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Template matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the ‘Matrix Profile’ (MP), a “correlate everything with everything” calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research.

 

Recent years have witnessed the superior performance of heterogeneous graph neural networks (HGNNs) in dealing with heterogeneous information networks (HINs). Nonetheless, the success of HGNNs often depends on the availability of sufficient labeled training data, which can be very expensive to obtain in real scenarios. Active learning provides an effective solution to tackle the data scarcity challenge. For the vast majority of the existing work regarding active learning on graphs, they mainly focus on homogeneous graphs, and thus fall in short or even become inapplicable on HINs. In this paper, we study the active learning problem with HGNNs and propose a novel meta-reinforced active learning framework MetRA. Previous reinforced active learning algorithms train the policy network on labeled source graphs and directly transfer the policy to the target graph without any adaptation. To better exploit the information from the target graph in the adaptation phase, we propose a novel policy transfer algorithm based on meta-Q-learning termed per-step MQL. Empirical evaluations on HINs demonstrate the effectiveness of our proposed framework. The improvement over the best baseline is up to 7% in Micro-F1. 

In superrosid species, root epidermal cells differentiate into root hair cells and nonhair cells. In some superrosids, the root hair cells and nonhair cells are distributed randomly (Type I pattern), and in others, they are arranged in a position‐dependent manner (Type III pattern). The model plant Arabidopsis (Arabidopsis thaliana) adopts the Type III pattern, and the gene regulatory network (GRN) that controls this pattern has been defined. However, it is unclear whether the Type III pattern in other species is controlled by a similar GRN as in Arabidopsis, and it is not known how the different patterns evolved.

In this study, we analyzed superrosid speciesRhodiola rosea,Boehmeria nivea, andCucumis sativusfor their root epidermal cell patterns. Combining phylogenetics, transcriptomics, and cross‐species complementation, we analyzed homologs of the Arabidopsis patterning genes from these species.

We identifiedR. roseaandB. niveaas Type III species andC. sativusas Type I species. We discovered substantial similarities in structure, expression, and function of Arabidopsis patterning gene homologs inR. roseaandB. nivea, and major changes inC. sativus.

We propose that in superrosids, diverse Type III species inherited the patterning GRN from a common ancestor, whereas Type I species arose by mutations in multiple lineages.

 

Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respect to node de- grees, resulting in worse predictive accuracy for low-degree nodes. We formulate the problem of mitigating the degree-related per- formance disparity in GCN from the perspective of the Rawlsian difference principle, which is originated from the theory of distribu- tive justice. Mathematically, we aim to balance the utility between low-degree nodes and high-degree nodes while minimizing the task- specific loss. Specifically, we reveal the root cause of this degree- related unfairness by analyzing the gradients of weight matrices in GCN. Guided by the gradients of weight matrices, we further propose a pre-processing method RawlsGCN-Graph and an in- processing method RawlsGCN-Grad that achieves fair predictive accuracy in low-degree nodes without modification on the GCN architecture or introduction of additional parameters. Extensive experiments on real-world graphs demonstrate the effectiveness of our proposed RawlsGCN methods in significantly reducing degree- related bias while retaining comparable overall performance. 

The three-level system in a diamond nitrogen vacancy center is used to engineer a tensor monopole. 

Warming due to climate change has profound impacts on regional crop yields, and this includes impacts from rising mean growing season temperature and heat stress events. Adapting to these two impacts could be substantially different, and the overall contribution of these two factors on the effects of climate warming and crop yield is not known. This study used the improved WheatGrow model, which can reproduce the effects of temperature change and heat stress, along with detailed information from 19 location-specific cultivars and local agronomic management practices at 129 research stations across the main wheat-producing region of China, to quantify the regional impacts of temperature increase and heat stress separately on wheat in China. Historical climate, plus two future low-warming scenarios (1.5 °C/2.0 °C warming above pre-industrial) and one future high-warming scenario (RCP8.5), were applied using the crop model, without considering elevated CO₂effects. The results showed that heat stress and its yield impact were more severe in the cooler northern sub-regions than the warmer southern sub-regions with historical and future warming scenarios. Heat stress was estimated to reduce wheat yield in most of northern sub-regions by 2.0%–4.0% (up to 29% in extreme years) under the historical climate. Climate warming is projected to increase heat stress events in frequency and extent, especially in northern sub-regions. Surprisingly, higher warming did not result in more yield-impacting heat stress compared to low-warming, due to advanced phenology with mean warming and finally avoiding heat stress events during grain filling in summer. Most negative impacts of climate warming are attributed to increasing mean growing-season temperature, while changes in heat stress are projected to reduce wheat yields by an additional 1.0%–1.5% in northern sub-regions. Adapting to climate change in China must consider the different regional and temperature impacts to be effective.

 

The two-way interaction between Madden–Julian oscillation (MJO) and higher-frequency waves (HFW) over the Maritime Continent (MC) during boreal winter of 1984–2005 is investigated. It is noted from observational analysis that strengthened (weakened) HFW activity appears to the west (east) of and under MJO convection during the MJO active phase and the opposite is seen during the MJO suppressed phase. Sensitivity model experiments indicate that the control of HFW activity by MJO is through change of the background vertical wind shear and specific humidity. The upscale feedbacks from HFW to MJO through nonlinear rectification of condensational heating and eddy momentum transport are also investigated with observational data. A significantly large amount (25%–40%) of positive heating anomaly ([Formula: see text]) at low levels to the east of MJO convection is contributed by nonlinear rectification of HFW. This nonlinear rectification is primarily attributed to eddy meridional moisture advection. A momentum budget diagnosis reveals that 60% of MJO zonal wind tendency at 850 hPa is attributed to the nonlinear interaction of HFW with other scale flows. Among them, the largest contribution arises from eddy zonal momentum flux divergence [Formula: see text]. Easterly (westerly) vertical shear to the west (east) of MJO convection during the MJO active phase causes the strengthening (weakening) of the HFW zonal wind anomaly. This leads to the increase (decrease) of eddy momentum flux activity to the east (west) of the MJO convection, which causes a positive (negative) eddy zonal momentum flux divergence in the zonal wind transitional region during the MJO active (suppressed) phase, favoring the eastward propagation of the MJO.