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General matrix multiplication (gemm) is a fundamental kernel in scientific computing and current frameworks for deep learning. Modern realisations of gemm are mostly written in C, on top of a small, highly tuned micro-kernel that is usually encoded in assembly. The high performance realisation of gemm in linear algebra libraries in general include a single micro-kernel per architecture, usually implemented by an expert. In this paper, we explore a couple of paths to automatically generate gemm micro-kernels, either using C++ templates with vector intrinsics or high-level Python scripts that directly produce assembly code. Both solutions can integrate high performance software techniques, such as loop unrolling and software pipelining, accommodate any data type, and easily generate micro-kernels of any requested dimension. The performance of this solution is tested on three ARM-based cores and compared with state-of-the-art libraries for these processors: BLIS, OpenBLAS and ArmPL. The experimental results show that the auto-generation approach is highly competitive, mainly due to the possibility of adapting the micro-kernel to the problem dimensions. [ABSTRACT FROM AUTHOR]

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients' facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell's facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients' condition. By replacing the VGG network with a more efficient backbone, we improved the model's accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation. [ABSTRACT FROM AUTHOR]

The book "Philosophy of Computer Science: An Introduction to the Issues and the Literature" by William J. Rapaport is a comprehensive monograph that analyzes the core issues defining the philosophy of computer science. It covers topics such as the nature of computer science, the definition of algorithms, the Church-Turing Thesis, the ontology of computer programs, and the relationship between computer science, ethics, and artificial intelligence. The book is intended for specialists in the field but can also be used as an introductory textbook for undergraduate teaching, requiring no background in philosophy or computer science. [Extracted from the article]

Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

This paper analyzes the tradeoffs between performance and resilience against cyber attacks of applications organized as workflows. The static nature of current workflows is a major benefit to attackers. To combat this advantage, a promising new approach inspired to Moving Target Defense (MTDs) was developed to increase a workflow's robustness to cyber attacks. This approach is based on dynamic reconfigurations of workflow tasks to reduce an attacker's probability of succeeding in completing the reconnaissance phase before launching an attack. Dynamic reconfigurations increase the resilience of a workflow against cyber attacks but increase its execution time due to the overhead of reconfigurations. As a part of this paper, we developed metrics that capture the impact of reconfigurations on a workflow's execution time and resilience against cyber attacks. The paper also presents recursive algorithms for computing the execution time and the reconnaissance function of a workflow. Our analysis relied on extensive trace-driven simulations of workflows from five different traces from the Workflow Trace Archive (WTA) and we used 6000 workflows from three different domains: scientific computing, engineering, and industrial. Our analysis of the results showed that there is a significant difference at the 95% confidence level due to reconfiguration on the resilience of workflows and demonstrated a consistent behavior across all five trace domains. [ABSTRACT FROM AUTHOR]

Python has become an essential programming language for scientific computing and data analysis and processing. Various multiple-point statistics (MPS) algorithms are used to characterize complex heterogeneous structures and phenomena in earth sciences. However, there is currently no Python library that integrates mainstream MPS methods for simulation and computation in geosciences. Aiming to establish a stable MPS tool, we developed an open-source Python library of commonly used MPS methods, named pyMPSLib. pyMPSLib consists of ENESIM, SNESIM, and DS algorithms and provides a flexible and convenient API interface. To ensure the maintainability of pyMPSLib, the Python objects and toolkits of MPS algorithms are defined and implemented. To improve the compatibility and extensibility of the presented library, uniform coding standard is adopted in pyMPSLib. We performed the parameter sensitivity analysis under multiple configurations to validate the performance of the library. This open-source library also provides optional tools to quantitatively evaluate the realizations of the integrated MPS methods. [ABSTRACT FROM AUTHOR]

Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures. [ABSTRACT FROM AUTHOR]

Python is becoming increasingly popular in scientific computing. The package MPI for Python (mpi4py) allows writing efficient parallel programs that scale across multiple nodes. However, it does not support non-contiguous data via slices, which is a well-known feature of NumPy. In this work, we therefore evaluate several methods to support the direct transfer of non-contiguous arrays in mpi4py. This significantly simplifies the code, while the performance basically stays the same. In a PingPong-, Stencil- and Lattice-Boltzmann-Benchmark, we compare the common manual copying, a NumPy-Copy design and a design that is based on MPI derived datatypes. In one case, the MPI derived datatype design could achieve a speedup of 15% in a Stencil-Benchmark on four compute nodes. Our designs are superior to naive manual copies, but for maximum performance manual copies with pre-allocated buffers or MPI persistent communication will be a better choice. [ABSTRACT FROM AUTHOR]

Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures. [ABSTRACT FROM AUTHOR]

Letters of Recommendation (LORs) are widely utilized for admission to both undergraduate and graduate programs, and are becoming even more important with the decreasing role that standardized tests play in the admissions process. However, LORs are highly subjective and thus can inject recommender bias into the process, leading to an inequitable evaluation of the candidates' competitiveness and competence. Our study utilizes natural language processing methods and manually determined ratings to investigate gender and cultural differences and biases in LORs written for STEM Master's program applicants. We generate features to measure important characteristics of the LORs and then compare these characteristics across groups based on recommender gender, applicant gender, and applicant country of origin. One set of features, which measure the underlying sentiment, tone, and emotions associated with each LOR, is automatically generated using IBM Watson's Natural Language Understanding (NLU) service. The second set of features is measured manually by our research team and quantifies the relevance, specificity, and positivity of each LOR. We identify and discuss features that exhibit statistically significant differences across gender and culture study groups. Our analysis is based on approximately 4000 applications for the MS in Data Science and MS in Computer Science programs at Fordham University. To our knowledge, no similar study has been performed on these graduate programs. [ABSTRACT FROM AUTHOR]

Introduction: Clinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]). Methods: Sarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017–2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks. Results: Among sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients. Conclusions: In real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data. Plain Language Summary: Rheumatoid arthritis (RA) is a condition that may cause joint damage, if untreated. Sarilumab is an advanced medication, approved for treating moderate-to-severe RA in patients not responding to initial standard medicines. Clinical trials have shown that sarilumab improves RA symptoms; however, some people do not respond. This is a common problem in RA treatment. Physicians measure proteins in people's blood (called biomarkers; e.g., anticyclic citrullinated peptide antibodies [ACPA], C-reactive protein [CRP], and rheumatoid factor [RF]) to predict a medicine's response. A previous study showed that people with positive blood tests for ACPA and CRP (> 12.3 mg/l) responded well to sarilumab; this study was based on machine learning (a branch of science using computers) and identified factors that could be linked to treatment benefits. The present study analyzed routine data of 2949 people from the ACR-RISE Registry and showed an improvement in RA symptoms after 6 and 12 months of sarilumab, with a greater improvement noted in patients previously treated with other medicines. Biomarkers were tested in 205 people to check whether they could predict treatment response in day-to-day life. People were called rule-positive if they tested positive for RF and/or ACPA with CRP > 12.3 mg/l, and otherwise rule-negative. After 24 weeks of treatment, rule-positive people had a greater chance of disease improvement than rule-negative people. These results showed the benefits of sarilumab in RA in routine care and suggested the usefulness of machine learning in identifying biomarkers that physicians can use to make treatment decisions. [ABSTRACT FROM AUTHOR]

A number of key scientific computing applications that are based upon tensor-product grid constructions, such as numerical weather prediction (NWP) and combustion simulations, require property-preserving interpolation. Essentially non-oscillatory (ENO) interpolation is a classic example of such interpolation schemes. In the aforementioned application areas, property preservation often manifests itself as a requirement for either data boundedness or positivity preservation. For example, in NWP, one may have to interpolate between the grid on which the dynamics is calculated to a grid on which the physics is calculated (and back). Interpolating density or other key physical quantities without accounting for property preservation may lead to negative values that are nonphysical and result in inaccurate representations and/or interpretations of the physical data. Property-preserving interpolation is straightforward when used in the context of low-order numerical simulation methods. High-order property-preserving interpolation is, however, nontrivial, especially in the case where the interpolation points are not equispaced. In this paper, we demonstrate that it is possible to construct high-order interpolation methods that ensure either data boundedness or constrained positivity preservation. A novel feature of the algorithm is that the positivity-preserving interpolant is constrained; that is, the amount by which it exceeds the data values may be strictly controlled. The algorithm we have developed comes with theoretical estimates that provide sufficient conditions for data boundedness and constrained positivity preservation. We demonstrate the application of our algorithm on a collection of 1D and 2D numerical examples, and show that in all cases property preservation is respected. [ABSTRACT FROM AUTHOR]

The structural battery composite (SBC) is a novel class of multifunctional materials with the ability to work as a lithium-ion battery that can withstand mechanical loads. The motivation of this study is to address one of the major challenges in the development of SBCs, which is a strong conflict in the structural and electrical demands for its electrolyte (i.e., high stiffness and high ionic conductivity). Furthermore, there is a design requirement that the electrochemical cycling should not result in overheating of the SBC. The novelty of this study is the development of an efficient multi-objective multiphysics density-based topology optimization framework that considers electrochemical/thermal/structural physics to identify the optimized design of a structural battery electrolyte (SBE). The optimization methodology is defined as solving a multi-objective problem of maximizing effective ionic conductivity and minimizing compliance of SBE. The problem is subjected to constraints on volume fraction and the maximum allowable temperature. The normalized-normal-constraint approach is utilized to generate a Pareto-front curve for this multi-objective problem. The proposed method is computationally efficient owing to utilizing a low-fidelity resistance network approach, for the electrochemical module and parallelizes the workload using portable, and extendable toolkit for scientific computing and message-passing interface. Several numerical examples are solved to demonstrate the applicability of the proposed methodology under different loading scenarios. The results reveal that the proposed methodology provides a better understanding of the required microstructural design of SBE for the performance improvement of structural battery composites. [ABSTRACT FROM AUTHOR]

Deep learning-based surrogate modeling is becoming a promising approach for learning and simulating dynamical systems. However, deep-learning methods find it very challenging to learn stiff dynamics. In this paper, we develop DAE-PINN, the first effective physics-informed deep-learning framework for learning and simulating the solution trajectories of nonlinear differential-algebraic equations (DAE). DAEs are used to model complex engineering systems, e.g., power networks, and present a "form" of infinite stiffness, which makes learning their solution trajectories challenging. Our DAE-PINN bases its effectiveness on the synergy between implicit Runge–Kutta time-stepping schemes (designed specifically for solving DAEs) and physics-informed neural networks (PINN) (deep neural networks that we train to satisfy the dynamics of the underlying problem). Furthermore, our framework (i) enforces the neural network to satisfy the DAEs as (approximate) hard constraints using a penalty-based method and (ii) enables simulating DAEs for long-time horizons. We showcase the effectiveness and accuracy of DAE-PINN by learning the solution trajectories of a three-bus power network. [ABSTRACT FROM AUTHOR]

To deliver the full science potential of the square kilometer array (SKA) telescope, several SKA regional centres (SRCs) will be required to be constructed in different SKA member countries around the world. These SRCs will provide high performance compute and storage, for the generation, of advanced science data products from the basic data streams generated by the SKA science data handling and processing system, critically necessary to the success of the key science projects to be carried out by the SKA user community. They will also provide support to astronomers to enable them to carry out analysis on very large SKA datasets. Construction of such large data centre is a technical challenge for all SKA member nations. In such a situation, each country plans to construct a smaller SRC over the next few years (2022 onwards), known as a proto-SRC. In India, we propose to construct a proto-SRC, which will be used for the analysis of data from SKA pathfinders and precursors with strong Indian involvement, such as uGMRT, Meerkat and MWA. We describe our thinking on some aspects of the the storage, compute and network of the proto-SRC and how it will be used for data analysis as well as for carrying out various simulations related to SKA key science projects led by Indian astronomers. We also present our thoughts on how the proto-SRC plans to evaluate emerging hardware and software technologies and to also begin software development in areas of relevance to SKA data processing and analysis, such as algorithm implementation, pipeline development and data visualisation software. [ABSTRACT FROM AUTHOR]

Currently, there is a tendency to use Islamic Geometric Patterns (IGPs) as important identities and cultural elements of building design in the Middle East. Despite high demand, lack of information about the potential of IGPs principles have led to formal inspiration in the design of existing buildings. Many research studies have been carried out on the principles of IGPs. However, comprehensive studies relating to new possibilities, such as structure-based, sustainable-based, and aesthetic-based purposes, developed by computer science and related technologies, are relatively rare. This article reviews the state-of-the-art knowledge of IGPs, provides a survey of the main principles, presents the status quo, and identifies gaps in recent research directions. Finally, future prospects are discussed by focussing on different aspects of the principles in accordance with collected evidence obtained during the review process. [ABSTRACT FROM AUTHOR]

Spectral methods are an important part of scientific computing's arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE. The last decade has seen the emergence of deep learning as a strong contender in providing efficient representations of complex functions. In the current work, we present an approach for combining deep neural networks with spectral methods to solve PDEs. In particular, we use a deep learning technique known as the Deep Operator Network (DeepONet) to identify candidate functions on which to expand the solution of PDEs. We have devised an approach that uses the candidate functions provided by the DeepONet as a starting point to construct a set of functions that have the following properties: (1) they constitute a basis, (2) they are orthonormal, and (3) they are hierarchical, i.e., akin to Fourier series or orthogonal polynomials. We have exploited the favorable properties of our custom-made basis functions to both study their approximation capability and use them to expand the solution of linear and nonlinear time-dependent PDEs. The proposed approach advances the state of the art and versatility of spectral methods and, more generally, promotes the synergy between traditional scientific computing and machine learning. [ABSTRACT FROM AUTHOR]

By incorporating three-way decision model into formal concept analysis (FCA) methodology, an emerging novel data analysis methodology, termed three-way concept analysis (3WCA), has been widely used in both computer science and social science areas. However, the construction of three-way concept lattice is quite time-consuming and proved as an NP-complete problem. Thus, it makes the knowledge discovery from three-way concept lattice difficult. To facilitate the knowledge acquisition in three-way concept lattice, both attribute reduction and three-way concept stability are utilized for pruning the size of three-way concept lattice and extracting informative three-way concepts. Aiming to extract hidden knowledge efficiently, this paper first attempts to figure out the relations of three-way concept stability in the original three-way concept lattice and reduced three-way concept lattice. We then propose a theorem on the invariance of three-way concept stability for attribute reduction of three-way concept lattice. To validate the correctness of our finding, we also conducted an empirical case study. [ABSTRACT FROM AUTHOR]

In this work, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration. [ABSTRACT FROM AUTHOR]

In this paper, we describe the design, development, and implementation of a curriculum based on teaching computer science using an industry-standard game-design software: Unity 3D. We discuss the theoretical underpinnings of our instructional design process and steps we have taken to introduce complexity and maintain student motivation. We discuss the challenges of this implementation and possible solutions and detail the additional steps related to teacher professional development, which is a key element for success of new and innovative curricular implementations such as ours. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) have been widely used to solve various scientific computing problems. However, large training costs limit PINNs for some real-time applications. Although some works have been proposed to improve the training efficiency of PINNs, few consider the influence of initialization. To this end, we propose a New Reptile initialization-based Physics-Informed Neural Network (NRPINN). The original Reptile algorithm is a meta-learning initialization method based on labeled data. PINNs can be trained with less labeled data or even without any labeled data by adding partial differential equations (PDEs) as a penalty term into the loss function. Inspired by this idea, we propose the new Reptile initialization to sample more tasks from the parameterized PDEs and adapt the penalty term of the loss. The new Reptile initialization can acquire initialization parameters from related tasks by supervised, unsupervised, and semi-supervised learning. Then, PINNs with initialization parameters can efficiently solve PDEs. Besides, the new Reptile initialization can also be used for the variants of PINNs. Finally, we demonstrate and verify the NRPINN considering both forward problems, including solving Poisson, Burgers, and Schrödinger equations, as well as inverse problems, where unknown parameters in the PDEs are estimated. Experimental results show that the NRPINN training is much faster and achieves higher accuracy than PINNs with other initialization methods. [ABSTRACT FROM AUTHOR]

The focus of this article is to find some new exact solutions to the (2+1)-dimensional nonlinear Schrödinger equation (NLSE) with Kerr media by employing the auxiliary equation method. A set of novel travelling wave solutions are observed such as bright, dark, periodic and optical solitons (dromions). Moreover, the physical interpretation of nonlinear waves would also be demonstrated with the aid of scientific computing. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model. [ABSTRACT FROM AUTHOR]

Since Wing Communications of the ACM, 49, 33–35, (2006) popularized computational thinking (CT) as a skill for every student, it has gained significant traction as an approach to bring computer science tools and practices into K-12 classrooms. At the same time, teachers often see the relevance of CT as a tool to introduce problem solving and thinking strategies in the classroom. Despite the increasing use of CT in K-12, questions remain about its role in supporting teaching and learning of disciplinary ideas. While CT can be used to bring computer science to all students, we believe that it can serve a bigger purpose to explicitly teach metacognitive strategies, which play a significant role in academic outcomes. In the paper, we discuss the connections between CT and metacognition and how CT could support the development of metacognition in K-12 classrooms. [ABSTRACT FROM AUTHOR]

We present an introduction to some of the state of the art in reduced order and surrogate modeling in gravitational-wave (GW) science. Approaches that we cover include principal component analysis, proper orthogonal (singular value) decompositions, the reduced basis approach, the empirical interpolation method, reduced order quadratures, and compressed likelihood evaluations. We divide the review into three parts: representation/compression of known data, predictive models, and data analysis. The targeted audience is practitioners in GW science, a field in which building predictive models and data analysis tools that are both accurate and fast to evaluate, especially when dealing with large amounts of data and intensive computations, are necessary yet can be challenging. As such, practical presentations and, sometimes, heuristic approaches are here preferred over rigor when the latter is not available. This review aims to be self-contained, within reasonable page limits, with little previous knowledge (at the undergraduate level) requirements in mathematics, scientific computing, and related disciplines. Emphasis is placed on optimality, as well as the curse of dimensionality and approaches that might have the promise of beating it. We also review most of the state of the art of GW surrogates. Some numerical algorithms, conditioning details, scalability, parallelization and other practical points are discussed. The approaches presented are to a large extent non-intrusive (in the sense that no differential equations are invoked) and data-driven and can therefore be applicable to other disciplines. We close with open challenges in high dimension surrogates, which are not unique to GW science. [ABSTRACT FROM AUTHOR]

Paper-based biosensors featuring immunoconjugated gold nanoparticles have gained extraordinary momentum in recent times as the platform of choice in key cases of field applications, including the so-called rapid antigen tests for SARS-CoV-2. Here, we propose a revision of this format, one that may leverage on the most recent advances in materials science and data processing. In particular, we target an amplifiable DNA rather than a protein analyte, and we replace gold nanospheres with anisotropic nanorods, which are intrinsically brighter by a factor of ~ 10, and multiplexable. By comparison with a gold-standard method for dot-blot readout with digoxigenin, we show that gold nanorods entail much faster and easier processing, at the cost of a higher limit of detection (from below 1 to 10 ppm in the case of plasmid DNA containing a target transgene, in our current setup). In addition, we test a complete workflow to acquire and process photographs of dot-blot membranes with custom-made hardware and regression tools, as a strategy to gain more analytical sensitivity and potential for quantification. A leave-one-out approach for training and validation with as few as 36 sample instances already improves the limit of detection reached by the naked eye by a factor around 2. Taken together, we conjecture that the synergistic combination of new materials and innovative tools for data processing may bring the analytical sensitivity of paper-based biosensors to approach the level of lab-grade molecular tests. [ABSTRACT FROM AUTHOR]

Paper-based biosensors featuring immunoconjugated gold nanoparticles have gained extraordinary momentum in recent times as the platform of choice in key cases of field applications, including the so-called rapid antigen tests for SARS-CoV-2. Here, we propose a revision of this format, one that may leverage on the most recent advances in materials science and data processing. In particular, we target an amplifiable DNA rather than a protein analyte, and we replace gold nanospheres with anisotropic nanorods, which are intrinsically brighter by a factor of ~ 10, and multiplexable. By comparison with a gold-standard method for dot-blot readout with digoxigenin, we show that gold nanorods entail much faster and easier processing, at the cost of a higher limit of detection (from below 1 to 10 ppm in the case of plasmid DNA containing a target transgene, in our current setup). In addition, we test a complete workflow to acquire and process photographs of dot-blot membranes with custom-made hardware and regression tools, as a strategy to gain more analytical sensitivity and potential for quantification. A leave-one-out approach for training and validation with as few as 36 sample instances already improves the limit of detection reached by the naked eye by a factor around 2. Taken together, we conjecture that the synergistic combination of new materials and innovative tools for data processing may bring the analytical sensitivity of paper-based biosensors to approach the level of lab-grade molecular tests. [ABSTRACT FROM AUTHOR]

Centrality measure is an essential tool in network analysis and widely used in the domain of computer science, biology and sociology. Taking advantage of the speedup offered by quantum computation, various quantum centrality measures have been proposed. However, few work of quantum centrality involves weighted graphs, while the weight of edges should be considered in certain real-world networks. In this work, we extend the centrality measure based on continuous-time quantum walk to weighted graphs. We testify the feasibility and reliability of this quantum centrality using an ensemble of 41,675 graphs with various topologies and comparing with the eigenvector centrality measure. The average Vigna's correlation index of all the tested graphs with all edge weights in [1, 10] is as high as 0.967, indicating the pretty good consistency of rankings by the continuous-time quantum walk centrality and the eigenvector centrality. The intuitive consistency of the top-ranked vertices given by this quantum centrality measure and classical centrality measures is also demonstrated on large-scale weighted graphs. Moreover, the range of the continuous-time quantum walk centrality values is much bigger than that of classical centralities, which exhibits better distinguishing ability to pick the important vertices from the ones with less importance. All these results show that the centrality measure based on continuous-time quantum walk still works well on weighted graphs. [ABSTRACT FROM AUTHOR]

The integration of machine learning (Keplerian paradigm) and more general artificial intelligence technologies with physical modeling based on first principles (Newtonian paradigm) will impact scientific computing in engineering in fundamental ways. Such hybrid models combine first principle-based models with data-based models into a joint architecture. This paper will give some background, explain trends and showcase recent achievements from an applied mathematics and industrial perspective. Examples include characterization of superconducting accelerator magnets by blending data with physics, data-driven magnetostatic field simulation without an explicit model of the constitutive law, and Bayesian free-shape optimization of a trace pair with bend on a printed circuit board. [ABSTRACT FROM AUTHOR]

A. Kireeva et al. contributed another paper in the field of cellular automata: "Parallel simulation of drift-diffusion-recombination by cellular automata and global random walk algorithm". Parallel computing technologies enable the solution of large-scale numerical simulation and data processing problems in science and industry. The previously proposed contention-aware scheduling algorithm is formally described with this model, modifications of the algorithm are proposed: look-ahead technique, chunked data transfers, data caching, and peer-to-peer data transfers. [Extracted from the article]

The Sunway TaihuLight is the first supercomputer built entirely with domestic processors in China. On Sunway Taihulight, the local data memory (LDM) of the slave core is limited, so data transmission with the main memory is frequent during calculation, and the memory access efficiency is low. On the other hand, for many scientific computing programs, how to solve the storage problem of irregular access data is the key to program optimization. Software cache (SWC) is one of the effective means to solve these problems. Based on the characteristics of Sunway TaihuLight structure and irregular access, this paper designs and implements a new software cache structure by using part of the space in LDM to simulate the cache function, which uses new cache address mapping and conflicts solution to solve high data access overhead and storage overhead in a traditional cache. At the same time, the SWC uses the register communication between the slave cores to share on the different slave core LDMs, increasing the capacity of the software cache and improving the hit rate. In addition, we adopt a double buffer strategy to access regular data in batches, which hides the communication overhead between the slave core and the main memory. The test results on the Sunway TaihuLight platform show that the software cache structure in this paper can effectively reduce the program running time, improve the software cache hit rate, and achieve a better optimization effect. [ABSTRACT FROM AUTHOR]

Fortran is still widely used in scientific computing, and a very large corpus of legacy as well as new code is written in FORTRAN 77. In general this code is not type safe, so that incorrect programs can compile without errors. In this paper, we present a formal approach to ensure type safety of legacy Fortran code through automated program transformation. The objective of this work is to reduce programming errors by guaranteeing type safety. We present the first rigorous analysis of the type safety of FORTRAN 77 and the novel program transformation and type checking algorithms required to convert FORTRAN 77 subroutines and functions into pure, side-effect free subroutines and functions in Fortran 90. We have implemented these algorithms in a source-to-source compiler which type checks and automatically transforms the legacy code. We show that the resulting code is type safe and that the pure, side-effect free and referentially transparent subroutines can readily be offloaded to accelerators. [ABSTRACT FROM AUTHOR]

Large simulations with many independent sub-simulations are common in scientific computing. There are numerous challenges, however, associated with performing such simulations in shared computing environments. For example, sub-simulations may have wildly varying completion times or not complete at all, leading to unpredictable runtimes as well as unbalanced and inefficient use of human and computational resources. In this study, we use the actor model of concurrent computation to improve both the resource utilization and fault tolerance for large-scale scientific computing simulations. More specifically, we use actors in the SUMMA model to manage a large-scale hydrological simulation over the North American continent with over 500,000 independent sub-simulations. We find that the actors implementation outperforms a standard array job submission as well as the job submission tool GNU Parallel by better balancing the computational load across processors. The actors implementation also improves fault tolerance and can eliminate the user intervention required to detect and re-submit failed jobs. [ABSTRACT FROM AUTHOR]

Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research. [ABSTRACT FROM AUTHOR]

In modern scientific computing, the scientific workflow (SWF) is considered an essential tool for the description and implementation of complex applications. The workflow application is described as a directed acyclic graph (DAG) of tasks (vertices) with I/O data flow (edges) between them. However, this approach does not support the ability to handle data streams from different IoT sources, nor does it support independent deployment and scaling of individual computing tasks. One approach to optimizing a SWF is to partition it into multiple stages, but the implementation is complicated by the tight coupling relationship between the vertices. In the article, we propose Micro-Workflows (MWF) algorithms which automatically separate the partitioned monolith workflow into a set of independent smaller workflows called MWF by refactoring the edges between the vertices. [ABSTRACT FROM AUTHOR]

In the past few years, the ethical ramifications of AI technologies (in particular data science) have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, in this paper, we formulate a different approach to ethics in data science which is based on a different conception of "being ethical" and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities (e.g., virtues), which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than "applying ethics" (which can be mindless), data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. [ABSTRACT FROM AUTHOR]

Purpose: Allogeneic stem cell transplantation would benefit from re-engineering care towards an integrated eHealth-facilitated care model. With this paper we aim to: (1) describe the development of an integrated care model (ICM) in allogeneic SteM-cell-transplantatIon faciLitated by eHealth (SMILe) by combining implementation, behavioral, and computer science methods (e.g., contextual analysis, Behavior Change Wheel, and user-centered design combined with agile software development); and (2) describe that model's characteristics and its application in clinical practice. Methods: The SMILe intervention's development consisted of four steps, with implementation science methods informing each: (1) planning its set-up within a theoretical foundation; (2) using behavioral science methods to develop the content; (3) choosing and developing its delivery method (human/technology) using behavioral and computer science methods; and (4) describing its characteristics and application in clinical practice. Results: The SMILe intervention is embedded within the eHealth enhanced Chronic Care Model, entailing four self-management intervention modules, targeting monitoring and follow-up of important medical and symptom-related parameters, infection prevention, medication adherence, and physical activity. Interventions are delivered partly face-to-face by a care coordinator embedded within the transplant team, and partly via the SMILeApp that connects patients to the transplant team, who can monitor and rapidly respond to any relevant changes within 1 year post-transplant. Conclusion: This paper provides stepwise guidance on how implementation, behavioral, and computer science methods can be used to develop interventions aiming to improve care for stem cell transplant patients in real-world clinical settings. This new care model is currently being tested in a hybrid I effectiveness-implementation trial. [ABSTRACT FROM AUTHOR]

SpMV (Sparse matrix-vector multiplication) is an essential component in scientific computing and has attracted the attention of researchers in related fields at home and abroad. With the continuous expansion of matrix data, the efficient parallel SpMV algorithm has become a research hotspot for research experts in related fields. The sparse matrix compression format as a critical point to improve computing performance can effectively save storage space and efficiently cooperate with the advantages of the processor system structure to give full play to performance. This paper proposes a new sparse matrix storage format CSR2 (Compressed Sparse Row 2). It is a new single format and suitable for processor platforms with SIMD (Single Instruction Multiple Data) vectorizations. The format operation of CSR2 is easy to implement with a low overhead of conversion. We compared the SpMV algorithm based on CSR2 with the most advanced single format CSR5 (Compressed Sparse Row 5) and Intel MKL (Intel Math Kernel Library) on the mainstream high-performance processor Intel Xeon E5-2670 v3 CPU. We choose 48 sets of matrices to be used as a benchmark suite. Experimental results show that CSR2 has a remarkable performance improvement compared with CSR5 and MKL. Compared to CSR5, CSR2 can achieve an average acceleration of 1.401 × (up to 1.861 ×). Compared to MKL, CSR2 can achieve an average acceleration of 1.261 × (up to 5.921 ×). In reality, for applications with multiple iterations, using our CSR2 can bring low-overhead format conversion and high-throughput computing performance. [ABSTRACT FROM AUTHOR]

Fortress provides a nice set of abstractions used widely in scientific computing. The use of such abstractions enhances the productivity of programmers/users. Also, in scientific computations, boilerplate code has extensive usage. Keeping this in view, we embed Fortress abstractions in an X10 environment so that we can get better productivity without losing performance. In this paper, we transform Fortress into X10 through a transcompilation system. We describe compilation strategies for a few important constructs and discuss the performance of the generated X10 code with respect to the original Fortress code. The translated X10 code outperforms the original Fortress code with a maximum of 206x speedup achieved in the best case. The system also supports the multiresolution language approach that simplifies parallel programming by allowing domain scientists to write programs in the Fortress syntax that is closer to the mathematical notation. The translated X10 code, which can further be compiled to either C++ or Java, implicitly assures performance and may further be optimized for performance by utilizing the low-level features of X10 (or C++/Java). [ABSTRACT FROM AUTHOR]

The heterogeneous many-core architecture plays an important role in the fields of high-performance computing and scientific computing. It uses accelerator cores with on-chip memories to improve performance and reduce energy consumption. Scratchpad memory (SPM) is a kind of fast on-chip memory with lower energy consumption compared with a hardware cache. However, data transfer between SPM and off-chip memory can be managed only by a programmer or compiler. In this paper, we propose a compiler-directed multithreaded SPM data transfer model (MSDTM) to optimize the process of data transfer in a heterogeneous many-core architecture. We use compile-time analysis to classify data accesses, check dependences and determine the allocation of data transfer operations. We further present the data transfer performance model to derive the optimal granularity of data transfer and select the most profitable data transfer strategy. We implement the proposed MSDTM on the GCC complier and evaluate it on Sunway TaihuLight with selected test cases from benchmarks and scientific computing applications. The experimental result shows that the proposed MSDTM improves the application execution time by 5.49 × and achieves an energy saving of 5.16 × on average. [ABSTRACT FROM AUTHOR]

The displacement and gradient of a finite element solution are of primary interest and a high accuracy displacement and gradient approximation are always desirable in scientific computing. In this paper, we introduce a new state of the art way to reconstruct a high accuracy approximated gradient. The proposed method inherits the advantages of both the extrapolation technique and the interpolation post-processing technique. We theoretically justify the fifth-order local ultraconvergence of the post-processing gradient. In the meanwhile, an ultraconvergent displacement can be obtained from this post-processing procedure. The theoretical result is numerically verified and validated. [ABSTRACT FROM AUTHOR]

Workflows that consume and produce large amounts of data are being widely used in modern scientific computing and data processing pipelines. Scheduling of data-intensive workflows requires a careful management of data transfers between tasks, since network contention can significantly impact the workflow execution time. The paper presents and evaluates several scheduling algorithms, data transfer strategies and optimizations aimed at efficient execution of data-intensive workflows. The studied approaches reduce or completely avoid network contention by explicit scheduling of data transfers and incorporate several optimizations, such as data caching, chunked and peer-to-peer data transfers. The results of experimental study demonstrate that the relative performance of different approaches depends on the workflow properties, data staging strategy and system configuration. The proposed CAS-L1 heuristic with additional data transfer optimizations achieves the best results. [ABSTRACT FROM AUTHOR]

To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these circumstances, a vast number of possible scenarios, incorporating uncertain supply and demand, has to be simulated ahead of time. This many-query gas network simulation task can be accelerated by model reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model order reduction algorithms. For this industrial application, we bring together the scientific computing topics of: mathematical modeling of gas transport networks, numerical simulation of hyperbolic partial differential equation, and parametric model reduction for nonlinear systems. This research resulted in the morgen (Model Order Reduction for Gas and Energy Networks) software platform, which enables modular testing of various combinations of models, solvers, and model reduction methods. In this work we present the theoretical background on systemic modeling and structured, data-driven, system-theoretic model reduction for gas networks, as well as the implementation of morgen and associated numerical experiments testing model reduction adapted to gas network models. [ABSTRACT FROM AUTHOR]

Tree balance plays an important role in different research areas like theoretical computer science and mathematical phylogenetics. For example, it has long been known that under the Yule model, a pure birth process, imbalanced trees are more likely than balanced ones. Also, concerning ordered search trees, more balanced ones allow for more efficient data structuring than imbalanced ones. Therefore, different methods to measure the balance of trees were introduced. The Sackin index is one of the most frequently used measures for this purpose. In many contexts, statements about the minimal and maximal values of this index have been discussed, but formal proofs have only been provided for some of them, and only in the context of ordered binary (search) trees, not for general rooted trees. Moreover, while the number of trees with maximal Sackin index as well as the number of trees with minimal Sackin index when the number of leaves is a power of 2 are relatively easy to understand, the number of trees with minimal Sackin index for all other numbers of leaves has been completely unknown. In this manuscript, we extend the findings on trees with minimal and maximal Sackin indices from the literature on ordered trees and subsequently use our results to provide formulas to explicitly calculate the numbers of such trees. We also extend previous studies by analyzing the case when the underlying trees need not be binary. Finally, we use our results to contribute both to the phylogenetic as well as the computer scientific literature using the new findings on Sackin minimal and maximal trees to derive formulas to calculate the number of both minimal and maximal phylogenetic trees as well as minimal and maximal ordered trees both in the binary and non-binary settings. All our results have been implemented in the Mathematica package SackinMinimizer, which has been made publicly available. [ABSTRACT FROM AUTHOR]

Computer science (CS) education finds itself at a pivotal moment to reckon with what it means to accept, use, and create technologies, with the continued recruitment of minoritized students into the field. In this paper, we build on the oral traditions of educating with stories, and take the reader on two journeys. We begin with a story that leads us in thinking about where computer science education is, in the wake of slavery, under the New Jim Code. Within a BlackCrit framework, we shake the grounds of the computer science field, where technologies are often promoted as objective, but reflect and reproduce existing inequalities. In tune with maintaining current systems of power, efforts to broaden participation in computer science have been heavily driven by industry, government, and military interests. These interests ultimately push us farther away from sustainable relations with the earth and with each other, and risk the very lives of the same communities the field claims to help. However, we can rewrite the narratives of the role of technology in our lives. We present a second story in which we place abolitionist theories and practices in conversation with computer science education. In this paper we explore (1) In what ways does computing education support systems that enable Black death? and (2) How might integrating an abolitionist framework into computer science open up possibilities for world-building and dreaming in the name of Black Life? We imagine a different future where computer science is used as a tool in life-affirming, world-building projects. We invite readers to engage with this piece as a part of an active dialogue towards combating anti-Black logics in the field of computer science education. [ABSTRACT FROM AUTHOR]

Sparse triangular solve (SpTRSV) is an important kernel in many scientific computing applications. In traditional viewpoints, accelerating SpTRSV by parallelizing the solution process is a challenging task. Dependencies among the variables that exist in the solution process not only restrict the parallelism that can be achieved, but also introduce large synchronization overhead among the parallel tasks. Moreover, a time-consuming pre-processing phase is commonly required to identify calculations that can be parallelized. However, we have observed that a large number of dependencies among the variables can be eliminated if we only calculate partial values of the variables first and add them together to obtain the final values later. By using such a strategy, starting to solve a variable does not need to wait for all of its prerequisite variables having been solved. In consequence, parallelism of the SpTRSV can be increased significantly. In this paper, we transform above mentioned observations into a subtree-based parallel algorithm to accelerate SpTRSV. The proposed algorithm calculates partial values of the variable along with an implicit subtree traversal and utilizes hardware atomic operation to implement accumulation of the partial values. This not only introduces no pre-processing overhead, but also avoids any barrier synchronization among the parallel threads. We evaluate the proposed algorithm on 2135 matrices from SuiteSparse Matrix Collection based on a generic GPU platform. Experimental results demonstrate that our scheme outperforms the state-of-the-art GPU and CPU vendor libraries in 1949 and 1782 matrices, respectively. Compared with the latest synchronization-free method, our scheme outperforms in 1779 matrices. [ABSTRACT FROM AUTHOR]

The focus of this article is to find some new exact solutions to the M-fractional paraxial nonlinear Schrödinger equation with Kerr media by employing the modified simple equation method and the auxiliary equation method. A set of novel travelling wave solutions are observed such as bright, dark, periodic and optical solitons. Moreover, the physical interpretation of nonlinear waves would also be demonstrated with the aid of scientific computing. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is integral to Industry 4.0 and the evolution of smart factories. To realize this future, material processing industries are embarking on adopting AI technologies into their enterprise and plants; however, like all new technologies, there is always the potential for misuse or the false belief that the outcomes are reliable. The goal of this paper is to provide context for the application of machine learning to materials processing. The general landscapes of data science and materials processing are presented, using the foundry and the metal casting industry as an exemplar. The challenges that exist with typical foundry data are that the data are unbalanced, semi-supervised, heterogeneous, and limited in sample size. Data science methods to address these issues are presented and discussed. The elements of a data science project are outlined and illustrated by a case study using sand cast foundry data. Finally, a prospective view of the application of data science to materials processing and the impact this will have in the field are given. [ABSTRACT FROM AUTHOR]

Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained grid-based simulations. In this paper, we investigate data-driven modelling of Bose-Einstein Condensates (BECs). In particular, we use Gaussian Processes (GPs) to model the ground state wave function of BECs as a function of scattering parameters from the dimensionless Gross Pitaveskii Equation (GPE). Experimental results illustrate the ability of GPs to accurately reproduce ground state wave functions using a limited number of data points from simulations. Consistent performance across different configurations of BECs, namely Scalar and Vectorial BECs generated under different potentials, including harmonic, double well and optical lattice potentials pronounces the versatility of our method. Comparison with existing data-driven models indicates that our model achieves similar accuracy with only a small fraction ( 1 50 th) of data points used by existing methods, in addition to modelling uncertainty from data. When used as a simulator post-training, our model generates ground state wave functions 36 × faster than Trotter Suzuki, a numerical approximation technique that uses Imaginary time evolution. Our method is quite general; with minor changes it can be applied to similar quantum many-body problems. [ABSTRACT FROM AUTHOR]

In scientific computing and cryptography, there are many applications that involve large integer multiplication, which is a time-consuming operation. To reduce the computational complexity, number theoretic transform is widely used, wherein the multiplication can be performed in the frequency domain with reduced complexity. However, the speed performance of large integer multiplication is still not satisfactory if the operand size is very large (e.g., more than 100K-bit). In view of that, several researchers had proposed to accelerate the implementation of number theoretic transform using massively parallel GPU architecture. In this paper, we proposed several techniques to improve the performance of number theoretic transform implementation, which is faster than the state-of-the-art work by Dai et al. The proposed techniques include register-based twiddle factors storage and multi-stream asynchronous computation, which leverage on the features offered in new GPU architectures. The proposed number theoretic transform implementation was applied to CMNT fully homomorphic encryption scheme proposed by Coron et al. With the proposed implementation technique, homomorphic multiplications in CMNT take 0.27 ms on GTX1070 desktop GPU and 7.49 ms in Jetson TX1 embedded system, respectively. This shows that the proposed implementation is suitable for practical applications in server environment as well as embedded system. [ABSTRACT FROM AUTHOR]