Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. However, it remains notoriously challenging to train and inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because as the graph size grows, the sheer number of node features and the large adjacency matrix can easily explode the required memory and data movements. To tackle the aforementioned challenges, we explore the possibility of drawing lottery tickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the adjacency matrix yet are capable of achieving accuracy comparable to or even better than their full graphs. Specifically, we for the first time discover the existence of graph early-bird (GEB) tickets that emerge at the very early stage when sparsifying GCN graphs, and propose a simple yet effective detector to automatically identify the emergence of such GEB tickets. Furthermore, we advocate graph-model co-optimization and develop a generic efficient GCN early-bird training framework dubbed GEBT that can significantly boost the efficiency of GCN training by (1) drawing joint early-bird tickets between the GCN graphs and models and (2) enabling simultaneously sparsification of both the GCN graphs and models. Experiments on various GCN models and datasets consistently validate our GEB finding and the effectiveness of our GEBT, e.g., our GEBT achieves up to 80.2% ~ 85.6% and 84.6% ~ 87.5% savings of GCN training and inference costs while offering a comparable or even better accuracy as compared to state-of-the-art methods. Our source code and supplementary appendix are available at https://github.com/RICE-EIC/Early-Bird-GCN.
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Trends in bird abundance differ among protected forests but not bird guilds
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Abstract Various types of radar systems are increasingly being used to monitor aerial biodiversity. Each of these types has different detection capabilities and sensitivities to environmental conditions, which affect the quantity and quality of the measured objects of interest. Radar wind profilers have long been known to detect birds, but their use in ornithology has remained limited, largely because of biologists' unfamiliarity with these systems. Although the potential of radar wind profilers for quantitative bird monitoring has been illustrated with time series of raw data, a comparison with a similar radar system more established in biology is missing. Here, we compare nocturnal bird migration patterns observed by a radar wind profiler during October 2019 and April 2021 with those from a dedicated bird radar BirdScan MR1. The systems were located 50 km apart with an altitudinal difference of about 850 m. The nightly migration intensities measured with both systems were highly correlated in both spring and autumn (Pearson correlation coefficient ≈ 0.8,
P < 0.001), but estimated traffic measured by the radar wind profiler was on average five times higher in spring and nine times higher in autumn. Low ratios of the migration traffic rates of the Birdscan MR1 to those of the radar wind profiler occurred primarily in clear conditions. In both radar systems, migration occurred at significantly higher altitudes in spring than in autumn. Discrepancies in absolute numbers between both systems are likely due to both system‐inherent and external environmental and topographical factors, but also different quantification approaches. These findings support the capacity of radar wind profilers for aerial biomonitoring, independent of environmental conditions, and open up further avenues for studying the impact of weather on bird migration at detailed temporal and altitudinal scales. -
Abstract Rasopone Schmidt and Shattuck is a poorly known lineage of ants that live in Neotropical forests. Informed by phylogenetic results from thousands of ultraconserved elements (UCEs) and mitochondrial DNA barcodes, we revise the genus, providing a new morphological diagnosis and a species-level treatment. Analysis of UCE data from many Rasopone samples and select outgroups revealed non-monophyly of the genus. Monophyly of Rasopone was restored by transferring several species to the unrelated genus Mayaponera Schmidt and Shattuck. Within Rasopone, species are morphologically very similar, and we provide a ‘bird guide’ approach to identification rather than the traditional dichotomous key. Species are arranged by size in a table, along with geographic range and standard images. Additional diagnostic information is then provided in individual species accounts. We recognize a total of 15 named species, of which the following are described as new species: R. costaricensis, R. cryptergates, R. cubitalis, R. guatemalensis, R. mesoamericana, R. pluviselva, R. politognatha, R. subcubitalis, and R. titanis. An additional 12 morphospecies are described but not formally named due to insufficient material. Rasopone panamensis (Forel, 1899) is removed from synonymy and elevated to species. The following species are removed from Rasopone and made new combinations in Mayaponera: M. arhuaca (Forel, 1901), M. becculata (Mackay and Mackay, 2010), M. cernua (Mackay and Mackay, 2010), M. conicula (Mackay and Mackay, 2010), M. longidentata (Mackay and Mackay, 2010), and M. pergandei (Forel, 1909).more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/eap.2377
