In the development of predictive models, the problem of missing data is a critical issue that traditionally requires a two-step analysis. Data scientists analyze the patterns of missing values, select variables, impute missing values on the basis of domain knowledge, and then train a model. Models typically have their input sizes hardcoded, and have limitations in handling data with high missing rates or changes in available variables. We propose an attention-based neural network combined with a novel real number representation, which requires little work on manually selecting variables, and in which missing data can be overlooked, making imputation unnecessary. In this proposed model, data analysis can be one step, omitting the first step of imputing missing values. The study included data on 32,709 intensive care unit (ICU) admissions and 60 healthcare variables from the Medical Information Mart for Intensive Care (MIMIC)-IV. The proposed algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CIs: 0.828–0.856) when predicting prolonged length of stay in the ICU, outperforming current approaches using imputation methods. The proposed algorithm can be applied to a range of problems in data science, as it addresses the issue of incomplete data with automatic variable selection.

We introduce in this paper a new problem of post-mask engineering change order (ECO) timing optimization using spare-cell rewiring and present a two-phase framework for this problem. Spare-cell rewiring is a popular technique for incremental timing optimization and/or functional change after the placement stage. The spare-cell rewiring problem is very challenging because of its dynamic wiring cost nature for selecting a spare cell, while the existing related problems consider only static wiring cost: once a standard cell is placed, its physical location is fixed and so is its wiring cost. For the spare-cell rewiring problem, each rewiring could make some spare cells become ordinary standard cells and some standard cells become new spare cells simultaneously. As a result, the wiring cost becomes dynamic and further complicates the optimization process. For the addressed problem, we present a two-phase framework of 1) buffer insertion and gate sizing followed by 2) technology remapping. For Phase 1, we present a dynamic programming algorithm considering the dynamic cost, called dynamic cost programming, for the ECO timing optimization with spare cells. Without loss of solution optimality, we further present an effective pruning method by selecting spare cells only inside an essential bounding polygon to reduce the solution space. For those ECO timing paths that cannot be fixed during Phase 1, we apply technology remapping on the spare cells to restructure the circuit to fix the timing violations. The whole framework is integrated into a commercial design flow. Experimental results based on five industry benchmarks show that our method is very effective and efficient in fixing the timing violations of ECO paths.

We introduce in this paper a new problem of ECO timing optimization using spare-cell rewiring and present the first work for this problem. Spare-cell rewiring is a popular technique for incremental timing optimization and/or functional change after the placement stage. The spare-cell rewiring problem is very challenging because of its dynamic wiring cost nature for selecting a spare cell, while the existing related problems consider only static wiring cost. For the addressed problem, we present a framework of buffer insertion and gate sizing to handle it. In this framework, we present a dynamic programming algorithm considering the dynamic cost, called dynamic cost programming (DCP), for the ECO timing optimization with spare cells. Without loss of solution optimality, we further present an effective pruning method by selecting spare cells only inside an essential bounding polygon to reduce the solution space. The whole framework is integrated into a commercial design flow. Experimental results based on five industry benchmarks show that our method is very effective and efficient in fixing the timing violations of ECO paths.