Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mixed-Methods Exploration of Color Encodings for Two-Dimensional Scalar Fields

Over the last several decades, visualization research has generated an extensive body of literature on the subject of color encodings for two-dimensional (2D) scalar data. When working with experts from other domains, however, visualization designers commonly encounter real-world practices that conflict with visualization guidance, especially where color is concerned. This dissertation focuses on the problem of empirically interrogating conflicting domain guidance related to color utilization in 2D scalar visualizations through a mixed-methods research program. As a primary contribution, this work uses guidance conflicts observed during a formative, qualitative design study conducted with meteorologists to motivate subsequent quantitative studies that revise and refine longstanding visualization guidance regarding color usage. As a secondary contribution, we also briefly touch on the subjects of ecological validity, generalizability, and experimental control. The dissertation begins with two pieces of formative work. The first is our meteorological design study, which provides a grounded understanding of color usage in meteorological practice. The second is a pilot study aimed at improving ecological validity in assessing the impact of uncertainty visualizations in meteorological forecasting. This formative work subsequently acts as a lens for two additional studies investigating specific color guidance conflicts between visualization and other domains: a web-based study looking into the impact of discretized color on meteorological tasks, and a laboratory study assessing the potential role of hue-based banding in implicitly discretizing 2D rainbow color-mapped datasets. Lastly, we discuss the broader implications of our mixed-methods research program and outline potential directions for future work. To my parents, Phil and Cyndy Quinan.
Nonlinear behavior analysis and control of the atomic force microscope and circuit implementation

In this paper an analysis of the nonlinear dynamic behavior and control of an atomic force microscope system is described. Phase plane trajectories, spectrum analysis, bifurcation analysis, the Poincare cross-section, the maximal Lyapunov exponent, and other numerical analysis methods were used to observe and verify the dynamic characteristics and the differential equations for the system. The results showed that at specific excitation frequencies, the system will exhibit nonlinear behavior that may be cyclic, multi-cyclic or acyclic. A Psim circuit simulation using the same parameters showed the same nonlinear behavior, as did laboratory circuit implementations. The results also showed than an understanding and control of the dynamic characteristics would not be an easy task. However, they could be used as a basis for the suppression and control of nonlinear behavior and vibration in atomic force microscope systems. A proportional-derivative system was also used, with particle swarm optimization, to find the control parameters Kp and KD and a fuzzy controller was used to compare the results. The controller simulation and hardware implementation both effectively inhibited the nonlinear behavior and were most helpful for the control and enhancement of measurement accuracy.
Affective Polarity Recognition and Human Needs Categorization for Affective Events

Though many improvements have been achieved in Natural Language Processing, the task of enabling computers to understand events that we experience is still far from achieved. Events that are stereotypically desirable (positive) or undesirable (negative) for experiencers are called Affective Events. Acquiring knowledge about affective events holds promise for obtaining a deeper understanding of narrative texts such as stories and conversations. In this dissertation, I present research on automatically learning knowledge about affective events: (1) the affective polarity of events indicates how experiencers are affected by the events (e.g., “I broke my leg” is typically undesirable), and (2) the human needs associated with affective events provide a general explanation for why an event is positive or negative (e.g., “I broke my leg” is negative because it violates the human need to be physically healthy). My research designed two graph-based semi-supervised models to identify the affective polarity of events. The first Event Context Graph model identifies affective events by using discourse context and event collocation information. The second Semantic Consistency Graph model recognizes the affective polarity of events by optimizing the semantic consistency among events. Experimental results show that the Semantic Consistency Graph model outperformed previous methods and learned over 110,000 affective events with >90% precision for positive events and >80% precision for negative events. My research also studied a new task of categorizing affective events into human need categories: Physiological, Health, Leisure, Social, Financial, Cognition, and Freedom Needs, which were developed to explain why events are affective. To automatically recognize human needs of affective events, I designed a co-training model that learns from unlabeled data by simultaneously training classifiers based on an event expression view and an event context view in an iterative learning process. Experimental results demonstrate that the co-training model achieved good performance, and outperformed each individual supervised classifier and a self-training model. For my parents and my wife.
Towards Building Efficient Error Detectors for Improving System Resilience

Current scaling trends in transistor technology, in pursuit of larger component counts and improving power efficiency, are making the hardware increasingly less reliable. Due to extreme transistor miniaturization, it is becoming easier to flip a bit stored in memory elements built using these transistors. Given that soft errors can cause transient bit-flips in memory elements, caused due to alpha particles and cosmic rays striking those elements, soft errors have become one of the major impediments in system resilience as we move towards exascale computing. Soft errors escaping the hardware-layer may silently corrupt the runtime application data of a program, causing silent data corruption in the output. Also, given that soft errors are transient in nature, it is notoriously hard to trace back their origins. Therefore, techniques to enhance system resilience hinge on the availability of efficient error detectors that have high detection rates, low false positive rates, and lower computational overhead. It is equally important to have a flexible infrastructure capable of simulating realistic soft error models to promote an effective evaluation of newly developed error detectors. In this work, we present a set of techniques for efficiently detecting soft errors affecting control-flow, data, and structured address computations in an application. We evaluate the efficacy of the proposed techniques by evaluating them on a collection of benchmarks through fault-injection driven studies. As an important requirement, we also introduce two new LLVM-based fault injectors, KULFI and VULFI, which are geared towards scalar and vector architectures, respectively. Through this work, we aim to make contributions to the system resilience community by making our research tools (in the form of error detectors and fault injectors) publicly available.
Physics-based animation of large-scale splashing liquids, elastoplastic solids, and model-reduced flow

Physical simulation has become an essential tool in computer animation. As the use of visual effects increases, the need for simulating real-world materials increases. In this dissertation, we consider three problems in physics-based animation: large-scale splashing liquids, elastoplastic material simulation, and dimensionality reduction techniques for fluid simulation. Fluid simulation has been one of the greatest successes of physics-based animation, generating hundreds of research papers and a great many special effects over the last fifteen years. However, the animation of large-scale, splashing liquids remains challenging. We show that a novel combination of unilateral incompressibility, mass-full FLIP, and blurred boundaries is extremely well-suited to the animation of large-scale, violent, splashing liquids. Materials that incorporate both plastic and elastic deformations, also referred to as elastioplastic materials, are frequently encountered in everyday life. Methods for animating such common real-world materials are useful for effects practitioners and have been successfully employed in films. We describe a point-based method for animating elastoplastic materials. Our primary contribution is a simple method for computing the deformation gradient for each particle in the simulation. Given the deformation gradient, we can apply arbitrary constitutive models and compute the resulting elastic forces. Our method has two primary advantages: we do not store or compare to an initial rest configuration and we work directly with the deformation gradient. The first advantage avoids poor numerical conditioning and the second naturally leads to a multiplicative model of deformation appropriate for finite deformations. One of the most significant drawbacks of physics-based animation is that ever-higher fidelity leads to an explosion in the number of degrees of freedom. This problem leads us to the consideration of dimensionality reduction techniques. We present several enhancements to model-reduced fluid simulation that allow improved simulation bases and two-way solid-fluid coupling. Specifically, we present a basis enrichment scheme that allows us to combine data-driven or artistically derived bases with more general analytic bases derived from Laplacian Eigenfunctions. Additionally, we handle two-way solid-fluid coupling in a time-splitting fashion—we alternately timestep the fluid and rigid body simulators, while taking into account the effects of the fluid on the rigid bodies and vice versa. We employ the vortex panel method to handle solid-fluid coupling and use dynamic pressure to compute the effect of the fluid on rigid bodies. Taken together, these contributions have advanced the state-of-the art in physics-based animation and are practical enough to be used in production pipelines.
Introspective Polyvariance for Control-Flow Analyses

Static analysis aims to approximate dynamic program behaviors statically. This task brings one up against the fundamental limits of computability. As identifying any nontrivial program property is undecidable in the general case, we must compromise by allowing for imprecision in order to ensure computability—with yet further compromises permitting us to place strict upper bounds on analysis complexity. Deciding how this compromise should be struck is the central challenge of static analysis. This dissertation extends the methodology of abstracting abstract machines (AAM), a systematic approach to the abstract interpretation of abstract-machine semantics, to encompass a well-defined notion of polyvariance. The polyvariance of a static analysis, broadly construed, is the degree to which variables or syntactic program points are differentiated into a multiplicity of static approximations of their dynamic behavior. Increasing the polyvariance of an analysis adjusts its compromise in favor of precision, although particular flavors of polyvariance may vary widely in their efficiency (and efficacy) for particular targets. We propose that, in an AAM setting, allocation characterizes polyvariance; that is, the polyvariance of an analysis can be entirely determined by the allocation of abstract addresses. Some forms of polyvariance cannot be implemented without necessary dynamic information; however, if the instrumentation of an analysis is left open as a parameter, we can show both that every form of polyvariance has an abstract allocator and that every tuning of abstract allocation represents a sound form of polyvariance. Polyvariance as a design space is not all-encompassing; however, we observe it to be surprisingly broad, and include techniques from type systems, abstract interpretations, and other methods, along with fundamental variations on the structure of analyses. Our investigation culminates in a novel form of introspective polyvariance that yields a guarantee of perfect precision in its modeling of the call stack and incurs no asymptotic complexity overhead. We show how this form of polyvariance may be implemented in AAMstyle analyses with only a trivial change to existing code and present a machine-verified proof of its bisimulation with an incomputable model of perfect stack precision. Adams (2011); Anderson and Giannini (2004); Clarke et al. (1992, 2000); Cousot and Cousot (1992); Gilray and Might (2013a,b); Gilray et al. (2016); Hudak (1986); Jagannathan et al. (1998); Sergey et al. (2013); Van Horn (2009)
Modeling, Rendering, and Simulating Knits

Knitting is one of the most popular techniques to manufacture clothing. In this dissertation we present a framework for modeling, fabricating, and simulating knit structures. We first introduce a fully automatic pipeline to convert arbitrary 3D shapes into a stitch mesh, which provides a mesh-based representation of the yarn-level knit structure. We also extend the concept of stitch mesh modeling with a list of novel shaping techniques to allow modeling more complex structures. We then introduce a scheduling algorithm to convert the structure into step-by-step hand knitting instructions for knitters. We further embed low-level machine knitting operations to each face and allow users to edit the stitch mesh in a way that preserves the machine knittability. Moreover, we present a real-time rendering method to display knitwear containing more than a hundred million individual fiber curves at real-time frame rates with shadows and ambient occlusion on the GPU. We also present a GPU parallelization method to address the computational challenges of using the Material Point Method to simulate knits. For my wife, Zhen Li.
Leveraging Mixed Process Three-Dimensional Die Stacking Technology for Cache Hierarchies and Reliability

Aggressive technology scaling has led to smaller transistor sizes and steadily improved transistor performance. However, on-chip global wires have not scaled at the same rate causing interconnects to emerge as one of the key bottlenecks. This has an especially pronounced effect on future cache hierarchy design where interconnects exchange data between numerous cores and caches. In addition to rendering processors communication-bound, technology scaling has also decreased processor reliability. Shrinking transistor sizes and lower supply voltages have increased the vulnerability of computer systems towards soft errors (also known as transient faults). Emerging three-dimensional (3D) integration technology enables vertical stacking of silicon dies, yielding high density and low latency interconnects. This results in increased processor performance as well as reduced power consumption because of shorter on-chip wires. Another salient advantage of 3D stacking is the ability to stack heterogeneous dies that have been fabricated in disparate process technologies. This dissertation explores novel applications for 3D die stacking at the micro-architecture level with an emphasis on this mixed-process integration. We find that reliability and cache-hierarchy design can benefit from 3D die stacking and we explore the various solutions that can help mitigate these challenges in future processor design. In the first part of this thesis, we propose a reliable processor architecture that uses an in-order checker processor optimized to reduce the power/performance overheads of redundancy. We then leverage the “snap-on” functionality provided by 3D integration and propose implementing this redundant checker processor on a second die. This allows manufacturers to easily create a family of reliable processors without significantly impacting the cost or performance for customers that care less about reliability. Most importantly, we advocate the use of an older process technology for implementing the checker die that is more error-resilient, showcasing the advantage of mixed-process 3D integration. In the second part of this thesis, we propose the design of a reconfigurable heterogeneous SRAM-DRAM cache. By stacking a DRAM die on top of a SRAM die, the SRAM cache can grow vertically by enabling the DRAM bank above based on the application’s working set requirement. This artifact would not have been possible without exploiting mixed-process 3D integration. Such a heterogenous cache design allows us to reap the benefits of DRAM’s density and SRAM’s superior power and delay characteristics.
Improving Information Extraction from Clinical Notes with Multiple Domain Models and Clustering-Based Instance Selection

Extracting information from electronic health records is a crucial task to acquire empirical evidence relevant to patient care. In this dissertation research, I aim to improve two clinical information extraction tasks: medical concept extraction and relation classification. First, my research investigates methods for creating effective concept extractors for specialty clinical notes. I present three new specialty area datasets consisting of Cardiology, Neurology, and Orthopedics clinical notes manually annotated with medical concepts. I analyze the medical concepts in each dataset and compare them with the widely used i2b2 2010 corpus. Then, I create several types of concept extraction models and examine the effects of training supervised learners with specialty area data versus i2b2 data. I find substantial differences in performance across the datasets, and obtain the best results for all three specialty areas by training with both i2b2 and specialty data. I also explore strategies to improve concept extraction on specialty notes with ensemble methods. I compare two types of ensemble methods (voting and stacking) and a domain adaptation model, and show that a stacked learning ensemble of classifiers trained with i2b2 and specialty data yields the best performance. Next, my research aims to improve relation classification using weakly supervised learning. Due to limited labeled data and extremely unbalanced class distributions, medical relation classification systems struggle to achieve good performance on less common relation types, which capture valuable information that is important to identify. I present two clusteringbased instance selection methods that acquire a diverse and balanced set of additional training instances from unlabeled data. The first method selects one representative instance from each cluster containing only unlabeled data. The second method selects a counterpart for each training instance using clusters containing both labeled and unlabeled data. These new instance selection methods for weakly supervised learning achieve substantial performance gains for the minority relation classes compared to supervised learning, while yielding comparable performance on the majority relation classes. For my wife, Jaeshin.
Particle Transport–Based Triboelectric Nanogenerator for Self‐Powered Mass‐Flow Detection and Explosion Early Warning

In the process of particle transport in pipes, particles get charged, and the discharge process can cause fire and explosion, which causes great harm to the safe production. In this paper, a particle transport triboelectric nanogenerator (PT‐TENG) based on particle transport in pipes is developed. The PT‐TENG can be utilized to monitor the mass‐flow of the particles and make explosion early warning without supplying the power. Under the condition of particles passing one by one, the moving speed of the particles can be obtained. When the particles transport continuously, the PT‐TENG can be used as a self‐powered detector to detect the mass flow of the particles in time. The mass flow detector can be installed both inside and outside the transport pipe. The PT‐TENG can be used as a power source to charge capacitors and light up light‐emitting diodes (LEDs). Moreover, a self‐powered explosion early warning device has been developed based on the PT‐TENG, which will have widespread application in the safety production.
Interparty Visibility in a Cloud Computing Platform

In recent years, it has become commonplace for organizations to deploy their services in a cloud environment. However, as this new computation ecosystem matured, the unique challenges of it also started to emerge. Due to the inherent multiparty, multilayer environment of public clouds, both cloud providers and cloud tenants have limited visibility into the whole system. This limited visibility complicates problems involving multiple parties in a cloud, such as accounting resource usage, resource demand estimation, and security monitoring for cloud providers as well as troubleshooting, virtual resource performance estimation/optimization, and automating service deployment for cloud tenants. In existing cloud platforms, solutions for these problems often require time-consuming and expensive interaction between different parties. Given that resource optimization and cost saving are becoming top priorities not only for cloud providers but also for tenants, resolving this lack of visibility problem is becoming a critical challenge of cloud computing platforms. However, extending visibility is not a simple task since, for security and privacy, cloud platforms were originally designed to reduce interparty visibility. In addition, for cloud providers, it could be prohibitively expensive to support such extended visibility for every single tenant. In this dissertation, we study different aspects related to enhancing visibility in a multitenant cloud environment. In the first part of the dissertation, we present a framework to offer cloud tenants better visibility into the cloud infrastructure for better understanding and troubleshooting, where both cloud providers and tenants may save cloud management costs. In the second part, we focus on the cloud provider’s visibility into the tenants’ network traffic, and enhance it by adopting a widely used traffic matrix estimation model for ISP networks and addressing two key challenges to apply the model to datacenter networks: the sparse traffic matrix problem and the interior traffic sink/source problem. In the last part, we study unintended ways to enhance visibility of tenants in the cloud infrastructure. We focus on cloud tenants’ visibility into the states of the virtual firewalls, which are typically supposed to be unknown to cloud tenants, devise a novel method to monitor the states of firewalls, and exploit them as a side channel into the host machine’s infrastructure level activities.