Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and embedding gradients (all referred to as messages) across devices bring significant communication overhead for nodes with remote neighbors on other devices (marginal nodes) and unnecessary waiting time for nodes without remote neighbors (central nodes) in the training graph. This paper proposes an efficient GNN training system, AdaQP, to expedite distributed full-graph GNN training. We stochastically quantize messages transferred across devices to lower-precision integers for communication traffic reduction and advocate communication-computation parallelization between marginal nodes and central nodes. We provide theoretical analysis to prove fast training convergence (at the rate of O(T^{-1}) with T being the total number of training epochs) and design an adaptive quantization bit-width assignment scheme for each message based on the analysis, targeting a good trade-off between training convergence and efficiency. Extensive experiments on mainstream graph datasets show that AdaQP substantially improves distributed full-graph training's throughput (up to 3.01 X) with negligible accuracy drop (at most 0.30%) or even accuracy improvement (up to 0.19%) in most cases, showing significant advantages over the state-of-the-art works

Gaussian-preserved, non-volatile shape morphing in three-dimensional microstructures for dual-functional electronic devices

Motile plant structures such as Mimosa pudica leaves, Impatiens glandulifera seedpods, and Dionaea muscipula leaves exhibit fast nastic movements in a few seconds or less. This motion is stimuli-independent mechanical movement following theorema egregium rules. Artificial analogs of tropistic motion in plants are exemplified by shape-morphing systems, which are characterized by high functional robustness and resilience for creating 3D structures. However, all shape-morphing systems developed so far rely exclusively on continuous external stimuli and result in slow response. Here, we report a Gaussian-preserved shape-morphing system to realize ultrafast shape morphing and non-volatile reconfiguration. Relying on the Gaussian-preserved rules, the transformation can be triggered by mechanical or thermal stimuli within a microsecond. Moreover, as localized energy minima are encountered during shape morphing, non-volatile configuration is preserved by geometrically enhanced rigidity. Using this system, we demonstrate a suite of electronic devices that are reconfigurable, and therefore, expand functional diversification

Associations of Dietary Patterns during Pregnancy with Gestational Hypertension: The “Born in Shenyang” Cohort Study

The literature on maternal dietary patterns and gestational hypertension (GH) risk is largely ambiguous. We investigated the associations of maternal dietary patterns with GH risk among 1092 pregnant women in a Chinese pre-birth cohort. We used both three-day food diaries (TFD) and food frequency questionnaires (FFQ) to assess the diets of pregnant women. Principal components analysis with varimax rotation was used to identify dietary patterns from the TFD and FFQ, respectively. In total, 14.5% of the participants were diagnosed with GH. Maternal adherence to a “Wheaten food–coarse cereals pattern (TFD)” was associated with a lower risk of GH (quartile 3 [Q3] vs. Q1, odds ratio [OR] = 0.53, 95%CI: 0.31, 0.90). Maternal adherence to a “Sweet food–seafood pattern (TFD)” was associated with lower systolic blood pressure (Q4 vs. Q1, β = −2.57, 95%CI: −4.19, −0.96), and mean arterial pressure (Q4 vs. Q1, β = −1.54, 95%CI: −2.70, −0.38). The protective associations of the “Sweet food-seafood (TFD)” and “Fish–seafood pattern (FFQ)” with the risk of GH were more pronounced among women who were overweight/obese before pregnancy (p for interaction < 0.05 for all). The findings may help to develop interventions and better identify target populations for hypertension prevention during pregnancy

The Association between Dietary Patterns and Pre-Pregnancy BMI with Gestational Weight Gain: The “Born in Shenyang” Cohort

The reported associations of maternal dietary patterns during pregnancy with gestational weight gain are inconsistent, especially among the less studied Asian Chinese populations. In a prospective pre-birth cohort study conducted in northern China, we determined the associations between maternal dietary patterns and the probability of excess gestational weight gain (EGWG) among 1026 pregnant women. We used 3-day food diaries to assess maternal diet and performed principal component analysis to identify dietary patterns. Maternal adherence to a traditional pattern, which was characterized by a higher intake of tubers, vegetables, fruits, red meat, and rice, was associated with a higher probability of EGWG (quartile 3 vs. quartile 1, odds ratio [OR] = 1.62, 95% confidence interval [CI] = 1.10−2.38). This risk association was more pronounced among women who were overweight/obese before pregnancy (quartile 4 vs. quartile 1, OR = 5.17, 95% CI = 1.45–18.46; p for interaction p for interaction < 0.01). These findings may help to develop interventions and better define target populations for EGWG prevention

Low-Temperature Solution-Processed Tin Oxide as an Alternative Electron Transporting Layer for Efficient Perovskite Solar Cells

Lead halide perovskite solar cells with the high efficiencies typically use high-temperature processed TiO₂ as the electron transporting layers (ETLs). Here, we demonstrate that low-temperature solution-processed nanocrystalline SnO₂ can be an excellent alternative ETL material for efficient perovskite solar cells. Our best-performing planar cell using such a SnO₂ ETL has achieved an average efficiency of 16.02%, obtained from efficiencies measured from both reverse and forward voltage scans. The outstanding performance of SnO₂ ETLs is attributed to the excellent properties of nanocrystalline SnO₂ films, such as good antireflection, suitable band edge positions, and high electron mobility. The simple low-temperature process is compatible with the roll-to-roll manufacturing of low-cost perovskite solar cells on flexible substrates