficult to justify. Here we leverage multi-agent reinforcement learning (RL) to expand the capabilities of ABMs with the introduction of 'fully rational' agents that learn their policy by interacting with the environment and maximising a reward function. Specifically, we propose a 'Rational macro ABM' (R-MABM) framework by extending a paradigmatic macro ABM from the economic literature. We show that gradually substituting ABM firms in the model with RL agents, trained to maximise profits, allows for studying the impact of rationality on the economy. We find that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality. We also find that RL agents with independent policies, and without the ability to communicate with each other, spontaneously learn to segregate into different strategic groups, thus increasing market power and overall profits. Finally, we find that a higher number of rational (RL) agents in the economy always improves the macroeconomic environment as measured by total output. Depending on the specific rational policy, this can come at the cost of higher instability. Our R-MABM framework allows for stable multi-agent learning, is available in open source, and represents a principled and robust direction to extend economic simulators., Comment: 9 pages, 4 figures

The kinematic information of the Radcliffe Wave (RW) is essential for determining its existence and gaining insights into its origin and evolution. In this work, we present an accurate measurement of the vertical velocity ($V_Z$) of RW by incorporating the radial velocity (RV) measures through two methods, which is crucial but was neglected previously. First, the velocities are measured towards young stars, using their RV measurements from APOGEE-2 and proper motion measurements from Gaia DR3. Second, we combine RV measurements toward clouds with proper motion measurements of associated Young Stellar Objects (YSOs) to determine the vertical velocities of the clouds. The results reveal that the oscillations in $V_Z$ are not synchronous with the vertical coordinate $Z$, which differs from the conclusions of previous studies. Instead, we find a 5 km$\cdot$s$^{-1}$$\cdot$kpc$^{-1}$ gradient in $V_Z$ along the RW, exhibiting a dipole-like pattern. Consequently, the kinematic arrangement does not show a corresponding coherence with the spatial arrangement, bringing the Radcliffe Wave model into question., Comment: 9 pages, 6 figures, Accepted for publication in ApJ

In many applied fields it is desired to make predictions with the aim of assessing the plausibility of more severe events than those already recorded to safeguard against calamities that have not yet occurred. This problem can be analysed using extreme value theory. We consider the popular peaks over a threshold method and show that the generalised Pareto approximation of the true predictive densities of both a future unobservable excess or peak random variable can be very accurate. We propose both a frequentist and a Bayesian approach for the estimation of such predictive densities. We show the asymptotic accuracy of the corresponding estimators and, more importantly, prove that the resulting predictive inference is asymptotically reliable. We show the utility of the proposed predictive tools analysing extreme temperatures in Milan in Italy.

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$_2$ emissions, demonstrating its potential for effective traffic management, Comment: This paper has been submitted to IEEE ECC24

The ages of young star clusters are fundamental clocks to constrain the formation and evolution of pre-main-sequence stars and their protoplanetary disks and exoplanets. However, dating methods for very young clusters often disagree, casting doubts on the accuracy of the derived ages. We propose a new method to derive the kinematic age of star clusters based on the evaporation ages of their stars. The method is validated and calibrated using hundreds of clusters identified in a supernova-driven simulation of the interstellar medium forming stars for approximately 40 Myr within a 250 pc region. We demonstrate that the clusters' evaporation-age uncertainty can be as small as about 10% for clusters with a large enough number of evaporated stars and small but realistic observational errors. We have obtained evaporation ages for a pilot sample of 10 clusters, finding a good agreement with their published isochronal ages. The evaporation ages will provide important constraints for modeling the pre-main-sequence evolution of low-mass stars, as well as to investigate the star-formation and gas-evaporation history of young clusters. These ages can be more accurate than isochronal ages for very young clusters, for which observations and models are more uncertain., Comment: 13 pages, 11 figures, 2 tables, Submitted to A&A on Nov 14th 2023, Accepted 9 January 2024

The paper studies conical, convex, and affine models in the framework of behavioral systems theory. We investigate basic properties of such behaviors and address the problem of constructing models from measured data. We prove that closed, shift-invariant, conical, convex, and affine models have the intersection property, thereby enabling the definition of most powerful unfalsified models based on infinite-horizon measurements. We then provide necessary and sufficient conditions for representing conical, convex, and affine finite-horizon behaviors using raw data matrices, expressing persistence of excitation requirements in terms of non-negative rank conditions. The applicability of our results is demonstrated by a numerical example arising in population ecology.

We study the controller implementability problem, which seeks to determine if a controller can make the closed-loop behavior of a given plant match that of a desired reference behavior. We establish necessary and sufficient conditions for controller implementability which only rely on raw data. Subsequently, we consider the problem of constructing controllers directly from data. By leveraging the concept of canonical controller, we provide a formula to directly construct controllers that implement plant-compatible reference behaviors using measurements of both reference and plant behaviors.

The Peaks Over Threshold (POT) method is the most popular statistical method for the analysis of univariate extremes. Even though there is a rich applied literature on Bayesian inference for the POT method there is no asymptotic theory for such proposals. Even more importantly, the ambitious and challenging problem of predicting future extreme events according to a proper probabilistic forecasting approach has received no attention to date. In this paper we develop the asymptotic theory (consistency, contraction rates, asymptotic normality and asymptotic coverage of credible intervals) for the Bayesian inference based on the POT method. We extend such an asymptotic theory to cover the Bayesian inference on the tail properties of the conditional distribution of a response random variable conditionally to a vector of random covariates. With the aim to make accurate predictions of severer extreme events than those occurred in the past, we specify the posterior predictive distribution of a future unobservable excess variable in the unconditional and conditional approach and we prove that is Wasserstein consistent and derive its contraction rates. Simulations show the good performances of the proposed Bayesian inferential methods. The analysis of the change in the frequency of financial crises over time shows the utility of our methodology.

Fixpoints are ubiquitous in computer science as they play a central role in providing a meaning to recursive and cyclic definitions. Bisimilarity, behavioural metrics, termination probabilities for Markov chains and stochastic games are defined in terms of least or greatest fixpoints. Here we show that our recent work which proposes a technique for checking whether the fixpoint of a function is the least (or the largest) admits a natural categorical interpretation in terms of gs-monoidal categories. The technique is based on a construction that maps a function to a suitable approximation and the compositionality properties of this mapping are naturally interpreted as a gs-monoidal functor. This guides the realisation of a tool, called UDEfix that allows to build functions (and their approximations) like a circuit out of basic building blocks and subsequently perform the fixpoints checks. We also show that a slight generalisation of the theory allows one to treat a new relevant case study: coalgebraic behavioural metrics based on Wasserstein liftings.

Marginal expected shortfall is unquestionably one of the most popular systemic risk measures. Studying its extreme behaviour is particularly relevant for risk protection against severe global financial market downturns. In this context, results of statistical inference rely on the bivariate extreme values approach, disregarding the extremal dependence among a large number of financial institutions that make up the market. In order to take it into account we propose an inferential procedure based on the multivariate regular variation theory. We derive an approximating formula for the extreme marginal expected shortfall and obtain from it an estimator and its bias-corrected version. Then, we show their asymptotic normality, which allows in turn the confidence intervals derivation. Simulations show that the new estimators greatly improve upon the performance of existing ones and confidence intervals are very accurate. An application to financial returns shows the utility of the proposed inferential procedure. Statistical results are extended to a general $\beta$-mixing context that allows to work with popular time series models with heavy-tailed innovations.

Characterizing prestellar cores in star-forming regions is an important step towards the validation of theoretical models of star formation. Thanks to their sub-arcsecond resolution, ALMA observations can potentially provide samples of prestellar cores up to distances of a few kpc, where regions of massive star formation can be targeted. However, the extraction of real cores from dust-continuum observations of turbulent star-forming clouds is affected by complex projection effects. In this work, we study the problem of core extraction both in the idealized case of column-density maps and in the more realistic case of synthetic 1.3\,mm ALMA observations. The analysis is carried out on 12 regions of high column density from our 250 pc simulation. We find that derived core masses are highly unreliable, with only {\em a weak correlation between the masses of cores selected in the synthetic ALMA maps and those of the corresponding three-dimensional cores}. The fraction of real three-dimensional cores detected in the synthetic maps increases monotonically with mass and remains always below 50\%. Above $\sim 1\,M_{\odot}$, the core mass function derived from the column-density maps is steeper than that of the three-dimensional cores, while the core mass function from the synthetic ALMA maps has a slope closer to that of the real three-dimensional cores. Because of the mass uncertainties, proper guidance from realistic simulations is essential if ALMA observations of protoclusters at kpc distances are to be used to test star-formation models., Comment: 20 pages, 21 figures, MNRAS, in press

Extreme Value Theory plays an important role to provide approximation results for the extremes of a sequence of independent random variables when their distribution is unknown. An important one is given by the {generalised Pareto distribution} $H_\gamma(x)$ as an approximation of the distribution $F_t(s(t)x)$ of the excesses over a threshold $t$, where $s(t)$ is a suitable norming function. In this paper we study the rate of convergence of $F_t(s(t)\cdot)$ to $H_\gamma$ in variational and Hellinger distances and translate it into that regarding the Kullback-Leibler divergence between the respective densities.

The mass distribution of dense cores is a potential key to understand the process of star formation. Applying dendrogram analysis to the CARMA-NRO Orion C$^{18}$O ($J$=1--0) data, we identify 2342 dense cores, about 22 \% of which have virial ratios smaller than 2, and can be classified as gravitationally bound cores. The derived core mass function (CMF) for bound starless cores which are not associate with protostars has a slope similar to Salpeter's initial mass function (IMF) for the mass range above 1 $M_\odot$, with a peak at $\sim$ 0.1 $M_\odot$. We divide the cloud into four parts based on the declination, OMC-1/2/3, OMC-4/5, L1641N/V380 Ori, and L1641C, and derive the CMFs in these regions. We find that starless cores with masses greater than 10 $M_\odot$ exist only in OMC-1/2/3, whereas the CMFs in OMC-4/5, L1641N, and L1641C are truncated at around 5--10 $M_\odot$. From the number ratio of bound starless cores and Class II objects in each subregion, the lifetime of bound starless cores is estimated to be 5--30 free-fall times, consistent with previous studies for other regions. In addition, we discuss core growth by mass accretion from the surrounding cloud material to explain the coincidence of peak masses between IMFs and CMFs. The mass accretion rate required for doubling the core mass within a core lifetime is larger than that of Bondi-Hoyle accretion by a factor of order 2. This implies that more dynamical accretion processes are required to grow cores., Comment: 58 pages, 33 figures, 21 tables, accepted by ApJS

The use of expectiles in risk management has recently gathered remarkable momentum due to their excellent axiomatic and probabilistic properties. In particular, the class of elicitable law-invariant coherent risk measures only consists of expectiles. While the theory of expectile estimation at central levels is substantial, tail estimation at extreme levels has so far only been considered when the tail of the underlying distribution is heavy. This article is the first work to handle the short-tailed setting where the loss (e.g. negative log-returns) distribution of interest is bounded to the right and the corresponding extreme value index is negative. We derive an asymptotic expansion of tail expectiles in this challenging context under a general second-order extreme value condition, which allows to come up with two semiparametric estimators of extreme expectiles, and with their asymptotic properties in a general model of strictly stationary but weakly dependent observations. A simulation study and a real data analysis from a forecasting perspective are performed to verify and compare the proposed competing estimation procedures.

Willems' Fundamental Lemma provides a powerful data-driven parametrization of all trajectories of a controllable linear time-invariant system based on one trajectory with persistently exciting (PE) input. In this paper, we present a novel proof of this result which is inspired by the classical adaptive control literature and differs from existing proofs in multiple aspects. The proof involves a quantitative and directional PE notion, allowing to characterize robust PE properties via singular value bounds, as opposed to binary rank-based PE conditions. Further, the proof is constructive, i.e., we derive an explicit PE lower bound for the generated data. As a contribution of independent interest, we generalize existing PE results from the adaptive control literature and reveal a crucial role of the system's zeros., Comment: Final version, accepted for presentation at the American Control Conference (ACC) 2023

Strategy iteration is a technique frequently used for two-player games in order to determine the winner or compute payoffs, but to the best of our knowledge no general framework for strategy iteration has been considered. Inspired by previous work on simple stochastic games, we propose a general formalisation of strategy iteration for solving least fixpoint equations over a suitable class of complete lattices, based on MV-chains. We devise algorithms that can be used for non-expansive fixpoint functions represented as so-called min-, respectively, max-decompositions. Correspondingly, we develop two different techniques: strategy iteration from above, which has to solve the problem that iteration might reach a fixpoint that is not the least, and from below, which is algorithmically simpler, but requires a more involved correctness argument. We apply our method to solve energy games and compute behavioural metrics for probabilistic automata.

The block maxima method is one of the most popular approaches for extreme value analysis with independent and identically distributed observations in the domain of attraction of an extreme value distribution. The lack of a rigorous study on the Bayesian inference in this context has limited its use for statistical analysis of extremes. In this paper we propose an empirical Bayes procedure for inference on the block maxima law and its related quantities. We show that the posterior distributions of the tail index of the data distribution and of the return levels (representative of future extreme episodes) satisfy a number of important theoretical properties. These guarantee the reliability of posterior-based inference and extend to the posterior predictive distribution, the key tool in Bayesian probabilistic forecasting. Posterior computations are readily obtained via an efficient adaptive Metropolis-Hasting type of algorithm. Simulations show its excellent inferential performances already with modest sample sizes. The utility of our proposal is showcased analysing extreme winds generated by hurricanes in the Atlantic basin.

This paper explores the problem of uncertainty quantification in the behavioral setting for data-driven control. Building on classical ideas from robust control, the problem is regarded as that of selecting a metric which is best suited to a data-based description of uncertainties. Leveraging on Willems' fundamental lemma, restricted behaviors are viewed as subspaces of fixed dimension, which may be represented by data matrices. Consequently, metrics between restricted behaviors are defined as distances between points on the Grassmannian, i.e., the set of all subspaces of equal dimension in a given vector space. A new metric is defined on the set of restricted behaviors as a direct finite-time counterpart of the classical gap metric. The metric is shown to capture parametric uncertainty for the class of autoregressive (AR) models. Numerical simulations illustrate the value of the new metric with a data-driven mode recognition and control case study., Comment: Submitted to the 61st IEEE Conference on Decision and Control

Continued fractions are classical representations of complex objects (for example, real numbers) as sums and inverses of simpler objects (for example, integers). The analogy in linear circuit theory is a chain of series/parallel one-ports: the port behavior is a continued fraction containing the port behaviors of its elements. Truncating a continued fraction is a classical method of approximation, which corresponds to deleting the circuit elements furthest from the port. We apply this idea to chains of series/parallel one-ports composed of arbitrary nonlinear relations. This gives a model reduction method which automatically preserves properties such as incremental positivity. The Scaled Relative Graph (SRG) gives a graphical representation of the original and truncated port behaviors. The difference of these SRGs gives a bound on the approximation error, which is shown to be competitive with existing methods., Comment: Submitted to CDC2022

The dynamical state of massive clumps is key to our understanding of the formation of massive stars. In this work, we study the kinematic properties of massive clumps using synthetic observations. We have previously compiled a very large catalog of synthetic dust-continuum compact sources from our 250 pc, SN-driven, star formation simulation. Here, we compute synthetic $\rm N_{2}H^{+}$ line profiles for a subsample of those sources and compare their properties with the observations and with those of the corresponding three-dimensional (3D) clumps in the simulation. We find that the velocity dispersion of the sources estimated from the $\rm N_{2}H^{+}$ line is a good estimate of that of the 3D clumps, although its correlation with the source size is weaker than the velocity-size correlation of the 3D clumps. The relation between the mass of the 3D clumps, $M_{\rm main}$, and that of the corresponding synthetic sources, $M_{\rm SED}$, has a large scatter and a slope of 0.5, $M_{\rm main} \propto M_{\rm SED}^{0.5}$, due to uncertainties arising from the observational band-merging procedure and from projection effects along the line of sight. As a result, the virial parameters of the 3D clumps are not correlated with the clump masses, even if a negative correlation is found for the compact sources, and the virial parameter of the most massive sources may significantly underestimate that of the associated clumps., Comment: 19 pages, 12 figures, 1 table. Accepted for publication in MNRAS

This paper investigates pooling strategies for tail index and extreme quantile estimation from heavy-tailed data. To fully exploit the information contained in several samples, we present general weighted pooled Hill estimators of the tail index and weighted pooled Weissman estimators of extreme quantiles calculated through a nonstandard geometric averaging scheme. We develop their large-sample asymptotic theory across a fixed number of samples, covering the general framework of heterogeneous sample sizes with different and asymptotically dependent distributions. Our results include optimal choices of pooling weights based on asymptotic variance and MSE minimization. In the important application of distributed inference, we prove that the variance-optimal distributed estimators are asymptotically equivalent to the benchmark Hill and Weissman estimators based on the unfeasible combination of subsamples, while the AMSE-optimal distributed estimators enjoy a smaller AMSE than the benchmarks in the case of large bias. We consider additional scenarios where the number of subsamples grows with the total sample size and effective subsample sizes can be low. We extend our methodology to handle serial dependence and the presence of covariates. Simulations confirm that our pooled estimators perform virtually as well as the benchmark estimators. Two applications to real weather and insurance data are showcased.

The paper addresses the model reduction problem for linear and nonlinear systems using the notion of least squares moment matching. For linear systems, the main idea is to approximate a transfer function by ensuring that the interpolation conditions imposed by moment matching are satisfied in a least squares sense. The paper revisits this idea using tools from output regulation theory to provide a new time-domain characterization of least squares moment matching. It is shown that least squares moment matching can be characterized in terms of an optimization problem involving an invariance equation and in terms of the steady-state behavior of an error system. This characterization, in turn, is then used to define a nonlinear enhancement of the notion of least squares moment matching and to develop a model reduction theory for nonlinear systems based on the notion of least squares moment matching. Parameterized families of models achieving least squares moment matching are determined both for linear and nonlinear systems. The new parameterizations are shown to admit natural geometric and system-theoretic interpretations. The theory is illustrated by worked-out numerical examples., Comment: Submitted to the IEEE Transactions on Automatic Control. arXiv admin note: substantial text overlap with arXiv:2109.11869

The paper addresses the model reduction problem by least squares moment matching for continuous-time, linear, time-invariant systems. The basic idea behind least squares moment matching is to approximate a transfer function by ensuring that the interpolation conditions imposed by moment matching are satisfied in a least squares sense. This idea is revisited using invariance equations and steady-state responses to provide a new time-domain characterization of least squares moment matching. The characterization, in turn, is then used to obtain a parameterized family of models achieving least squares moment matching. The theory is illustrated by a worked-out numerical example., Comment: Submitted to the 60th Conference on Decision and Control (CDC)

Systematic surveys of massive clumps have been carried out to study the conditions leading to the formation of massive stars. These clumps are typically at large distances and unresolved, so their physical properties cannot be reliably derived from the observations alone. Numerical simulations are needed to interpret the observations. To this end, we generate synthetic Herschel observations using our large-scale star-formation simulation, where massive stars explode as supernovae driving the interstellar-medium turbulence. From the synthetic observations, we compile a catalog of compact sources following the exact same procedure as for the Hi-GAL compact source catalog. We show that the sources from the simulation have observational properties with statistical distributions consistent with the observations. By relating the compact sources from the synthetic observations to their three-dimensional counterparts in the simulation, we find that the synthetic observations overestimate the clump masses by about an order of magnitude on average due to line-of-sight projection, and projection effects are likely to be even worse for Hi-GAL Inner Galaxy sources. We also find that a large fraction of sources classified as protostellar are likely to be starless, and propose a new method to partially discriminate between true and false protostellar sources., Comment: 17 pages, 15 figures, submitted to MNRAS

Applying dendrogram analysis to the CARMA-NRO C$^{18}$O ($J$=1--0) data having an angular resolution of $\sim$ 8", we identified 692 dense cores in the Orion Nebula Cluster (ONC) region. Using this core sample, we compare the core and initial stellar mass functions in the same area to quantify the step from cores to stars. About 22 \% of the identified cores are gravitationally bound. The derived core mass function (CMF) for starless cores has a slope similar to Salpeter's stellar initial mass function (IMF) for the mass range above 1 $M_\odot$, consistent with previous studies. Our CMF has a peak at a subsolar mass of $\sim$ 0.1 $M_\odot$, which is comparable to the peak mass of the IMF derived in the same area. We also find that the current star formation rate is consistent with the picture in which stars are born only from self-gravitating starless cores. However, the cores must gain additional gas from the surroundings to reproduce the current IMF (e.g., its slope and peak mass), because the core mass cannot be accreted onto the star with a 100\% efficiency. Thus, the mass accretion from the surroundings may play a crucial role in determining the final stellar masses of stars., Comment: 13 pages, 5 figures, accepted by ApJL

Knaster-Tarski's theorem, characterising the greatest fixpoint of a monotone function over a complete lattice as the largest post-fixpoint, naturally leads to the so-called coinduction proof principle for showing that some element is below the greatest fixpoint (e.g., for providing bisimilarity witnesses). The dual principle, used for showing that an element is above the least fixpoint, is related to inductive invariants. In this paper we provide proof rules which are similar in spirit but for showing that an element is above the greatest fixpoint or, dually, below the least fixpoint. The theory is developed for non-expansive monotone functions on suitable lattices of the form $\mathbb{M}^Y$, where $Y$ is a finite set and $\mathbb{M}$ an MV-algebra, and it is based on the construction of (finitary) approximations of the original functions. We show that our theory applies to a wide range of examples, including termination probabilities, metric transition systems, behavioural distances for probabilistic automata and bisimilarity. Moreover it allows us to determine original algorithms for solving simple stochastic games.