Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/tree/master/cluster_gcn.Comment: In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Capulet and Slingshot share overlapping functions during Drosophila eye morphogenesis

BACKGROUND: CAP/Capulet (Capt), Slingshot (Ssh) and Cofilin/Twinstar (Tsr) are actin-binding proteins that restrict actin polymerization. Previously, it was shown that low resolution analyses of loss-of-function mutations in capt, ssh and tsr all show ectopic F-actin accumulation in various Drosophila tissues. In contrast, RNAi depletion of capt, tsr and ssh in Drosophila S2 cells all affect actin-based lamella formation differently. Whether loss of these three related genes might cause the same effect in the same tissue remains unclear. METHODS: Loss-of-function mutant clones were generated using the MARCM or EGUF system whereas overexpression clones were generated using the Flip-out system. Immunostaining were then performed in eye imaginal discs with clones. FRAP was performed in cultured eye discs. RESULTS: Here, we compared their loss-of-function phenotype at single-cell resolution, using a sheet of epithelial cells in the Drosophila eye imaginal disc as a model system. Surprisingly, we found that capt and ssh, but not tsr, mutant cells within and posterior to the morphogenetic furrow (MF) shared similar phenotypes. The capt/ssh mutant cells possessed: (1) hexagonal cell packing with discontinuous adherens junctions; and (2) largely complementary accumulation of excessive phosphorylated myosin light chain (p-MLC) and F-actin rings at the apical cortex. We further showed that the capt/ssh mutant phenotypes depended on the inactivation of protein kinase A (PKA) and activation of Rho. CONCLUSIONS: Although Capt, Ssh and Tsr were reported to negatively regulate actin polymerization, we found that Capt and Ssh, but not Tsr, share overlapping functions during eye morphogenesis

Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation

Vertical Heating Structures Associated with the MJO as Characterized by TRMM Estimates, ECMWF Reanalyses, and Forecasts: A Case Study during 1998/99 Winter

The Madden–Julian oscillation (MJO) is a fundamental mode of the tropical atmosphere variability that exerts significant influence on global climate and weather systems. Current global circulation models, unfortunately, are incapable of robustly representing this form of variability. Meanwhile, a well-accepted and comprehensive theory for the MJO is still elusive. To help address this challenge, recent emphasis has been placed on characterizing the vertical structures of the MJO. In this study, the authors analyze vertical heating structures by utilizing recently updated heating estimates based on the Tropical Rainfall Measuring Mission (TRMM) from two different latent heating estimates and one radiative heating estimate. Heating structures from two different versions of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses/forecasts are also examined. Because of the limited period of available datasets at the time of this study, the authors focus on the winter season from October 1998 to March 1999. The results suggest that diabatic heating associated with the MJO convection in the ECMWF outputs exhibits much stronger amplitude and deeper structures than that in the TRMM estimates over the equatorial eastern Indian Ocean and western Pacific. Further analysis illustrates that this difference might be due to stronger convective and weaker stratiform components in the ECMWF estimates relative to the TRMM estimates, with the latter suggesting a comparable contribution by the stratiform and convective counterparts in contributing to the total rain rate. Based on the TRMM estimates, it is also illustrated that the stratiform fraction of total rain rate varies with the evolution of the MJO. Stratiform rain ratio over the Indian Ocean is found to be 5% above (below) average for the disturbed (suppressed) phase of the MJO. The results are discussed with respect to whether these heating estimates provide enough convergent information to have implications on theories of the MJO and whether they can help validate global weather and climate models

A Fuzzy-based Dynamic Channel Allocation

[[abstract]]In traditional wireless networks, fixed allocation of spectrum is one of the main reason causing low utilization of spectrum. In order to solve this problem, a new wireless communication model has been proposed, which called Cognitive Radio Networks (CRN). CRN adopts Dynamic Spectrum Access (DSA) technology, thus it can flexibly use the spectrum which primary user temporarily unused. In cognitive radio networks, due to each secondary user (SU) has different location and surrounding spectrum environment, it may have variety of available channels. How to assign these available channels is the crucial point of system performance. However, existing methods doesn’t consider the problem of multipath fading; therefore, this study proposed an improved channel allocation scheme. We consider the received signal strength to define the channel access priority of secondary users applied by fuzzy theory. Finally, the simulation results show the superior of our approach and verify the effectiveness of the proposed scheme.[[sponsorship]]University of Colombo School of Computing, Sri Lanka[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20150823~20150826[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Colombo, Sri Lank

Traditional Chinese Medicine ZHENG Identification Provides a Novel Stratification Approach in Patients with Allergic Rhinitis

Background. We aimed to apply the ZHENG identification to provide an easy and useful tool to stratify the patients with allergic rhinitis (AR) through exploring the correlation between the quantified scores of AR symptoms and the TCM ZHENGs. Methods. A total of 114 AR patients were enrolled in this observational study. All participants received the examinations of anterior rhinoscopy and acoustic rhinometry. Their blood samples were collected for measurement of total serum immunoglobulin E (IgE), blood eosinophil count (Eos), and serum eosinophil cationic protein (ECP). They also received two questionnaire to assess the severity scores of AR symptoms and quantified TCM ZHENG scores. Multiple linear regression analysis was used to determine explanatory factors for the score of AR manifestations. Results. IgE and ECP level, duration of AR, the 2 derived TCMZHENG scores of “Yin-Xu − Yang-Xu”, and “Qi-Xu + Blood-Xu” were 5 explanatory variables to predict the severity scores of AR symptoms. The patients who had higher scores of “Yin-Xu − Yang-Xu” or “Qi-Xu + Blood-Xu” tended to manifest as “sneezer and runner” or “blockers,” respectively. Conclusions. The TCM ZHENG scores correlated with the severity scores of AR symptoms and provided an easy and useful tool to stratify the AR patients

The Study on Antecedents of Consumer Buying Impulsiveness in an Online Context

The global recession caused by the financial tsunami has seriously impacted numerous industries. Although the market scale of global e-commerce market has declined, global online shopping continues to grow. Many previous researches focused on the effect of website design characteristics on online impulsive buying behavior, and few have explored such behavior from consumer individual internal factor perspectives. This paper aims to explore and integrate individual internal factors influencing consumer online buying impulsiveness, and further to recognize the relationships among these factors. The results showed as follows: (1) hedonic consumption needs, impulsive buying tendency, positive affect and normative evaluations positively influence buying impulsiveness, respectively; (2) hedonic consumption needs positively influence positive affect; (3) impulsive buying tendency positively influences normative evaluations; (4) normative evaluations positively influence positive affect