Modern insurance and reinsurance companies use stochastic simulation techniques for portfolio risk analysis. Their risk portfolios may consist of thousands of reinsurance contracts covering millions of individually insured locations. To quantify risk and to help ensure capital adequacy, each portfolio must be evaluated in up to a million simulation trials, each capturing a different possible sequence of catastrophic events (e.g., earthquakes, hurricanes, etc.) over the course of a contractual year.We present a flexible framework for portfolio risk analysis that can answer a rich variety of catastrophic risk queries. Rather than aggregating simulation data in order to produce a small set of high-level risk metrics efficiently (as done in production risk management systems), our focus is on queries on unaggregated or partially aggregated data. The goal is to allow analysts to obtain answers to a wide variety of unanticipated but natural ad hoc queries, which can help actuaries or underwriters to better understand the multiple dimensions (e.g., spatial correlation, seasonality, peril features, construction features, financial terms, etc.) that can impact portfolio risk and thus company solvency.We implemented a prototype system, called QuPARA, using Apache's Hadoop implementation of the MapReduce paradigm. This allows the user to utilize large parallel compute servers in order to answer ad hoc queries efficiently even on very large data sets typically encountered in practice. We describe the design and implementation of QuPARA and present experimental results that demonstrate its feasibility.
In this paper we propose MO-PBIL, a parallel multidimensional variant of the Population Based Incremental Learning (PBIL) technique that executes efficiently on both multi-core and many-core architectures. We show how MO-PBIL can be used to address an important problem in Reinsurance Risk Analytics namely the Reinsurance Contract Optimization problem. A mix of vectorization and multithreaded parallelism is used to accelerate the three main computational steps: objective function evaluation, multidimensional dominance calculations, and multidimensional clustering. Using MO-PBIL, reinsurance contract optimization problems with a 5% discretization and 7 or less contractual layers (subcontracts) can be solved in under a 1 minute on a single workstation or server. Problems with up to 15 layers, which previously took a month or more of computation to solve, can now be solved in less than 10 minutes.
Purpose: To describe a case of postoperative persistent loculated subretinal fluid (SRF) that developed after pars plana vitrectomy (PPV) for vitreomacular traction (VMT) syndrome. Methods: A case was analyzed and a literature review performed. Results: A healthy 64-year-old man with no significant ocular history presented with persistent, symptomatic VMT. Combined phacoemulsification and PPV, epiretinal membrane and internal limiting membrane peeling, and gas–fluid exchange were performed. Postoperative spectral-domain optical coherence tomography imaging showed loculated foveal SRF. The SRF persisted for 8 months, with minimal change in size and little BCVA improvement. Conclusions: Although persistent loculated SRF has been reported after vitrectomy for rhegmatogenous retinal detachment (RD) in high myopia and tractional RD in diabetes, it has not yet been reported postoperatively after PPV for VMT. Studies of the pathophysiology and long-term course of persistent SRF after PPV for VMT are needed to inform management decisions for this rare complication.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.