Stresses Within the Actin Meshwork Control the Turnover of Fimbrin During Clathrin-Mediated Endocytosis

In this dissertation, I investigated the molecular mechanism of clathrin-mediated endocytosis (CME) in fission yeast with a sparse labeling strategy to track endocytic proteins at the single molecule level. CME is involved in a variety of biological processes, such as nutrient internalization and receptor recycling. CME is also a well-conserved biological process from yeast to mammalian cells. During clathrin-mediated endocytosis, about 60 different endocytic proteins are recruited to the endocytic site in a highly reproducible order. During the endocytic event, endocytic proteins assemble into endocytic structures, contributing to membrane invagination and endocytic vesicle formation. Based on the single molecule endocytic protein trajectories I obtained, I proved the significance of stresses within the actin meshwork. I also investigated the dwell-time distribution of single molecules of fimbrin (a protein that crosslinks actin filaments) and provide new mechanisms for fimbrin-actin binding mechanism. To study the single-molecule endocytic protein dynamics, I upgraded a two-color Total Internal Reflection Fluorescence (TIRF) microscopy system to study the single molecule dynamics of endocytic proteins. The two-color imaging system can be applied to probe relative motions between endocytic proteins in further studies

Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast

Video-based remote physiological measurement utilizes facial videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements have been shown to achieve good performance. However, the drawback of these methods is that they require facial videos with ground truth (GT) physiological signals, which are often costly and difficult to obtain. In this paper, we propose Contrast-Phys+, a method that can be trained in both unsupervised and weakly-supervised settings. We employ a 3DCNN model to generate multiple spatiotemporal rPPG signals and incorporate prior knowledge of rPPG into a contrastive loss function. We further incorporate the GT signals into contrastive learning to adapt to partial or misaligned labels. The contrastive loss encourages rPPG/GT signals from the same video to be grouped together, while pushing those from different videos apart. We evaluate our methods on five publicly available datasets that include both RGB and Near-infrared videos. Contrast-Phys+ outperforms the state-of-the-art supervised methods, even when using partially available or misaligned GT signals, or no labels at all. Additionally, we highlight the advantages of our methods in terms of computational efficiency, noise robustness, and generalization

Protecting Privacy When Releasing Search Results from Medical Document Data

Health information technologies have greatly facilitated sharing of personal health data for secondary use, which is critical to medical and health research. However, there is a growing concern about privacy due to data sharing and publishing. Medical and health data typically contain unstructured text documents, such as clinical narratives, pathology reports, and discharge summaries. This study concerns privacy-preserving extraction, summary, and release of information from medical documents. Existing studies on privacy-preserving data mining and publishing focus mostly on structured data. We propose a novel approach to enable privacy-preserving extract, summarize, query and report patients’ demographic, health and medical information from medical documents. The extracted data is represented in a semi-structured, set-valued data format, which can be stored in a health information system for query and analysis. The privacy preserving mechanism is based on the cutting-edge idea of differential privacy, which offers rigorous privacy guarantee

Parameterizing Topic Models for Empirical Research

Machine learning techniques have been increasingly employed in business research to discover or extract new simple features from large and unstructured data. These machine learned features (MLFs) are then used as independent or explanatory variables in the main econometric models for empirical research. Despite this growing trend, there has been little research regarding the impact of using MLFs on statistical inference for empirical research. In this paper, we undertake parameter estimation issues related to the use of topics/features extracted by Latent Dirichlet Allocation, a popular machine learning technique for text mining. We propose a novel method to extract features that result in the minimum-variance estimation of the regression model parameters. This enables a better use of unstructured text data for econometric modeling in empirical research. The effectiveness of the proposed method is validated with an experimental evaluation study on real-world text data

Feature Selection with Cost Constraint

When acquiring consumer data for marketing or new business initiatives, it is important to decide what features of potential customers should be acquired. We study feature selection and acquisition problem with cost constraint in the context of regression prediction. We formulate the feature selection and acquisition problem as a nonlinear programming problem that minimizes prediction error and number of features used in the model subject to a budget constraint. We derive the analytical properties of the solution for this problem and provide a computational procedure for solving the problem. The results of a preliminary experiment demonstrate the effectiveness of our approach

Counterfactual Explanations for Incorrect Predictions Made by AI Models

Advanced AI models are powerful in making accurate predictions for complex problems. However, these models often operate as black boxes. This lack of interpretability poses significant challenges, especially in high-stakes applications such as finance, healthcare, and criminal justice. Explainable AI seeks to address the challenges by developing methods that can provide meaningful explanations for humans to understand. When black box models are used for prediction, they inevitably produce errors. It is important to appropriately explain incorrect predictions. This problem, however, has not been addressed in the literature. In this study, we propose a novel method to provide explanations for misclassified cases made by black box models. The proposed method takes a counterfactual explanation approach. It builds a decision tree to find the best counterfactual examples for explanations. Incorrect predictions are rectified using a trust score measure. We validate the proposed method in an evaluation study using real-world data

Exploiting Topic Modeling and Neural Word Embeddings for Interpretable Retail Item Recommendations

Digital platforms have used recommender systems to recommend relevant products to their users based on their historical interactions. Recently, neural network-based recommender systems that generate embedding vectors have gained popularity in both research and practice and show improved performance over traditional methods. However, it is often difficult to explain why and how the recommended items are provided to specific users by these black box systems. In this study, we propose a novel user-centric approach to recommending retail items by exploiting the latent intent of the users from transaction histories. The latent theme is learned using a Latent Dirichlet Allocation topic modeling method. The proposed method can explain the intent of the focal user and other similar users. A preliminary evaluation study shows our method outperform the baseline methods in both the accuracy and the interpretability of the recommended items

To Cooperate or to Compete in the Gig Economy? Endorsements and the Performance of Freelancers in Online Labor Markets

Online labor markets connect buyers with gig workers across several task categories. A buyer evaluates workers’ quality based on their past performance encapsulated in ratings and reviews. However, these ratings can be inflated and arguably fail to assess workers’ true quality. Literature shows that worker characteristics like skills, experience, and heuristic cues can measure worker quality. In this study, we explore how gig workers’ personality traits in terms of Social Value Orientation (SVO) affects their performance on an online labor platform. We measure SVO from peer endorsements among workers on an online labor platform. Our results show that a cooperative SVO, where gig workers endorse each other, is more beneficial to the stakeholders of online labor platforms than competitive and individualistic SVO. We also explore how such cooperative behavior evolves by leveraging social network analysis methods to examine the endorsements generated among gig workers. We observe that reciprocity, homophily and worker popularity induces such cooperative behavior