AN EXAMINATION OF HYDROXYL RADICAL: OUR CURRENT UNDERSTANDING OF THE OXIDATIVE CAPACITY OF THE TROPOSPHERE THROUGH EMPIRICAL, BOX, AND GLOBAL MODELING APPROACHES

Hydroxyl radical (OH) is the primary oxidant in the troposphere, initiating the removal of numerous atmospheric species including greenhouse gases, pollutants that are detrimental to human health, and ozone-depleting substances. Because of the complexity of OH chemistry, models vary widely in their OH chemistry schemes and resulting methane (CH4) lifetimes. The current state of knowledge concerning global OH abundances is often contradictory. This body of work encompasses three projects that investigate tropospheric OH from a modeling perspective, with the goal of improving the tropospheric community’s knowledge of the atmospheric lifetime of CH4. First, measurements taken during the airborne CONvective TRansport of Active Species in the Tropics (CONTRAST) field campaign are used to evaluate OH in global models. A box model constrained to measured variables is utilized to infer concentrations of OH along the flight track. Results are used to evaluate global model performance, suggest against the existence of a proposed “OH Hole” in the tropical Western Pacific, and investigate implications of high O3/low H2O filaments on chemical transport to the stratosphere. While methyl chloroform-based estimates of global mean OH suggest that models are overestimating OH, we report evidence that these models are actually underestimating OH in the tropical Western Pacific. The second project examines OH within global models to diagnose differences in CH4 lifetime. I developed an approach to quantify the roles of OH precursor field differences (O3, H2O, CO, NOx, etc.) using a neural network method. This technique enables us to approximate the change in CH4 lifetime resulting from variations in individual precursor fields. The dominant factors driving CH4 lifetime differences between models are O3, CO, and J(O3-O1D). My third project evaluates the effect of climate change on global fields of OH using an empirical model. Observations of H2O and O3 from satellite instruments are combined with a simulation of tropical expansion to derive changes in global mean OH over the past 25 years. We find that increasing H2O and increasing width of the tropics tend to increase global mean OH, countering the increasing CH4 sink and resulting in well-buffered global tropospheric OH concentrations.

Figer: A Quadraphonic Electroacoustic Composition

Figer (to congeal, to solidify) is a quadraphonic electroacoustic composition. It was completed in the fall of 2003. Several software programs were used in creating and assembling the piece (C-Sound, Grain Mill, AL/Erwin (grain generator), Sound Forge and Acid Music). The sounds used in the piece are of two general types: synthesized and sampled, both of which were subjected to various processing techniques. The most important of these techniques, and one that formally defines large portions of the piece, is granular synthesis. Form The notion of time perception is of great importance in this piece. Figer addresses this question in several ways. In one sense, the form of Figer is simple. There are three layers of activity (see diagram). Layer 1 is continuous and non-sectional and supplies a backdrop (not necessarily a background) for the other two. The second and third layers overlap and interrupt one another. Each consists of two blocks of sound. The layers, and blocks within, relate to each other in various ways. Layer 1 is formally continuous. Layer 2 consists of well-defined columns of sound that evolve from soft and mild to loud and abrasive. The layer is, in reality, a whole that is simply cut into two parts (block 1 and block 2). In contrast, the blocks of layer 3 do not constitute a whole. Each is a complete unit and has its own self-contained evolutionary path. Those paths, however, do cross the paths of other units (layers, blocks), influencing them and absorbing some of their essence. At the heart of Figer lies a constant process of presenting materials or ideas and immediately, or, at times, simultaneously, commenting, reflecting on, or reinterpreting that material. All of the layers of this piece deal, both at local and global levels, with the problem of time and its perception relative to the materials, sonic or otherwise, that occupy it and the manner in which they unfold and relate to each other.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.