Cohen-Macaulay permutation graphs

In this article, we characterize Cohen-Macaulay permutation graphs. In particular, we show that a permutation graph is Cohen-Macaulay if and only if it is well-covered and there exists a unique way of partitioning its vertex set into $r$ disjoint maximal cliques, where $r$ is the cardinality of a maximal independent set of the graph. We also provide some sufficient conditions for a comparability graph to be a uniquely partially orderable (UPO) graph.Comment: 8 pages, 2 figures; comments are welcom

End-to-End Learning on Multimodal Knowledge Graphs

Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in graphs' structure: raw values, encoded as literal nodes, are either omitted completely or treated as regular nodes without consideration for their values. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including numbers, texts, dates, images and geometries, which are projected into a joint representation space together with their relational information. We implement and demonstrate our model on node classification and link prediction for artificial and real-worlds datasets, and evaluate the effect that each modality has on the overall performance in an inverse ablation study. Our results indicate that end-to-end multimodal learning from any arbitrary knowledge graph is indeed possible, and that including multimodal information can significantly affect performance, but that much depends on the characteristics of the data.Comment: Under submission. arXiv admin note: substantial text overlap with arXiv:2003.1238

The GOAL study: a prospective examination of the impact of factor V Leiden and ABO(H) blood groups on haemorrhagic and thrombotic pregnancy outcomes

Factor V Leiden (FVL) and ABO(H) blood groups are the common influences on haemostasis and retrospective studies have linked FVL with pregnancy complications. However, only one sizeable prospective examination has taken place. As a result, neither the impact of FVL in unselected subjects, any interaction with ABO(H) in pregnancy, nor the utility of screening for FVL is defined. A prospective study of 4250 unselected pregnancies was carried out. A venous thromboembolism (VTE) rate of 1·23/1000 was observed, but no significant association between FVL and pre-eclampsia, intra-uterine growth restriction or pregnancy loss was seen. No influence of FVL and/or ABO(H) on ante-natal bleeding or intra-partum or postpartum haemorrhage was observed. However, FVL was associated with birth-weights >90th centile [odds ratio (OR) 1·81; 95% confidence interval (CI<sub>95</sub>) 1·04–3·31] and neonatal death (OR 14·79; CI<sub>95</sub> 2·71–80·74). No association with ABO(H) alone, or any interaction between ABO(H) and FVL was observed. We neither confirmed the protective effect of FVL on pregnancy-related blood loss reported in previous smaller studies, nor did we find the increased risk of some vascular complications reported in retrospective studies

Physical Orbit for Lambda Virginis and a Test of Stellar Evolution Models

Lambda Virginis (LamVir) is a well-known double-lined spectroscopic Am binary with the interesting property that both stars are very similar in abundance but one is sharp-lined and the other is broad-lined. We present combined interferometric and spectroscopic studies of LamVir. The small scale of the LamVir orbit (~20 mas) is well resolved by the Infrared Optical Telescope Array (IOTA), allowing us to determine its elements as well as the physical properties of the components to high accuracy. The masses of the two stars are determined to be 1.897 Msun and 1.721 Msun, with 0.7% and 1.5% errors respectively, and the two stars are found to have the same temperature of 8280 +/- 200 K. The accurately determined properties of LamVir allow comparisons between observations and current stellar evolution models, and reasonable matches are found. The best-fit stellar model gives LamVir a subsolar metallicity of Z=0.0097, and an age of 935 Myr. The orbital and physical parameters of LamVir also allow us to study its tidal evolution time scales and status. Although currently atomic diffusion is considered to be the most plausible cause of the Am phenomenon, the issue is still being actively debated in the literature. With the present study of the properties and evolutionary status of LamVir, this system is an ideal candidate for further detailed abundance analyses that might shed more light on the source of the chemical anomalies in these A stars.Comment: 43 Pages, 13 figures. Accepted for publication in Ap

Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center

Gain without population inversion in V-type systems driven by a frequency-modulated field

We obtain gain of the probe field at multiple frequencies in a closed three-level V-type system using frequency modulated pump field. There is no associated population inversion among the atomic states of the probe transition. We describe both the steady-state and transient dynamics of this system. Under suitable conditions, the system exhibits large gain simultaneously at series of frequencies far removed from resonance. Moreover, the system can be tailored to exhibit multiple frequency regimes where the probe experiences anomalous dispersion accompanied by negligible gain-absorption over a large bandwidth, a desirable feature for obtaining superluminal propagation of pulses with negligible distortion.Comment: 10 pages + 8 figures; To appear in Physical Review

Drilling their own graves:How the European oil and gas supermajors avoid sustainability tensions through mythmaking

This study explores how paradoxical tensions between economic growth and environmental protection are avoided through organizational mythmaking. By examining the European oil and gas supermajors’ ‘‘CEOspeak’’ about climate change, we show how mythmaking facilitates the disregarding, diverting, and/or displacing of sustainability tensions. In doing so, our findings further illustrate how certain defensive responses are employed: (1) regression, or retreating to the comforts of past familiarities, (2) fantasy, or escaping the harsh reality that fossil fuels and climate change are indeed irreconcilable, and (3) projecting, or shifting blame to external actors for failing to address climate change. By highlighting the discursive effects of enacting these responses, we illustrate how the European oil and gas supermajors self-determine their inability to substantively address the complexities of climate change. We thus argue that defensive responses are not merely a form of mismanagement as the paradox and corporate sustainability literature commonly suggests, but a strategic resource that poses serious ethical concerns given the imminent danger of issues such as climate change

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer