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No description available.

No description available.

No description available.

No description available.

No description available.

Previous research on Temporal Self-Appraisal Theory demonstrated that people make downward comparisons to their past selves. Researchers have discussed this tendency to denigrate past selves as a self-esteem maintenance strategy; however, little research has been done on how people recall their past selves following active threats to their self-esteem. Reconstruing one’s past self for self-enhancement may lead to changes in construal level. I conducted three studies in which participants were randomly assigned to either an intelligence self-esteem threat or control condition and then tasked to recall an autobiographical memory, rate attributes of their recalled past self, and complete a measure of construal level. In the pilot study (N = 113), participants were free to recall any memory of their choosing. In Studies 1 and 2, participants recalled and rated two memories from early high-school: pre- and post-threat manipulation. Participants in Study 1 (N = 240) recalled their academic experience, while participants in Study 2 (N = 243) recalled their interpersonal relationships. A pattern emerged across studies suggesting that when people recall autobiographical memories related to the domain in which their self-esteem has been threatened (an academic memory and intelligence threat), threatened participants are more likely to denigrate their past selves (lower endorsement of positive self-attributes post-manipulation than pre-test) than controls in threat-relevant traits (competence, knowledge). This pattern is accompanied by an increased likelihood to recall positive transformations and periods of growth, as opposed to stability. However, a relationship with construal level was not observed. Further, when people recall autobiographical memories unrelated to the domain in which their self-esteem has been threatened (an interpersonal memory and intelligence threat), threatened participants are more likely to idealize their past selves (higher endorsement of positive self-attributes post-manipulation than pre-test) than controls in threat-irrelevant traits (likeable, attractive). However, this pattern was only observed for those who demonstrated fixed mindsets, emphasizing stability, and was not associated with an impact on construal level. / Psychology

In this dissertation, we develop feedback control strategies that can be used for evacuating people. Pedestrian models are based on macroscopic or microscopic behavior. We use the macroscopic modeling approach, where pedestrians are treated in an aggregate way and detailed interactions are overlooked. The models representing evacuation dynamics are based on the laws of conservation of mass and momentum and are described by nonlinear hyperbolic partial differential equations. As such the system is distributed in nature. We address the design of feedback control for these models in a distributed setting where the problem of control and stability is formulated directly in the framework of partial differential equations. The control goal is to design feedback controllers to control the movement of people during evacuation and avoid jams and shocks. We design the feedback controllers for both diffusion and advection where the density of people diffuses as well as moves in a specified direction with time. In order to achieve this goal we are assuming that the control variables have no bounds. However, it is practically impossible to have unbounded controls so we modify the controllers in order to take the effect of control saturation into account. We also discuss the feedback control for these models in presence of uncertainties where the goal is to design controllers to minimize the effect of uncertainties on the movement of people during evacuation. The control design technique adopted in all these cases is feedback linearization which includes backstepping for higher order two-equation models, Lyapunov redesign for uncertain models and robust backstepping for two-equation uncertain models. The work also focuses on abstraction of evacuation system which focuses on obtaining models with lesser number of partial differential equations than the original one. The feedback control design of a higher level two-equation model is more difficult than the lower order one-equation model. Therefore, it is desirable to perform control design for a simpler abstracted model and then transform control design back to the original model. / Ph. D.

Abstraction-guided simulation is a promising semi-formal framework for design validation in which an abstract model of the design is used to guide a logic simulator towards a target property. However, key issues still need to be addressed before this framework can truly deliver on it's promise. Concretizing, or finding a real trace from an abstract trace, remains a hard problem. Abstract traces are often spurious, for which no corresponding real trace exits. This is a direct consequence of the abstraction being an over-approximation of the real design. Further, the way in which the abstract model is constructed is an open-ended problem which has a great impact on the performance of the simulator. In this work, we propose a novel approaches to address these issues. First, we present a genetic algorithm to select sets of state variables directly from the gate-level net-list of the design, which are highly correlated to the target property. The sets of selected variables are used to build the Partition Navigation Tracks (PNTs). PNTs capture the behavior of expanded portions of the state space as they related to the target property. Moreover, the computation and storage costs of the PNTs is small, making them scale well to large designs. Our experiments show that we are able to reach many more hard-to-reach states using our proposed techniques, compared to state-of-the-art methods. Next, we propose a novel abstraction strengthening technique, where the abstract design is constrained to make it more closely resemble the concrete design. Abstraction strengthening greatly reduces the need to refine the abstract model for hard to reach properties. To achieve this, we efficiently identify sequentially unreachable partial sates in the concrete design via intelligent partitioning, resolution and cube enlargement. Then, these partial states are added as constraints in the abstract model. Our experiments show that the cost to compute these constraints is low and the abstract traces obtained from the strengthened abstract model are far easier to concretize. / Master of Science