Heterogeneous multiprocessor architecture is frequently employed as an economical and efficient means of providing excellent parallel processing capabilities while keeping production cost and power consumption under control. Although this architecture achieves significant performance enhancement and cost reduction, it results in a serious task allocation and scheduling problem, especially for periodic tasks with data dependencies, all of which should be reasonably scheduled and executed in a timely manner such that their deadlines and dependence requirements could be satisfied even if the worst happens. In this article, we concentrate on the non-preemptive scheduling problem of periodic tasks with data dependencies upon heterogeneous multiprocessor platforms. First, with models of data-dependent tasks and heterogeneous processors, we analyze the time, space, precedence, and data dependence constraints of tasks and design an exact formulation based on the mixed integer linear programming to completely explore the solution space and produce the optimal solutions. Then, by constructing a directed acyclic graph to depict the dependence relationship of jobs generated by tasks, we propose an efficient off-line list-based scheduling algorithm to provide a reasonable time and processor allocation for each job, with a view to minimizing the completion time of jobs. Experiments with randomly generated tasks are performed to evaluate the effectiveness and efficiency of the proposed algorithm, and the experimental results show that our algorithm can averagely enhance the scheduling success ratio by 28.5%, and, respectively, reduce the task completion time and the deviation ratio by 23.3% and 17.2%, on average.

Converting natural language text into executable SQL queries significantly impacts the healthcare domain, specifically when applied to electronic medical records. Given that electronic medical records store extensive patient information in a relational multitable database, developing a Text-to-SQL parser would enable the correlation of intricate medical terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new databases. A critical step towards this goal involves schema linking - accurately identifying references to previously unseen columns or tables during SQL creation. In response to these key challenges, we propose a novel framework—Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records (LI-EMRSQL). This model leverages the Poincaré distance metric detection procedure, utilizing induced relations to enhance the performance of pre-existing graph-based parsers and improve schema linkage. To enhance the generalizability of LI-EMRSQL, the detection process is completely unsupervised and does not necessitate additional parameters. On two conventional Text2SQL datasets and two EMRs Text2SQL datasets, the system delivers SOTA performance. Furthermore, notable enhancements in the model's comprehension and alignment of schemas are observed.

The challenge of natural language processing is from natural language to logical form (SQL). In this article, we present an fuzzy semantic to structured query language (F-SemtoSql) neural approach that is a fuzzy decision semantic deep network query model based on demand aggregation. It aims to address the problem of the complex and cross-domain text-to-SQL generation task. The corpus is trained as the input word vector of the model with LSTM and Word2Vec embedding technology. Combined with the dependency graph method, the problem of SQL statement generation is converted to slot filling. Complex tasks are divided into four levels via F-SemtoSql and constructed by the need of aggregation. At the same time, to avoid the order problem in the traditional model effectively, we have adopted the attention mechanism and used a fuzzy decision mechanism to improve the model decision. On the challenging text-to-SQL benchmark Spider and the other three datasets, F-SemtoSql achieves faster convergence and occupies the first position.

Sepsis is the third-highest mortality disease in intensive care units(ICU) and expensive treatment costs, but the best treatment strategy remains uncertain. In this paper, we proposed a pre-training bidirectional LSTM Networks to predict the Sepsis severity of patients in ICU. Most previous models for severity prediction rely on the multi-task recurrent neural networks. In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of organ systems that may be the most crucial features for severity prediction. To address these issues, we propose an end-to-end recurrent neural model which incorporates simultaneously analyses different organ systems and intuitively reflect the condition of the patients in a timely fashion. Specifically, we apply a pre-training technique in our model to combines it with labeled data via multi-task learning. Experimental results on the real-world clinical dataset (MIMIC-III), one of the most popular sepsis severity prediction tasks, demonstrate that our model outperforms existing state-of-the-art models.