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• 41. [Article] Portland : Recommended North Interstate Corridor plan

  107 pp. Bookmarks supplied by UO. Maps, tables, illus., appendices. Published June 19, 2008. Captured April 27, 2009.

  Citation × Citation Citation Abstract Copy Title: Portland : Recommended North Interstate Corridor plan Author: EnviroIssues (Firm), Emmons Architects, Portland Development Commission, Portland (Or.). Bureau of Planning, Portland (Or.). Bureau of Transportation, Portland (Or.) Year: 2009, 2008 107 pp. Bookmarks supplied by UO. Maps, tables, illus., appendices. Published June 19, 2008. Captured April 27, 2009. Title: Portland : Recommended North Interstate Corridor plan Author: EnviroIssues (Firm), Emmons Architects, Portland Development Commission, Portland (Or.). Bureau of Planning, Portland (Or.). Bureau of Transportation, Portland (Or.) Year: 2009, 2008 107 pp. Bookmarks supplied by UO. Maps, tables, illus., appendices. Published June 19, 2008. Captured April 27, 2009. Close

• 42. [Article] Portland : Natural resource inventory GIS model

  42 pp. Bookmarks supplied by UO. Charts, tables, figures. Published August 19, 2008. Captured April 27, 2009.

  Citation × Citation Citation Abstract Copy Title: Portland : Natural resource inventory GIS model Author: Portland (Or.). Bureau of Planning, Portland (Or.) Year: 2009, 2008 42 pp. Bookmarks supplied by UO. Charts, tables, figures. Published August 19, 2008. Captured April 27, 2009. Title: Portland : Natural resource inventory GIS model Author: Portland (Or.). Bureau of Planning, Portland (Or.) Year: 2009, 2008 42 pp. Bookmarks supplied by UO. Charts, tables, figures. Published August 19, 2008. Captured April 27, 2009. Close

• 43. [Article] Albany : Strategic plan

  22 pp. Revised February 23, 2005. Captured November 5, 2005.

  Citation × Citation Citation Abstract Copy Title: Albany : Strategic plan Author: Albany (Or.) Year: 2005 22 pp. Revised February 23, 2005. Captured November 5, 2005. Title: Albany : Strategic plan Author: Albany (Or.) Year: 2005 22 pp. Revised February 23, 2005. Captured November 5, 2005. Close

• 44. [Article] Blurring the Boundaries: Applying Gender Studies and Material Culture Studies to "Fine Art," Decorative Art, and Craft

  132 p. Examining committee chair: Dr. Douglas Blandy

  Citation × Citation Citation Abstract Copy Title: Blurring the Boundaries: Applying Gender Studies and Material Culture Studies to "Fine Art," Decorative Art, and Craft Author: Schaup, Lisa A. Year: 2005 132 p. Examining committee chair: Dr. Douglas Blandy Title: Blurring the Boundaries: Applying Gender Studies and Material Culture Studies to "Fine Art," Decorative Art, and Craft Author: Schaup, Lisa A. Year: 2005 132 p. Examining committee chair: Dr. Douglas Blandy Close

• 45. [Article] Superstar CEOs and Innovation

  I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial ...

  Citation × Citation Citation Abstract Copy Title: Superstar CEOs and Innovation Author: Park, Keun Jae Year: 2015 I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial entrenchment and reduce managers’ efforts to create valuable innovations because they will either pursue value-decreasing pet projects or simply enjoy the quiet life (agency hypothesis). Conversely, the managerial myopia theory predicts that these awards may increase managerial job security and allow the CEO to focus on long-term innovative projects rather than on boosting the stock price through current earnings. Finally, the overconfidence theory predicts that these awards increase managerial incentives to innovate if they make CEOs overconfident. In my first set of tests, I find evidence that is consistent with the managerial myopia and overconfidence theories: award-winning CEOs innovate more than otherwise similar CEOs who do not win awards, both in terms of the number of patents and the number of citations per patent during the period 1992–2002. After further examination, the evidence continues to be consistent with the managerial myopia theory: there is a clear increase in CEO power and job security after winning a prestigious award and no such increase in overconfidence. I also document that the positive effect of CEO awards on innovation is weaker for firms with high institutional ownership. Overall, the evidence suggests that winning a prestigious award relieves managerial myopia and brings positive long-term benefits for the firm. Title: Superstar CEOs and Innovation Author: Park, Keun Jae Year: 2015 I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial entrenchment and reduce managers’ efforts to create valuable innovations because they will either pursue value-decreasing pet projects or simply enjoy the quiet life (agency hypothesis). Conversely, the managerial myopia theory predicts that these awards may increase managerial job security and allow the CEO to focus on long-term innovative projects rather than on boosting the stock price through current earnings. Finally, the overconfidence theory predicts that these awards increase managerial incentives to innovate if they make CEOs overconfident. In my first set of tests, I find evidence that is consistent with the managerial myopia and overconfidence theories: award-winning CEOs innovate more than otherwise similar CEOs who do not win awards, both in terms of the number of patents and the number of citations per patent during the period 1992–2002. After further examination, the evidence continues to be consistent with the managerial myopia theory: there is a clear increase in CEO power and job security after winning a prestigious award and no such increase in overconfidence. I also document that the positive effect of CEO awards on innovation is weaker for firms with high institutional ownership. Overall, the evidence suggests that winning a prestigious award relieves managerial myopia and brings positive long-term benefits for the firm. Close

• 46. [Article] Korean Agricultural cooperatives as a model for development of the Lane County

  Examining committee: Michael Hibbard, chair, Edward Weeks

  Citation × Citation Citation Abstract Copy Title: Korean Agricultural cooperatives as a model for development of the Lane County Author: Lee, Kwangsoo Year: 2009, 2008 Examining committee: Michael Hibbard, chair, Edward Weeks Title: Korean Agricultural cooperatives as a model for development of the Lane County Author: Lee, Kwangsoo Year: 2009, 2008 Examining committee: Michael Hibbard, chair, Edward Weeks Close

• 47. [Article] Tower Fire ecosystem analysis forest vegetation report and forest vegetation BAER report

  72 pp. Tables, figures, maps, references, appendix, illus. "Pre-fire forest types were very diverse, largely in response to a relatively steep elevational gradient ranging from 3,000 feet near the North ...

  Citation × Citation Citation Abstract Copy Title: Tower Fire ecosystem analysis forest vegetation report and forest vegetation BAER report Author: North Fork John Day Ranger District (Or.), Powell, David C., Erickson, Vicky Year: 2008, 1997 72 pp. Tables, figures, maps, references, appendix, illus. "Pre-fire forest types were very diverse, largely in response to a relatively steep elevational gradient ranging from 3,000 feet near the North Fork of the John Day River at the southwestern corner of the fire perimeter to 6,850 feet at Tower Mountain lookout on the extreme eastern edge of the analysis area." Date of BAER report is September 1996. Captured June 18, 2008. Title: Tower Fire ecosystem analysis forest vegetation report and forest vegetation BAER report Author: North Fork John Day Ranger District (Or.), Powell, David C., Erickson, Vicky Year: 2008, 1997 72 pp. Tables, figures, maps, references, appendix, illus. "Pre-fire forest types were very diverse, largely in response to a relatively steep elevational gradient ranging from 3,000 feet near the North Fork of the John Day River at the southwestern corner of the fire perimeter to 6,850 feet at Tower Mountain lookout on the extreme eastern edge of the analysis area." Date of BAER report is September 1996. Captured June 18, 2008. Close

• 48. [Article] Essays on the Recreational Value of Avian Biodiversity

  This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity ...

  Citation × Citation Citation Abstract Copy Title: Essays on the Recreational Value of Avian Biodiversity Author: Kolstoe, Sonja Year: 2016 This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity to bird watchers. Panel data (i.e. longitudinal data) are highly desirable for preference estimation. Fortuitously, the diaries of birding excursions by eBird members provide a rich source of spatial data on trips taken, over time by the same individuals, to a variety of birding destinations. Origin and destination data can be combined with exogenous species prevalence information. These combined data sources permit estimation of utility-theoretic choice models that allow derivation of the marginal utilities of avian biodiversity measures as well as the marginal utility of net income (i.e. consumption of other goods and services). Ratios of these marginal utilities yield marginal willingness to pay (MWTP) estimates for numbers of bird species (or numbers of species of different types, in richer specifications). MWTP for levels of other attributes of birding destinations are also derived (e.g. ecosystem type, management regime, seasonal variations, a time trend).\\ The chapters are organized as follows: Chapter 2 is a stand-alone paper that demonstrates the feasibility of a travel-cost based random utility model with the eBird data. This chapter focuses on measuring the total number of bird species at each birding hotspot in Washington and Oregon states. This chapter does not differentiate among types of birders beyond using their recent birding activities in an analysis of habit formation or variety-seeking behavior. For this model, beyond past behavior, a representative consumer is postulated. Chapter 3 starts from the basic specifications identified in Chapter 2 and explores heterogeneous preferences among consumers as well as their preferences for species richness and for different categories of birds. This chapter explores whether different types of birds are relatively more attractive to different types of birders (for example, by gender or by age or by neighborhood characteristics and educational attainment). Chapter 4 is an extension of the work in Chapter 3 to explore how changing site attributes in the face of climate change effects birder welfare. This dissertation includes previously unpublished co-authored material. Title: Essays on the Recreational Value of Avian Biodiversity Author: Kolstoe, Sonja Year: 2016 This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity to bird watchers. Panel data (i.e. longitudinal data) are highly desirable for preference estimation. Fortuitously, the diaries of birding excursions by eBird members provide a rich source of spatial data on trips taken, over time by the same individuals, to a variety of birding destinations. Origin and destination data can be combined with exogenous species prevalence information. These combined data sources permit estimation of utility-theoretic choice models that allow derivation of the marginal utilities of avian biodiversity measures as well as the marginal utility of net income (i.e. consumption of other goods and services). Ratios of these marginal utilities yield marginal willingness to pay (MWTP) estimates for numbers of bird species (or numbers of species of different types, in richer specifications). MWTP for levels of other attributes of birding destinations are also derived (e.g. ecosystem type, management regime, seasonal variations, a time trend).\\ The chapters are organized as follows: Chapter 2 is a stand-alone paper that demonstrates the feasibility of a travel-cost based random utility model with the eBird data. This chapter focuses on measuring the total number of bird species at each birding hotspot in Washington and Oregon states. This chapter does not differentiate among types of birders beyond using their recent birding activities in an analysis of habit formation or variety-seeking behavior. For this model, beyond past behavior, a representative consumer is postulated. Chapter 3 starts from the basic specifications identified in Chapter 2 and explores heterogeneous preferences among consumers as well as their preferences for species richness and for different categories of birds. This chapter explores whether different types of birds are relatively more attractive to different types of birders (for example, by gender or by age or by neighborhood characteristics and educational attainment). Chapter 4 is an extension of the work in Chapter 3 to explore how changing site attributes in the face of climate change effects birder welfare. This dissertation includes previously unpublished co-authored material. Close

• 49. [Article] Effects of noise on teleseismic T* estimation and attenuation tomography of the Yellowstone region

  xv, 108 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.

  Citation × Citation Citation Abstract Copy Title: Effects of noise on teleseismic T* estimation and attenuation tomography of the Yellowstone region Author: Adams, David C., 1952- Year: 2010, 2009 xv, 108 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. Title: Effects of noise on teleseismic T* estimation and attenuation tomography of the Yellowstone region Author: Adams, David C., 1952- Year: 2010, 2009 xv, 108 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. Close

• 50. [Article] Robust Large Margin Approaches for Machine Learning in Adversarial Settings

  Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform ...

  Citation × Citation Citation Abstract Copy Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Close