An automatic abstraction technique for verifying featured, parameterised systems

A general technique combining model checking and abstraction is presented that allows property based analysis of systems consisting of an arbitrary number of featured components. We show how parameterised systems can be specified in a guarded command form with constraints placed on variables which occur in guards. We prove that results that hold for a small number of components can be shown to scale up. We then show how featured systems can be specified in a similar way, by relaxing constraints on guards. The main result is a generalisation theorem for featured systems which we apply to two well known examples

Predicting blood pressure response to fluid bolus therapy in the ICU using attention-based stacked neural networks for clinical interpretability

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages [38]-[40]).Fluid bolus therapy (FBT) is a treatment commonly administered to treat seriously ill hypotensive patients in intensive care units (ICUs). Unfortunately, only a fraction of hypotensive patients respond positively to FBT, and emergency room physicians are constantly challenged in determining whether administering FBT will result in a corresponding increase in blood pressure. In this thesis, we utilized regression models and attention-based recurrent neural network (RNN) algorithms to predict the response of hypotensive patients to FBT from a multi-clinical information system large-scale database. We investigated time-series modeling with the use of the stacked long short term memory network (LSTM) and the gated recurrent units network (GRU) models by altering the representation of our data and time-aggregated modeling using logistic regression algorithms with regularization on our original representation. Additionally, we applied the attention mechanism for clinical interpretability on our RNN models applied on the time-series representation. Among all the modeling strategies and data representations, the stacked LSTM with the attention mechanism predicted the success or failure of the FBT on hypotensive patients with a highest accuracy of 0.852 and area under the curve (AUC) value of 0.925. The aim of the study is to help identify hypotensive patients in ICUs who will experience a sufficient increase in blood pressure after FBT administration. The end goal of these results would be to develop a clinically actionable decision support tool for intensive care management.by Uma M. Girkar.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Australian systematic botany : an international journal devoted to the taxonomy, biogeography and evolution of all plant groups

Abstract. The on-line visualization and the computational steering of parallel simulations come up against a serious coherence problem. Indeed, data distributed over parallel processes must be accessed carefully to ensure they are presented to the visualization system in a meaningful way. In this paper, we present a solution to the coherence problem for structured parallel simulations. We introduce a hierarchical task model that allows to better grasp the complexity of simulations, too often considered as “single-loop ” applications. Thanks to this representation, we can schedule in parallel the request treatments on the simulation processes and satisfy the temporal coherence.

Modeling Multi-grain Parallelism on Heterogeneous Multicore Processors: A Case Study of the Cell BE

Abstract. Heterogeneous multi-core processors invest the most significant portion of their transistor budget in customized “accelerator ” cores, while using a small number of conventional low-end cores for supplying computation to accelerators. To maximize performance on heterogeneous multi-core processors, programs need to expose multiple dimensions of parallelism simultaneously. Unfortunately, programming with multiple dimensions of parallelism is to date an ad hoc process, relying heavily on the intuition and skill of programmers. Formal techniques are needed to optimize multi-dimensional parallel program designs. We present a model of multi-dimensional parallel computation for steering the parallelization process on heterogeneous multi-core processors. The model predicts with high accuracy the execution time and scalability of a program using conventional processors and accelerators simultaneously. More specifically, the model reveals optimal degrees of multi-dimensional, task-level and data-level concurrency, to maximize performance across cores. We use the model to derive mappings of two full computational phylogenetics applications on a multi-processor based on the IBM Cell Broadband Engine.