Weaving Entities into Relations: From Page Retrieval to Relation Mining on the Web

With its sheer amount of information, the Web is clearly an important frontier for data mining. While Web mining must start with content on the Web, there is no effective ``search-based'' mechanism to help sifting through the information on the Web. Our goal is to provide a such online search-based facility for supporting query primitives, upon which Web mining applications can be built. As a first step, this paper aims at entity-relation discovery, or E-R discovery, as a useful function-- to weave scattered entities on the Web into coherent relations. To begin with, as our proposal, we formalize the concept of E-R discovery. Further, to realize E-R discovery, as our main thesis, we abstract tuple ranking-- the essential challenge of E-R discovery-- as pattern-based cooccurrence analysis. Finally, as our key insight, we observe that such relation mining shares the same core functions as traditional page-retrieval systems, which enables us to build the new E-R discovery upon today's search engines, almost for free. We report our system prototype and testbed, WISDM-ER, with real Web corpus. Our case studies have demonstrated a high promise, achieving 83%-91% accuracy for real benchmark queries-- and thus the real possibilities of enabling ad-hoc Web mining tasks with online E-R discovery

Multi-gigabit CMOS analog-to-digital converter and mixed-signal demodulator for low-power millimeter-wave communication systems

The objective of the research is to develop high-speed ADCs and mixed-signal demodulator for multi-gigabit communication systems using millimeter-wave frequency bands in standard CMOS technology. With rapid advancements in semiconductor technologies, mobile communication devices have become more versatile, portable, and inexpensive over the last few decades. However, plagued by the short lifetime of batteries, low power consumption has become an extremely important specification in developing mobile communication devices. The ever-expanding demand of consumers to access and share information ubiquitously at faster speeds requires higher throughputs, increased signal-processing functionalities at lower power and lower costs. In today’s technology, high-speed signal processing and data converters are incorporated in almost all modern multi-gigabit communication systems. They are key enabling technologies for scalable digital design and implementation of baseband signal processors. Ultimately, the merits of a high performance mixed-signal receiver, such as data rate, sensitivity, signal dynamic range, bit-error rate, and power consumption, are directly related to the quality of the embedded ADCs. Therefore, this dissertation focuses on the analysis and design of high-speed ADCs and a novel broadband mixed-signal demodulator with a fully-integrated DSP composed of low-cost CMOS circuitry. The proposed system features a novel dual-mode solution to demodulate multi-gigabit BPSK and ASK signals. This approach reduces the resolution requirement of high-speed ADCs, while dramatically reducing its power consumption for multi-gigabit wireless communication systems.PhDGee-Kung Chang - Committee Chair; Chang-Ho Lee - Committee Member; Geoffrey Ye Li - Committee Member; Paul A. Kohl - Committee Member; Shyh-Chiang Shen - Committee Membe

Informing Makerspace Outcomes Through a Linguistic Analysis of Written and Video-Recorded Project Assessments

A growing body of research focuses on what outcomes to assess in makerspaces, and appropriate formats for capturing those outcomes (e.g. reflections, surveys, and port-folios). Linguistic analysis as a data mining technique holds promise for revealing different dimensions of learning exhibited by students in makerspaces. In this study, student reflections on makerspace projects were gathered in 2 formats over 2 years: private written assessments captured in the 3D GameLab gamification platform, and semi-public video-recorded assessments posted in the more social FlipGrid platform. Transcripts of student assessments were analyzed using Linguistic Inquiry Word Count (LIWC) to generate 4 summary variables thought to inform makerspace outcomes of interest (i.e. analytical thinking, authenticity, clout, and emotional tone). Comparative findings indicate that written assessments may elicit more analytical thinking about maker projects compared with less analytical conversation in videos, while video assessments may elicit somewhat higher clout scores as evidence of social scaffolding along with a much more positive emotional tone. Recommendations are provided for layering assessment approaches to maximize the potential benefits of each format, including reflective writing for social spaces, in social groups, and about design processes and procedures

Healing LER using directed self assembly: treatment of EUVL resists with aqueous solutions of block copolymers

Overcoming the resolution-LER-sensitivity trade-off is a key challenge for the development of novel resists and processes that are able to achieve the ITRS targets for future lithography nodes. Here, we describe a process that treats lithographic patterns with aqueous solutions of block copolymers to facilitate a reduction in LER. A detailed understanding of parameters affecting adhesion and smoothing is gained by first investigating the behavior of the polymers on planar smooth and rough surfaces. Once healing was established in these model systems the methodology is tested on lithographically printed features where significant healing is observed, making this a promising technology for LER remediation

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g., mass or elasticity), and that those properties affect the outcome of physical events. While there has been great progress in physical and video prediction models in recent years, benchmarks to test their performance typically do not require an understanding that objects have individual physical properties, or at best test only those properties that are directly observable (e.g., size or color). This work proposes a novel dataset and benchmark, termed Physion++, that rigorously evaluates visual physical prediction in artificial systems under circumstances where those predictions rely on accurate estimates of the latent physical properties of objects in the scene. Specifically, we test scenarios where accurate prediction relies on estimates of properties such as mass, friction, elasticity, and deformability, and where the values of those properties can only be inferred by observing how objects move and interact with other objects or fluids. We evaluate the performance of a number of state-of-the-art prediction models that span a variety of levels of learning vs. built-in knowledge, and compare that performance to a set of human predictions. We find that models that have been trained using standard regimes and datasets do not spontaneously learn to make inferences about latent properties, but also that models that encode objectness and physical states tend to make better predictions. However, there is still a huge gap between all models and human performance, and all models' predictions correlate poorly with those made by humans, suggesting that no state-of-the-art model is learning to make physical predictions in a human-like way. Project page: https://dingmyu.github.io/physion_v2

Racial discrimination and anti-discrimination : the impact of the COVID-19 pandemic on Chinese restaurants in North America

The COVID-19 pandemic has led to an increase in cases of racial discrimination against Asians, especially Chinese people. Despite an emerging stream of studies investigating various aspects of the COVID-19 pandemic, research on the behavioral consequences of racial discrimination during the pandemic remains scarce. In this work, we examined how racial discrimination stemming from the COVID-19 pandemic and subsequent anti-discrimination were manifested on online platforms. By conducting difference-in-differences analyses on two large-scale panel datasets from Yelp.com and SafeGraph, we explored the impact of COVID-19 on Chinese restaurants, relative to non-Chinese restaurants, at different phases of the COVID-19 pandemic. We found that the COVID-19 pandemic led to an immediate increase in racial discrimination, which was reflected in a significant drop in the customer patronage frequency of Chinese restaurants as compared to that of non-Chinese restaurants. Furthermore, analyses using multiple behavioral indicators generated by text mining and machine learning techniques consistently suggested that increased discrimination triggered anti-discrimination actions of customers on online platforms after the COVID-19 outbreak. This study contributes to the literature on racial discrimination by investigating a subtle but more factual form of racial discrimination evidenced by the customer patronage of Chinese restaurants, as well as user-generated content, by demonstrating that consumers can fight discrimination on online platforms