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Recent theoretical work and targeted observational studies suggest that filaments are sites of galaxy preprocessing. The aim of the WISESize project is to directly probe galaxies over the full range of environments to quantify and characterize extrinsic galaxy quenching in the local Universe. In this paper, we use GALFIT to measure the infrared 12$\mu$m ($R_{12}$) and 3.4$\mu$m ($R_{3.4}$) effective radii of 603 late-type galaxies in and surrounding the Virgo cluster. We find that Virgo cluster galaxies show smaller star-forming disks relative to their field counterparts at the $2.5\sigma$ level, while filament galaxies show smaller star-forming disks to almost $1.5\sigma$. Our data, therefore, show that cluster galaxies experience significant effects on their star-forming disks prior to their final quenching period. There is also tentative support for the hypothesis that galaxies are preprocessed in filamentary regions surrounding clusters. On the other hand, galaxies belonging to rich groups and poor groups do not differ significantly from those in the field. We additionally find hints of a positive correlation between stellar mass and size ratio for both rich group and filament galaxies, though the uncertainties on these data are consistent with no correlation. We compare our size measurements with the predictions from two variants of a state-of-the-art semi-analytic model (SAM), one which includes starvation and the other incorporating both starvation and ram-pressure stripping (RPS). Our data appear to disfavor the SAM, which includes RPS for the rich group, filament, and cluster samples, which contributes to improved constraints for general models of galaxy quenching., Comment: 21 pages, 11 figures. Resubmitted second time to ApJ with only minor revision requested

We introduce ontology-mediated planning, in which planning problems are combined with an ontology. Our formalism differs from existing ones in that we focus on a strong separation of the formalisms for describing planning problems and ontologies, which are only losely coupled by an interface. Moreover, we present a black-box algorithm that supports the full expressive power of OWL DL. This goes beyond what existing approaches combining automated planning with ontologies can do, which only support limited description logics such as DL-Lite and description logics that are Horn. Our main algorithm relies on rewritings of the ontology-mediated planning specifications into PDDL, so that existing planning systems can be used to solve them. The algorithm relies on justifications, which allows for a generic approach that is independent of the expressivity of the ontology language. However, dedicated optimizations for computing justifications need to be implemented to enable an efficient rewriting procedure. We evaluated our implementation on benchmark sets from several domains. The evaluation shows that our procedure works in practice and that tailoring the reasoning procedure has significant impact on the performance., Comment: Extended version of a paper accepted at ECAI 2024

Concrete domains have been introduced in the context of Description Logics to allow references to qualitative and quantitative values. In particular, the class of $\omega$-admissible concrete domains, which includes Allen's interval algebra, the region connection calculus (RCC8), and the rational numbers with ordering and equality, has been shown to yield extensions of $\mathcal{ALC}$ for which concept satisfiability w.r.t. a general TBox is decidable. In this paper, we present an algorithm based on type elimination and use it to show that deciding the consistency of an $\mathcal{ALC}(\mathfrak{D})$ ontology is ExpTime-complete if the concrete domain $\mathfrak{D}$ is $\omega$-admissible and its constraint satisfaction problem is decidable in exponential time. While this allows us to reason with concept and role assertions, we also investigate feature assertions $f(a,c)$ that can specify a constant $c$ as the value of a feature $f$ for an individual $a$. We show that, under conditions satisfied by all known $\omega$-admissible domains, we can add feature assertions without affecting the complexity., Comment: This is the extended version of a paper presented at DL 2024: 37th International Workshop on Description Logics

Printed mathematical expression recognition (MER) models are usually trained and tested using LaTeX-generated mathematical expressions (MEs) as input and the LaTeX source code as ground truth. As the same ME can be generated by various different LaTeX source codes, this leads to unwanted variations in the ground truth data that bias test performance results and hinder efficient learning. In addition, the use of only one font to generate the MEs heavily limits the generalization of the reported results to realistic scenarios. We propose a data-centric approach to overcome this problem, and present convincing experimental results: Our main contribution is an enhanced LaTeX normalization to map any LaTeX ME to a canonical form. Based on this process, we developed an improved version of the benchmark dataset im2latex-100k, featuring 30 fonts instead of one. Second, we introduce the real-world dataset realFormula, with MEs extracted from papers. Third, we developed a MER model, MathNet, based on a convolutional vision transformer, with superior results on all four test sets (im2latex-100k, im2latexv2, realFormula, and InftyMDB-1), outperforming the previous state of the art by up to 88.3%., Comment: 12 pages, 6 figures

Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation. Understanding human behavior naturally helps to improve and optimize the underlying infrastructure or user interfaces. Typically, human navigation is represented by sequences of transitions between states. Previous work suggests to use hypotheses, representing different intuitions about the navigation to analyze these transitions. To mathematically grasp this setting, first-order Markov chains are used to capture the behavior, consequently allowing to apply different kinds of graph comparisons, but comes with the inherent drawback of losing information about higher-order dependencies within the sequences. To this end, we propose to analyze entire sequences using autoregressive language models, as they are traditionally used to model higher-order dependencies in sequences. We show that our approach can be easily adapted to model different settings introduced in previous work, namely HypTrails, MixedTrails and even SubTrails, while at the same time bringing unique advantages: 1. Modeling higher-order dependencies between state transitions, while 2. being able to identify short comings in proposed hypotheses, and 3. naturally introducing a unified approach to model all settings. To show the expressiveness of our approach, we evaluate our approach on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users who interact with voice assistants.

While classical planning languages make the closed-domain and closed-world assumption, there have been various approaches to extend those with DL reasoning, which is then interpreted under the usual open-world semantics. Current approaches for planning with DL ontologies integrate the DL directly into the planning language, and practical approaches have been developed based on first-order rewritings or rewritings into datalog. We present here a new approach in which the planning specification and ontology are kept separate, and are linked together using an interface. This allows planning experts to work in a familiar formalism, while existing ontologies can be easily integrated and extended by ontology experts. Our approach for planning with those ontology-mediated planning problems is optimized for cases with comparatively small domains, and supports the whole OWL DL fragment. The idea is to rewrite the ontology-mediated planning problem into a classical planning problem to be processed by existing planning tools. Different to other approaches, our rewriting is data-dependent. A first experimental evaluation of our approach shows the potential and limitations of this approach., Comment: Extended version of a paper accepted at 36th International Workshop on Description Logics (DL 2023)

Understanding logical entailments derived by a description logic reasoner is not always straight-forward for ontology users. For this reason, various methods for explaining entailments using justifications and proofs have been developed and implemented as plug-ins for the ontology editor Prot\'eg\'e. However, when the user expects a missing consequence to hold, it is equally important to explain why it does not follow from the ontology. In this paper, we describe a new version of $\rm E{\scriptsize VEE}$, a Prot\'eg\'e plugin that now also provides explanations for missing consequences, via existing and new techniques based on abduction and counterexamples.

Logic-based approaches to AI have the advantage that their behavior can in principle be explained with the help of proofs of the computed consequences. For ontologies based on Description Logic (DL), we have put this advantage into practice by showing how proofs for consequences derived by DL reasoners can be computed and displayed in a user-friendly way. However, these methods are insufficient in applications where also numerical reasoning is relevant. The present paper considers proofs for DLs extended with concrete domains (CDs) based on the rational numbers, which leave reasoning tractable if integrated into the lightweight DL $\mathcal{E}\hspace{-0.1em}\mathcal{L}_\bot$. Since no implemented DL reasoner supports these CDs, we first develop reasoning procedures for them, and show how they can be combined with reasoning approaches for pure DLs, both for $\mathcal{E}\hspace{-0.1em}\mathcal{L}_\bot$ and the more expressive DL $\mathcal{ALC}$. These procedures are designed such that it is easy to extract proofs from them. We show how the extracted CD proofs can be combined with proofs on the DL side into integrated proofs that explain both the DL and the CD reasoning., Comment: Extended version of a paper accepted at the 7th International Joint Conference on Rules and Reasoning (RuleML+RR 2023). The software and the dataset described in the paper are available online, see https://doi.org/10.5281/zenodo.8208780

We present a method for extracting general modules for ontologies formulated in the description logic ALC. A module for an ontology is an ideally substantially smaller ontology that preserves all entailments for a user-specified set of terms. As such, it has applications such as ontology reuse and ontology analysis. Different from classical modules, general modules may use axioms not explicitly present in the input ontology, which allows for additional conciseness. So far, general modules have only been investigated for lightweight description logics. We present the first work that considers the more expressive description logic ALC. In particular, our contribution is a new method based on uniform interpolation supported by some new theoretical results. Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in a significantly shorter time. Moreover, our method can be used for, and in fact improves, the computation of uniform interpolants and classical modules., Comment: This is the extended version of a paper accepted at IJCAI 2023

Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended functionality of these systems does not introduce unreasonable risks to stakeholders. In this work, we critically analyze the ISO 21448 standard which contains requirements and guidance on how the SOTIF can be provably validated. Emphasis lies on developing a consistent terminology as a basis for the subsequent definition of a validation strategy when using quantitative acceptance criteria. In the broad picture, we aim to achieve a well-defined risk decomposition that enables rigorous, quantitative validation approaches for the SOTIF of automated driving systems., Comment: 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

The Baryonic Tully-Fisher Relation (BTFR) has applications in galaxy evolution as a testbed for the galaxy-halo connection and in observational cosmology as a redshift-independent secondary distance indicator. We use the 31,000+ galaxy ALFALFA sample -- which provides redshifts, velocity widths, and HI content for a large number of gas-bearing galaxies in the local universe -- to fit and test an extensive local universe BTFR. This BTFR is designed to be as inclusive of ALFALFA and comparable samples as possible. Velocity widths measured via an automated method and $M_{b}$ proxies extracted from survey data can be uniformly and efficiently measured for other samples, giving this analysis broad applicability. We also investigate the role of sample demographics in determining the best-fit relation. We find that the best-fit relations are changed significantly by changes to the sample mass range and to second order, mass sampling, gas fraction, different stellar mass and velocity width measurements. We use a subset of ALFALFA with demographics that reflect the full sample to measure a robust BTFR slope of $3.30\pm0.06$. We apply this relation and estimate source distances, finding general agreement with flow-model distances as well as average distance uncertainties of $\sim0.17$ dex for the full ALFALFA sample. We demonstrate the utility of these distance estimates by applying them to a sample of sources in the Virgo vicinity, recovering signatures of infall consistent with previous work., Comment: 23 pages, 10 figures, submitted to ApJ

We present SAMI-HI, a survey of the atomic hydrogen content of 296 galaxies with integral field spectroscopy available from the SAMI Galaxy Survey. The sample spans nearly 4 dex in stellar mass ($M_\star = 10^{7.4}-10^{11.1}~ \rm M_\odot$), redshift $z<0.06$, and includes new Arecibo observations of 153 galaxies, for which we release catalogues and HI spectra. We use these data to compare the rotational velocities obtained from optical and radio observations and to show how systematic differences affect the slope and scatter of the stellar-mass and baryonic Tully-Fisher relations. Specifically, we show that H$\alpha$ rotational velocities measured in the inner parts of galaxies (1.3 effective radii in this work) systematically underestimate HI global measurements, with HI/H$\alpha$ velocity ratios that increase at low stellar masses, where rotation curves are typically still rising and H$\alpha$ measurements do not reach their plateau. As a result, the H$\alpha$ stellar mass Tully-Fisher relation is steeper (when $M_\star$ is the independent variable) and has larger scatter than its HI counterpart. Interestingly, we confirm the presence of a small fraction of low-mass outliers of the H$\alpha$ relation that are not present when HI velocity widths are used and are not explained by "aperture effects". These appear to be highly disturbed systems for which H$\alpha$ widths do not provide a reliable estimate of the rotational velocity. Our analysis reaffirms the importance of taking into account differences in velocity definitions as well as tracers used when interpreting offsets from the Tully-Fisher relation, at both low and high redshifts and when comparing with simulations., Comment: 18 pages, 12 figures. Accepted for publication in MNRAS

In this paper, we propose an approach how connected and highly automated vehicles can perform cooperative maneuvers such as lane changes and left-turns at urban intersections where they have to deal with human-operated vehicles and vulnerable road users such as cyclists and pedestrians in so-called mixed traffic. In order to support cooperative maneuvers the urban intersection is equipped with an intelligent controller which has access to different sensors along the intersection to detect and predict the behavior of the traffic participants involved. Since the intersection controller cannot directly control all road users and - not least due to the legal situation - driving decisions must always be made by the vehicle controller itself, we focus on a decentralized control paradigm. In this context, connected and highly automated vehicles use some carefully selected game theory concepts to make the best possible and clear decisions about cooperative maneuvers. The aim is to improve traffic efficiency while maintaining road safety at the same time. Our first results obtained with a prototypical implementation of the approach in a traffic simulation are promising.

The verification and validation of automated driving systems at SAE levels 4 and 5 is a multi-faceted challenge for which classical statistical considerations become infeasible. For this, contemporary approaches suggest a decomposition into scenario classes combined with statistical analysis thereof regarding the emergence of criticality. Unfortunately, these associational approaches may yield spurious inferences, or worse, fail to recognize the causalities leading to critical scenarios, which are, in turn, prerequisite for the development and safeguarding of automated driving systems. As to incorporate causal knowledge within these processes, this work introduces a formalization of causal queries whose answers facilitate a causal understanding of safety-relevant influencing factors for automated driving. This formalized causal knowledge can be used to specify and implement abstract safety principles that provably reduce the criticality associated with these influencing factors. Based on Judea Pearl's causal theory, we define a causal relation as a causal structure together with a context, both related to a domain ontology, where the focus lies on modeling the effect of such influencing factors on criticality as measured by a suitable metric. As to assess modeling quality, we suggest various quantities and evaluate them on a small example. As availability and quality of data are imperative for validly estimating answers to the causal queries, we also discuss requirements on real-world and synthetic data acquisition. We thereby contribute to establishing causal considerations at the heart of the safety processes that are urgently needed as to ensure the safe operation of automated driving systems.

In ontology-mediated query answering, access to incomplete data sources is mediated by a conceptual layer constituted by an ontology, which can be formulated in a description logic (DL) or using existential rules. In the literature, there exists a multitude of complex techniques for incorporating ontological knowledge into queries. However, few of these approaches were designed for explainability of the query answers. We tackle this challenge by adapting an existing proof framework toward conjunctive query answering, based on the notion of universal models. We investigate the data and combined complexity of determining the existence of a proof below a given quality threshold, which can be measured in different ways. By distinguishing various parameters such as the shape of the query, we obtain an overview of the complexity of this problem for several Horn DLs., Comment: Extended version of a paper accepted at 6th International Joint Conference on Rules and Reasoning (RuleML+RR 2022). arXiv admin note: substantial text overlap with arXiv:2206.09758

In ontology-mediated query answering, access to incomplete data sources is mediated by a conceptual layer constituted by an ontology. To correctly compute answers to queries, it is necessary to perform complex reasoning over the constraints expressed by the ontology. In the literature, there exists a multitude of techniques incorporating the ontological knowledge into queries. However, few of these approaches were designed for comprehensibility of the query answers. In this article, we try to bridge these two qualities by adapting a proof framework originally applied to axiom entailment for conjunctive query answering. We investigate the data and combined complexity of determining the existence of a proof below a given quality threshold, which can be measured in different ways. By distinguishing various parameters such as the shape of a query, we obtain an overview of the complexity of this problem for the lightweight ontology languages DL-Lite_R and EL, and also have a brief look at temporal query answering., Comment: Extended version of a paper accepted at 35th International Workshop on Description Logics (DL 2022). Preliminary proceedings are available at https://dai.fmph.uniba.sk/events/dl2022/proceedings/Vol-XXX/

When working with description logic ontologies, understanding entailments derived by a description logic reasoner is not always straightforward. So far, the standard ontology editor Prot\'eg\'e offers two services to help: (black-box) justifications for OWL 2 DL ontologies, and (glass-box) proofs for lightweight OWL EL ontologies, where the latter exploits the proof facilities of reasoner ELK. Since justifications are often insufficient in explaining inferences, there is thus only little tool support for explaining inferences in more expressive DLs. In this paper, we introduce EVEE-LIBS, a Java library for computing proofs for DLs up to ALCH, and EVEE-PROTEGE, a collection of Prot\'eg\'e plugins for displaying those proofs in Prot\'eg\'e. We also give a short glimpse of the latest version of EVONNE, a more advanced standalone application for displaying and interacting with proofs computed with EVEE-LIBS.

Explanations for description logic (DL) entailments provide important support for the maintenance of large ontologies. The "justifications" usually employed for this purpose in ontology editors pinpoint the parts of the ontology responsible for a given entailment. Proofs for entailments make the intermediate reasoning steps explicit, and thus explain how a consequence can actually be derived. We present an interactive system for exploring description logic proofs, called Evonne, which visualizes proofs of consequences for ontologies written in expressive DLs. We describe the methods used for computing those proofs, together with a feature called signature-based proof condensation. Moreover, we evaluate the quality of generated proofs using real ontologies.

Abduction in description logics finds extensions of a knowledge base to make it entail an observation. As such, it can be used to explain why the observation does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic EL, where the observation is a concept inclusion and the background knowledge is a TBox, i.e., a set of concept inclusions. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion follows Occam's razor by rejecting hypotheses that use concept inclusions unrelated to the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain., Comment: This paper is the technical report version, including appendices, of an IJCAR 2022 paper (to appear)

Virgo is the nearest galaxy cluster; it is thus ideal for studies of galaxy evolution in dense environments in the local Universe. It is embedded in a complex filamentary network of galaxies and groups, which represents the skeleton of the large scale Laniakea supercluster. Here we assemble a comprehensive catalog of galaxies extending up to ~12 virial radii in projection from Virgo to revisit the Cosmic Web structure around it. This work is the foundation of a series of papers that will investigate the multi-wavelength properties of galaxies in the Cosmic Web around Virgo. We match spectroscopically confirmed sources from several databases and surveys including HyperLeda, NASA Sloan Atlas, NED, and ALFALFA. The sample consists of ~7000 galaxies. By exploiting a tomographic approach, we identify 13 filaments, spanning several Mpc in length. Long > 17 Mpc/h filaments, tend to be thin (< 1 Mpc/h in radius) and with a low density contrast (< 5), while shorter filaments show a larger scatter in their structural properties. Overall, we find that filaments are a transitioning environment between the field and cluster in terms of local densities, galaxy morphologies, and fraction of barred galaxies. Denser filaments have a higher fraction of early type galaxies, suggesting that the morphology-density relation is already in place in the filaments, before galaxies fall into the cluster itself. We release the full catalog of galaxies around Virgo and their associated properties., Comment: 32 pages, version after the proof corrections, ApJS in press

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness.

The inexpressive Description Logic (DL) $\mathcal{FL}_0$, which has conjunction and value restriction as its only concept constructors, had fallen into disrepute when it turned out that reasoning in $\mathcal{FL}_0$ w.r.t. general TBoxes is ExpTime-complete, i.e., as hard as in the considerably more expressive logic $\mathcal{ALC}$. In this paper, we rehabilitate $\mathcal{FL}_0$ by presenting a dedicated subsumption algorithm for $\mathcal{FL}_0$, which is much simpler than the tableau-based algorithms employed by highly optimized DL reasoners. Our experiments show that the performance of our novel algorithm, as prototypically implemented in our $\mathcal{FL}_o$wer reasoner, compares very well with that of the highly optimized reasoners. $\mathcal{FL}_o$wer can also deal with ontologies written in the extension $\mathcal{FL}_{\bot}$ of $\mathcal{FL}_0$ with the top and the bottom concept by employing a polynomial-time reduction, shown in this paper, which eliminates top and bottom. We also investigate the complexity of reasoning in DLs related to the Horn-fragments of $\mathcal{FL}_0$ and $\mathcal{FL}_{\bot}$., Comment: This paper is under consideration in Theory and Practice of Logic Programming (TPLP)