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

Data_Sheet_1_Changing discourses of Chinese language maintenance in Australia: unpacking language ideologies of first-generation Chinese immigrant parents from People’s Republic of China.docx

Parental agency of their children’s language learning is often determined by their perceptions of the significance of the language in both family and society levels. Based on a larger ethnography conducted in Sydney from 2017 to 2020, this study investigates the language ideologies of Chinese immigrant parents from the People’s Republic of China in the recent decades, regarding the maintenance of their children’s Chinese heritage language(s). Drawing on the concept of language as pride and profit shifting between communities across time and space, this study reveals that Chinese parents primarily emphasize the economic benefits associated with Chinese languages when it comes to preserving their heritage language(s). While the significance of cultural pride and identity remains evident, there is a notable shift where the concept of pride is merging with that of profit concerning the importance of Chinese heritage language. However, the commodification of Chinese and identity, privileging “national” mandarin while marginalizing “regional” others, impedes the transmission of diverse Chinese heritage languages other than Mandarin. Simultaneously, the value-laden calculation of language prioritizes the “most” prestigious English, often at the expense of “heritage” Mandarin, regardless of its acknowledged economic potential. The findings illustrate how language ideologies and practices within the Chinese diaspora are shaped by power conflicts between English and Mandarin Chinese, hierarchical distinctions between Mandarin and non-Mandarin Chinese, and subtle stratification within regional Chinese languages. The research underscores the challenges faced by minority communities in preserving their heritage languages, particularly those with limited economic capital or political influence.</p

Data_Sheet_2_Changing discourses of Chinese language maintenance in Australia: unpacking language ideologies of first-generation Chinese immigrant parents from People’s Republic of China.docx

Parental agency of their children’s language learning is often determined by their perceptions of the significance of the language in both family and society levels. Based on a larger ethnography conducted in Sydney from 2017 to 2020, this study investigates the language ideologies of Chinese immigrant parents from the People’s Republic of China in the recent decades, regarding the maintenance of their children’s Chinese heritage language(s). Drawing on the concept of language as pride and profit shifting between communities across time and space, this study reveals that Chinese parents primarily emphasize the economic benefits associated with Chinese languages when it comes to preserving their heritage language(s). While the significance of cultural pride and identity remains evident, there is a notable shift where the concept of pride is merging with that of profit concerning the importance of Chinese heritage language. However, the commodification of Chinese and identity, privileging “national” mandarin while marginalizing “regional” others, impedes the transmission of diverse Chinese heritage languages other than Mandarin. Simultaneously, the value-laden calculation of language prioritizes the “most” prestigious English, often at the expense of “heritage” Mandarin, regardless of its acknowledged economic potential. The findings illustrate how language ideologies and practices within the Chinese diaspora are shaped by power conflicts between English and Mandarin Chinese, hierarchical distinctions between Mandarin and non-Mandarin Chinese, and subtle stratification within regional Chinese languages. The research underscores the challenges faced by minority communities in preserving their heritage languages, particularly those with limited economic capital or political influence.</p

Mean scores for participants' evaluation of the fairness of offers made by attractive and less attractive proposers of the same or the opposite sex (Standard deviations are in parentheses).

<p>Mean scores for participants' evaluation of the fairness of offers made by attractive and less attractive proposers of the same or the opposite sex (Standard deviations are in parentheses).</p

Synthesis of Sequence-Regulated Polymers: Alternating Polyacetylene through Regioselective Anionic Polymerization of Butadiene Derivatives

We hereby report a strategy to synthesize sequence-regulated substituted polyacetylenes using living anionic polymerization of designed monomers, that is, 2,4-disubstituted butadienes. It is found that proper substituents, such as 2-isopropyl-4-phenyl, lead to nearly 100% 1,4-addition during the polymerization, thus, giving product with high regioregularity, precise molecular weight, and narrow molecular weight distribution. The product is convertible into sequence-regulated substituted polyacetylene by oxidative dehydrogenation using 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ). Block copolymers containing polyacetylene segment are also prepared. Owing to the versatility of the anionic reactions, the present strategy can serve as a powerful tool of precise control on polymer chain microstructure, architecture, and functionalities in the same time

Mean scores for participants' intention to punish attractive and less attractive proposers as a function of the reasonableness of offer and sex congruence between the proposers and the participants.

<p>5/5  =  equal division of 100 yuan between the proposer and the recipient; 6/4  =  the proposer received 60 yuan while the recipient received 40 yuan; 8/2  =  the proposer received 80 yuan while the recipient received 20 yuan; and 9/1  =  the proposer received 90 yuan while the recipient received 10 yuan. Error bars represent standard errors.</p

Image_3_Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer.jpeg

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.</p

The SAX-3 Receptor Stimulates Axon Outgrowth and the Signal Sequence and Transmembrane Domain Are Critical for SAX-3 Membrane Localization in the PDE Neuron of <i>C. elegans</i>

<div><p>SAX-3, a receptor for Slit in <i>C. elegans</i>, is well characterized for its function in axonal development. However, the mechanism that regulates the membrane localization of SAX-3 and the role of SAX-3 in axon outgrowth are still elusive. Here we show that SAX-3::GFP caused ectopic axon outgrowth, which could be suppressed by the loss-of-function mutation in <i>unc-73</i> (a guanine nucleotide exchange factor for small GTPases) and <i>unc-115</i> (an actin binding protein), suggesting that they might act downstream of SAX-3 in axon outgrowth. We also examined genes related to axon development for their possible involvement in the subcellular localization of SAX-3. We found the <i>unc-51</i> mutants appeared to accumulate SAX-3::GFP in the neuronal cell body of the posterior deirid (PDE) neuron, indicating that UNC-51 might play a role in SAX-3 membrane localization. Furthermore, we demonstrate that the N-terminal signal sequence and the transmembrane domain are essential for the subcellular localization of SAX-3 in the PDE neurons.</p></div

First-stage merging of Cartesian product clusters based on bipartite clustering.

(a) Bipartite clustering yields super-clusters, each containing multiple clusters in every view. A super-cluster is marked by a given color, and the same super color is shown by different shapes in the two views. Any super-cluster of interaction effects will be treated as a merged product cluster in later analysis. (b) The off-diagonal white blocks correspond to unmatched product (UP) super-clusters. The diagonal colored blocks correspond to matched product (MP) super-clusters. (c) A simple case that the true clusters are the product clusters from two views. The information from the two views is fully complementary.</p

Image_2_Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer.jpeg

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.</p