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The search for simple principles that underlie the spatial structure and dynamics of plant communities is a long-standing challenge in ecology. In particular, the relationship between species coexistence and the spatial distribution of plants is challenging to resolve in species-rich communities. Here we present a comprehensive analysis of the spatial patterns of 720 tree species in 21 large forest plots and their consequences for species coexistence. We show that species with low abundance tend to be more spatially aggregated than more abundant species. Moreover, there is a latitudinal gradient in the strength of this negative aggregation–abundance relationship that increases from tropical to temperate forests. We suggest, in line with recent work, that latitudinal gradients in animal seed dispersal and mycorrhizal associations may jointly generate this pattern. By integrating the observed spatial patterns into population models8, we derive the conditions under which species can invade from low abundance in terms of spatial patterns, demography, niche overlap and immigration. Evaluation of the spatial-invasion condition for the 720 tree species analysed suggests that temperate and tropical forests both meet the invasion criterion to a similar extent but through contrasting strategies conditioned by their spatial patterns. Our approach opens up new avenues for the integration of observed spatial patterns into ecological theory and underscores the need to understand the interaction among spatial patterns at the neighbourhood scale and multiple ecological processes in greater detail. 

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. 

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. 

Summary 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. 

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

Abstract 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.