Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Thermally driven spin injection from a ferromagnet into a non-magnetic metal

Creating, manipulating and detecting spin polarized carriers are the key elements of spin based electronics. Most practical devices use a perpendicular geometry in which the spin currents, describing the transport of spin angular momentum, are accompanied by charge currents. In recent years, new sources of pure spin currents, i.e., without charge currents, have been demonstrated and applied. In this paper, we demonstrate a conceptually new source of pure spin current driven by the flow of heat across a ferromagnetic/non-magnetic metal (FM/NM) interface. This spin current is generated because the Seebeck coefficient, which describes the generation of a voltage as a result of a temperature gradient, is spin dependent in a ferromagnet. For a detailed study of this new source of spins, it is measured in a non-local lateral geometry. We developed a 3D model that describes the heat, charge and spin transport in this geometry which allows us to quantify this process. We obtain a spin Seebeck coefficient for Permalloy of -3.8 microvolt/Kelvin demonstrating that thermally driven spin injection is a feasible alternative for electrical spin injection in, for example, spin transfer torque experiments

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Predator-Induced Vertical Behavior of a Ctenophore

Although many studies have focused on Mnemiopsis leidyi predation, little is known about the role of this ctenophore as prey when abundant in native and invaded pelagic systems. We examined the response of the ctenophore M. leidyi to the predatory ctenophore Beroe ovata in an experiment in which the two species could potentially sense each other while being physically separated. On average, M. leidyi responded to the predator’s presence by increasing variability in swimming speeds and by lowering their vertical distribution. Such behavior may help explain field records of vertical migration, as well as stratified and near-bottom distributions of M. leidyi

Brain-Derived Neurotrophic Factor Gene Val66Met Polymorphism Modulates Reversible Cerebral Vasoconstriction Syndromes

BACKGROUND: Reversible cerebral vasoconstriction syndrome (RCVS) could be complicated by cerebral ischemic events. Hypothetical mechanisms of RCVS involve endothelial dysfunction and sympathetic overactivity, both of which were reported to be related to brain-derived neurotrophic factor (BDNF). The study investigated the association between functional BDNF Val66Met polymorphism and RCVS. METHODS: Patients with RCVS and controls were prospectively recruited and genotyped for the BDNF Val66Met polymorphism. Magnetic resonance angiography (MRA) and transcranial color-coded Doppler sonography were employed to evaluate cerebral vasoconstriction. Genotyping results, clinical parameters, vasoconstriction scores, mean flow velocities of the middle cerebral artery (V(MCA)), and Lindegaard indices were analyzed. Split-sample approach was employed to internally validate the data. PRINCIPAL FINDINGS: Ninety Taiwanese patients with RCVS and 180 age- and gender-matched normal controls of the same ethnicity completed the study. The genotype frequencies did not differ between patients and controls. Compared to patients with Met/Met homozygosity, patients with Val allele had higher mean vasoconstriction scores of all arterial segments (1.60±0.72 vs. 0.87±0.39, p<0.001), V(MCA) values (116.7±36.2 vs. 82.7±17.9 cm/s, p<0.001), and LI (2.41±0.91 vs. 1.89±0.41, p = 0.001). None of the Met/Met homozygotes, but 38.9% of the Val carriers, had V(MCA) values of >120 cm/s (p<0.001). Split-sample validation by randomization, age, entry time or residence of patients demonstrated concordant findings. CONCLUSIONS: Our findings link BDNF Val66Met polymorphism with the severity of RCVS for the first time and implicate possible pathogenic mechanisms for vasoconstriction in RCVS

The effect of distractions in the operating room during endourological procedures

Contains fulltext : 98421.pdf (publisher's version ) (Closed access)BACKGROUND: Professionals working in the operating room (OR) are subject to various distractions that can be detrimental to their task performance and the quality of their work. This study aimed to quantify the frequency, nature, and effect on performance of (potentially) distracting events occurring during endourological procedures and additionally explored urologists' and residents' perspectives on experienced ill effects due to distracting factors. METHODS: First, observational data were collected prospectively during endourological procedures in one OR of a teaching hospital. A seven-point ordinal scale was used to measure the level of observed interference with the main task of the surgical team. Second, semistructured interviews were conducted with eight urologists and seven urology residents in two hospitals to obtain their perspectives on the impact of distracting factors. RESULTS: Seventy-eight procedures were observed. A median of 20 distracting events occurred per procedure, which corresponds to an overall rate of one distracting event every 1.8 min. Equipment problems and procedure-related and medically irrelevant communication were the most frequently observed causes of interruptions and identified as the most distracting factors in the interviews. Occurrence of distracting factors in difficult situations requiring high levels of concentration was perceived by all interviewees as disturbing and negatively impacting performance. The majority of interviewees (13/15) thought distracting factors impacted more strongly on residents' compared to urologists' performance due to their different levels of experience. CONCLUSION: Distracting events occur frequently in the OR. Equipment problems and communication, the latter both procedure-related and medically irrelevant, have the largest impact on the sterile team and regularly interrupt procedures. Distracting stimuli can influence performance negatively and should therefore be minimized. Further research is required to determine the direct effect of distraction on patient safety

Anointed or appointed? Father–daughter succession within the family business

With the focus on events and outcomes shaping most of the existing family business research on intra-family succession, the subtleties of the incumbent-successor relationship and the dynamic nature of succession as a process of becoming is somewhat neglected. In particular, we have limited understanding of how successor identities are constructed as legitimate between incumbent and successor during father-daughter succession. This article addresses this gap in understanding by exploring how the daughter successor engages in identity work with the father incumbent during the process of succession and the role of father-daughter gendered relations in shaping her successor identity. Using a two-stage research design strategy, we draw upon empirical evidence derived from 14 individual and joint semi-structured interviews to present a narrative analysis of five father-daughter dyads. In so doing, we unveil how the daughter’s successor identity was co-constructed as legitimate and how father-daughter gendered relations influenced this process. Although daughters rely on certain father-daughter relations (preparation, endorsement and osmotic credibility) for legitimacy, they also need to develop independently from their father in order to heighten their own visibility and establish credibility

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas