Modeling Temporal and Structural Information in Time Series Data

Time series data is a sequence of data with temporal information at each position in the sequence. Such data widely exists in various disciplines. In computer science, different areas such as computational biology, signal processing, anomaly detection, and user behavior modeling benefit significantly from time series data. When modeling and analyzing time series data, there are two essential aspects embedded in time series data. The first one is structural information. Structural information contains the relationships and dependencies that inherently exist in time series data. The second indispensable aspect is temporal information. Temporal information is the key to distinguish time series data from other sequence data such as sentences (sequences of words). This thesis proposes novel approaches for modeling structural and temporal information to improve performance on various machine learning tasks. It demonstrates that the same methodologies can be used for diverse machine learning tasks, including activity recognition, dynamic network prediction, hypothesis testing, and recommendation. For activity recognition, I propose a novel adversarial prediction approach to model structured outputs, which outperforms the state-of-the-art approaches. I also design adversarial structural prediction approach that provides robust guarantees and superior performance for dynamic network prediction on real-world network prediction datasets. Additionally, I demonstrate that new temporal features are capable of capturing favorable information for the dynamic network prediction task. Another proposed approach in this thesis is temporal filtering which is introduced to advance the learning tasks of hypothesis testing and recommendation

Additional file 1 of Grounded theory-based model of the influence of digital communication on handicraft intangible cultural heritage

Additional file 1: Appendix A. Initial Interview Outline. Appendix B. CSSCI papers searched on CNKI (2016-2021). Appendix c. Open coding categories and original records

Additional file 4 of Diagnostic and prognostic values of pyroptosis-related genes for the hepatocellular carcinoma

Additional file 4. The gene set associated with the immune cell subtypes

Additional file 8 of Diagnostic and prognostic values of pyroptosis-related genes for the hepatocellular carcinoma

Additional file 8. The pyroptosis related DEGs between the non-tumor and tumor samples in the ICGC cohort

Additional file 6 of Diagnostic and prognostic values of pyroptosis-related genes for the hepatocellular carcinoma

Additional file 6. Table S6. The clinical information of the IMvirgor210 cohort

Additional file 1 of Diagnostic and prognostic values of pyroptosis-related genes for the hepatocellular carcinoma

Additional file 1. Table S1. The clinical information of the paired samples in the TCGA cohort and ICGC cohort

Description statistics of main variables.

Does government audit affect employee allocation of enterprises? Using the quasi natural experiment and differences-in-differences(DID) model, this paper empirically tests the impact of government audit on employees efficiency and labor cost stickiness of state-owned enterprises (SOEs) in China. The results show that after the implementation of government audit, excess employees and labor cost stickiness of the audited enterprise is significantly reduced, which confirms the supervision and governance function of government audit on employee efficiency of SOEs. However, labor costs of the audited enterprise are not significantly reduced. These indicate from the side that the compensation system of SOEs conforms to market mechanisms, and there is no problem of employee over payment in China. This study enriches the relevant literature on the impact of government audit on employee policy of SOEs, and also provides empirical support for the full coverage of government audit.</div

Comparative analysis of excess employee(<i>Exemp</i>2) in SOEs that is audited and not audited.

Comparative analysis of excess employee(Exemp2) in SOEs that is audited and not audited.</p

The test of relationship between government audit and excess employees based on compensate incentive grouping.

The test of relationship between government audit and excess employees based on compensate incentive grouping.</p