An Adaptive Deep Learning for Causal Inference Based on Support Points With High-Dimensional Data

The Sample splitting method in semiparametric statistics could introduce inconsistency in inference and estimation. Thus, to make adaptive learning based on observational data and establish valid learning that helps in the estimation and inference of the parameters and hyperparameters using double machine learning, this study introduces an efficient sample splitting technique for causal inference in the semiparametric framework, in other words, the support points sample splitting( SPSS), a subsampling method based on the energy distance concept is employed for causal inference under double machine learning paradigm. This work is based on the idea that the support points sample splitting (SPSS) is an optimal representative point of the data in a random sample versus the counterpart of random splitting, which implies that the support points sample splitting is an optimal sub-representation of the underlying data generating distribution. To my best knowledge, the conceptual foundation of the support points-based sample splitting is a cutting-edge method of subsampling and the best representation of a full big data set in the sense that the unit structural information of the underlying distribution via the traditional random data splitting is most likely not preserved. Three estimators were applied for double/debiased machine learning causal inference a paradigm that estimates the causal treatment effect from observational data based on machine learning algorithms with the support points sample splitting (SPSS). This study is considering Support Vector Machine (SVM) and Deep Learning (DL) as the predictive estimators. A comparative study is conducted between the SVM and DL with the support points technique to the benchmark results of Chernozhukov et al. (2018) that used instead, the random forest, the neural network, and the regression trees with random k-fold cross-fitting technique. An ensemble machine learning algorithm is proposed that is a hybrid of the super learner and the deep learning with the support points splitting to compare it to the results of Chernozhukov et al. (2018). Finally, a socio-economic real-world dataset, for the 401(k)-pension plan, is used to investigate and evaluate the proposed methods to those in Chernozhukov et al. (2018). The result of this study was under 162 simulations, shows that the three proposed models converge, support vector machine (SVM) with support points sample splitting (SPSS) under double machine learning (DML), the deep learning (DL) with support points sample splitting under double machine learning (DML), and the hybrid of super learning (SL) and deep learning with support points sample splitting under double machine learning. However, the performance of the three models differs. The first model, support vector machine (SVM) with support points sample splitting (SPSS) under double machine learning (DML) has the lowest performance compared to the other two models. In terms of the quality of the causal estimators, it has a higher MSE and inconsistency of the simulation results on all three data dimension levels, low-high-dimensional (p = 20,50,80), moderate-high-dimensional (p = 100, 200, 500), and big-high-dimensional p = (1000, 2000, 5000). The two other models, deep learning (DL) with support points sample splitting under double machine learning (DML), and the hybrid of super learning (SL) and deep learning with support points sample splitting under double machine learning have produced a competing performance and results in terms of the best estimation compared to the two other methods. The first model was time efficient to estimate the causal inference compared to the third one. But the third model was better performing in terms of the estimation quality by producing the lowest MSE compared to the other two models. The results of this research are consistent with the recent development of machine learning. The support vector machine learning has been introduced in the previous century, and it looks like it is no longer showing efficiency and quality estimation with the recent emerging double machine learning. However, cutting-edge methods such as deep learning and super learner have shown superior performance in the estimation of the causal double machine learning target estimator, and efficiency in the time of computation

C++ programming language for an abstract massively parallel SIMD architecture

The aim of this work is to define and implement an extended C++ language to support the SIMD programming paradigm. The C++ programming language has been extended to express all the potentiality of an abstract SIMD machine consisting of a central Control Processor and a N-dimensional toroidal array of Numeric Processors. Very few extensions have been added to the standard C++ with the goal of minimising the effort for the programmer in learning a new language and to keep very high the performance of the compiled code. The proposed language has been implemented as a porting of the GNU C++ Compiler on a SIMD supercomputer.Comment: 10 page

Spatial support vector regression to detect silent errors in the exascale era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research Program, under Contract DE-AC02-06CH11357, by FI-DGR 2013 scholarship, by HiPEAC PhD Collaboration Grant, the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402, and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field