On the Landau-Ginzburg description of Boundary CFTs and special Lagrangian submanifolds

We consider Landau-Ginzburg (LG) models with boundary conditions preserving A-type N=2 supersymmetry. We show the equivalence of a linear class of boundary conditions in the LG model to a particular class of boundary states in the corresponding CFT by an explicit computation of the open-string Witten index in the LG model. We extend the linear class of boundary conditions to general non-linear boundary conditions and determine their consistency with A-type N=2 supersymmetry. This enables us to provide a microscopic description of special Lagrangian submanifolds in C^n due to Harvey and Lawson. We generalise this construction to the case of hypersurfaces in P^n. We find that the boundary conditions must necessarily have vanishing Poisson bracket with the combination (W(\phi)-\bar{W}(\bar{\phi})), where W(\phi) is the appropriate superpotential for the hypersurface. An interesting application considered is the T^3 supersymmetric cycle of the quintic in the large complex structure limit.Comment: 28+1 pages; no figures; requires JHEP.cls, amssymb; (v2) typo corrected; (v3) references adde

A comparative analysis of leading relational database management systems

http://deepblue.lib.umich.edu/bitstream/2027.42/96903/1/MBA_JayaramanS_1996Final.pd

Object reational data base management systems and applications in document retrieval

http://deepblue.lib.umich.edu/bitstream/2027.42/96902/1/MBA_JayaramanaF_1996Final.pd

Human Behavior Modeling and Human Behavior-aware Control of Automated Vehicles for Trustworthy Navigation

First and foremost, I would like to thank my advisor, Professor Dawn Tilbury, for her constant guidance and encouragement. She has been extremely helpful in developing my technical, research, and personal skills and immensely supportive of my ideas and endeavors throughout graduate school. She has been an excellent mentor and has always been there in my time of need, encouraging and boosting my confidence when I needed them the most. I would like to specially thank my committee members and collaborators, Professors Lionel Robert and Jessie Yang, for their support and encouragement, right from the start of my graduate program. The multi-disciplinary nature of the research initiated by these three Professors is what first drew me towards pursuing a Ph.D. I would also like to thank my other committee members Professors Ilya Kolmanovsky and Ram Vasudevan, for providing their support and feedback that improved the dissertation. I would like to thank the Department of Mechanical Engineering, Rackham Graduate School, and the University of Michigan for giving me the opportunity to pursue the doctoral degree and providing financial support during my time at the university. In addition, I would like to thank the Toyota Research Institute and the Automotive Research Center for providing financial assistance. I really appreciate the support I received from the MAVRIC lab members. The multi-disciplinary culture and environment that the Professors have fostered in the MAVRIC lab have deeply broadened my perspectives. Specically, I would like to thank Hebert Azevedo-Sa. He is usually the first person I discuss my ideas with and has been an excellent critique. I would also like to thank Connor Esterwood, Na Du, Qiaoning Zhang, and Huajing Zhao for the numerous discussions and help with my user studies; especially Connor, who took on a variety of roles to help with my user study|from an engineer to a tailor, to even a hidden driver. Outside of the University of Michigan, I would like to thank my undergraduate advisor, Professor Madhu M., and my internship advisor at the Indian Institute of Technology-Madras, Professor Saravanan Gurunathan. They encouraged me to pursue research and provided me with the necessary opportunities. A special thanks to Sajaysurya Ganesh, a close friend, and collaborator in my early research projects, with who I discuss ideas even now. Last but not least, I would like to thank my family and friends for supporting me during the past several years. My friends at Ann Arbor made life away from home much easier; they are like my second family. A long list of people from my Master's and Ph.D. programs at the University of Michigan has played an essential role in my graduate experience. Still, I would like to especially thank Sandipp Krishnan Ravi, Subramaniam Balakrishna, Rahasudha Kannan, and Paavai Pari for all their love and support. I will fondly remember my time at the University of Michigan and in Ann Arbor because of all of the people I encountered, the friends I made, and the experiences I had. My parents, wife, and extended family have all been incredibly supportive of the pursuit of my degree, and I am eternally grateful for their love and guidance.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169640/1/jskumaar_1.pd

Synthesis, fluorescence and photoisomerization studies of azobenzene-functionalized poly(alkyl aryl ether) dendrimers

A series of azobenzene-functionalized poly(alkyl aryl ether) dendrimers have been synthesized and their photochemical and photophysical properties in solution and as thin films have been investigated. Although the photochemical behavior of the azodendrimers in solution indicated that the azobenzene units behave independently, very similar to the constituent monomer azobenzene unit, the properties of thin solid films of the dendrimers were distinctly different. The azodendrimers, AzoG1, AzoG2, and AzoG3 were observed to form stable supercooled glasses, which showed long-wavelength absorption and red emission characteristics of J-aggregates of the azobenzene chromophores. Reversible photoinduced isomerization of the azodendrimers in the glassy state is described

Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model

For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians’ crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians’ gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians’ crossing behaviors. The BMPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios.Automotive Research Center (ARC) at the University of Michigan, with funding from government contract DoD-DoA W56HZV14-2-0001, through the U.S. Army Combat Capabilities Development Command (CCDC) /Ground Vehicle Systems Center (GVSC).National Science FoundationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154113/1/Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdfDescription of Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdf : MainFil

Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians’ trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians’ gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further, we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.Toyota Research Institute (TRI) provided funds to assist the authors with their research, but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity. The work was also supported in part by the National Science Foundation and supported in part by the Automotive Research Center at the University of Michigan, with funding from government contract Department of the Army W56HZV- 14-2-0001 through the U.S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC).Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154053/1/ICRA_2020_Analysis_and_Prediction_of_Pedestrian_Final_revised_03_03_20.pdfDescription of ICRA_2020_Analysis_and_Prediction_of_Pedestrian_Final_revised_03_03_20.pdf : Main fil

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

Automated vehicles (AVs) that intelligently interact with drivers must build a trustworthy relationship with them. A calibrated level of trust is fundamental for the AV and the driver to collaborate as a team. Techniques that allow AVs to perceive drivers’ trust from drivers’ behaviors and react accordingly are, therefore, needed for context-aware systems designed to avoid trust miscalibrations. This letter proposes a framework for the management of drivers’ trust in AVs. The framework is based on the identification of trust miscalibrations (when drivers’ undertrust or overtrust the AV) and on the activation of different communication styles to encourage or warn the driver when deemed necessary. Our results show that the management framework is effective, increasing (decreasing) trust of undertrusting (overtrusting) drivers, and reducing the average trust miscalibration time periods by approximately 40%. The framework is applicable for the design of SAE Level 3 automated driving systems and has the potential to improve the performance and safety of driver–AV teams.U.S. Army CCDC/GVSCAutomotive Research CenterNational Science FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162571/1/Azevedo-Sa et al. 2020 with doi.pdfSEL