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The role of analysts in corporate governance has been examined extensively in the accounting literature. Two conflicting representations of the influence of analysts have emerged. Analysts are either viewed as external monitors of corporate behavior, thereby reducing agency costs; or they are viewed as exerting additional pressure on management to meet earnings forecasts, which may contribute to aggressive corporate behavior. Studies exist that examine the impact of analyst coverage in a financial reporting context. The purpose of this study is to examine the role of analysts in the corporate tax reporting context. This dissertation examines the impact of analyst coverage on corporate tax aggressiveness using a cross-section of publicly traded firms between 1992 and 2006. Permanent discretionary book-tax differences are used to proxy for tax aggressiveness. The relation is examined using ordinary least squares regression as well as two-stage least squares regression using expected coverage and inclusion in the S&P 500 index as instrumented variables to account for the endogeneity of analyst coverage selections. Additional analyses investigate the impact of analyst characteristics: experience as an analyst, experience covering a specific firm and identification as a top analyst. Results indicate that analyst coverage is associated with lower levels of tax aggressiveness. This finding suggests that analysts serve as external monitors of corporate tax behavior. In addition, more experienced analysts are associated with lower levels of tax aggressiveness indicating an improvement in monitoring ability with experience. Analysts identified as All-American analysts by Institutional Investor magazine are associated with higher levels of tax aggressiveness. This result suggests that top analysts may view aggressive tax behavior as a wealth creation tool for firms. / Ph. D.

Large-scale sensor networks have become a reality due to recent developments in sensor node hardware and algorithms. Sensor networks can provide real-time information based on detection and tracking. This information cannot be reliable if little is known about the sensor coverage of the network, which can be defined as the total sensing range of the network due to contributions from each sensor node. Knowledge about coverage can also be useful in determining if there is any gap in coverage in the region of interest as well as improving the algorithm that determines the placement of nodes. Although coverage estimation is this thesis's central concern, other factors such as energy-efficiency and network lifespan that affect the network performance are investigated. Energy-efficiency and network lifespan depend on the communication model used for obtaining coverage information from each sensor node. This thesis proposes the use of B-splines for describing coverage efficiently. The properties of B-splines also enable communication models such as directed diffusion and hierarchical clustering to provide better performance as compared to a centralized scheme. Results obtained from simulation experiments indicate that hierarchical clustering and directed diffusion can be used effectively for coverage measurement. The hierarchical clustering model, however, exhibited some drawbacks such as a dependency on the routing scheme and poor node-failure recovery. / Master of Science

In this thesis, we propose to use relevant potential invariants in a simulation-based swarmintelligence-based test generation technique to generate relevant test vectors for design validation at the Register Transfer Level (RTL). Providing useful guidance to the test generator for such techniques is critical. In our approach, we provide guidance by exploiting potential invariants in the design. These potential invariants are obtained using random stimuli such that they are true under these stimuli. Since these potential invariants are only likely to be true, we try to generate stimuli that can falsify them. Any such vectors would help reach some corners of the design. However, the space of potential invariants can be extremely large. To reduce execution time, we also implement a two-layer filter to remove the irrelevant potential invariants that may not contribute in reaching difficult states. With the filter, the vectors generated thus help to reduce the overall test length while still reach the same coverage as considering all unfiltered potential invariants. Experimental results show that with only the filtered potential invariants, we were able to reach equal or better branch coverage than that reported by BEACON in the ITC99 benchmarks, with considerable reduction in vector lengths, at reduced execution time. / Master of Science / Over the recent years, size and complexity of hardware designs are increasing at an enormous rate. Due to this, verification of these designs is of utmost importance and demands much more resources and time than designing of these hardware. To project the information of the designs, developers use Hardware Descriptive Languages (HDL), that includes the important decision points of the system, also called branches of the circuit. There are several methodologies proposed to check how many branches of the design can be traversed by set of inputs. This practice is important to confirm correct functionality of the design as we can catch all the faults in the design at these decision points. Some of these methodologies include checking with random inputs, exhaustively checking for every possible input, investing many hours of labor to verify with appropriate inputs, or simply automating the process of generating inputs. In this thesis, we focus on one such automated process called BEACON or Branch-oriented Evolutionary Ant Colony OptimizatioN. We propose a modification to improve this method by using standard properties of the design. These properties, also known as invariants, help to cover those branches that require extra effort in terms of both inputs and time, and are thus, hard to cover. When we add these significant invariants to the design, modified BEACON is able to cover almost all accessible branches in the system with significantly less amount of time and lesser number of vectors than original BEACON itself, which helps save a lot of resources.

This research focuses on developing a model to predict the yield of a printed circuit board manufactured on a given assembly line. Based on an extensive literature review as well as discussion with industrial partners, it was determined that there is no tool available for assisting engineers in determining reliable estimates of their production capabilities as they introduce new board designs onto their current production lines. Motivated by this need, a more in-depth study of manufacturing yield as well as the electronic assembly process was undertaken. The relevant literature research was divided into three main fields: process modeling, board design, and PCB testing. The model presented in this research combines elements from process modeling and board design into a single yield model. An optimization model was formulated to determine the fault probabilities that minimize the difference between actual yield values and predicted yield values. This model determines fault probabilities (per component type) based on past production yields for the different board designs assembled. These probabilities are then used to estimate the yields of future board designs. Two different yield models were tested and their assumptions regarding the nature of the faults were validated. The model that assumes independence between faults provided better yield predictions. A preliminary case study was performed to compare the performance of the presented model with that of previous models using data available from the literature. The proposed yield model predicts yield within 3% of the actual yield value, outperforming previous regression models that predicted yield within 10%, and artificial neural network models that predicted yield within 5%. A second case study was performed using data gathered from actual production lines. The proposed yield model continued to provide very good yield predictions. The average difference with respect to the actual yields from this case study ranged between 1.25% and 2.27% for the lines studied. Through sensitivity analysis, it was determined that certain component types have a considerably higher effect on yield than others. Once the proposed yield model is implemented, design suggestions can be made to account for manufacturability issues during the design process. / Master of Science

The purpose of this study is to examine and analyze how Swedish ice hockey clubs portray (image and text) and report (coverage) their male and female representative teams through the club's channels (Instagram & Website), and to compare portray with the reporting in Swedish sports tabloids (Expressen & Aftonbladet). Through methodological triangulation consisting of quantitative content analysis and multimodal analysis, data is collected and analyzed to identify potential differences, similarities, and patterns. The analysis time was October 1–31, 2023.  Hypothesis:  What space is allocated to male and female hockey players on Instagram, websites, and in tabloids? How are the different genders represented in terms of images and text? Are there similarities or differences in how external sports tabloids report on each gender compared to the team's own channels? The study utilizes statistics and graphical analysis to measure the frequency and extent of gender representation within the clubs' communications. The results indicate significant differences in the amount of coverage and the type of content given to women's and men's teams, on the clubs' own platforms. The type of textual content in articles given to women and men in external sports tabloids wasn't any bigger differences. Women's teams generally received less coverage and different image and text presentations compared to men's teams on the hockey club's platforms, there were big differences in coverage of gender between the best club and the worst in the analysis. The tabloids showed an even greater inequality in reporting, with a markedly higher frequency of articles about men's teams. Some teams are more balanced than others when it comes to coverage and space allocated in the hockey clubs.

Yes / This study uses the quality of coverage decisions as a new metric to evaluate the performance of star and non-star analysts. We find that the coverage decisions of star analysts are better predictors of returns than those of non-star analysts. The return predictability of star analysts’ coverage decisions is stronger for informationally opaque stocks. We further exploit the staggered short selling deregulations, Google’s withdrawal, and the anti-corruption campaign as three quasi-natural experiments that create plausibly exogenous variations in the quality of information environment. These experiments show that the predictive power of star analysts’ coverage decisions strengthens (weakens) following a sharp deterioration (improvement) in firms’ information environment, consistent with the notion that star analysts possess superior ability to identify mispriced stocks. Overall, star analysts make better coverage decisions and play a superior role as information intermediaries, especially in poor information environment.