122 research outputs found

    Coexistence and asymptotic periodicity in a competitor–competitor–mutualist model

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    AbstractIn this paper, the competitor–competitor–mutualist three-species Lotka–Volterra model is discussed. Firstly, by Schauder fixed point theory, the coexistence state of the strongly coupled system is given. Applying the method of upper and lower solutions and its associated monotone iterations, the true solutions are constructed. Our results show that this system possesses at least one coexistence state if cross-diffusions and cross-reactions are weak. Secondly, the existence and asymptotic behavior of T-periodic solutions for the periodic reaction–diffusion system under homogeneous Dirichlet boundary conditions are investigated. Sufficient conditions which guarantee the existence of T-periodic solution are also obtained

    Three Essays on Residential Mortgage Payment Behavior

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    This dissertation is comprised of three essays on residential mortgage payment behavior. The first chapter analyzes the simultaneous mortgage-termination risks of 90-day delinquency and prepayment, the second and third chapters study borrower mortgage payment and exiting behavior in the CARES Act Mortgage Forbearance program. Ding, Tian, Yu, and Guo (2012) analyze transformations of the binomial logit duration model for which the results are an exact binomial logit duration model when the transformation parameter equals one and an interval censored proportional hazard model as the transformation parameter limits to zero. In the first chapter, which is co-authored with Ran An and Jan Ondrich, we incorporate one of the Ding et al. transformations into a model with more than one mortgage-termination risk. In this case the resulting model is multinomial logit when the transformation parameter equals one. The resulting model as the transformation parameter approaches zero is not an interval censored competing risk proportional hazard model (see An and Qi 2012). However, it may approximate one and is in any case a valid statistical model. We analyze the simultaneous mortgage-termination risks of 90-day delinquency and prepayment for single-family 30-year fixed-rate mortgages securitized by Fannie Mae using the Fannie Mae public use data. We show that the transformation can control for over-dispersion in the data and that transformed models perform better than the corresponding models without the transformation. The second chapter uses borrower mortgage payment behavior in the CARES Act Mortgage Forbearance program to predict the mode of exit from the program. The CARES Act permits borrowers to postpone mortgage payments without penalty. In the empirical work, this chapter extends the beta-logistic model in Heckman and Willis (1977) to the Dirichlet nested logit model, which allows the state dependence of choices to vary across different nests. The results show that the beta distribution of probabilities of choices within the nest and between nests are both J shaped, which indicates that the payment behavior probability of relatively few borrowers is near the average. Moreover, borrowers who make curtailment payments are more likely to exit forbearance with prepayment or reinstatement. In comparison, borrowers who frequently forbear payments are more likely to leave with payment deferral or trial/modification. The models in the second chapter estimate the effect of payment behavior in the CARES Act forbearance program as the program continues through time. For a given exit time, the likelihoods contained information on only those mortgages that failed at that exit time. Results were presented for three exit times: 6, 12, and 18 months. A two-step estimation technique was used and standard errors were corrected in the second step. The first improvement in the final chapter incorporates information on all mortgages that survive until a given time into the likelihood functions. I show that the estimation can be accomplished in a single step. The accuracy of the two-step estimation and single-step estimation results are compared. The second improvement in the final chapter is to construct a single model, estimated in a single step, that uses information for all of the first six months. The accuracy rate of the estimation for this new model is substantially higher than the accuracy rate of the estimation for the model with a single survival time of six months. Future work is to extend the estimation to cover the entire length of the program

    More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

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    For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL). Website: https://sites.google.com/view/more-than-a-feelin

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat
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