The application of growth curve modeling for the analysis of diachronic corpora

This paper introduces growth curve modeling for the analysis of language change in corpus linguistics. In addition to describing growth curve modeling, which is a regression-based method for studying the dynamics of a set of variables measured over time, we demonstrate the technique through an analysis of the relative frequencies of words that are increasing or decreasing over time in a multi-billion word diachronic corpus of Twitter. This analysis finds that increasing words tend to follow a trajectory similar to the s-curve of language change, whereas decreasing words tend to follow a decelerated trajectory, thereby showing how growth curve modeling can be used to uncover and describe underlying patterns of language change in diachronic corpora

Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction

Traffic prediction is a crucial task in many real-world applications. The task is challenging due to the implicit and dynamic spatio-temporal dependencies among traffic data. On the one hand, the spatial dependencies among traffic flows are latent and fluctuate with environmental conditions. On the other hand, the temporal dependencies among traffic flows also vary significantly over time and locations. In this paper, we propose Adaptive Spatio-Temporal Convolutional Network (ASTCN) to tackle these challenges. First, we propose a spatial graph learning module that learns the dynamic spatial relations among traffic data based on multiple influential factors. Furthermore, we design an adaptive temporal convolution module that captures complex temporal traffic dependencies with environment-aware dynamic filters. We conduct extensive experiments on three real-world traffic datasets. The results demonstrate that the proposed ASTCN consistently outperforms state-of-the-arts.Peer reviewe

EGFR exon 19-deletion aberrantly regulate ERCC1 expression that may partly impaired DNA damage repair ability in non-small cell lung cancer

Background Epidermal growth factor receptor (EGFR) activating mutations are usually associated with DNA damage repair (DDR) deficiency. However, the precise mechanism has remained elusive. In this study, we aimed to investigate whether EGFR exon 19 deletion mutation downstream signals contributed to DDR deficiency by downregulation of excision repair cross-complementation group-1 (ERCC1), a key factor in DDR, expression and function. Methods We first measured cell survival, DNA damage (gamma-H2AX foci formation) and damage repair (ERCC1 and RAD51 foci formation) ability in response to DNA cross-linking drug in EGFR exon 19 deletion and EGFR wild-type cells separately. We then investigated the involvement of EGFR downstream signals in regulating ERCC1 expression and function in EGFR exon 19 deletion cells as compared with EGFR wild-type ones. Results We observed increased gamma-H2AX, but impaired ERCC1 and RAD51 nuclear foci formation in EGFR exon 19 deletion cells as compared with EGFR wild-type ones treated with DNA cross-linker. In addition, we identified that inhibition of EGFR exon 19 deletion signals increased ERCC1 expression, whereas blocked wild-type EGFR signals decreased ERCC1 expression, on both mRNA and protein levels. Furthermore, EGFR exon 19 deletion downstream signals not only inhibited ERCC1 expression but also influenced ERCC1 foci formation in response to DNA cross-linker. Conclusion Our findings indicated that the aberrant EGFR exon 19 deletion signals were not only associated with decreased expression of ERCC1 but were also involved in impaired ERCC1 recruitment in response to DNA cross-link damage, thereby providing us with more evidence for exploring the mechanism of DDR deficiency in EGFR mutant NSCLC.Peer reviewe

Impact of Enteromorpha Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China

Between 2008 and 2016, there were mass summer blooms of Enteromorpha in the Yellow Sea, China. They covered an area of thousands of square kilometers annually, lasting an average of 90 days. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the Enteromorpha blooms were used in this study of the Qianliyan Island area. Underwater robots survey trepang, wrinkles abalone, and submarine ecological status. We found that the time taken by Enteromorpha to cover the Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The Enteromorpha made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. Enteromorpha sedimentation during outbreaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of trepang and wrinkles abalone were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramatic and rapid changes in the world environment, the assumption that data obey Independent Identically Distribution is undermined by the subsequent changes in data distribution, known as concept drift, leading to weak replicability and transferability of the model over unseen data. To address the issue, previous approaches typically retrain the model, forcing it to fit the most recent observed data. However, retraining is problematic in that it leads to model lag, consumption of resources, and model re-invalidation, causing the drift problem to be not well solved in realistic scenarios. In this study, we propose a new urban time series prediction model for the concept drift problem, which encodes the drift by considering the periodicity in the data and makes on-the-fly adjustments to the model based on the drift using a meta-dynamic network. Experiments on real-world datasets show that our design significantly outperforms state-of-the-art methods and can be well generalized to existing prediction backbones by reducing their sensitivity to distribution changes.Comment: Accepted by CIKM 202

The effect of Matmo typhoon on mixed zone between the Yellow sea and Bohai sea

The results of remote sensing, buoy and profile based on measured data indicate that the wind speed, H-1/3 and salinity increased, sea surface temperature declined, and wind direction changed greatly during the transit of the Matmo typhoon on July 25. It was found that the typhoon transport the Yellow Sea Cold Water Mass into the the Yellow and Bohai seas mixed zone

Analyzing lexical emergence in modern American English online

This paper introduces a quantitative method for identifying newly emerging word forms in large time-stamped corpora of natural language and then describes an analysis of lexical emergence in American social media using this method based on a multi-billion word corpus of Tweets collected between October 2013 and November 2014. In total 29 emerging word forms, which represent various semantic classes, grammatical parts-of speech, and word formations processes, were identified through this analysis. These 29 forms are then examined from various perspectives in order to begin to better understand the process of lexical emergence