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With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss. [ABSTRACT FROM AUTHOR]

This study uses a national longitudinal sample of 612 Latinx STEM bachelor's degree recipients across 171 higher education institutions to examine the STEM undergraduate experiences and institutional contexts that predict their values toward conducting research that will have a meaningful impact on underserved communities. Results show that several background and precollege characteristics, particular STEM majors, learning about science and technology in society, and institutional characteristics predict this empowerment outcome. While attending a Hispanic-Serving Institution (HSI) did not have a direct effect on the outcome, it was found to reduce the negative effect of majoring in math/statistics and computer science/tech. Implications for our current understanding of Latinx STEM success and the role of HSIs and PWIs are provided. [ABSTRACT FROM AUTHOR]

This paper proposes two novel approaches to measure the similarity of co-cited authors for the task of document clustering, a) paragraph-level content-based author co-citation analysis (PCACA) and b) section-level content-based author co-citation analysis (SCACA), by mining the textual cited reference at the paragraph and the section level within a given scientific publication, respectively. Using over 2000 full-text publications, indexed in the field of Computer and Information Sciences, indexed in PLOS.org, we extract the useful information from a full-text publication such as citing sentences, location of citing sentences, cited first author name, and title of cited. We show that our proposed SCACA method of clustering outperforms existing clustering methods by exhibiting more optimal clusters with minimum graph density and an average degree in SCACA, i.e., 0.327 and 27.143, respectively. Finally, we show that SCACA produces the optimum number of clusters that comprehensively explains sub-disciplines of co-cited author pairs. [ABSTRACT FROM AUTHOR]

Workflow computing has become an essential part of many scientific and engineering fields, while workflow scheduling has long been a well-known NP-complete research problem. Major previous works can be classified into two categories: heuristic-based and guided random-search-based workflow scheduling methods. Monte Carlo Tree Search (MCTS) is a recently proposed search methodology with great success in AI research on game playing, such as Computer Go. However, researchers found that MCTS also has potential application in many other domains, including combinatorial optimization, task scheduling, planning, and so on. In this paper, we present a new workflow scheduling approach based on MCTS, which is still a rarely explored direction. Several new mechanisms are developed for the major steps in MCTS to improve workflow execution schedules further. Experimental results show that our approach outperforms previous methods significantly in terms of execution makespan. [ABSTRACT FROM AUTHOR]

"Deep Learning and Scientific Computing with R torch" by Sigrid Keydana is a book that introduces an R interface for PyTorch, a popular deep learning framework. The book is well-written and easy to follow, with plenty of illustrations and examples. It is divided into three parts: the basics of torch, the implementation of deep neural networks, and the utility of torch beyond deep learning. The book covers topics such as tensors, automatic differentiation, neural network architecture, optimization algorithms, and various applications of torch. Overall, it is a valuable resource for R users interested in deep learning and scientific computing. [Extracted from the article]

Full waveform inversion (FWI) plays a major role in the oil and gas industry as a state-of-the-art technique that produces quantitative subsurface structures with high-fidelity images. Various FWI studies have been conducted, and these suggest that FWI is a promising inversion method. Recently, many attempts have been made toward three-dimensional (3D) and four-dimensional FWI applications (which were difficult to perform in the past) because of the progress made in computer science and the growth of computer resources. To manage the very large data requirement of 3D problems, a time-domain FWI that is relatively efficient in terms of memory demands must be implemented. However, it could encounter practical issues, leading to failure in its convergence. In this paper, we introduce these practical issues and several alternative methods for mitigating them. The first issue is the bandpass filtering of the observed seismograms. We suggest that the frequency-domain filter based on a reference wavelet would be optimal in terms of both bandpass filtering and source wavelet estimation. The second issue is related to acoustic approximation. We show that a simple density model comprising only water and solid layers is a reasonable option to address seafloor reflectivity properly. The last issue is the accumulation of round-off errors due to the massive computation of the objective function. We demonstrate that a simple modification of the error calculation can resolve this round-off error problem. [ABSTRACT FROM AUTHOR]

GPU accelerators are massively parallel in nature and tailored for processing numerically intensive high-performance computing applications. But most of the applications that involve heavy computations take excess time to process as the dataset gets larger and lead to more power consumption. Hence, among all the factors in sustainable computing that contribute to operartional costs of GPU, power and time management is one of the major challenging issue. This paper presents a methodology to reduce power consumption in GPUs, meanwhile keeping parallel execution of jobs as high as possible. To achieve this, a power and time aware framework is created by integrating TensorFlow library, InterPSS, and Dynamic Voltage Frequency Scaling (DVFS) techniques. For most of the scientific computing, matrix operations are considered as the fundamental building block. So, the performance, power consumption, and power efficiency of GPU for matrix applications are analyzed under proposed model. Experimental results reveal that proposed methodology substantially reduces peak power of GPUs by 20%, with improved speedup in execution time around 15%. [ABSTRACT FROM AUTHOR]

Spatial boundary analysis has attained considerable attention in several disciplines including engineering, shape analysis, spatial statistics, and computer science. The inferential question of interest is often to identify rapid surface change of an unobserved latent process. Curvilinear wombling and crisp wombling (or fuzzy) are two major approaches that have emerged in Bayesian spatial statistics literature. These methods are limited to a single spatial scale even though data with multiple spatial scales are often accessible. Thus, we propose a multiscale representation of the directional derivative Karhunen–Loéve expansion to perform directionally based boundary detection. Taking a multiscale spatial perspective allows us, for the first time, to consider the concept of curvilinear boundary fallacy (CBF) error, which is a boundary detection analog to the ecological fallacy that is often studied in spatial change of support literature. Furthermore, we propose a directionally based multiscale curvilinear boundary error criterion to quantify CBF. We refer to this metric as the criterion for boundary aggregation error (BAGE), and use it to perform boundary detection. Several theoretical results are derived to motivate BAGE. In particular, we show that no BAGE exists when the directional derivatives of eigenfunctions of a KL expansion are constant across spatial scales. We illustrate the use of our model through a simulated example and an analysis of Mediterranean wind measurements data. for this article are available online. [ABSTRACT FROM AUTHOR]

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum," the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this article, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom. [ABSTRACT FROM AUTHOR]

Elementary computer science (CS) can be engaging and challenging for some students with disabilities who struggle with complex problem solving. This study examined academic engagement of students with autism spectrum disorder (ASD) in elementary CS instruction. A mixed methods case study was used to study how three elementary students with ASD participated in CS instruction that involved a peer collaboration strategy. All students were engaged with the computational tasks, with variations in academic engagement. All experienced challenges with the computing activities that resulted in disengagement or limited independent problem solving. Students received "in the moment" supports, with little evidence of planned instructional scaffolding. Students did not receive individualized support. With such supports, they may have had a more positive experience. It is important to continue to study the experiences of students with disabilities during elementary CS instruction to better support these learners. [ABSTRACT FROM AUTHOR]

Background and Context: In this theory paper, we explore the concept of translanguaging from bilingual education, and its implications for teaching and learning programming and computing in especially computer science (CS) for all initiatives. Objective: We use translanguaging to examine how programming is and isn't like using human languages. We frame CS as computational literacies. We describe a pedagogical approach for teaching computational literacies. Method: We review theory from applied linguistics, literacy, and computational literacy. We provide a design narrative of our pedagogical approach by describing activities from bilingual middle school classrooms integrating Scratch into academic subjects. Findings: Translanguaging pedagogy can leverage learners' (bilingual and otherwise) full linguistic repertoires as they engage with computational literacies. Implications: Our data helps demonstrate how translanguaging can be mobilized to do CS, which has implications for increasing equitable participation in computer science. [ABSTRACT FROM AUTHOR]

The research focus of an institution indicates the academic strength of the institution. This academic strength goes a long way to determine the quality of academic product expected from such an institution. To this effect, this study aims at analyzing the research publication strength of 70 Nigerian universities that are visible in Scopus database for Computer Science from inception to 22 December 2017. They include 35 federal, 27 state and 8 private universities. This study is divided into two phases; the first phase consists of six major steps in a waterfall model and the second is carried out by adopting a knowledge discovery model using a clustering algorithm for pattern discovery in an unstructured data model. The data source for the analysis is the Scopus indexing database recommended by the Times Higher Education for international ranking of higher institutions. From the first phase, the predominant outlet for each institution was discovered alongside the percentage of publications in the dominant outlet and the number of publications by the institution within the database over the period of the analysis. In the second phase, the predominant areas of research of each institution within the field of Computer Science was determined. In particular, it was observed that such areas as deep learning and data science are under researched. The findings also revealed networking as the most published area in Nigerian universities, and a number of Nigerian computer science scholars engage in application research which link Computer Science with other disciplines with Nigeria and Africa as case studies. The findings from this research can support postgraduate students' decision in making the right choice of institution based on their research interest. It will also serve as an eye-opener for industries in promoting a result-oriented university-industry partnership. [ABSTRACT FROM AUTHOR]

Overlaying Computer Science (CS) courses on top of inequitable schooling systems will not move us toward "CS for All." This paper prioritizes the perspectives of minoritized students enrolled in high school CS classrooms across a large, urban school district in the Western United States, to help inform how CS can truly be for all. This paper explores what student agency looks like while answering the research question "From the perspective of minoritized students historically underrepresented in computing, what makes a critical difference in their sense of agency in introductory CS high school classes?" Our research-practice partnership used qualitative data (including classroom observations, interviews, student artifacts, and video/photos) and surveys to surface the perspectives and visions of minoritized youth. The research describes what student agency looks like as youth – who have had no prior CS learning experiences – use CS as a tool to resist marginalization and dehumanizing school contexts, while declaring their own "rightful presence" in CS classrooms. Findings demonstrate the importance for CS curricula and pedagogy to center the lives of students in ways that are consequential for minoritized youth. This would support deeper engagement with content learning and student agency with computing. [ABSTRACT FROM AUTHOR]

Promoting open-ended projects presents new opportunities and challenges for inclusive teaching in CS classrooms. While efforts have been made to develop inclusive curricula, little research has focused on ways teachers apply curricula in their classrooms to promote inclusion. To understand the challenges faced in facilitating an open-ended unit and the pedagogical strategies enacted to address those challenges, we analyze the self-reported teaching practices that experienced teachers developed in their implementation of a constructionist electronic textiles unit in Exploring Computer Science. We inductively analyzed and coded 17 experienced teachers' weekly surveys and post-interviews. Teachers leveraged local resources and contexts to adapt classroom activities as well as developed new perspectives on computing as ways to foster inclusivity. We propose further opportunities for CS teachers to consciously reflect and draw upon the assets and funds of knowledge of their students' communities when facilitating open-ended, inclusive activities. [ABSTRACT FROM AUTHOR]

The use of Scratch programming in introducing text-based programming to novices at all levels of education has gained prominence in computer science but is still hardly known among pre-service teachers. With affordances of Scratch in learning text-based programming, we present an experience report on how we supported our first-year pre-service teachers' learning of procedural programming concepts with Scratch for the first time. The study follows an action research strategy conducted over two cycles with 58 pre-service teachers who were purposively sampled. Findings revealed that Scratch supported the learning of procedural programming by our first-year pre-service teachers to some extent. We, therefore, recommend that pre-service teachers be exposed to more exercises while focusing on challenging concepts such as algorithms, use of variables, repetition and control structures. [ABSTRACT FROM AUTHOR]

Advanced approaches to predicting offending are increasingly transpiring without the forensic psychology discipline's involvement – an area that it has piloted and influenced for many decades. Computer science experts have built an impressive decade-long literature base on risk assessment – a technical literature that is not only progressing at a fast pace, but appears to function independently, for the most part, from the forensic risk assessment literature. This paper outlines the potential utility of machine learning approaches and the broader 'algorithmic culture', for forensic risk assessment, and the implications their use (and non-use) may have for the discipline. [ABSTRACT FROM AUTHOR]

Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions. [ABSTRACT FROM AUTHOR]

Three studies examine the relationship between women's expression of interest in computer science and identity expression threat, the concern about conveying an identity inconsistent with one's gender role. Undergraduates perceive academic majors to signal who they are to peers (Study 1). Women imagining majoring in computer science report greater identity expression threat from their peers outside computer science than from those inside the field (Study 2). Women report greater identity expression threat in computer science (but not biology or English) than do men. Identity expression threat mediates gender differences in reported likelihood of downplaying interest in computer science (Study 3). Women considering computer science perceive they will be doubly isolated, both from those within and outside the field. [ABSTRACT FROM AUTHOR]

Computer hardware, software, numerical algorithms, and science and engineering applications are traced for a half century from the author's perspective. [ABSTRACT FROM AUTHOR]

The history of computing in Mexico cannot be thought without the name of Prof. Harold V. McIntosh (1929–2015). For almost 50 years, in Mexico, he contributed to the development of computer science with wide international recognition. Approximately in 1964, McIntosh began working in the Physics Department of the Advanced Studies Center (CIEA) of the National Polytechnic Institute (IPN), now called CINVESTAV. In 1965, at the National Center of Calculus (CeNaC), he was a founding member of the Master in Computing, first in Latin America. With the support of Mario Baez Camargo and Enrique Melrose, McIntosh continues his research of Martin-Baltimore Computer Center and University of Florida at IBM 709. [ABSTRACT FROM AUTHOR]

In 37 states, an accepted computer science course may qualify or "flex" as a high school graduation requirement in lieu of a traditional requirement, such as mathematics. Applying a first- and second-order barriers framework (i.e., tangible impediments and personal beliefs), we examined views of 60 high school administrators. The administrators were at schools that had not yet adopted flex credit policies and were surveyed regarding readiness of their schools and communities, as well as their personal beliefs, about adopting such a policy. Key findings included learning that the greatest perceived obstacles centered on developing and maintaining computer science courses (e.g., qualified teachers). Administrators were most positive about leadership support and student capacity, although less sure about student interest. Overall, first-order barriers posed greater impediments than second-order barriers. However, one-third of administrators did not favor computer science taking the place of traditional graduation requirements. [ABSTRACT FROM AUTHOR]

This paper concerns the implementation and application of the extended one-shot approach including additional equality constraints to achieve a direct transition from simulation to optimization. The approach can be applied for different areas of scientific computing where partial differential equations are treated by using a fixed-point solver. The solver is extended in a semi-automated fashion. In a first step it is augmented with a consistent adjoint solver using algorithmic differentiation. Then the obtained reduced derivative information is directly employed to simultaneously achieve optimality and primal as well as adjoint feasibility. The methodology is implemented in the multi-physics package SU2 and applied for multi-objective aerodynamic shape optimization. [ABSTRACT FROM AUTHOR]

Coccaro, Silvio, B I La crittologia classica in azione, Classical Cryptology at play, Silcosoft i b , https://www.silcosoft.it/ Silvio Coccaro is an Italian physician and software engineer and a passionate lover of classical cryptography. He created this little great app for Windows, designed mainly for educational purposes; the users have the opportunity to try a lot of ciphers, seeing a slow-motion animation of the method I at play i . The app presents a menu of many well-known ciphers from Caesar to Vigenère up to the Vic cipher, monoalphabetic, polyalphabetic, polygraphic ciphers, a few nomenclators, and also many less known ciphers; there are also non secret codes like Morse alphabet, Polybius checkboard or naval flag code. [Extracted from the article]

We would like to congratulate Professor Brad Efron for the well-deserved honor of winning the 2019 International Statistics Prize, and for his thought-provoking paper discussing the diverging paths of so-called traditional statistical regression and pure prediction approaches. In this discussion, we provide a portrayal of our own perspectives on the intersection of statistics and machine learning methods, and compare them with Professor Efron's. Professor Efron discusses this idea but dismisses these modern attribution attempts as being suboptimal, dampened by the pure prediction method's focus on prediction using many weak learners that do not prioritize individual strong predictors. [Extracted from the article]

Our current approach to science has engendered excessive individual and institutional competition and complexity in funding instruments. We contend that a future science system will require a more collaborative approach that supports competition for ideas within a greater ‘mission-led’ environment. We build on our experiences within the New Zealand science sector to envisage a more collaborative and open framework for delivery, which will provide long-term career opportunities and recognise the importance of vibrant universities, Crown Research Institutes and other private sector participants. A new approach will be founded on successful implementation of the National Science Challenges through a balance of science excellence, effective boards, creating best teams free of institutional constraints, a focus on delivering outcomes and benefits, sharing of data and infrastructure, and engaging with wider society. [ABSTRACT FROM AUTHOR]

Sampling from complex distributions is an important but challenging topic in scientific and statistical computation. We synthesize three ideas, tempering, resampling, and Markov moving, and propose a general framework of resampling Markov chain Monte Carlo (MCMC). This framework not only accommodates various existing algorithms, including resample-move, importance resampling MCMC, and equi-energy sampling, but also leads to a generalized resample-move algorithm. We provide some basic analysis of these algorithms within the general framework, and present three simulation studies to compare these algorithms together with parallel tempering in the difficult situation where new modes emerge in the tails of previous tempering distributions. Our analysis and empirical results suggest that generalized resample-move tends to perform the best among all the algorithms studied when the Markov kernels lead to fast mixing or even locally so toward restricted distributions, whereas parallel tempering tends to perform the best when the Markov kernels lead to slow mixing, without even converging fast to restricted distributions. Moreover, importance resampling MCMC and equi-energy sampling perform similarly to each other, often worse than independence Metropolis resampling MCMC. Therefore, different algorithms seem to have advantages in different settings. [ABSTRACT FROM PUBLISHER]

This paper attempts to show how the “big data” paradigm is changing science through offering access to millions of database elements in real time and the computational power to rapidly process those data in ways that are not initially obvious. In order to gain a proper understanding of these changes and their implications, we propose applying an extended cognition model to the novel scenario. [ABSTRACT FROM PUBLISHER]

This article suggests that substantial and often disturbing change is the only way that libraries can achieve a bright future in an increasingly digital world. Libraries must quickly adopt mobile technology, cloud computing, eScience, and the systematic organization and dissemination of quality information—and do so within the context of collaboration with other libraries in the region, nation, and world. Fiscal shortages can not be used as an excuse to postpone imperative changes; money has to be found. [ABSTRACT FROM AUTHOR]

Techniques for library-centric application design have already proven to be very useful in the past. The current gain in computer performance is shifted towards the utilisation of multi-core processors which extends the importance of this type of application design in the field of scientific computing, which also poses new difficulties. A parallel generic scientific simulation environment has been developed to ease this transition from single-core to multi-core systems without additional development activity. [ABSTRACT FROM AUTHOR]

The challenging art of multi-paradigmatic application development, which only few languages currently support, greatly aids the development of highly efficient and reusable software components. A link of two such languages, namely Python and C++, is presented. Thereby data structures and algorithms realised in C++ using features such as compile-time meta-programming are made available to the run-time environment of Python. Several generic components and modules for application design in the area of scientific computing are presented. Compile times and run-times are discussed to show the advantages of the proposed combination of both languages. [ABSTRACT FROM AUTHOR]

In modern geoscience, understanding the climate depends on the information about the oceans. Covering two-thirds of the Earth, oceans play an important role. Oceanic phenomena are, for example: oceanic circulation; water exchanges between atmosphere, land, and ocean; or temporal changes of the total water volume. All these features require new methods in constructive approximation, since they are regionally bounded and not globally observable. This article deals with a new and alternative method of handling data with locally supported basis functions, modeling them in a multiscale scheme involving a wavelet approximation and presenting the main results for the dynamic topography and the geostrophic flow, e.g., in the Northern Atlantic. Further, it is demonstrated that compressional rates of the occurring wavelet transforms can be achieved by using of locally supported wavelets and investigating the signal distribution within different frequency bands. [ABSTRACT FROM AUTHOR]

Particle tracking methods are central to a wide spectrum of scientific computing applications. To support such applications, this paper presents a compact software architecture that can be used to interface parallel particle tracking software to computational mesh management systems. A detailed description is presented of the in-element particle tracking framework supported by this software architecture--a framework that encompasses most particle tracking applications. The use of this parallel software architecture is illustrated through the implementation of two differential equation solvers, the forward Euler and an implicit trapezoidal method, on a distributed, unstructured. computational mesh. A design goal of this software effort has been to interface to software libraries such as Scalable Unstructured Mesh Algorithms and Applications (SUMAA3d) in addition to application codes (e.g. FEMWATER). This goal of portability is achieved through a software architecture that specifies a lightweight functional interface that maintains the full functionality required by particle-mesh methods. The use of this approach in parallel programming environments written in C and Fortran is demonstrated. [ABSTRACT FROM AUTHOR]

Examines the Science Operations Centre (SOC) for spectrum-ultraviolet radiation project. Role of SOC to evaluate instrument performance; Activities of SOC to access Science Archive by Trend Analysis; Use of strict programming rules and software development to implement industry standards.