SUMMARY: Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. AVAILABILITY AND IMPLEMENTATION: BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Point of Interest (POI) data constitute the cornerstone of any application, service or product even remotely related to our physical surroundings. From navigation applications to social networks, tourism, and logistics, we use POI data to search, communicate, decide and plan our actions. POIs are semantically diverse and spatio-temporally evolving entities, having geographical, temporal and thematic relations. Currently, integrating POI data to increase their coverage, timeliness, accuracy and value is a resource-intensive and mostly manual process, with no specialized software available to address the specific challenges of this task. In this paper, we present an integrated toolkit for transforming, linking, fusing and enriching POI data, and extracting additional value from them. In particular, we demonstrate how Linked Data technologies can address the limitations, gaps and challenges of the current landscape in Big POI data integration. We have built a prototype application that enables users to define, manage and execute scalable POI data integration workflows built on top of state-of-the-art software for geospatial Linked Data. The application abstracts and hides away the underlying complexity, automates quality-assured integration, scales efficiently for world-scale integration tasks and lowers the entry barrier for end-users. Validated against real-world POI datasets in several application domains, our system has shown great potential to address the requirements and needs of cross-sector, cross-border and cross-lingual integration of Big POI data.

In two previous publications we have, on the one hand, extended the description logic (DL) ALCQ by more expressive number restrictions using numerical and set constraints expressed in the quantifier-free fragment of Boolean Algebra with Presburger Arithmetic (QFBAPA). The resulting DL was called ALCSCC . On the other hand, we have extended the terminological formalism of the well-known description logic ALC from concept inclusions (CIs) to more general cardinality constraints expressed in QFBAPA, which we called extended cardinality constraints. Here, we combine the two extensions, i.e., we consider extended cardinality constraints on ALCSCC concepts. We show that this does not increase the complexity of reasoning, which is NExpTime-complete both for extended cardinality constraints in the DL ALC and in its extension ALCSCC . The same is true for a restricted version of such cardinality constraints, where the complexity of reasoning decreases to ExpTime, not just for ALC , but also for ALCSCC.

In two previous publications we have, on the one hand, extended the description logic (DL) ALCQ by more expressive number restrictions using numerical and set constraints expressed in the quantifier-free fragment of Boolean Algebra with Presburger Arithmetic (QFBAPA). The resulting DL was called ALCSCC. On the other hand, we have extended the terminological formalism of the well-known description logic ALC from concept inclusions (CIs) to more general cardinality constraints expressed in QFBAPA, which we called extended cardinality constraints. Here, we combine the two extensions, i.e., we consider extended cardinality constraints on ALCSCC concepts. We show that this does not increase the complexity of reasoning, which is NExpTime-complete both for extended cardinality constraints in ALC and ALCSCC. The same is true for a restricted version of such cardinality constraints, where the complexity of reasoning decreases to ExpTime, not just for ALC, but also for ALCSCC.

This paper continues the rather recent line of research on the dynamics of non-monotonic formalisms. In particular, we consider semantic changes in Dung's abstract argumentation formalism. One of the most studied problems in this context is the so-called enforcing problem which is concerned with manipulating argumentation frameworks (AFs) such that a certain desired set of arguments becomes an extension. Here we study the inverse problem, namely the extension removal problem: is it possible -- and if so how -- to modify a given argumentation framework in such a way that certain undesired extensions are no longer generated? Analogously to the well known AGM paradigm we develop an axiomatic approach to the removal problem, i.e. a certain set of axioms will determine suitable manipulations. Although contraction (that is, the elimination of a particular belief) is conceptually quite different from extension removal, there are surprisingly deep connections between the two: it turns out that postulates for removal can be directly obtained as reformulations of the AGM contraction postulates. We prove a series of formal results including conditional and unconditional existence and semantical uniqueness of removal operators as well as various impossibility results -- and show possible ways out.

Among the most expressive knowledge representation formalisms are the description logics of the Z family. For well-behaved fragments of ZOIQ, entailment of positive two-way regular path queries is well known to be 2EXPTIME-complete under the proviso of unary encoding of numbers in cardinality constraints. We show that this assumption can be dropped without an increase in complexity and EXPTIME-completeness can be achieved when bounding the number of query atoms, using a novel reduction from query entailment to knowledge base satisfiability. These findings allow to strengthen other results regarding query entailment and query containment problems in very expressive description logics. Our results also carry over to GC2, the two-variable guarded fragment of first-order logic with counting quantifiers, for which hitherto only conjunctive query entailment has been investigated.

OBJECTIVE: Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. METHODS: 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. RESULTS: Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. CONCLUSIONS: We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics. Among others, PAN contributions include original corpora, tailored benchmarks, and standardized experimentation platforms. In this paper we review, theoretically and practically, the authorship verification task and conclude that the underlying experiment design cannot guarantee pushing forward the state of the art---in fact, it allows for top benchmarking with a surprisingly straightforward approach. In this regard, we present a ``Basic and Fairly Flawed'' (BAFF) authorship verifier that is on a par with the best approaches submitted so far, and that illustrates sources of bias that should be eliminated. We pinpoint these sources in the evaluation chain and present a refined authorship corpus as effective countermeasure.

Authorship verification is the task of determining whether two texts were written by the same author. We deal with the adversary task, called authorship obfuscation: preventing verification by altering a to-be-obfuscated text. Our new obfuscation approach (1) models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and (2) manipulates an author's subconsciously encoded writing style in a sophisticated manner using heuristic search. To obfuscate, we analyze the huge space of textual variants for a paraphrased version of the to-be-obfuscated text that has a sufficient Jensen-Shannon distance at minimal costs in terms of text quality. We analyze, quantify, and illustrate the rationale of this approach, define paraphrasing operators, derive obfuscation thresholds, and develop an effective obfuscation framework. Our authorship obfuscation approach defeats state-of-the-art verification approaches, including unmasking and compression models, while keeping text changes at a minimum.

Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff. Our generalized approach therefore reduces the required material by orders of magnitude, making unmasking applicable to authorship cases of more practical proportions. The new approach is on par with other state-of-the-art techniques that are optimized for texts of this length: it achieves accuracies of 75--80%, while also allowing for easy adjustment to forensic scenarios that require higher levels of confidence in the classification.

In this paper we derive the optimal linear shrinkage estimator for the high-dimensional mean vector using random matrix theory. The results are obtained under the assumption that both the dimension p and the sample size n tend to infinity in such a way that p∕n$rightarrow$c$in$(0,$infty$). Under weak conditions imposed on the underlying data generating mechanism, we find the asymptotic equivalents to the optimal shrinkage intensities and estimate them consistently. The proposed nonparametric estimator for the high-dimensional mean vector has a simple structure and is proven to minimize asymptotically, with probability 1, the quadratic loss when c$in$(0,1). When c$in$(1,$infty$) we modify the estimator by using a feasible estimator for the precision covariance matrix. To this end, an exhaustive simulation study and an application to real data are provided where the proposed estimator is compared with known benchmarks from the literature. It turns out that the existing estimators of the mean vector, including the new proposal, converge to the sample mean vector when the true mean vector has an unbounded Euclidean norm.

Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.

BACKGROUND: Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical information from these data. This information can be used to develop process models of cellular dynamics. However, so far there has been no structured, standardized way of annotating and storing the tracking results, which is critical for comparative analysis and data integration. The key requirement to be satisfied by an ontology is the representation of a cell's change over time. Unfortunately, popular ontology languages, such as Web Ontology Language (OWL), have limitations for the representation of temporal information. The current paper addresses the fundamental problem of modeling changes of qualities over time in biomedical ontologies specified in OWL. RESULTS: The presented analysis is a result of the lessons learned during the development of an ontology, intended for the annotation of cell tracking experiments. We present, discuss and evaluate various representation patterns for specifying cell changes in time. In particular, we discuss two patterns of temporally changing information: n-ary relation reification and 4d fluents. These representation schemes are formalized within the ontology language OWL and are aimed at the support for annotation of cell tracking experiments. We analyze the performance of each pattern with respect to standard criteria used in software engineering and data modeling, i.e. simplicity, scalability, extensibility and adequacy. We further discuss benefits, drawbacks, and the underlying design choices of each approach. CONCLUSIONS: We demonstrate that patterns perform differently depending on the temporal distribution of modeled information. The optimal model can be constructed by combining two competitive approaches. Thus, we demonstrate that both reification and 4d fluents patterns can work hand in hand in a single ontology. Additionally, we have found that 4d fluents can be reconstructed by two patterns well known in the computer science community, i.e. state modeling and actor-role pattern.

A previsão da demanda de água é fundamental para decisões relacionadas à gestão de recursos hídricos a longo prazo. No entanto, a variabilidade espacial do consumo de água é um desafio para uma previsão adequada. O principal objetivo do presente estudo é investigar como os aspectos socioeconômicos da população afetam o futuro consumo urbano de água. Para que a previsão tenha bom desempenho e precisão, um subconjunto significativo de variáveis explicativas deve ser definido. Para isso, vários métodos de seleção de variáveis do tipo filtro e envoltório, baseados Regressão de Mínimos Quadrados Parciais (PLSR, do inglês Partial Least Square Regression) foram testados, juntamente com uma classificação baseada em Florestas Aleatórias (RF, do inglês Random Forest). Os subconjuntos de dados foram em seguida utilizados como entrada para um modelo preditivo. Duas técnicas de aprendizado de máquina foram testadas: RF e Rede Neural Artificial (RNA). O desempenho do modelo foi avaliado através do coeficiente de Nash-Sutfcliffe, Raiz do erro quadrático médio (RMSE, do inglês Root Mean Square Error) e correlação de Pearson. O conjunto de dados consistiu no consumo de água e dados do Censo de 2010 associados a 182 Unidades de Desenvolvimento Humano (UDH) em Fortaleza, Ceará. Importância da variável em projeção, Procedimento de eliminação regularizada e RF forneceram os subconjuntos de variáveis que levaram ao melhor desempenho de previsão entre os sete métodos de seleção. A expectativa de vida ao nascer, a renda per capita e residentes com educação primária e secundária …

[en] Planning for the capacity expansion of water supply systems requires efficient operation of available water resources and the expansion of existing infrastructure. The need to attend water demand is associated with climate, social and economic uncertainties, increasing the supply costs. Besides, most water supply systems are no longer able to meet increasing demands, resulting in the need to include nonconventional water sources. Previous work applied simulation and optimization techniques through Dynamic Programming (Fraga et al. 2017), Mixed-Integer Programming (Beh et al. 2014) and Genetic Algorithms (Zhang and Babovic 2012) for expansion of urban water systems. This paper proposes a multistage modelling approach to define operation and expansion strategies that reduce the supply costs under risks of water shortage, incorporating reservoir operation and alternative sources. Besides, the impact of climate uncertainty on the system expansion costs will be assessed as well as the efficiency of incorporating alternative sources. The method is applied in Fortaleza Metropolitan Region (FMR), Brazil. FMR consists of eight storage reservoirs and transmission canals that transfer water from the Jaguaribe basin, where is located the largest reservoir of the state (6.2 billion m³). Rainfall variability and recurrent droughts reduce the reliability on the system, that is in a stage of insufficient adaptation to urban and industrial activity growth.

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years. In this article, we provide an overview over these recent advancements, focusing on neural network based question answering systems over knowledge graphs. We introduce readers to the challenges in the tasks, current paradigms of approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point, and ease their process of making informed decisions while creating their own QA system.

We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines. Most previous methods treats price as an extrapolatable time series, those analyze the relation between prices and news either trim their price data correspondingly to a public news dataset, manually annotate headlines or use off-the-shelf tools. In comparison to off-the-shelf tools, our event extraction method detects not only the occurrence of phenomena but also the changes in attribution and characteristics from public sources. Instead of using sentence embedding as a feature, we use every word of the extracted events, encode and organize them before feeding to the learning models. Empirical results show favorable results, in terms of prediction performance, money saved and scalability.

Abstract This paper aims to study the asymptotic behaviour of the fundamental solutions (heat kernels) of non-local (partial and pseudo differential) equations with fractional operators in time and space. In particular, we obtain exact asymptotic formulas for the heat kernels of time-changed Brownian motions and Cauchy processes. As an application, we obtain exact asymptotic formulas for the fundamental solutions to the n-dimensional fractional heat equations in both time and space $beginarray displaystyle fracpartial^betapartial t^betau(t,x) = -(-Delta_x)^gamma u(t,x), quad beta,gammain(0,1). endarray$

BACKGROUND: The overall survival of patients with advanced and refractory oesophageal squamous cell carcinoma, mostly aged 65 years and older, is poor. Treatment with PD-1 antibodies showed improved progression-free survival and overall survival. We assessed the safety and efficacy of combined nivolumab and ipilimumab therapy in this population. METHODS: This multicentre, open-label, phase 2 trial done in 32 sites in Germany included patients aged 65 years and older with oesophageal squamous cell carcinoma and disease progression or recurrence following first-line therapy. Patients were treated with nivolumab (240 mg fixed dose once every 2 weeks, intravenously) in the safety run-in phase and continued with nivolumab and ipilimumab (nivolumab 240 mg fixed dose once every 2 weeks and ipilimumab 1 mg/kg once every 6 weeks, intravenously). The primary endpoint was overall survival, which was compared with a historical cohort receiving standard chemotherapy in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT03416244. FINDINGS: Between March 2, 2018, and Aug 20, 2020, we screened 75 patients with advanced oesophageal squamous cell carcinoma. We enrolled 66 patients (50 [76%] men and 16 [24%] women; median age 70·5 years [IQR 67·0-76·0]), 44 (67%) of whom received combined nivolumab and ipilimumab therapy and 22 (33%) received nivolumab alone. Median overall survival time at the prespecified data cutoff was 7·2 months (95% CI 5·7-12·4) and significantly higher than in a historical cohort receiving standard chemotherapy (p=0·0063). The most common treatment-related adverse events were fatigue (12 [29%] of 42), nausea (11 [26%]), and diarrhoea (ten [24%]). Grade 3-5 treatment-related adverse events occurred in 13 (20%) of 66 patients. Treatment-related death occurred in one patient with bronchiolitis obliterans while on nivolumab and ipilimumab treatment. INTERPRETATION: Patients aged at least 65 years, with advanced oesophageal squamous cell carcinoma might benefit from combined nivolumab and ipilimumab therapy in second-line treatment. FUNDING: Bristol Myers Squibb.

Privacy-preserving record linkage (PPRL) is increasingly demanded in real-world applications, e.g., in the health-care domain, to combine person-related data for data analysis while preserving the privacy of individuals. However, the adoption of PPRL is hampered by the absence of easy-to-use and powerful PPRL tools covering the entire PPRL process. We therefore demonstrate Primat, a flexible and scalable tool that enables the definition and application of tailored PPRL workflows as well as the comparative evaluation of different PPRL methods. We introduce the main requirements for PPRL tools and discuss previous tool efforts that do not fully meet the requirements and have not been applied in practice. By contrast, Primat covers the whole PPRL life-cycle and improves applicability by providing various components for data owners and the central linkage to be executed by a trusted linkage unit.

AbstractRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text. Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in ir_datasets, Zenodo and Hugging Face. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on Hugging Face and files.webis.de. The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the associated paper. If you use this dataset, please cite @InProceedingsfroebe:2022a, address = Berlin Heidelberg New York, author = Maik Fr``obe and Sebastian G''unther and Maximilian Probst and Martin Potthast and Matthias Hagen, booktitle = Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022), editor = Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil Norvrag and Vinay Setty, month = apr, publisher = Springer, series = Lecture Notes in Computer Science, site = Stavanger, Norway, title = The Power of Anchor Text in the Neural Retrieval Era, year = 2022

Superbubbles are a class of induced subgraphs in digraphs that play an essential role in assembly algorithms for high-throughput sequencing data. They are connected with the remainder of the host digraph by a single entrance and a single exit vertex. Linear-time algorithms for the enumeration superbubbles recently have become available. Current approaches require the decomposition of the input digraph into strongly-connected components, which are then analyzed separately. In principle, a single depth-first search could be used, provided one can guarantee that the root of the depth-first search (DFS)-tree is not itself located in the interior or the exit point of a superbubble. Here, we describe a linear-time algorithm to determine suitable roots for a DFS-forest that is guaranteed to identify the superbubbles in a digraph correctly. In addition to the advantages of a more straightforward implementation, we observe a nearly three-fold gain in performance on real-world datasets. We present a reference implementation of the new algorithm that accepts many commonly-used input formats for digraphs. It is available as open source from github.

Pattern Detection, Time Series Analysis, Anomaly Detection, Neural Networks, Noise Mitigation.

Rapid growing complexity of HPC systems in response to demand for higher computing performance, results in higher probability of failures. Early detection of failures significantly reduces the damages caused by failure via impeding their propagation through system. Various anomaly detection mechanism are proposed to detect failures in their early stages. Insufficient amount of failure samples in addition to privacy concerns extremely limits the functionality of available anomaly detection approaches. Advances in machine learning techniques, significantly increased the accuracy of unsupervised anomaly detection methods, addressing the challenge of insufficient failure samples. However, available approaches are either domain specific, inaccurate, or require comprehensive knowledge about the underlying system. Furthermore, processing certain monitoring data such as system logs raises high privacy concerns. In addition, noises in monitoring data severely impact the correctness of data analysis. This work proposes an unsupervised and privacy-aware approach for detecting abnormal behaviors in general HPC systems. Preliminary results indicate high potentials of autoencoders for automatic detection of abnormal behaviors in HPC systems via analyzing anonymized system logs using fast-trainable noise-resistant models.

In response to the demand for higher computational power, the number of computing nodes in high performance computers (HPC) increases rapidly. Exascale HPC systems are expected to arrive by 2020. With drastic increase in the number of HPC system components, it is expected to observe a sudden increase in the number of failures which, consequently, poses a threat to the continuous operation of the HPC systems. Detecting failures as early as possible and, ideally, predicting them, is a necessary step to avoid interruptions in HPC systems operation. Anomaly detection is a well-known general purpose approach for failure detection, in computing systems. The majority of existing methods are designed for specific architectures, require adjustments on the computing systems hardware and software, need excessive information, or pose a threat to users' and systems' privacy. This work proposes a node failure detection mechanism based on a vicinity-based statistical anomaly detection approach using passively collected and anonymized system log entries. Application of the proposed approach on system logs collected over 8 months indicates an anomaly detection precision between 62% to 81%.

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong``: there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

Good benchmarks are hard to find because they require a substantial effort to keep them representative for the constantly changing challenges of a particular field. Synthetic benchmarks are a common approach to deal with this, and methods from machine learning are natural candidates for synthetic benchmark generation. In this paper we investigate the usefulness of machine learning in the prominent CLgen benchmark generator. We re-evaluate CLgen by comparing the benchmarks generated by the model with the raw data used to train it. This re-evaluation indicates that, for the use case considered, machine learning did not yield additional benefit over a simpler method using the raw data. We investigate the reasons for this and provide further insights into the challenges the problem could pose for potential future generators.

Crowdsourced knowledge bases like Wikidata suffer from low-quality edits and vandalism, employing machine learning-based approaches to detect both kinds of damage. We reveal that state-of-the-art detection approaches discriminate anonymous and new users: benign edits from these users receive much higher vandalism scores than benign edits from older ones, causing newcomers to abandon the project prematurely. We address this problem for the first time by analyzing and measuring the sources of bias, and by developing a new vandalism detection model that avoids them. Our model FAIR-S reduces the bias ratio of the state-of-the-art vandalism detector WDVD from 310.7 to only 11.9 while maintaining high predictive performance at 0.963 ROC and 0.316 PR.

Arousal affects cognition, emotion, and behavior and has been implicated in the etiology of psychiatric disorders. Although environmental conditions substantially contribute to the level of arousal, stable interindividual characteristics are well-established and a genetic basis has been suggested. Here we investigated the molecular genetics of brain arousal in the resting state by conducting a genome-wide association study (GWAS). We selected N = 1877 participants from the population-based LIFE-Adult cohort. Participants underwent a 20-min eyes-closed resting state EEG, which was analyzed using the computerized VIGALL 2.1 (Vigilance Algorithm Leipzig). At the SNP-level, GWAS analyses revealed no genome-wide significant locus (p < 5E-8), although seven loci were suggestive (p < 1E-6). The strongest hit was an expression quantitative trait locus (eQTL) of TMEM159 (lead-SNP: rs79472635, p = 5.49E-8). Importantly, at the gene-level, GWAS analyses revealed significant evidence for TMEM159 (p = 0.013, Bonferroni-corrected). By mapping our SNPs to the GWAS results from the Psychiatric Genomics Consortium, we found that all corresponding markers of TMEM159 showed nominally significant associations with Major Depressive Disorder (MDD; 0.006 $leq$ p $leq$ 0.011). More specifically, variants associated with high arousal levels have previously been linked to an increased risk for MDD. In line with this, the MetaXcan database suggests increased expression levels of TMEM159 in MDD, as well as Autism Spectrum Disorder, and Alzheimer's Disease. Furthermore, our pathway analyses provided evidence for a role of sodium/calcium exchangers in resting state arousal. In conclusion, the present GWAS identifies TMEM159 as a novel candidate gene which may modulate the risk for psychiatric disorders through arousal mechanisms. Our results also encourage the elaboration of the previously reported interrelations between ion-channel modulators, sleep-wake behavior, and psychiatric disorders.

INTRODUCTION: Chronic pancreatitis (CP) may be caused by oxidative stress. An important source of reactive oxygen species (ROS) is the methylglyoxal-derived formation of advanced glycation endproducts (AGE). Methylglyoxal is detoxified by Glyoxalase I (GLO1). A reduction in GLO1 activity results in increased ROS. Single nucleotide polymorphisms (SNPs) of GLO1 have been linked to various inflammatory diseases. Here, we analyzed whether common GLO1 variants are associated with alcoholic (ACP) and non-alcoholic CP (NACP). METHODS: Using melting curve analysis, we genotyped a screening cohort of 223 ACP, 218 NACP patients, and 328 controls for 11 tagging SNPs defined by the SNPinfo LD TAG SNP Selection tool and the functionally relevant variant rs4746. For selected variants the cohorts were extended to up to 1,441 patient samples. RESULTS: In the ACP cohort, comparison of genotypes for rs1937780 between patients and controls displayed an ambiguous result in the screening cohort (p = 0.08). However, in the extended cohort of 1,441 patients no statistically significant association was found for the comparison of genotypes (p = 0.11), nor in logistic regression analysis (p = 0.214, OR 1.072, 95% CI 0.961-1.196). In the NACP screening cohort SNPs rs937662, rs1699012, and rs4746 displayed an ambiguous result when patients were compared to controls in the recessive or dominant model (p = 0.08, 0.08, and 0.07, respectively). Again, these associations were not confirmed in the extended cohorts (rs937662, dominant model: p = 0.07, logistic regression: p = 0.07, OR 1.207, 95% CI 0.985-1.480) or in the replication cohorts for rs4746 (Germany, p = 0.42, OR 1.080, 95% CI 0.673-1.124; France, p = 0.19, OR 0.90, 95% CI 0.76-1.06; China, p = 0.24, OR 1.18, 95% CI 0.90-1.54) and rs1699012 (Germany, Munich; p = 0.279, OR 0.903, 95% CI 0.750-1.087). CONCLUSIONS: Common GLO1 variants do not increase chronic pancreatitis risk.

We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

Advances in Data Management methods have resulted in a wide array of storage solutions having varying query capabilities and supporting different data formats. Traditionally, heterogeneous data was transformed off-line into a unique format and migrated to a unique data management system, before being uniformly queried. However, with the increasing amount of heterogeneous data sources, many of which are dynamic, modern applications prefer accessing directly the original fresh data. Addressing this requirement, we designed and developed Squerall, a software framework that enables the querying of original large and heterogeneous data on-the-fly without prior data transformation. Squerall is built from the ground up with extensibility in consideration, e.g., supporting more data sources. Here, we explain Squerall's extensibility aspect and demonstrate step-by-step how to add support for RDF data, a new extension to the previously supported range of data sources.

Whereas the availability of data has seen a manyfold increase in past years, its value can be only shown if the data variety is effectively tackled ---one of the prominent Big Data challenges. The lack of data interoperability limits the potential of its collective use for novel applications. Achieving interoperability through the full transformation and integration of diverse data structures remains an ideal that is hard, if not impossible, to achieve. Instead, methods that can simultaneously interpret different types of data available in different data structures and formats have been explored. On the other hand, many query languages have been designed to enable users to interact with the data, from relational, to object-oriented, to hierarchical, to the multitude emerging NoSQL languages. Therefore, the interoperability issue could be solved not by enforcing physical data transformation, but by looking at techniques that are able to query heterogeneous sources using one uniform language. Both industry and research communities have been keen to develop such techniques, which require the translation of a chosen 'universal' query language to the various data model specific query languages that make the underlying data accessible. In this article, we survey more than forty query translation methods and tools for popular query languages, and classify them according to eight criteria. In particular, we study which query language is a most suitable candidate for that 'universal' query language. Further, the results enable us to discover the weakly addressed and unexplored translation paths, to discover gaps and to learn lessons that can benefit future research in the area.

SpiNNaker is an ARM-based processor platform optimized for the simulation of spiking neural networks. This brief describes the roadmap in going from the current SPINNaker1 system, a 1 Million core machine in 130nm CMOS, to SpiNNaker2, a 10 Million core machine in 22nm FDSOI. Apart from pure scaling, we will take advantage of specific technology features, such as runtime adaptive body biasing, to deliver cutting-edge power consumption. Power management of the cores allows a wide range of workload adaptivity, i.e. processor power scales with the complexity and activity of the spiking network. Additional numerical accelerators will enhance the utility of SpiNNaker2 for simulation of spiking neural networks as well as for executing conventional deep neural networks. These measures should increase the simulation capacity of the machine by a factor $>$50. The interplay between the two domains, i.e. spiking and rate based, will provide an interesting field for algorithm exploration on SpiNNaker2. Apart from the platforms' traditional usage as a neuroscience exploration tool, the extended functionality opens up new application areas such as automotive AI, tactile internet, industry 4.0 and biomedical processing.

The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG, in general, is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows approx 8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.

Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly metadata have been made vailable as knowledge graphs from the diversity of data providers and agents. However, these high-quantities of data remain far from quality criteria in terms of completeness while growing at a rapid pace. Most of the attempts in completing such KGs are following traditional data digitization, harvesting and collaborative curation approaches. Whereas, advanced AI-related approaches such as embedding models - specifically designed for such tasks - are usually evaluated for standard benchmarks such as Freebase and Wordnet. The tailored nature of such datasets prevents those approaches to shed the lights on more accurate discoveries. Application of such models on domain-specific KGs takes advantage of enriched meta-data and provides accurate results where the underlying domain can enormously benefit. In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata. The results show a significant shift in the accuracy and performance evaluation of the model on a dataset with scholarly metadata. The newly proposed version of TransE obtains 99.9% for link prediction task while original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms other embedding models such as ComplEx, TransH and TransR experimented over scholarly knowledge graphs

Knowledge graph embedding models have gained significant attention in AI research. Recent works have shown that the inclusion of background knowledge, such as logical rules, can improve the performance of embeddings in downstream machine learning tasks. However, so far, most existing models do not allow the inclusion of rules. We address the challenge of including rules and present a new neural based embedding model (LogicENN). We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph. To the best of our knowledge, this has not been proved so far for the neural based family of embedding models. Moreover, we derive formulae for the inclusion of various rules, including (anti-)symmetric, inverse, irreflexive and transitive, implication, composition, equivalence and negation. Our formulation allows to avoid grounding for implication and equivalence relations. Our experiments show that LogicENN outperforms the state-of-the-art models in link prediction.

We outline the use of the tool FAMER to address the schema and entity matching tasks for the DI2KG 2019 challenge. FAMER supports both the static and incremental matching and clustering of entities from multiple sources. To alleviate entity matching, we first identify matching properties in the provided datasets based on the similarity of property names and instance values. This approach utilizes the given training data to derive property matches from entity matches. For entity matching, we consider multiple configurations to determine entity similarities with the optional use of word embeddings.

This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.

[en] The application of mathematical optimization techniques to reservoir system operation, besides allowing a more rational use of water, can also help to justify allocation decisions and solve conflicts. However, this application is not a simple task since it must deal with the inherent uncertainties, nonlinearities and multiobjectivity of the hydrosystems (Rani and Moreira 2009). In the literature, reservoir systems operation optimization is often modelled as a multistage stochastic programming problem and solved with a Stochastic Dynamic Programming (SDP) algorithm. However, the application of SDP is limited to systems with few reservoirs due to its curse of dimensionality. Pereira and Pinto (1991) developed the Stochastic Dual Dynamic Programming (SDDP), an extension of SDP that attenuates the curse of dimensionality by avoiding the variables discretization. The SDDP was first developed for the optimization of large hydropower generation systems (Pereira and Pinto 1991; Scott and Read 1996; Mo et al. 2001) and today its application to multipurpose reservoir systems is becoming more usual (Tilmant et al. 2008; Marques and Tilmant 2013; Raso et al. 2017). The present work seeks to apply and evaluate the performance of the SDDP algorithm in the optimization of the joint operation of two multipurpose reservoir systems. The hydrossystem chosen for this study is the Jaguaribe-Metropolitano wich supplies water for most of the Ceará state (Brazil). The hydrosystem is composed by the Jaguaribe and Metropolitan basins reservoir systems. Jaguaribe comprises the greatest reservoirs of the State, presenting a predominant demand for …

CONTEXT: Steroid hormones are important regulators of physiological processes in humans and are under genetic control. A link to coronary artery disease (CAD) is supposed. OBJECTIVE: Our main objective was to identify genetic loci influencing steroid hormone levels. As a secondary aim, we searched for causal effects of steroid hormones on CAD. DESIGN: We conducted genome-wide meta-association studies for eight steroid hormones: cortisol, dehydroepiandrosterone sulfate (DHEAS), estradiol, and testosterone in two independent cohorts (LIFE-Adult, LIFE-Heart, maximum n = 7667), and progesterone, 17-hydroxyprogesterone, androstenedione, and aldosterone in LIFE-Heart only (maximum n = 2070). All genome-wide significant loci were tested for sex interactions. Furthermore, we tested whether previously reported CAD single-nucleotide polymorphisms were associated with our steroid hormone panel and investigated causal links between hormone levels and CAD status using Mendelian randomization (MR) approaches. RESULTS: We discovered 15 novel associated loci for 17-hydroxyprogesterone, progesterone, DHEAS, cortisol, androstenedione, and estradiol. Five of these loci relate to genes directly involved in steroid metabolism, that is, CYP21A1, CYP11B1, CYP17A1, STS, and HSD17B12, almost completing the set of steroidogenic enzymes with genetic associations. Sexual dimorphisms were found for seven of the novel loci. Other loci correspond, for example, to the WNT4/$beta$-catenin pathway. MR revealed that cortisol, androstenedione, 17-hydroxyprogesterone, and DHEA-S had causal effects on CAD. We also observed enrichment of cortisol and testosterone associations among known CAD hits. CONCLUSION: Our study greatly improves insight into genetic regulation of steroid hormones and their dependency on sex. These results could serve as a basis for analyzing sexual dimorphism in other complex diseases.

[en] The uncertainty in the capacity of water supply due to changes in nature and gaps in observations leads to the need for greater efficiency within the water resources management. An examination of the vulnerability of a water system is an essential step in the development of a strategy in the scope of management of drought and water safety. IPCC (2001) divided vulnerability into three main factors: sensitivity, exposure and adaptive capacity. This paper proposes a method to quantify sensitivity to drought through the analysis of social, economic, environmental and hydric aspects. Sensitivity represents the degree of preparedness the system is in, reflecting its ability to respond to changes in the climate, whether beneficial or harmful. It also represents the system's ability to absorb impacts without suffering long-term damage or any significant changes of state (Thomas et al. 2016; Fontaine 2007; Abraham 2006). The study was applied to the states of São Paulo and Ceará, located in southeast and northeast of Brazil, respectively. The states were chosen because they faced during this decade severe water crisis even though they present different climate characteristics and different social dynamic and economic activity.

[en] In the face of increasingly frequent water crises, human supply becomes an even more complex challenge. In large urban centers, due to changes in consumption patterns, the values of residential water demand are high. The need for an offer that meets this growing demand pushes decision makers to an analysis of increasingly critical scenarios in order to achieve a balance in supply and demand (Fielding et al. 2013). The decision-making process, when done from a robust, technical and scientific base, promotes benefits and efficiencies that result in positive impacts at all levels: social, economic and environmental. In order to provide subsidies for the decision-making process, this paper proposes to analyze the variation of demand in urban centers through the application of a pool regression model, seeking to identify the existence of some statistical correlation between the water consumption variation pattern and mean income. The area of application is the city of Fortaleza, in the northeast of Brazil..

[en] Large-scale climate oscillations have been known to have a direct impact into the variability of hydrological systems and their relationship have been documented in different parts of the world. Those indices play an important role in the hydrological time series behaviour, thus identifying their relationship is an important tool for an accurate time series forecast, especially under uncertain hydrological time series and multiobjective demands from reservoirs. This paper proposes to use random forests, a powerful ensemble learning technique, to evaluate the relative relationship of each climate index to the streamflow’s time series. Such nonlinear machine learning methods have been successfully used in different areas of knowledge. Then, a regression decision tree is built aiming to extract useful information about dry and wet periods from streamflow data related to the climate oscillation and adjust the decision tree model to forecast future values of streamflow. The data used in this study belongs to the gauge station of Itaipu’s hydropower plant located in the Paraná River (Brazil). The basin has an annual cycle of discharges with large-amplitude and high flows in the summer.

The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and show their use in a bibliographic scenario to analyze temporal citation patterns.

The analysis of large graphs has received considerable attention recently but current solutions are typically hard to use. In this demonstration paper, we report on an effort to improve the usability of the open-source system Gradoop for processing and analyzing large graphs. This is achieved by integrating Gradoop into the popular open-source software KNIME to visually create graph analysis workflows, without the need for coding. We outline the integration approach and discuss what will be demonstrated.

Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero. They also lose information when learning relations with reflexive patterns since they become symmetric and transitive. We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms. Our solution is based on two principles: 1) we use a limit-based loss instead of a margin ranking loss and, 2) by learning independent embedding vectors for each of the terms we can collectively train and predict using contradicting distance terms. We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns. We propose MDE as a neural network model that allows us to map non-linear relations between the embedding vectors and the expected output of the score function. Our empirical results show that MDE performs competitively to state-of-the-art embedding models on several benchmark datasets.

Informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients. Meanwhile, social media is an infrastructure that allows public instant sharing of information, thus it has recently become popular in medical applications. In this study, we apply Multi Distance Knowledge Graph Embeddings (MDE) to link physicians and surgeons to the latest medical breakthroughs that are shared as the research results on Twitter. Our study shows that using this method physicians can be informed about the new findings in their field given that they have an account dedicated to their profession.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

The coordination of cell movements across spatio-temporal scales ensures precise positioning of organs during vertebrate gastrulation. Mechanisms governing such morphogenetic movements have been studied only within a local region, a single germlayer or in whole embryos without cell identity. Scale-bridging imaging and automated analysis of cell dynamics are needed for a deeper understanding of tissue formation during gastrulation. Here, we report pan-embryo analyses of formation and dynamics of all three germlayers simultaneously within a developing zebrafish embryo. We show that a distinct distribution of cells in each germlayer is established during early gastrulation via cell movement characteristics that are predominantly determined by their position in the embryo. The differences in initial germlayer distributions are subsequently amplified by a global movement, which organizes the organ precursors along the embryonic body axis, giving rise to the blueprint of organ formation. The tools and data are available as a resource for the community.

Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i.e. QA pipelines). The research community has paid substantial attention to CQAs since they support reusability and scalability of the available components in addition to the flexibility of pipelines. CQA frameworks attempt to build such pipelines automatically by solving two optimisation problems: 1) local collective performance of QA components per QA task and 2) global performance of QA pipelines. In spite offering several advantages over monolithic QA systems, the effectiveness and efficiency of CQA frameworks in answering questions is limited. In this paper, we tackle the problem of local optimisation of CQA frameworks and propose a three fold approach, which applies feature selection techniques with supervised machine learning approaches in order to identify the best performing components efficiently. We have empirically evaluated our approach over existing benchmarks and compared to existing automatic CQA frameworks. The observed results provide evidence that our approach answers a higher number of questions than the state of the art while reducing: i) the number of used features by 50% and ii) the number of components used by 76%.

In this paper we present Sparklify: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. In particular, we demonstrate a W3C SPARQL endpoint powered by our SANSA framework's RDF partitioning system and Apache Spark for querying the DBpedia knowledge base. This work is motivated by the lack of Big Data SPARQL systems that are capable of exposing large-scale heterogeneous RDF datasets via a Web SPARQL endpoint.

Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.

In a project called Ä Library of a Billion Words" we needed an implementation of the CTS protocol that is capable of handling a text collection containing at least 1 billion words. Because the existing solutions did not work for this scale or were still in development I started an implementation of the CTS protocol using methods that MySQL provides. Last year we published a paper that introduced a prototype with the core functionalities without being compliant with the specifications of CTS (Tiepmar et al., 2013). The purpose of this paper is to describe and evaluate the MySQL based implementation now that it is fulfilling the specifications version 5.0 rc.1 and mark it as finished and ready to use. Further information, online instances of CTS for all described datasets and binaries can be accessed via the projects website.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Abstract argumentation frameworks are by now a major research area in knowledge representation and reasoning. Various aspects of AFs have been extensively studied over the last 25 years. Contributing to understanding the expressive power of AFs, researchers found lower and upper bounds for the maximal number of extensions, that is, acceptable points of view, in AFs. One of the classical and most important concepts in AFs are so-called complete extensions. Surprisingly, the exact bound for the maximal number of complete extensions in an AF has not yet been formally established, although there is a reasonable conjecture tracing back at least to 2015. Recently the notion of modularization was introduced and it was shown that this concept plays a key role for the understanding of relations between semantics as well as intrinsic properties. In this paper, we will use this property to give a formal proof of the conjecture regarding complete semantics.

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.

CONTEXT: Despite the emerging evidence on the role of oxytocin (OXT) in metabolic diseases, there is a lack of well-powered studies addressing the relationship of circulating OXT with obesity and diabetes. OBJECTIVES AND DESIGN: Here, we measured OXT in a study cohort (n = 721; 396 women, 325 men; mean age $pm$ SD, 47.7 $pm$ 15.2 years) with subphenotypes related to obesity, including anthropometric traits such as body mass index [BMI (mean $pm$ SD), 26.8 $pm$ 4.6 kg/m2], waist-to-hip ratio (WHR; 0.88 $pm$ 0.09), blood parameters (glucose, 5.32 $pm$ 0.50 mmol/L; insulin, 5.3 $pm$ 3.3 µU/mL), and oral glucose tolerance test to clarify the association with OXT. We also tested in a genome-wide association study (GWAS) whether the interindividual variation in OXT serum levels might be explained by genetic variation. RESULTS: The OXT concentration was increased in subjects with elevated BMI and positively correlated with WHR, waist circumference, and triglyceride levels. The OXT concentration in subjects with BMI 30 kg/m2 (n = 137; 106.4 pg/mL, P = 8 $times$ 10-6). OXT levels were also positively correlated with plasma glucose and insulin and were elevated in subjects with impaired glucose tolerance (P = 4.6 $times$ 10-3). Heritability of OXT was estimated at 12.8%. In a GWAS, two hits in linkage disequilibrium close (19 kb) to the OXT reached genome-wide significant association (top-hit rs12625893, P = 3.1 $times$ 10-8, explained variance 3%). CONCLUSIONS: Our data show that OXT is genetically affected by a variant near OXT and is associated with obesity and impaired glucose tolerance.

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, which makes them worthy research subjects in many respects, not least profiling. With this paper we introduce the Webis Celebrity Corpus 2019. For its construction the Twitter feeds of 71,706 verified accounts have been carefully linked with their respective Wikidata items, crawling both. After cleansing, the resulting profiles contain an average of 29,968 words per profile and up to 239 pieces of personal information. A cross-evaluation that checked the correct association of Twitter account and Wikidata item revealed an error rate of only 0.6%, rendering the profiles highly reliable. Our corpus comprises a wide cross-section of local and global celebrities, forming a unique combination of scale, profile comprehensiveness, and label reliability. We further establish the state of the art's profiling performance by evaluating the winning approaches submitted to the PAN gender prediction tasks in a transfer learning experiment. They are only outperformed by our own deep learning approach, which we also use to exemplify celebrity occupation prediction for the first time.

MOTIVATION: RNA secondary structure is a useful representation for studying the function of RNA, which captures most of the free energy of RNA folding. Using empirically determined energy parameters, secondary structures of nucleic acids can be efficiently computed by recursive algorithms. Several software packages supporting this task are readily available. As RNA secondary structures are outerplanar graphs, they can be drawn without intersection in the plane. Interpretation by the practitioner is eased when these drawings conform to a series of additional constraints beyond outerplanarity. These constraints are the reason why RNA drawing is difficult. Many RNA drawing algorithms therefore do not always produce intersection-free (outerplanar) drawings. RESULTS: To remedy this shortcoming we propose here the RNApuzzler algorithm which is guaranteed to produce intersection-free drawings. It is based on a drawing algorithm respecting constraints based on nucleotide distances (RNAturtle). We investigate relaxations of these constraints allowing for intersection-free drawings. Based on these relaxations, we implemented a fully automated, simple, and robust algorithm that produces aesthetic drawings adhering to previously established guidelines. We tested our algorithm using the RFAM database and found that we can compute intersection-free drawings of all RNAs therein efficiently. AVAILABILITY AND IMPLEMENTATION: The software can be accessed freely at: https://github.com/dwiegreffe/RNApuzzler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE chieves the state-of-the-art on link prediction over four temporal KGs.

The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents of the question and the corresponding entries in the database. As a result, parsing the questions in order to determine their main elements, which are required for answer retrieval, becomes crucial. However, most datasets for QA systems lack gold annotations for parsing, i.e., labels are only available in the form of (question, formal-query, answer). In this paper, we propose a distantly supervised learning framework based on reinforcement learning to learn the mentions of entities and relations in questions. We leverage the provided formal queries to characterize delayed rewards for optimizing a policy gradient objective for the parsing model. An empirical evaluation of our approach shows a significant improvement in the performance of entity and relation linking compared to the state of the art. We also demonstrate that a more accurate parsing component enhances the overall performance of QA systems.