Browsing by Subject "dynamic"
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Item Open Access Distortions in Perceived Direction of Motion Predicted by Population Response in Visual Cortex(2009) Wu, WeiThe visual system is thought to represent the trajectory of moving objects in the activity of large populations of cortical neurons that respond preferentially to the direction of stimulus motion. Here I employed in vivo voltage sensitive dye (VSD) imaging to explore how abrupt changes in the trajectory of a moving stimulus impact the population coding of motion direction in ferret primary visual cortex (V1). For motion in a constant direction, the peak of the cortical population response reliably signaled the stimulus trajectory; but for abrupt changes in motion direction, the peak of the population response departed significantly from the stimulus trajectory in a fashion that depended on the size of the direction deviation. For small direction deviation angles, the peak of the active population shifted from values consistent with the initial direction of motion to those consistent with the final direction of motion by progressing smoothly through intermediate directions not present in the stimulus. In contrast, for large direction deviation angles, peak values consistent with the initial motion direction were followed by: a small deviation away from the final motion direction, a rapid 180° jump, and a gradual shift to the final direction. These departures of the population response from the actual trajectory of the stimulus predict specific misperceptions of motion direction that were confirmed by human psychophysical experiments. I conclude that cortical dynamics and population coding mechanisms combine to place constraints on the accuracy with which abrupt changes in direction of motion can be represented by cortical circuits.
Item Open Access Dynamic Models of Human Capital Investment(2015) Ashworth, JaredMy dissertation examines human capital investments and their role in individual's labor market outcomes. Chapter 2 analyzes how public school teachers decide to make human capital investments and the effects that these decisions have on their future labor market outcomes. In particular, I look at the decisions of employed teachers to obtain an advanced degree. Teachers' education and career decisions are modeled via a dynamic framework in the presence of teacher-specific unobserved heterogeneity. I find that teachers' decisions to obtain master's degrees are motivated by more than just an increase in salary. In particular, I observe teachers with master's degrees receiving a better draw on job characteristics, as measured by school quality, and that teachers are willing to pay between $1,500 and $20,000 to to move up one quartile in school quality. I also find that teachers value having broad access to online degree programs more than they dislike tuition costs. Counterfactual simulations by unobserved ability are consistent with a story that high-type teachers value both the salary increase and a better draw in career prospects, whereas low-type teachers are mostly interested in the salary increase.
Chapter 3 investigates the evolution over the last two decades in the wage returns to schooling and early work experience. Using data from the 1979 and 1997 panels of the National Longitudinal Survey of Youth, we isolate changes in skill prices from changes in composition by estimating a dynamic model of schooling and work decisions. Importantly, this allows us to account for the endogenous nature of the changes in educational and accumulated work experience over this time period. We find an increase over this period in the returns to working in high school, but a decrease in the returns to working while in college. We also find an increase in the incidence of working in college, but that any detrimental impact of in-college work experience is offset by changes in other observable characteristics. Overall, our decomposition of the evolution in skill premia suggests that both price and composition effects play an important role. The role of unobserved ability is also important.
Item Open Access Poleward Transport of TPX2 in the Mammalian Mitotic Spindle Requires Dynein, Eg5, and Microtubule Flux(2010) Ma, Nan; Tulu, US; Ferenz, Nick P; Fagerstrom, Carey; Wilde, Andrew; Wadsworth, PatriciaTPX2 is a Ran-regulated spindle assembly factor that is required for kinetochore fiber formation and activation of the mitotic kinase Aurora A. TPX2 is enriched near spindle poles and is required near kinetochores, suggesting that it undergoes dynamic relocalization throughout mitosis. Using photoactivation, we measured the movement of PA-GFP-TPX2 in the mitotic spindle. TPX2 moves poleward in the half-spindle and is static in the interzone and near spindle poles. Poleward transport of TPX2 is sensitive to inhibition of dynein or Eg5 and to suppression of microtubule flux with nocodazole or antibodies to Kif2a. Poleward transport requires the C terminus of TPX2, a domain that interacts with Eg5. Overexpression of TPX2 lacking this domain induced excessive microtubule formation near kinetochores, defects in spindle assembly and blocked mitotic progression. Our data support a model in which poleward transport of TPX2 down-regulates its microtubule nucleating activity near kinetochores and links microtubules generated at kinetochores to dynein for incorporation into the spindle.Item Open Access Research and Development Competition in the Chemicals Industry(2008-04-24) Finger, Stephen RThis dissertation is composed of two related chapters dealing with research and development. I evaluate the effects of the Research and Experimentation Tax Credit on the Chemicals Industry and then examine the determinants of research joint ventures and technological licenses. The first chapter evaluates the equilibrium effects of the Research and Experimentation Tax Credit, taking into consideration firm interactions. The tax credit was put into place to counteract the underinvestment in private R&D caused by firms not internalizing the benefits of technological spillovers from their research. However, this rationale ignored the impact of product market competition. I propose and estimate a structural dynamic oligopoly model of competition in intellectual assets to capture the impact of interactions between firms in the industry. I estimate the dynamic parameters of the model using methods from Bajari, Benkard, and Levin (2007). I build upon previous estimators by incorporating unobserved firm-level heterogeneity using techniques from Arcidiacono and Miller (2007). I use publicly available panel data on firms' R&D expenditures and their patenting activities to measure innovations. In the data, I observe firms that persistently invest more in research and generate more innovations than other firms that are observationally similar. I model this heterogeneity as an unobserved state that raises a firm's research productivity. In my analysis, I find that increased investment in R&D by more advanced firms due to the subsidy, was largely offset by decreases by smaller firms because of the substitutability of knowledge in product market. This greatly reduced the effectiveness of the policy to spur innovation and limited its impact on social welfare. The second chapter examines the cooperation between innovating firms either through technology licensing or research joint ventures. Both of these types of arrangements help to facilitate the dissemination of productive knowledge permitting the increased application of beneficial innovations. As opposed to the first chapter which considers how untargeted, and unintended transfers of knowledge in the form of spillovers, effected an industry, this chapter examines directed transfers of knowledge. I analyze a cross industry data set of joint ventures and technology licensing deals to examine how industry features affect the manner in which knowledge is shared and how the sharing effects research capabilities of deal participants.Item Open Access Succinct Data Structures(2007-12-14) Gupta, AnkurThe world is drowning in data. The recent explosion of web publishing, XML data, bioinformation, scientific data, image data, geographical map data, and even email communications has put a strain on our ability to manage the information contained there. In general, the influx of massive data sets for all kinds of data present a number of difficulties with storage, organization of information, and data accessibility. A primary computing challenge in these cases is how to compress the data but still allow it to be queried quickly.In real-life situations, many instances of data are highly compressible, presenting a major opportunity for space savings. In mobile applications, such savings are critical, since space and the power to access information are at a premium. In a streaming environment, where new data are being generated constantly, compression can aid in prediction as well. In the case of bioinformatics, understanding succinct representations of DNA sequences could lead to a more fundamental understanding of the nature of our own "data stream," perhaps even giving hints on secondary and tertiary structure, gene evolution, and other important topics.In this thesis, we focus our attention on the important problem of compressed text indexing<\i>, where the goal is to compress a text document and allow arbitrary searching for patterns in the best possible time without first decompressing the text<\i>. We develop a number of compressed data structures that either solve this problem directly, or are used as smaller components of an overall text indexing solution. Each component has a number of applications beyond text indexing as well. For each structure, we provide a theoretical study of its space usage and query performance on a suite of operations crucial to access the stored data. In each case, we relate its space usage to the compressed size of the original data and show that the supported operations function in near-optimal or optimal time. We also present a number of experimental results that validate our theoretical findings, showing that our methodology is competitive with the state-of-the-art.