The problem of computing the reachable set for a given system is a quintessential question in nonlinear control theory. Motivated by prior work on safety-critical online planning, this paper considers an environment where the only available information about system dynamics is that of dynamics at a single point. Limited to such knowledge, we study the problem of describing the set of all states that are guaranteed to be reachable regardless of the unknown true dynamics. We show that such a set can be underapproximated by a reachable set of a related known system whose dynamics at every state depend on the velocity vectors that are available in all control systems consistent with the assumed knowledge. Complementing the theory, we discuss a simple model of an aircraft in distress to verify that such an underapproximation is meaningful in practice.Motion planning in an unknown environment demands synthesis of an optimal control policy that balances between exploration and exploitation. check details In this paper, we present the environment as a labeled graph where the labels of states are initially unknown, and consider a motion planning objective to fulfill a generalized reach-avoid specification given on these labels in minimum time. By describing the record of visited labels as an automaton, we translate our problem to a Canadian traveler problem on an adapted state space. We propose a strategy that enables the agent to perform its task by exploiting possible a priori knowledge about the labels and the environment and incrementally revealing the environment online. Namely, the agent plans, follows, and replans the optimal path by assigning edge weights that balance between exploration and exploitation, given the current knowledge of the environment. We illustrate our strategy on the setting of an agent operating on a two-dimensional grid environment.We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However, the output space of such structured prediction models is often too large to adapt binary or multi-class calibration methods directly. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for named-entity-recognition, part-of-speech and question answering. We also improve our model's performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-ofdomain test scenarios as well.Vitamin D, which is increasingly in demand in pharmacies and increasingly prescribed, could be an asset in the treatment of Covid-19 by reducing mortality or the severity of the condition. Its potential immunomodulatory effect is currently being studied by numerous international teams of researchers. A Susceptible-Exposed-Infected-Removed​ (SEIR) model was developed to forecast the spread of the novel coronavirus (SARS-CoV-2) in the United States and the implications of re-opening and hospital resource utilization. The model relies on the specification of various parameters that characterize the virus and the population being modeled. However, several of these parameters can be expected to vary significantly between states. Therefore, a genetic algorithm was developed that adjusts these population-dependent parameters to fit the SEIR model to data for any given state. Publicly available data was collected from each state in terms of the number of positive COVID-19 cases and the number of COVID-19-caused deaths and used as inputs into a SEIR model to predict the spread of COVID infections in a given population. A genetic algorithm was designed where the genes are the state-dependent parameters from the model. The algorithm operates by determining the fitness of a given set of genes, applying selection, using selected agents to reproduce with cross-over, applying random mutation, and simulating several generations. Use of the genetic algorithm produces exceptionally good agreement between the model and available data. Deviations in the parameters were examined to see if the trends were reasonable.Use of the genetic algorithm produces exceptionally good agreement between the model and available data. Deviations in the parameters were examined to see if the trends were reasonable. Medical education all over the country has been forced to shift to e-learning, mainly online classes. In this scenario, the medical education department (MEU) of a teaching hospital under a deemed university felt the need to study the satisfaction and usefulness of these classes, as perceived by the undergraduate medical students. Questionnaire survey was planned. A specially designed questionnaire was created, keeping in mind, the study objectives. It was validated, and a pilot was conducted, for modifications and to calculate sample size. The questionnaire was administered by email as Google Forms. The responses, which included quantitative and qualitative responses, were analysed, and feedback points noted. Percentage level of satisfaction and usefulness was calculated with 95% confidence interval. To test the statistical significance of the association of satisfaction levels amongst students of different Phases, chi square test was used. On a scale scoring for satisfaction, 53.6% scored moderate, 31ient feedback to be shared with all stakeholders, regarding improvements in the online classes.