Learning Edge Representations via Low-Rank Asymmetric Projections

We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the "graph likelihood", which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller

Spin Resolution of the Electron-Gas Correlation Energy: Positive same-spin contribution

The negative correlation energy per particle of a uniform electron gas of density parameter $r_s$ and spin polarization $\zeta$ is well known, but its spin resolution into up-down, up-up, and down-down contributions is not. Widely-used estimates are incorrect, and hamper the development of reliable density functionals and pair distribution functions. For the spin resolution, we present interpolations between high- and low-density limits that agree with available Quantum Monte Carlo data. In the low-density limit for $\zeta = 0$, we find that the same-spin correlation energy is unexpectedly positive, and we explain why. We also estimate the up and down contributions to the kinetic energy of correlation.Comment: new version, to appear in PRB Rapid Communicatio

Integrating Climatological-Hydrodynamic Modeling and Paleohurricane Records to Assess Storm Surge Risk

Sediment cores from blue holes have emerged as a promising tool for extending the record of long-term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological-hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias-correct the climatological-hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500-year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias-corrected climatological-hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500-year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2-4.5 and SSP5-8.5, respectively by the end of the 21st century

A Taxonomy of Causality-Based Biological Properties

We formally characterize a set of causality-based properties of metabolic networks. This set of properties aims at making precise several notions on the production of metabolites, which are familiar in the biologists' terminology. From a theoretical point of view, biochemical reactions are abstractly represented as causal implications and the produced metabolites as causal consequences of the implication representing the corresponding reaction. The fact that a reactant is produced is represented by means of the chain of reactions that have made it exist. Such representation abstracts away from quantities, stoichiometric and thermodynamic parameters and constitutes the basis for the characterization of our properties. Moreover, we propose an effective method for verifying our properties based on an abstract model of system dynamics. This consists of a new abstract semantics for the system seen as a concurrent network and expressed using the Chemical Ground Form calculus. We illustrate an application of this framework to a portion of a real metabolic pathway

Correlation energy contribution to nuclear masses

The ground state correlation energies associated with collective surface and pairing vibrations are calculated for Pb- and Ca-isotopes. It is shown that this contribution, when added to those predicted by one of the most accurate modern nuclear mass formula (HFBCS MSk7 mass formula), reduces the associated rms error by an important factor, making mean field theory, once its time dependence is taken into account, a quantitative predictive tool for nuclear masses.Comment: 4 pages, 2 figures, RevTeX

Correlation energy and spin polarization in the 2D electron gas

The ground state energy of the two--dimensional uniform electron gas has been calculated with fixed--node diffusion Monte Carlo, including backflow correlations, for a wide range of electron densities as a function of spin polarization. We give a simple analytic representation of the correlation energy which fits the density and polarization dependence of the simulation data and includes several known high- and low-density limits. This parametrization provides a reliable local spin density energy functional for two-dimensional systems and an estimate for the spin susceptibility. Within the proposed model for the correlation energy, a weakly first--order polarization transition occurs shortly before Wigner crystallization as the density is lowered.Comment: Minor typos corrected, see erratum: Phys. Rev. Lett. 91, 109902(E) (2003

Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems

I consider the problem of deriving couplings of a statistical model from measured correlations, a task which generalizes the well-known inverse Ising problem. After reminding that such problem can be mapped on the one of expressing the entropy of a system as a function of its corresponding observables, I show the conditions under which this can be done without resorting to iterative algorithms. I find that inverse problems are local (the inverse Fisher information is sparse) whenever the corresponding models have a factorized form, and the entropy can be split in a sum of small cluster contributions. I illustrate these ideas through two examples (the Ising model on a tree and the one-dimensional periodic chain with arbitrary order interaction) and support the results with numerical simulations. The extension of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure

Pan-GWAS of Streptococcus agalactiae highlights lineage-specific genes associated with virulence and niche adaptation

Streptococcus agalactiae (Group B streptococcus, GBS) is a coloniser of the gastrointestinal and urogenital tracts, and an opportunistic pathogen of infants and adults. The worldwide population of GBS is characterised by Clonal Complexes (CCs) with different invasive potentials. CC17 for example, is a hypervirulent lineage commonly associated with neonatal sepsis and meningitis, while CC1 is less invasive in neonates and more commonly causes invasive disease in adults with co-morbidities. The genetic basis of GBS virulence and to what extent different CCs have adapted to different host environments remain uncertain. We have therefore applied a pan-genome wide association study approach to 1988 GBS strains isolated from different hosts and countries. Our analysis identified 279 CC-specific genes associated with virulence, disease, metabolism and regulation of cellular mechanisms that may explain the differential virulence potential of particular CCs. In CC17 and CC23 for example, we have identified genes encoding for pilus, quorum sensing proteins, and proteins for the uptake of ions and micronutrients which are absent in less invasive lineages. Moreover, in CC17, carriage and disease strains were distinguished by the allelic variants of 21 of these CC-specific genes. Together our data highlight the lineage-specific basis of GBS niche adaptation and virulence, and suggest that human-associated GBS CCs have largely evolved in animal hosts before crossing to the humans and then spreading clonally

Pan-GWAS of Streptococcus agalactiae Highlights Lineage-Specific Genes Associated with Virulence and Niche Adaptation

Streptococcus agalactiae (group B streptococcus; GBS) is a colonizer of the gastrointestinal and urogenital tracts, and an opportunistic pathogen of infants and adults. The worldwide population of GBS is characterized by clonal complexes (CCs) with different invasive potentials. CC17, for example, is a hypervirulent lineage commonly associated with neonatal sepsis and meningitis, while CC1 is less invasive in neonates and more commonly causes invasive disease in adults with comorbidities. The genetic basis of GBS virulence and the extent to which different CCs have adapted to different host environments remain uncertain. We have therefore applied a pan-genome-wide association study (GWAS) approach to 1,988 GBS strains isolated from different hosts and countries. Our analysis identified 279 CC-specific genes associated with virulence, disease, metabolism, and regulation of cellular mechanisms that may explain the differential virulence potential of particular CCs. In CC17 and CC23, for example, we have identified genes encoding pilus, quorum-sensing proteins, and proteins for the uptake of ions and micronutrients which are absent in less invasive lineages. Moreover, in CC17, carriage and disease strains were distinguished by the allelic variants of 21 of these CC-specific genes. Together our data highlight the lineage-specific basis of GBS niche adaptation and virulence.IMPORTANCE GBS is a leading cause of mortality in newborn babies in high- and low-income countries worldwide. Different strains of GBS are characterized by different degrees of virulence, where some are harmlessly carried by humans or animals and others are much more likely to cause disease.The genome sequences of almost 2,000 GBS samples isolated from both animals and humans in high- and low- income countries were analyzed using a pan-genome-wide association study approach. This allowed us to identify 279 genes which are associated with different lineages of GBS, characterized by a different virulence and preferred host. Additionally, we propose that the GBS now carried in humans may have first evolved in animals before expanding clonally once adapted to the human host.These findings are essential to help understand what is causing GBS disease and how the bacteria have evolved and are transmitted