Search

The stellar age and mass of galaxies have been suggested as the primary determinants for the dynamical state of galaxies, with environment seemingly playing no or only a very minor role. We use a sample of 77 galaxies at intermediate redshift (z~0.3) in the Middle-Ages Galaxies Properties with Integral field spectroscopy (MAGPI) Survey to study the subtle impact of environment on galaxy dynamics. We use a combination of statistical techniques (simple and partial correlations and principal component analysis) to isolate the contribution of environment on galaxy dynamics, while explicitly accounting for known factors such as stellar age, star formation histories and stellar masses. We consider these dynamical parameters: high-order kinematics of the line-of-sight velocity distribution (parametrised by the Gauss-Hermite coefficients $h_3$ and $h_4$), kinematic asymmetries $V_{\rm asym}$ derived using kinemetry and the observational spin parameter proxy $\lambda_{R_e}$. Of these, the mean $h_4$ is the only parameter found to have a significant correlation with environment as parametrised by group dynamical mass. This correlation exists even after accounting for age and stellar mass trends. Finally, we confirm that variations in the spin parameter $\lambda_{R_e}$ are most strongly (anti-)correlated with age as seen in local studies, and show that this dependence is well-established by z~0.3., Comment: 17 pages, 13 figures, 3 tables, accepted for publication in PASA

This work considers the impact of resolution in the construction of mock observations of simulated galaxies. In particular, when building mock integral field spectroscopic observations from galaxy formation models in cosmological simulations, we investigate the possible systematics that may arise given the assumption that all galaxies above some stellar mass limit will provide unbiased and meaningful observable stellar kinematics. We build a catalogue of N-body simulations to sample the range of stellar particle resolutions within the EAGLE Ref0050N0752 simulation box and examine how their observable kinematics vary relative to a higher-resolution N-body control. We use these models to compile a table of the minimum number of particles-per-pixel to reach a given uncertainty in the fitted line-of-sight velocity distribution parameters. Further, we introduce a Voronoi-binning module to the mock observation code, SimSpin, in order to meet these minimum numbers. Using EAGLE, we show the impact of this shot noise on the observed spin-ellipticity plane and the recovery of this space when observations are binned with increasing numbers of particles. In conclusion, we advise binning mock images to meet at least 200 particles-per-pixel to avoid systematically under-estimating the velocity dispersion along a given line-of-sight. We demonstrate that this is important for comparing galaxies extracted from the same simulation, as well as between simulations of varying mass resolution and observations of real galaxies., Comment: 19 pages, 11 figures (including 3 pages of appendix with 2 figures). Accepted 2024 November 3 for publication in MNRAS. Received 2024 October 28; in original form 2024 August 9

We present the formation histories of 19 massive ($>3X10^{10}M_\odot$) quiescent (sSFR$<0.15Gyr^{-1}$) galaxy candidates at z~3.0-4.5 observed using JWST/NIRSpec. This completes the spectroscopic confirmation of the 24 K-selected quiescent galaxy sample from the ZFOURGE and 3DHST surveys (Schreiber et al. 2018a). Utilizing Prism $1-5\mu m$ spectroscopy, we confirm that all 12 sources that eluded confirmation by ground-based spectroscopy lie at z>3, resulting in a spectroscopically confirmed number density of $\sim1.4X10^{-5}Mpc^{-3}$ between $z\sim3-4$. Rest-frame U-V vs V-J color selections show high effectiveness in identifying quiescent galaxies, with a purity of~90%. Our analysis shows that parametric star-formation histories (SFHs) from FAST++ and binned SFHs from Prospector on average yield consistent results, revealing diverse formation and quenching times. The oldest galaxy formed $~6X10^{10}M_\odot$ by $z\sim10$ and has been quiescent for over 1 Gyr at z~3.2. We detect two galaxies with ongoing star formation and six with active galactic nuclei (AGN). We demonstrate that the choice of stellar population models, stellar libraries, and nebular or AGN contributions does not significantly affect the derived average SFHs of the galaxies. We demonstrate that extending spectral fitting beyond the rest-frame optical regime reduces the inferred average star formation rates in the earliest time bins of the SFH reconstruction. The assumed SFH prior influences the star formation rate at early times, where spectral diagnostic power is limited. Simulated z~3 quiescent galaxies from IllustrisTNG, SHARK, and Magneticum broadly match the average SFHs of the observed sample but struggle to capture the full diversity, particularly at early stages. Our results emphasize the need for mechanisms that rapidly build stellar mass and quench star formation within the first billion years of the Universe., Comment: Accepted for publication in the ApJ

The dynamical interactions between young binaries can perturb the material distribution of their circumstellar disks, and modify the planet formation process. In order to constrain the impact and nature of the binary interaction in the RW Aur system (bound or unbound), we analyzed the circumstellar material at 1.3 mm wavelengths, as observed at multiple epochs by ALMA. We analyzed the disk properties through parametric visibility modeling, and we used this information to constrain the dust morphology and the binary orbital period. We imaged the dust continuum emission of RW Aur with a resolution of 3 au, and we find that the radius enclosing 90% of the flux (R90%) is 19 au and 14 au for RW Aur A and B, respectively. By modeling the relative distance of the disks at each epoch, we find a consistent trend of movement for the disk of RW Aur B moving away from the disk of RW Aur A at an approximate rate of 3 mas/yr (about 0.5 au/yr in sky-projected distance). By combining ALMA astrometry, historical astrometry, and the dynamical masses of each star, we constrain the RW Aur binary stars to be most likely in a high-eccentricity elliptical orbit with a clockwise prograde orientation relative to RW Aur A, although low-eccentricity hyperbolic orbits are not ruled out by the astrometry. Our analysis does not exclude the possibility of a disk collision during the last interaction, which occurred $295_{-74}^{+21}$ yr ago relative to beginning of 2024. Evidence for the close interaction is found in a tentative warp of 6 deg in the inner 3 au of the disk of RW Aur A, in the brightness temperature of both disks, and in the morphology of the gas emission. A narrow ring that peaks at 6 au around RW Aur B is suggestive of captured material from the disk around RW Aur A., Comment: Accepted in A&A. All self-calibrated data and products are publicly available in https://doi.org/10.5281/zenodo.12825068 . Short video summary in https://youtu.be/IOjEW8Vrj9Q

Observations show evidence that a significant fraction of protoplanetary disks contain warps. A warp in a disk evolves in time affecting the appearance of shadows and greatly influencing kinematic signatures. So far, many theoretical studies of warped disks have been conducted using Smoothed Particle Hydrodynamics (SPH) methods. In our approach, we use a grid-based method in spherical coordinates which has notable advantages: the method allows for accurate modelling of low viscosity values and the resolution does not depend on density or mass of the disk, which allows surface structures to be resolved. We perform 3D simulations using FARGO3D to simulate the evolution of a warped disk and compare the results to one-dimensional models using a ring code. Additionally, we extensively investigate the applicability of grid-based methods to misaligned disks and test their dependency on grid resolution as well as disk viscosity. We find that grid-based simulations are capable of simulating disks not aligned to the grid geometry. Our three-dimensional simulation of a warped disk compares well to one-dimensional models in evolution of inclination. However, we find a twist which is not captured in 1D models. After thorough analysis we suspect this to be a physical effect possibly caused by non-linear effects neglected in the one-dimensional equations. Evaluating the internal dynamics, we find sloshing and breathing motions as predicted in local shearing box analysis. They can become supersonic, which may have consequences on kinematic observations of warped disks. Warped disks can be accurately modelled in 3D grid-based simulations when using reasonably good resolution, especially in the $\theta$-direction. We find good agreement with the linear approximation of the sloshing motion which highlights the reliability of 1D models., Comment: accepted for publication in Astronomy & Astrophysics

Tidal features provide signatures of recent mergers and offer a unique insight into the assembly history of galaxies. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will enable an unprecedentedly large survey of tidal features around millions of galaxies. To decipher the contributions of mergers to galaxy evolution it will be necessary to compare the observed tidal features with theoretical predictions. Therefore, we use cosmological hydrodynamical simulations NewHorizon, EAGLE, IllustrisTNG, and Magneticum to produce LSST-like mock images of $z\sim0$ galaxies ($z\sim0.2$ for NewHorizon) with $M_{\scriptstyle\star,\text{ 30 pkpc}}\geq10^{9.5}$ M$_{\scriptstyle\odot}$. We perform a visual classification to identify tidal features and classify their morphology. We find broadly good agreement between the simulations regarding their overall tidal feature fractions: $f_{\text{NewHorizon}}=0.40\pm0.06$, $f_{\text{EAGLE}}=0.37\pm0.01$, $f_{\text{TNG}}=0.32\pm0.01$ and $f_{\text{Magneticum}}=0.32\pm0.01$, and their specific tidal feature fractions. Furthermore, we find excellent agreement regarding the trends of tidal feature fraction with stellar and halo mass. All simulations agree in predicting that the majority of central galaxies of groups and clusters exhibit at least one tidal feature, while the satellite members rarely show such features. This agreement suggests that gravity is the primary driver of the occurrence of visually-identifiable tidal features in cosmological simulations, rather than subgrid physics or hydrodynamics. All predictions can be verified directly with LSST observations., Comment: 25 pages, 14 figures, accepted in MNRAS

Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed -- a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measurement. We measure and validate the weak gravitational lensing (WL) signature in the shape of galaxies observed in the first 3 years of the DES Y3 caused by galaxy clusters selected in the first all-sky survey performed by SRG/eROSITA. These data are then used to determine the scaling between X-ray photon count rate of the clusters and their halo mass and redshift. We empirically determine the degree of cluster member contamination in our background source sample. The individual cluster shear profiles are then analysed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination, and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we perform mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allows us to establish that hydro-dynamical modelling uncertainties at low lens redshifts ($z<0.6$) are the dominant systematic limitation. At high lens redshift the uncertainties of the sources' photometric redshift calibration dominate. With regard to the X-ray count rate to halo mass relation, we constrain all its parameters. This work sets the stage for a joint analysis with the number counts of eRASS1 clusters to constrain a host of cosmological parameters. We demonstrate that WL mass calibration of galaxy clusters can be performed successfully with source galaxies whose calibration was performed primarily for cosmic shear experiments., Comment: 27 pages, 18 figures, 2 appendices, submitted to A\&A

A puzzling population of extremely massive quiescent galaxies at redshifts beyond z=3 has recently been revealed by JWST and ALMA, some of them with stellar ages that show their quenching times to be as high as z=6, while their stellar masses are already above 5e10Msun. These extremely massive yet quenched galaxies challenge our understanding of galaxy formation at the earliest stages. Using the hydrodynamical cosmological simulation suite Magneticum Pathfinder, we show that such massive quenched galaxies at high redshifts can be successfully reproduced with similar number densities as observed. The stellar masses, sizes, formation redshifts, and star formation histories of the simulated quenched galaxies match those determined with JWST. Following these quenched galaxies at z=3.4 forward in time, we find 20% to be accreted onto a more massive structure by z=2, and from the remaining 80% about 30% rejuvenate up to z=2, another 30% stay quenched, and the remaining 40% rejuvenated on a very low level of star formation. Stars formed through rejuvenation are mostly formed on the outer regions of the galaxies, not in the centres. Furthermore, we demonstrate that the massive quenched galaxies do not reside in the most massive nodes of the cosmic web, but rather live in side-nodes of approximately Milky-Way halo mass. Even at z=0, only about 10% end up in small-mass galaxy clusters, while most of the quenched galaxies at z=3.4 end up in group-mass halos, with about 20% actually not even reaching 1e13Msun in halo mass., Comment: 18 pages, 14 figures. Submitted to ApJ, Comments welcome

Recent observations with JWST and ALMA have revealed extremely massive quiescent galaxies at redshifts of z=3 and higher, indicating both rapid onset and quenching of star formation. Using the cosmological simulation suite Magneticum Pathfinder we reproduce the observed number densities and stellar masses, with 36 quenched galaxies of stellar mass larger than 3e10Msun at z=3.42. We find that these galaxies are quenched through a rapid burst of star-formation and subsequent AGN feedback caused by a particularly isotropic collapse of surrounding gas, occurring on timescales of around 200Myr or shorter. The resulting quenched galaxies host stellar components which are kinematically fast rotating and alpha-enhanced, while exhibiting a steeper metallicity and flatter age gradient compared to galaxies of similar stellar mass. The gas of the galaxies has been metal enriched and ejected. We find that quenched galaxies do not inhabit the densest nodes, but rather sit in local underdensities. We analyze observable metrics to predict future quenching at high redshifts, finding that on shorter timescales <500Myr the ratio M_bh/M_* is the best predictor, followed by the burstiness of the preceding star-formation, t50-t90 (time to go from 50% to 90% stellar mass). On longer timescales, >1Gyr, the environment becomes the strongest predictor, followed by t50-t90, indicating that at high redshifts the consumption of old and lack of new gas are more relevant for long-term prevention of star-formation than the presence of a massive AGN. We predict that relics of such high-z quenched galaxies should best be characterized by a strong alpha enhancement., Comment: 22 pages, 13 figures, Submitted to ApJ, Comments welcome

Cosmological simulations predict that during the evolution of galaxies, the specific star formation rate continuously decreases. In a previous study we showed that generally this is not caused by the galaxies running out of cold gas but rather a decrease in the fraction of gas capable of forming stars. To investigate the origin of this behavior, we use disk galaxies selected from the cosmological hydrodynamical simulation Magneticum Pathfinder and follow their evolution in time. We find that the mean density of the cold gas regions decreases with time. This is caused by the fact that during the evolution of the galaxies, the star-forming regions move to larger galactic radii, where the gas density is lower. This supports the idea of inside-out growth of disk galaxies., Comment: 19 pages, 15 figures, 1 table, accepted for publication in ApJ

Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modelling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic programming language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems., Comment: Under consideration in Theory and Practice of Logic Programming (TPLP)

Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty. A major limitation of current probabilistic NeSy systems, such as DeepProbLog, is their restriction to finite probability distributions, i.e., discrete random variables. In contrast, deep probabilistic programming (DPP) excels in modelling and optimising continuous probability distributions. Hence, we introduce DeepSeaProbLog, a neural probabilistic logic programming language that incorporates DPP techniques into NeSy. Doing so results in the support of inference and learning of both discrete and continuous probability distributions under logical constraints. Our main contributions are 1) the semantics of DeepSeaProbLog and its corresponding inference algorithm, 2) a proven asymptotically unbiased learning algorithm, and 3) a series of experiments that illustrate the versatility of our approach., Comment: 27 pages, 9 figures

Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics. However, the distribution semantics is limited to discrete random variables only. While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation. We contribute the measure semantics together with the hybrid PLP language DC-ProbLog (where DC stands for distributional clauses) and its inference engine infinitesimal algebraic likelihood weighting (IALW). These have the original distribution semantics, standard PLP languages such as ProbLog, and standard inference engines for PLP based on knowledge compilation as special cases. Thus, we generalize the state of the art of PLP towards hybrid PLP in three different aspects: semantics, language and inference. Furthermore, IALW is the first inference algorithm for hybrid probabilistic programming based on knowledge compilation

Substructures are known to be good tracers for the dynamical states and recent accretion histories of the most massive collapsed structures in the Universe, galaxy clusters. Observations find extremely massive substructures in some clusters, especially Abell 2744, which are potentially in tension with the $\Lambda$CDM paradigm since they are not found in simulations directly. However, the methods to measure substructure masses strongly differ between observations and simulations. Using the fully hydrodynamical cosmological simulation suite Magneticum Pathfinder we develop a method to measure substructure masses in projection from simulations, similar to the observational approach. We identify a simulated Abell 2744 counterpart that not only has eight substructures of similar mass fractions but also exhibits similar features in the hot gas component. This cluster formed only recently through a major merger together with at least 6 massive minor merger events since z=1, where prior the most massive component had a mass of less than $1\times10^{14}M_\odot$. We show that the mass fraction of all substructures and of the eighth substructure separately are excellent tracers for the dynamical state and assembly history for all galaxy cluster mass ranges, with high fractions indicating merger events within the last 2Gyr. Finally, we demonstrate that the differences between subhalo masses measured directly from simulations as bound and those measured in projection are due to methodology, with the latter generally 2-3 times larger than the former. We provide a predictor function to estimate projected substructure masses from SubFind masses for future comparison studies between simulations and observations., Comment: 25 pages, 15 figures, submitted to ApJ

Planes of satellites are observed around many galaxies. However, these observations are still considered a point of tension for the $\Lambda$CDM paradigm. We use the fully hydrodynamical cosmological $\Lambda$CDM state-of-the-art simulation Magneticum Pathfinder to investigate the existence of such planes over a large range of haloes, from Milky Way to galaxy cluster masses. To this end, we develop the Momentum in Thinnest Plane (MTP) method to identify planes and quantify the properties of their constituent satellites, considering both position and momentum. We find that thin planes ($20\%\cdot R_\mathrm{halo}$) containing at least $50\%$ of the total number of satellites can be found in almost all systems. In Milky Way mass-like systems, around 86\% of such planes are even aligned in momentum ($90\%$ of the total satellite momentum), where the fraction is smaller if more satellites are required to be inside the plane. We further find a mass dependency, with more massive systems exhibiting systematically thicker planes. This may point towards the change from continuous accretion of small objects along filaments and sheets for less massive haloes to the accretion of large objects (e.g., major mergers) dominating the growth of more massive haloes. There is no correlation between the existence of a plane and the main galaxy's morphology. Finally, we find a clear preference for the minor axes of the satellite planes and the host galaxy to be aligned, in agreement with recent observations., Comment: 15 pages, 11 figures

Quantitative extensions of logic programming often require the solution of so called second level inference tasks, i.e., problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus go beyond the well-known weighted or algebraic model counting setting of probabilistic logic programming under the distribution semantics. We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems. As 2AMC is to (algebraic) model counting what forall-exists-SAT is to propositional satisfiability, it is notoriously hard to solve. First level techniques based on Knowledge Compilation (KC) have been adapted for specific 2AMC instances by imposing variable order constraints on the resulting circuit. However, those constraints can severely increase the circuit size and thus decrease the efficiency of such approaches. We show that we can exploit the logical structure of a 2AMC problem to omit parts of these constraints, thus limiting the negative effect. Furthermore, we introduce and implement a strategy to generate a sufficient set of constraints statically, with a priori guarantees for the performance of KC. Our empirical evaluation on several benchmarks and tasks confirms that our theoretical results can translate into more efficient solving in practice. Under consideration for acceptance in TPLP., Comment: Paper presented at the 38th International Conference on Logic Programming (ICLP 2022), 16 pages

In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining the flexibility and modularity on the definitions of complex event rules, (iii) allowing the system to be trained in an end-to-end manner and (iv) being robust against noisily labelled data. Our approach makes use of DeepProbLog to create a neuro-symbolic architecture that combines a neural network to process the subsymbolic data with a probabilistic logic layer to allow the user to define the rules for the complex events. We demonstrate that our approach is capable of detecting complex events from an audio stream. We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data., Comment: 8 pages, 3 figures

We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is given by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. It is well-defined when each random sample uniquely determines the truth values of all logical atoms. Argumentation problems, however, represent an interesting practical application where this is not always the case. SMProbLog generalizes the semantics of ProbLog to the setting where multiple truth assignments are possible for a randomly sampled program, and implements the corresponding algorithms for both inference and learning tasks. We then show how this novel framework can be used to reason about probabilistic argumentation problems. Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.

The 1-D evolution equations for warped discs come in two flavors: For very viscous discs the internal torque vector G is uniquely determined by the local conditions in the disc, and warps tend to damp out rapidly if they are not continuously driven. For very inviscid discs, on the other hand, G becomes a dynamic quantity, and a warp will propagate through the disc as a wave. The equations governing both regimes are usually treated separately. A unified set of equations was postulated recently by Martin et al. (2019), but not yet derived from the underlying physics. The standard method for deriving these equations is based on a perturbation series expansion, which is a powerful, but somewhat abstract technique. A more straightforward method is to employ the warped shearing box framework of Ogilvie and Latter (2013), which so far has not yet been used to derive the equations for the wavelike regime. The goal of this paper is to analyze the warped disc equations in both regimes using the warped shearing box framework, to derive a unified set of equations, valid for small warps, and to discuss how our results can be interpreted in terms of the affine tilted-slab approach of Ogilvie (2018)., Comment: Accepted for publication in MNRAS

When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to overcome the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian learning from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities., Comment: Under submission to MACH

Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are solicited in all areas of logic programming and related areas, including but not restricted to: - Foundations: Semantics, Formalisms, Answer-Set Programming, Non-monotonic Reasoning, Knowledge Representation. - Declarative Programming: Inference engines, Analysis, Type and mode inference, Partial evaluation, Abstract interpretation, Transformation, Validation, Verification, Debugging, Profiling, Testing, Logic-based domain-specific languages, constraint handling rules. - Related Paradigms and Synergies: Inductive and Co-inductive Logic Programming, Constraint Logic Programming, Interaction with SAT, SMT and CSP solvers, Logic programming techniques for type inference and theorem proving, Argumentation, Probabilistic Logic Programming, Relations to object-oriented and Functional programming, Description logics, Neural-Symbolic Machine Learning, Hybrid Deep Learning and Symbolic Reasoning. - Implementation: Concurrency and distribution, Objects, Coordination, Mobility, Virtual machines, Compilation, Higher Order, Type systems, Modules, Constraint handling rules, Meta-programming, Foreign interfaces, User interfaces. - Applications: Databases, Big Data, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics, Education, Computational life sciences, Education, Cybersecurity, and Robotics.

Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic architecture based on Event Calculus that can perform Complex Event Processing (CEP). It leverages both a neural network to interpret inputs and logical rules that express the pattern of the complex event. Our approach is capable of training with much fewer labelled data than a pure neural network approach, and to learn to classify individual events even when training in an end-to-end manner. We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K., Comment: Accepted as extended abstract at ICLP2020

Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility., Comment: Accepted for presentation at The 7th International Conference on Agreement Technologies (AT 2020)

Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting. Automated negotiation has already seen increased usage in fields such as e-commerce and power distribution in smart girds, and recent advancements in opponent modelling have proven to deliver better outcomes. However, significant barriers to more widespread adoption remain, such as lack of predictable outcome over time and user trust. Additionally, there have been many recent advancements in the field of reasoning about uncertainty, which could help alleviate both those problems. As there is no recent survey on these two fields, and specifically not on their possible intersection we aim to provide such a survey here., Comment: pre-print. New version fixes mistake in title

State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios over knowledge graphs, while also providing fast approximations on classical benchmarks in the field.

Planetary migration is a key link between planet formation models and observed exoplanet statistics. So far the theory of migration has focused on the interaction of planets with an inviscid or viscously evolving disk. Turbulent viscosity is thought to be the main driver of disk evolution and is known to affect the migration process. Recently, the topic of wind-driven accretion is experiencing a renaissance, as evidence is mounting that PPDs may be less turbulent than previously thought, and 3-D non-ideal MHD modeling of the wind-launching process is maturing. Aim: We wish to investigate how wind-driven accretion may affect migration. We aim for a qualitative exploration of the main effects, rather than a quantitative prediction. Methods: We perform 2-D hydrodynamic simulations with the FARGO3D code in the $(r,\phi)$-plane. The vertical coordinate and the launching of the wind are not treated explicitly. Instead, the torque of the wind onto the disk is treated using a simple 2-parameter formula treating the wind mass loss rate and the lever arm. Results: We find that the wind-driven accretion process has a different way of replenishing the co-orbital region than the viscous accretion. The former always injects mass from the outer edge of the co-orbital region and removes mass from the inner edge, while the latter injects or removes mass from the co-orbital region depending on the radial density gradients in the disk. The migration behavior can differ very much and under certain conditions it can drive rapid type-III-like outward migration. We derive an analytic expression for the parameters under which this outward migration occurs. Conclusion: If wind-driven accretion plays a role in the secular evolution of PPDs, migration studies have to include this process as well, because it can strongly affect the resulting migration rate and direction., Comment: accepted for publication in Astronomy & Astrophysics

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples., Comment: Extended version of DeepProbLog: Neural Probabilistic Logic Programming (previously published at NeurIPS 2018). arXiv admin note: text overlap with arXiv:1805.10872