A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks

Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achieving state-of-the-art results on node and graph classification tasks. The proposed GNNs, however, often implement complex node and graph embedding schemes, which makes challenging to explain their performance. In this paper, we investigate the link between a GNN's expressiveness, that is, its ability to map different graphs to different representations, and its generalization performance in a graph classification setting. In particular , we propose a principled experimental procedure where we (i) define a practical measure for expressiveness, (ii) introduce an expressiveness-based loss function that we use to train a simple yet practical GNN that is permutation-invariant, (iii) illustrate our procedure on benchmark graph classification problems and on an original real-world application. Our results reveal that expressiveness alone does not guarantee a better performance, and that a powerful GNN should be able to produce graph representations that are well separated with respect to the class of the corresponding graphs

Quantum Transduction of Telecommunications-band Single Photons from a Quantum Dot by Frequency Upconversion

The ability to transduce non-classical states of light from one wavelength to another is a requirement for integrating disparate quantum systems that take advantage of telecommunications-band photons for optical fiber transmission of quantum information and near-visible, stationary systems for manipulation and storage. In addition, transducing a single-photon source at 1.3 {\mu}m to visible wavelengths for detection would be integral to linear optical quantum computation due to the challenges of detection in the near-infrared. Recently, transduction at single-photon power levels has been accomplished through frequency upconversion, but it has yet to be demonstrated for a true single-photon source. Here, we transduce the triggered single-photon emission of a semiconductor quantum dot at 1.3 {\mu}m to 710 nm with a total detection (internal conversion) efficiency of 21% (75%). We demonstrate that the 710 nm signal maintains the quantum character of the 1.3 {\mu}m signal, yielding a photon anti-bunched second-order intensity correlation, g^(2)(t), that shows the optical field is composed of single photons with g^(2)(0) = 0.165 < 0.5.Comment: 7 pages, 4 figure

The Stokes Phenomenon and Quantum Tunneling for de Sitter Radiation in Nonstationary Coordinates

We study quantum tunneling for the de Sitter radiation in the planar coordinates and global coordinates, which are nonstationary coordinates and describe the expanding geometry. Using the phase-integral approximation for the Hamilton-Jacobi action in the complex plane of time, we obtain the particle-production rate in both coordinates and derive the additional sinusoidal factor depending on the dimensionality of spacetime and the quantum number for spherical harmonics in the global coordinates. This approach resolves the factor of two problem in the tunneling method.Comment: LaTex 10 pages, no figur

Mass spectrometry imaging identifies palmitoylcarnitine as an immunological mediator during Salmonella Typhimurium infection

Salmonella Typhimurium causes a self-limiting gastroenteritis that may lead to systemic disease. Bacteria invade the small intestine, crossing the intestinal epithelium from where they are transported to the mesenteric lymph nodes (MLNs) within migrating immune cells. MLNs are an important site at which the innate and adaptive immune responses converge but their architecture and function is severely disrupted during S. Typhimurium infection. To further understand host-pathogen interactions at this site, we used mass spectrometry imaging (MSI) to analyse MLN tissue from a murine model of S. Typhimurium infection. A molecule, identified as palmitoylcarnitine (PalC), was of particular interest due to its high abundance at loci of S. Typhimurium infection and MLN disruption. High levels of PalC localised to sites within the MLNs where B and T cells were absent and where the perimeter of CD169+ sub capsular sinus macrophages was disrupted. MLN cells cultured ex vivo and treated with PalC had reduced CD4+CD25+ T cells and an increased number of B220+CD19+ B cells. The reduction in CD4+CD25+ T cells was likely due to apoptosis driven by increased caspase-3/7 activity. These data indicate that PalC significantly alters the host response in the MLNs, acting as a decisive factor in infection outcome

Observational Conditioning in Flower Choice Copying by Bumblebees (Bombus terrestris): Influence of Observer Distance and Demonstrator Movement

A. Avargues-Weber was funded by a postdoctoral fellowship from Fyssen fondation: http://www.fondationfyssen.fr/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

In vitro and in vivo insulinotropic properties of the multifunctional frog skin peptide hymenochirin-1B: a structure–activity study

Hymenochirin-1b (Hym-1B; IKLSPETKDNLKKVLKGAIKGAIAVAKMV.NH2) is a cationic, α-helical amphibian host-defense peptide with antimicrobial, anticancer, and immunomodulatory properties. This study investigates the abilities of the peptide and nine analogues containing substitutions of Pro5, Glu6, and Asp9 by either l-lysine or d-lysine to stimulate insulin release in vitro using BRIN-BD11 clonal β cells or isolated mouse islets and in vivo using mice fed a high-fat diet to produce obesity and insulin resistance. Hym-1B produced a significant and concentration-dependent increase in the rate of insulin release from BRIN-BD11 cells without cytotoxicity at concentrations up to 1 µM with a threshold concentration of 1 nM. The threshold concentrations for the analogues were: [P5K], [E6K], [D9K], [P5K, E6K] and [E6K, D9k] 0.003 nM, [E6K, D9K] and [D9k] 0.01 nM, [P5K, D9K] 0.1 nM and [E6k] 0.3 nM. All peptides displayed cytotoxicity at concentrations ≥1 µM except the [P5K] and [D9k] analogues which were non-toxic at 3 µM. The potency and maximum rate of insulin release from mouse islets produced by the [P5K] peptide were significantly greater than produced by Hym-1B. Neither Hym-1B nor the [P5K] analogue at 1 µM concentration had an effect on membrane depolarization or intracellular Ca2+. The [P5K] analogue (1 µM) produced a significant increase in cAMP concentration in BRIN-BD11 cells and stimulated GLP-1 secretion from GLUTag cells. Down-regulation of the protein kinase A pathway by overnight incubation with forskolin completely abolished the insulin-releasing effects of [P5K]hym-1B. Intraperitoneal administration of the [P5K] and [D9k] analogues (75 nmol/kg body weight) to high-fat-fed mice with insulin resistance significantly enhanced glucose tolerance with a concomitant increase in insulin secretion. We conclude that [P5K]hym-1B and [D9k]hym-1B show potential for development into anti-diabetic agents

Exploring miniature insect brains using micro-CT scanning techniques

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Googling Food Webs: Can an Eigenvector Measure Species' Importance for Coextinctions?

A major challenge in ecology is forecasting the effects of species' extinctions, a pressing problem given current human impacts on the planet. Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated, but interact instead in a complex network of ecological relationships. Because of their mutual dependence, the loss of a single species can cascade in multiple coextinctions. Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network. Moreover, we use the algorithm to bridge the gap between qualitative (who eats whom) and quantitative (at what rate) descriptions of food webs. We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks. Our approach relies on network structure, but applies regardless of the specific dynamical model of species' interactions, because it identifies the subset of coextinctions common to all possible models, those that will happen with certainty given the complete loss of prey of a given predator. Results show that previous measures of importance based on the concept of “hubs” or number of connections, as well as centrality measures, do not identify the most effective extinction sequence. The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems

Hydrophobicity and Charge Shape Cellular Metabolite Concentrations

What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ∼100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors