Oregon State University

About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Gene. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.

Negative refraction in semiconductor metamaterials.

Abstract: An optical metamaterial is a composite in which subwavelength features, rather than the constituent materials, control the macroscopic electromagnetic properties of the material. Recently, properly designed metamaterials have garnered much interest because of their unusual interaction with electromagnetic waves. Whereas nature seems to have limits on the type of materials that exist, newly invented metamaterials are not bound by such constraints. These newly accessible electromagnetic properties make these materials an excellent platform for demonstrating unusual optical phenomena and unique applications such as subwavelength imaging and planar lens design. 'Negative-index materials', as first proposed, required the permittivity, epsilon, and permeability, mu, to be simultaneously less than zero, but such materials face limitations. Here, we demonstrate a comparatively low-loss, three-dimensional, all-semiconductor metamaterial that exhibits negative refraction for all incidence angles in the long-wave infrared region and requires only an anisotropic dielectric function with a single resonance. Using reflection and transmission measurements and a comprehensive model of the material, we demonstrate that our material exhibits negative refraction. This is furthermore confirmed through a straightforward beam optics experiment. This work will influence future metamaterial designs and their incorporation into optical semiconductor devices.

Power to the People: The Role of Humans in Interactive Machine Learning

Abstract: Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.

Prioritizing test cases for regression testing

Abstract: Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection, is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.

A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes.

Abstract: The authors examined a new assessment of behavioral regulation and contributions to achievement and teacher-rated classroom functioning in a sample (N = 343) of kindergarteners from 2 geographical sites in the United States. Behavioral regulation was measured with the Head-Toes-Knees-Shoulders (HTKS) task, a structured observation requiring children to perform the opposite of a dominant response to 4 different oral commands. Results revealed considerable variability in HTKS scores. Evidence for construct validity was found in positive correlations with parent ratings of attentional focusing and inhibitory control and teacher ratings of classroom behavioral regulation. Hierarchical linear modeling indicated that higher levels of behavioral regulation in the fall predicted stronger levels of achievement in the spring and better teacher-rated classroom self-regulation (all ps < .01) but not interpersonal skills. Evidence for domain specificity emerged, in which gains in behavioral regulation predicted gains in mathematics but not in language and literacy over the kindergarten year (p < .01) after site, child gender, and other background variables were controlled. Discussion focuses on the importance of behavioral regulation for successful adjustment to the demands of kindergarten.