Setting priorities for private land conservation in fire-prone landscapes: Are fire risk reduction and biodiversity conservation competing or compatible objectives?

Although wildfire plays an important role in maintaining biodiversity in many ecosystems, fire management to protect human assets is often carried out by different agencies than those tasked for conserving biodiversity. In fact, fire risk reduction and biodiversity conservation are often viewed as competing objectives. Here we explored the role of management through private land conservation and asked whether we could identify private land acquisition strategies that fulfill the mutual objectives of biodiversity conservation and fire risk reduction, or whether the maximization of one objective comes at a detriment to the other. Using a fixed budget and number of homes slated for development, we simulated 20 years of housing growth under alternative conservation selection strategies, and then projected the mean risk of fires destroying structures and the area and configuration of important habitat types in San Diego County, California, USA. We found clear differences in both fire risk projections and biodiversity impacts based on the way conservation lands are prioritized for selection, but these differences were split between two distinct groupings. If no conservation lands were purchased, or if purchases were prioritized based on cost or likelihood of development, both the projected fire risk and biodiversity impacts were much higher than if conservation lands were purchased in areas with high fire hazard or high species richness. Thus, conserving land focused on either of the two objectives resulted in nearly equivalent mutual benefits for both. These benefits not only resulted from preventing development in sensitive areas, but they were also due to the different housing patterns and arrangements that occurred as development was displaced from those areas. Although biodiversity conflicts may still arise using other fire management strategies, this study shows that mutual objectives can be attained through land-use planning in this region. These results likely generalize to any place where high species richness overlaps with hazardous wildland vegetation.

Assessing variation in personal health service delivery and workplace smoking ban enforcement by local health departments

Thesis (Ph.D.)--University of Washington, 2016-08

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.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1979-June 30, 1980

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1973-June 30, 1974

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1974-June 30, 1975

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1975-June 30, 1976

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1976-June 30, 1977

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1977-June 30, 1978

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1978-June 30, 1979

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1980-June 30, 1981

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1982-June 30, 1983

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT of LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1983-June 30, 1984

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

ANNUAL REPORT OF LANDER COLLEGE TO THE GOVERNOR AND GENERAL ASSEMBLY July 1, 1984-June 30, 1985

Each year Lander University produces an annual accountability report for the South Carolina General Assembly and the Budget and Control Board. Included is an executive summary, agency discussion and analysis, and strategic planning documents.

As conferências nacionais do esporte na configuração da política esportiva e de lazer no governo Lula (2003-2010)

Universidade Estadual de Campinas . Faculdade de Educação Física