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Subgraph neural networks have recently gained prominence for subgraph-level predictive tasks, but existing methods either use simple pooling over graph convolutional networks that fail to capture essential subgraph properties or rely on rigid subgraph definitions that limit performance; moreover, they cannot model long-range dependencies between and within subgraphs—an important limitation given real-world networks’ diverse structures. To address this, we propose the first implicit subgraph neural network that captures dependencies across subgraphs and integrates label-aware subgraph-level information, formulating implicit subgraph learning as a bilevel optimization problem and introducing a provably convergent algorithm requiring fewer gradient estimations than standard bilevel methods, achieving superior performance on real-world benchmarks. 

Abstract Location and interaction data for workers in a hospital unit are useful for epidemiological research. This article describes a one-week study measuring contacts between healthcare professionals in a medical intensive care unit. Measurements capture the duration of contact, defined as being within six feet (1.8 ± 0.1 meters) distance between instrumented persons or between persons and selected locations throughout the unit. Within each patient room, measurements distinguish between three places: the sink, the computer, and the vitals monitor. Data from the study, approximately 15 million records, are processed into different formats that facilitate analysis. 

Abstract We study theT-system of type $A_{\mathrm{\infty }}$, also known as the octahedron recurrence/equation, viewed as a $2+1$-dimensional discrete evolution equation. Generalizing earlier work on arctic curves for the Aztec Diamond obtained from solutions of the octahedron recurrence with ‘flat’ initial data, we consider initial data along parallel ‘slanted’ planes perpendicular to an arbitrary admissible direction $(r,s,t)\in {\mathbb{Z}}_{+}^3$. The corresponding solutions of theT-system are interpreted as partition functions of dimer models on some suitable ‘pinecone’ graphs introduced by Bousquet–Mélou, Propp, and West in 2009. TheT-system formulation and some exact solutions in uniform or periodic cases allow us to explore the thermodynamic limit of the corresponding dimer models and to derive exact arctic curves separating the various phases of the system. This direct approach bypasses the standard general theory of dimers using the Kasteleyn matrix approach and uses instead the theory of Analytic Combinatorics in Several Variables, by focusing on a linear system obeyed by the dimer density generating function. 

In response to elevated temperatures, plants alter the activities of enzymes that affect lipid composition. While it has long been known that plant leaf membrane lipids become less unsaturated in response to heat, other changes, including polygalactosylation of galactolipids, head group acylation of galactolipids, increases in phosphatidic acid and triacylglycerols, and formation of sterol glucosides and acyl sterol glucosides, have been observed more recently. In this work, by measuring lipid levels with mass spectrometry, we confirm the previously observed changes in Arabidopsis thaliana leaf lipids under three heat stress regimens. Additionally, in response to heat, increased oxidation of the fatty acyl chains of leaf galactolipids, sulfoquinovosyldiacylglycerols, and phosphatidylglycerols, and incorporation of oxidized acyl chains into acylated monogalactosyldiacylglycerols are shown. We also observed increased levels of digalactosylmonoacylglycerols and monogalactosylmonoacylglycerols. The hypothesis that a defect in sterol glycosylation would adversely affect regrowth of plants after a severe heat stress regimen was tested, but differences between wild-type and sterol glycosylation-defective plants were not detected.