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Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. In this paper, we address this limitation by proposing GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem. To achieve this generalization, we make use of statistical learning techniques at the level of individual constraints. Specifically, we propose to train a classifier to predict, for any possible constraint and parameterization, whether the constraint belongs to the problem. We then show how, for some classes of classifiers, we can extract decision rules to construct interpretable constraint specifications. This enables the generation of ground constraints for any parameter instantiation. Additionally, we present a generate-and-test approach that can be used with any classifier, to generate the ground constraints on the fly. Our empirical results demonstrate that our approach achieves high accuracy and is robust to noise in the input instances.

Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while reducing the significant energy wastage resulting from curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants navigate numerous options, each presenting unique challenges and opportunities, underscoring the critical role of the trading strategy in maximizing profits. This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research enhances forecast assessment, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits and addressing market challenges. Finally, we modelled four commercial battery storage systems and evaluated their economic viability through a scenario analysis, with larger batteries showing a shorter return on investment.

Luminous, compact emission-line galaxies (LCGs) are the most abundant class of confirmed Lyman continuum (LyC) emitters. An optical integral field study of the nearby LCG NGC 2366 reveals an outflow originating at the star cluster 'knot B' thought to clear a channel via mechanical feedback that enables LyC escape. We observed NGC 2366 with the Chandra X-ray Observatory and detect X-ray emission from a point source coincident with the apex of the outflow at knot B. The point-like nature and variability of the X-ray emission suggests accretion onto a compact object. The accretion could produce sufficient kinetic energy to power the outflow., Comment: 5 pages, to appear in ApJ

Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing market is the closest to real-time and the most volatile among them. Its price forecasting literature is limited, inconsistent and outdated, with few deep learning attempts and no public dataset. This work applies to the Irish balancing market a variety of price prediction techniques proven successful in the widely studied day-ahead market. We compare statistical, machine learning, and deep learning models using a framework that investigates the impact of different training sizes. The framework defines hyperparameters and calibration settings; the dataset and models are made public to ensure reproducibility and to be used as benchmarks for future works. An extensive numerical study shows that well-performing models in the day-ahead market do not perform well in the balancing one, highlighting that these markets are fundamentally different constructs. The best model is LEAR, a statistical approach based on LASSO, which outperforms more complex and computationally demanding approaches.

Luminous infrared galaxies (LIRGs), the most extreme star-forming galaxies in the nearby (D$<$30 Mpc) Universe, show a notable X-ray emission deficiency (up to a factor of $\sim$10) compared with predictions from scaling relations of galaxy-wide high mass X-ray binary (HMXB) luminosity with star-formation rate. In the nearby ($\approx$20 Mpc) LIRG NGC 7552, the majority of the IR emission originates in a circumnuclear starburst ring, which has been resolved into several discrete knots of star formation. We present results from recent Chandra observations of NGC 7552, which reveal significant deficits in the 2-7 keV X-ray luminosities from two of the most powerful star-forming knots. We hypothesize that the expected luminous HMXB populations in these knots are either (1) obscured by very large column densities or (2) suppressed due to the knots having relatively high metallicity and/or very young ages ($\lesssim$ 5 Myr). We distinguish between these possibilities using data from recent NuSTAR observations, whose sensitivity above 10 keV is capable of uncovering heavily obscured HMXB populations, since emission at these energies is more immune to absorption effects. We find no evidence of a heavily obscured HMXB population in the central region of NGC 7552, suggesting suppressed HMXB formation. We further show that metallicity-dependent scaling relations cannot fully account for the observed deficit from the most powerful star-forming knots or the central region as a whole. Thus, we suggest that recent bursts in local star formation activity likely drive the high $L_{\rm{IR}}$ within these regions on timescales $\lesssim$ 5 Myr, shorter than the timescale required for the formation of HMXBs., Comment: Accepted for publication in ApJ (17 pages, 3 tables, 8 figures)

Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost. Modelling prediction uncertainty is also desirable to better inform the resource decision-making process, but research in this field is under-investigated. In this paper, we propose univariate and bivariate Bayesian deep learning models that provide predictions of future workload demand and its uncertainty. We run extensive experiments on Google and Alibaba clusters, where we first train our models with datasets from different cloud providers and compare them with LSTM-based baselines. Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications. Moreover, we investigate whether our models benefit transfer learning capabilities across different domains, i.e. dataset distributions. Experiments on the same workload datasets reveal that acceptable transfer learning performance can be achieved within the same provider (because distributions are more similar). Also, domain knowledge does not transfer when the source and target domains are very different (e.g. from different providers), but this performance degradation can be mitigated by increasing the training set size of the source domain.

We characterize the optical counterparts to the compact X-ray source population within the nearby spiral galaxy M81 using multi-band Hubble Space Telescope (HST) imaging data. By comparing the optical luminosities and colors measured for candidate donor stars and host clusters to stellar and cluster evolutionary models, respectively, we estimate the likely masses and upper age limits of the field and cluster X-ray binaries. We identify 15 low-mass X-ray binaries (i.e. donor star mass $\leq$ 3 solar masses) within ancient globular clusters, as well as 42 candidate high-mass X-ray binaries (i.e. donor star mass $\geq$ 8 solar masses). To estimate the likelihood of misclassifications, we inject 4,000 artificial sources into the HST mosaic image and conclude that our classifications of globular clusters and high-mass X-ray binaries are reliable at the >90% level. We find that globular clusters that host X-ray binaries are on average more massive and more compact than globular clusters that do not. However, there is no apparent correlation between the X-ray brightness of the clusters and their masses or densities, nor are X-ray binary hosts more X-ray luminous than the general field population of low-mass X-ray binaries. This work represents one of the first in-depth analyses of the population of X-ray binaries within globular clusters in a spiral galaxy., Comment: 14 pages of text, 12 figures, 3 tables

The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order's quantity is set to raise the inventory position to the order-up-to-level S. This paper introduces a new stochastic dynamic program (SDP) based heuristic to compute the (R, s, S) policy parameters for the non-stationary stochastic lot-sizing problem with backlogging of the excessive demand, fixed order and review costs, and linear holding and penalty costs. In a recent work, Visentin et al. (2021) present an approach to compute optimal policy parameters under these assumptions. Our model combines a greedy relaxation of the problem with a modified version of Scarf's (s, S) SDP. A simple implementation of the model requires a prohibitive computational effort to compute the parameters. However, we can speed up the computations by using K-convexity property and memorisation techniques. The resulting algorithm is considerably faster than the state-of-the-art, extending its adoptability by practitioners. An extensive computational study compares our approach with the algorithms available in the literature.

Chandra observations of the nearby, candidate Lyman-continuum (LyC) emitting galaxy Tol 0440-381 show brightening of an X-ray source by at least a factor of 4 to a luminosity of 1.6E40 erg/s over 3.8~days. The X-ray emission likely arises from either a low-luminosity AGN or an ultraluminous X-ray source. The properties of the X-ray source are similar to those found in Haro 11 and Tololo 1247-232, the only other LyC-emitting galaxies that have been resolved in X-rays. All three galaxies host luminous, variable, and hard spectrum X-ray sources that are likely accretion-powered. Accretion onto compact objects produces powerful outflows and ionizing radiation that could help enable LyC escape., Comment: 5 pages, to appear in Monthly Notices of the Royal Astronomical Society: Letters

In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem. This is a complex problem in which each the quality of each stratification from the set of all possible stratifications is measured its optimal sample allocation. EDAs are stochastic black-box optimization algorithms which can be used to estimate, build and sample probability models in the search for an optimal stratification. In this paper we enhance the exploitation properties of the EDA by adding a simulated annealing algorithm to make it a hybrid EDA. Results of empirical comparisons for atomic and continuous strata show that the HEDA attains the bests results found so far when compared to benchmark tests on the same data using a grouping genetic algorithm, simulated annealing algorithm or hill-climbing algorithm. However, the execution times and total execution are, in general, higher for the HEDA., Comment: 32

We used archival data from the \emph{Chandra X-ray Observatory} (\emph{Chandra}) and the \emph{Hubble Space Telescope}, to identify 334 candidate X-ray binary (XRB) systems and their potential optical counterparts in the interacting galaxy pair NGC 5194/5195 (M51). We present the catalog and data analysis of X-ray and optical properties for those sources, from the deep $892$ ks \emph{Chandra} observations, along with the magnitudes of candidate optical sources as measured in the $8.16$ ks \emph{HST} observations. The X-ray luminosity function of the X-ray sources above a few times $10^{36}\, {\rm erg\,s^{-1}}$ follows a power law $N(>L_{X,b})\propto L_{X,b}^{1-\alpha}$ with $\alpha=1.65\pm0.03$. Aproximately 80\% of sources are variable over a 30 day window. Nearly half of the X-ray sources (173/334) have an optical counterparts within $0{\mbox{$.\!\!^{\prime\prime}$}}5$., Comment: 10 pages; accepted for publication in ApJ; matches accepted version

In this paper we combine the k-means and/or k-means type algorithms with a hill climbing algorithm in stages to solve the joint stratification and sample allocation problem. This is a combinatorial optimisation problem in which we search for the optimal stratification from the set of all possible stratifications of basic strata. Each stratification being a solution the quality of which is measured by its cost. This problem is intractable for larger sets. Furthermore evaluating the cost of each solution is expensive. A number of heuristic algorithms have already been developed to solve this problem with the aim of finding acceptable solutions in reasonable computation times. However, the heuristics for these algorithms need to be trained in order to optimise performance in each instance. We compare the above multi-stage combination of algorithms with three recent algorithms and report the solution costs, evaluation times and training times. The multi-stage combinations generally compare well with the recent algorithms both in the case of atomic and continuous strata and provide the survey designer with a greater choice of algorithms to choose from., Comment: 39 pages, 20 tables, 8 Figures

Lyman continuum and line emission are thought to be important agents in the reionization of the early universe. Haro 11 is a rare example of a local galaxy in which Ly$\alpha$ and continuum emission have escaped without being absorbed or scattered by ambient gas and dust, potentially as a consequence of feedback from its X-ray sources. We build on our previous Chandra analysis of Haro 11 by analyzing three new observations. Our subpixel spatial analysis reveals that the two previously known X-ray sources are each better modelled as ensembles of at least 2 unresolved point sources. The spatial variability of these components reveals X1 as a dynamical system where one luminous X-ray source ($L_{\rm X} \sim 10^{41}$ erg s$^{-1}$) fades as a secondary source begins to flare. These might be intermediate mass black holes or low luminosity active galactic nuclei near the center of the galaxy in the process of merging. Optical emission line diagnostics drawn from the literature suggest that while the galaxy as a whole is consistent with starburst signatures of ionization, the individual regions wherein the X-ray sources reside are more consistent with AGN/composite classification. The sources in X2 exhibit some degree of flux variability. X2a dominates the flux of this region during most observations ($L_{\rm X} \sim 6\ \times\ 10^{40}$ erg s$^{-1}$), and gives the only evidence in the galaxy of a soft Ultra-Luminous X-ray source capable of high energy winds, which we suggest are responsible for allowing the coincident Ly$\alpha$ emission to escape., Comment: 18 pages, 12 figures, Accepted for publication in MNRAS

Building on recent work by Chandar et al. (2020), we construct X-ray luminosity functions (XLFs) for different classes of X-ray binary (XRB) donors in the nearby star-forming galaxy M83 through a novel methodology: rather than classifying low- vs. high-mass XRBs based on the scaling of the number of X-ray sources with stellar mass and star formation rate, respectively, we utilize multi-band Hubble Space Telescope imaging data to classify each Chandra-detected compact X-ray source as a low-mass (i.e. donor mass <~ 3 solar masses), high-mass (donor mass >~ 8 solar masses) or intermediate-mass XRB based on either the location of its candidate counterpart on optical color-magnitude diagrams or the age of its host star cluster. In addition to the the standard (single and/or truncated) power-law functional shape, we approximate the resulting XLFs with a Schechter function. We identify a marginally significant (at the 1-to-2 sigma level) exponential downturn for the high-mass XRB XLF, at logLx ~ 38.48^{+0.52}_{-0.33} (in log CGS units). In contrast, the low- and intermediate-mass XRB XLFs, as well as the total XLF of M83, are formally consistent with sampling statistics from a single power-law. Our method suggests a non-negligible contribution from low- and possibly intermediate-mass XRBs to the total XRB XLF of M83, i.e. between 20 and 50%, in broad agreement with X-ray based XLFs. More generally, we caution against considerable contamination from X-ray emitting supernova remnants to the published, X-ray based XLFs of M83, and possibly all actively star-forming galaxies., Comment: 18 pages (text, 44 with figures), 8 figures + 1 long figure, 1 table + 1 long table. Accepted by ApJ

A well-know control policy in stochastic inventory control is the (R, s, S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls below reorder-level s. To date, little or no work has been devoted to developing approaches for computing (R, s, S) policy parameters. In this work, we introduce a hybrid approach that exploits tree search to compute optimal replenishment cycles, and stochastic dynamic programming to compute (s, S) levels for a given cycle. Up to 99.8% of the search tree is pruned by a branch-and-bound technique with bounds generated by dynamic programming. A numerical study shows that the method can solve instances of realistic size in a reasonable time.

This study combines simulated annealing with delta evaluation to solve the joint stratification and sample allocation problem. In this problem, atomic strata are partitioned into mutually exclusive and collectively exhaustive strata. Each partition of atomic strata is a possible solution to the stratification problem, the quality of which is measured by its cost. The Bell number of possible solutions is enormous, for even a moderate number of atomic strata, and an additional layer of complexity is added with the evaluation time of each solution. Many larger scale combinatorial optimisation problems cannot be solved to optimality, because the search for an optimum solution requires a prohibitive amount of computation time. A number of local search heuristic algorithms have been designed for this problem but these can become trapped in local minima preventing any further improvements. We add, to the existing suite of local search algorithms, a simulated annealing algorithm that allows for an escape from local minima and uses delta evaluation to exploit the similarity between consecutive solutions, and thereby reduces the evaluation time. We compared the simulated annealing algorithm with two recent algorithms. In both cases, the simulated annealing algorithm attained a solution of comparable quality in considerably less computation time., Comment: 34 pages

We present a systematic study of the metallicity variations within the collisional ring galaxy NGC 922 based on long-slit optical spectroscopic observations. We find a metallicity difference between star-forming regions in the bulge and the ring, with metallicities ranging from almost solar to significantly sub-solar ($\rm{[12+\log(O/H)]\sim 8.2}$). We detect $\rm He\,_I$ emission in the bulge and the ring star-forming regions indicating ionization from massive stars associated with recent ($<10$ Myr) star-formation, in agreement with the presence of very young star-clusters in all studied regions. We find an anti-correlation between the X-ray luminosity and metallicity of the sub-galactic regions of NGC 922. The different regions have similar stellar population ages leaving metallicity as the main driver of the anti-correlation. The dependence of the X-ray emission of the different regions in NGC 922 on metallicity is in agreement with similar studies of the integrated X-ray output of galaxies and predictions from X-ray binary population models., Comment: 15 pages, 10 figures

In this work we compare several new computational approaches to an inventory routing problem, in which a single product is shipped from a warehouse to retailers via an uncapacitated vehicle. We survey exact algorithms for the Traveling Salesman Problem (TSP) and its relaxations in the literature for the routing component. For the inventory control component we survey classical mixed integer linear programming and shortest path formulations for inventory models. We present a numerical study comparing combinations of the two empirically in terms of cost and solution time., Comment: 14 pages