Toward Geo-social Information Systems: Methods and Algorithms

The widespread adoption of GPS-enabled tagging of social media content via smartphones and social media services (e.g., Facebook, Twitter, Foursquare) uncovers a new window into the spatio-temporal activities of hundreds of millions of people. These \footprints" open new possibilities for understanding how people can organize for societal impact and lay the foundation for new crowd-powered geo-social systems. However, there are key challenges to delivering on this promise: the slow adoption of location sharing, the inherent bias in the users that do share location, imbalanced location granularity, respecting location privacy, among many others. With these challenges in mind, this dissertation aims to develop the framework, algorithms, and methods for a new class of geo-social information systems. The dissertation is structured in two main parts: the rst focuses on understanding the capacity of existing footprints; the second demonstrates the potential of new geo-social information systems through two concrete prototypes. First, we investigate the capacity of using these geo-social footprints to build new geo-social information systems. (i): we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. With the help of a classi cation component for automatically identifying words in tweets with a strong local geo-scope, the location estimator places 51% of Twitter users within 100 miles of their actual location. (ii): we investigate a set of 22 million check-ins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. Concretely, we observe that users follow simple reproducible mobility patterns. (iii): we compare a set of 35 million publicly shared check-ins with a set of over 400 million private query logs recorded by a commercial hotel search engine. Although generated by users with fundamentally di erent intentions, we nd common conclusions may be drawn from both data sources, indicating the viability of publicly shared location information to complement (and replace, in some cases), privately held location information. Second, we introduce a couple of prototypes of new geo-social information systems that utilize the collective intelligence from the emerging geo-social footprints. Concretely, we propose an activity-driven search system, and a local expert nding system that both take advantage of the collective intelligence. Speci cally, we study location-based activity patterns revealed through location sharing services and nd that these activity patterns can identify semantically related locations, and help with both unsupervised location clustering, and supervised location categorization with a high con dence. Based on these results, we show how activity-driven semantic organization of locations may be naturally incorporated into location-based web search. In addition, we propose a local expert nding system that identi es top local experts for a topic in a location. Concretely, the system utilizes semantic labels that people label each other, people's locations in current location-based social networks, and can identify top local experts with a high precision. We also observe that the proposed local authority metrics that utilize collective intelligence from expert candidates' core audience (list labelers), signi cantly improve the performance of local experts nding than the more intuitive way that only considers candidates' locations. ii

SiGe-On-Insulator (SGOI) Technology and MOSFET Fabrication

In this work, we have developed two different fabrication processes for relaxed Si₁₋xGex-on-insulator (SGOI) substrates: (1) SGOI fabrication by etch-back approach, and (2) by "smart-cut" approach utilizing hydrogen implantation. Etch-back approach produces SGOI substrate with less defects in SiGe film, but the SiGe film uniformity is inferior. "Smart-cut" approach has better control on the SiGe film thickness and uniformity, and is applicable to wider Ge content range of the SiGe film. We have also fabricated strained-Si n-MOSFET’s on SGOI substrates, in which epitaxial regrowth was used to produce the surface strained Si layer on relaxed SGOI substrate, followed by large-area n-MOSFET’s fabrication on this structure. The measured electron mobility shows significant enhancement (1.7 times) over both the universal mobility and that of co-processed bulk-Si MOSFET’s. This SGOI process has a low thermal budget and thus is compatible with a wide range of Ge contents in Si₁₋xGex layer.Singapore-MIT Alliance (SMA

Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework

Development of a Simple Multiplex Electrochemiluminescence (ECL) Assay for Screening Pre-Type 1 Diabetes and Multiple Relevant Autoimmune Diseases

The presence of islet autoantibodies (iAbs) is currently the most reliable biomarker for type 1 diabetes (T1D). The current “gold” standard radio-binding assays that measure four major iAbs to insulin, IAA, GAD65, IA-2A and ZnT8, are laborious and do not fit for large-scale screenings. Around 40% of patients with T1D develop other autoimmune diseases like celiac disease, autoimmune thyroid disease, and so on. It is highly recommended to screen these closely related autoimmune diseases during T1D screening; however, there is no method available. Recently, on the platform of extensively validated high-sensitive and high-specific electrochemiluminescence (ECL) assay, we developed a multiplex ECL assay to combine up to 10 autoantibody assays into one single well with 5 μl of blood sample. It not only allows us to combine multiple iAbs into one but also makes it possible to simultaneously screen T1D and other multiple autoimmune diseases, which in turn facilitates large-scale screenings in the general population