No abstract

Our algorithm presents a good performance on multi-label short ECG classification with selected morphological features.

No abstract

The PROTEUS-MOC code is a three-dimensional (3D) neutron transport code based on the finite element mesh and the method of characteristics (MOC) which combines a 2D MOC with the discontinuous Galerkin finite element method for the axial direction. Thus, for PROTEUS-MOC, a 3D geometry is represented with by the implicit extrusion of a single 2D planar geometry with different material assignments allowable on each plane. The code requires four input files for a steady-state calculation without the thermal feedback: a driver input, a mesh input, a cross section input, and a material assignment input. With thermal feedback, a T/H input file is needed. For kinetics, a kinetics driver input file is required. The PROTEUS-MOC software produces a text output file as well as a data file which includes integral and average quantities such on fluxes, powers, temperatures, densities, etc. The detailed output file can be converted to the VTK format using the post processing code so that the outputs can be visualized using a visualization software such as VisIt.

Two micro reactor cores were simulated using the MOC solver of PROTEUS: MegaPower for a heat pipe micro reactor and Holos-Quad for a high-temperature gas-cooled micro reactor. Verification of PROTEUS standalone was performed by simulating 2D and 3D core benchmark problems developed based on MegaPower and Holos-Quad. Simulation results indicated that PROTEUS successfully modelled 2D and 3D cores of the MegaPower and Holos-Quad micro reactors and PROTEUS eigenvalues agreed well with the SERPENT Monte Carlo solutions.

Causal effect identification is one of the most prominent and well-understood problems in causal inference. Despite the generality and power of the results developed so far, there are still challenges in their applicability to practical settings, arguably due to the finitude of the samples. Simply put, there is a gap between causal effect identification and estimation. One popular setting in which sample-efficient estimators from finite samples exist is when the celebrated back-door condition holds. In this paper, we extend weighting-based methods developed for the back-door case to more general settings, and develop novel machinery for estimating causal effects using the weighting-based method as a building block. We derive graphical criteria under which causal effects can be estimated using this new machinery and demonstrate the effectiveness of the proposed method through simulation studies.

No abstract

No abstract