Towards Measuring Microlensing Event Rate in the Galactic Center: I. Events Detection from the UKIRT Microlensing Survey Data

To overcome the high optical extinction, near-infrared observations are needed for probing the microlensing events toward the Galactic center. The 2015-2019 UKIRT microlensing survey toward the Galactic center is the first dedicated precursor near-infrared (NIR) survey for the Nancy Grace Roman Space Telescope. We here analyze the online data from the UKIRT microlensing survey, reaching $l=b=0^\circ$. Using the event-finder algorithm of KMTNet with the $\Delta \chi^2$ threshold of 250, we find 522 clear events, 436 possible events, and 27 possible anomalous events. We fit a point-source point-lens (PSPL) model to all the clear events and derive the PSPL parameters with uncertainties using a Markov chain Monte Carlo method. Assuming perfect detection efficiency, we compute the uncorrected event rates, which should serve as the lower limits on the true event rate. We find that the uncorrected NIR event rates are likely rising toward the Galactic center and higher than the optical event rates.Comment: 16 pages, Accepted for publication at ApJ

Graph-Level Embedding for Time-Evolving Graphs

Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph's nodes. We then train a "document-level" language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.Comment: In Companion Proceedings of the ACM Web Conference 202

Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation

Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7% on average when given only 10% of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.Comment: In proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). Onlin

Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding methods suffer from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which require domain-specific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate task-specific embeddings. In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.Comment: In proceedings of the 35th AAAI Conference on Artificial Intelligenc

An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data

The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-generated text, such as tweets. In this paper, we present an experimental survey of a wide range of well-known text representation techniques for the task of text clustering on noisy Twitter data. Our results indicate that the more advanced models do not necessarily work best on tweets and that more exploration in this area is needed.Comment: In proceedings of the 6th Workshop on Noisy User-generated Text (W-NUT) at EMNLP 202

The expression profile analysis of NKX2-5 knock-out embryonic mice to explore the pathogenesis of congenital heart disease

AbstractBackgroundMutation of NKX2-5 could lead to the development of congenital heart disease (CHD) which is a common inherited disease. This study aimed to investigate the pathogenesis of CHD in NKX2-5 knock-out embryonic mice.MethodsThe expression profile in the NKX2-5 knock-out embryonic mice (GSE528) was downloaded from Gene Expression Omnibus. The heart tissues from the null/heterozygous embryonic day 12.5 mice were compared with wild-type mice to identify differentially expressed genes (DEGs), and then DEGs corresponding to the transcriptional factors were filtered out based on the information in the TRANSFAC database. In addition, a transcriptional regulatory network was constructed according to transcription factor binding site information from the University of California Santa Cruz database. A pathway interaction network was constructed by latent pathways identification analysis.ResultsThe 42 DEGs corresponding to transcriptional factors from the null and heterozygous embryos were identified. The transcriptional regulatory networks included five down-regulated DEGs (SP1, SRY, JUND, STAT6, and GATA6), and six up-regulated DEGs [POU2F1, NFY (NFYA/NFYB/NFYC), USF2 and MAX]. Latent pathways analysis demonstrated that ribosome, glycolysis/gluconeogenesis, and dilated cardiomyopathy pathways significantly interacted.ConclusionThe identified DEGs and latent pathways could provide new comprehensive view for understanding the pathogenesis of CHD