Disturbance, response, and persistence in self-organized forested communities: Analysis of robustness and resilience in five communities in Southern Indiana

We develop an analytic framework for the analysis of robustness in social-ecological systems (SESs) over time. We argue that social robustness is affected by the disturbances that communities face and the way they respond to them. Using Ostrom's ontological framework for SESs, we classify the major factors influencing the disturbances and responses faced by five Indiana intentional communities over a 15-year time frame. Our empirical results indicate that operational and collective-choice rules, leadership and entrepreneurship, monitoring and sanctioning, economic values, number of users, and norms/social capital are key variables that need to be at the core of future theoretical work on robustness of self-organized systems. © 2010 by the author(s).

Incontinence and gait disturbance after intraventricular extension of intracerebral hemorrhage.

OBJECTIVE: We tested the hypothesis that intraventricular hemorrhage (IVH) is associated with incontinence and gait disturbance among survivors of intracerebral hemorrhage (ICH) at 3-month follow-ups. METHODS: The Genetic and Environmental Risk Factors for Hemorrhagic Stroke study was used as the discovery set. The Ethnic/Racial Variations of Intracerebral Hemorrhage study served as a replication set. Both studies performed prospective hot-pursuit recruitment of ICH cases with 3-month follow-up. Multivariable logistic regression analyses were computed to identify risk factors for incontinence and gait dysmobility at 3 months after ICH. RESULTS: The study population consisted of 307 ICH cases in the discovery set and 1,374 cases in the replication set. In the discovery set, we found that increasing IVH volume was associated with incontinence (odds ratio [OR] 1.50; 95% confidence interval [CI] 1.10-2.06) and dysmobility (OR 1.58; 95% CI 1.17-2.15) after controlling for ICH location, initial ICH volume, age, baseline modified Rankin Scale score, sex, and admission Glasgow Coma Scale score. In the replication set, increasing IVH volume was also associated with both incontinence (OR 1.42; 95% CI 1.27-1.60) and dysmobility (OR 1.40; 95% CI 1.24-1.57) after controlling for the same variables. CONCLUSION: ICH subjects with IVH extension are at an increased risk for developing incontinence and dysmobility after controlling for factors associated with severity and disability. This finding suggests a potential target to prevent or treat long-term disability after ICH with IVH.

Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge