Engineering topics

Turton, Terece L.

Publications and source records attributed to Turton, Terece L..

6 records

DOE OSTI2024

Toward the validation of crowdsourced experiments for lightness perception

Crowdsource platforms have been used to study a range of perceptual stimuli such as the graphical perception of scatterplots and various aspects of human color perception. Given the lack of control over a crowdsourced participant’s experimental setup, there are valid concerns on the use of crowdsourcing for color studies as the perception of the stimuli is highly dependent on the stimulus presentation. Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. Based on this, we provide a mathematical estimate for the sample size needed to produce a crowdsourced experiment with the same power as the corresponding in-person study. We tested this claim by replicating a large-scale, crowdsourced study of human lightness perception with a diverse sample with a highly controlled, in-person study with a sample taken from psychology undergraduates. Our claim was supported by the replication of the results from the latter. These findings suggest that, with sufficient sample size, color vision studies may be completed online, giving access to a larger and more representative sample. With this framework at hand, experimentalists have the validation that choosing either many online participants or few in person participants will not sacrifice the impact of their results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

The ECP ALPINE project: In situ and post hoc visualization infrastructure and analysis capabilities for exascale

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Information-Theoretic Exploration of Multivariate Time-Varying Image Databases

Modern scientific simulations produce very large datasets, making interactive exploration of such data computationally prohibitive. An increasingly common data reduction technique is to store visualizations and other data extracts in a database. The Cinema project is one such approach, storing visualizations in an image database for post hoc exploration and interactive image-based analysis. This work focuses on developing efficient algorithms that can quantify various types of multivariate dependencies existing within multi-variable datasets. It applies specific mutual information measures for the quantification of salient regions from multivariate image data. Here, using such information measures, the opacity of the images is modulated so that the salient regions are automatically highlighted and the domain scientists can interactively explore the most relevant regions for scientific discovery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

ECP Software Technology Capability Assessment Report V3.0

The Exascale Computing Project (ECP) Software Technology (ST) focus area is responsible for (1) developing critical software capabilities that will enable the successful execution of ECP applications and (2) providing key components of a productive and sustainable exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high-performance computing (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every 6–12 months). Highlights from this version of the report are presented here. This version of the CAR contains the following updates relative to the previous revision: (1) This report highlights the progress with the Extreme-scale Scientific Software Stack (E4S) efforts. In particular, this report discusses how E4S continues to gain traction as a first-class entity in the HPC ecosystem, enabling new conversations with users, facilities, vendors, other US agencies, and international partners. (2) The several-page summaries of each ECP Level 4 project were updated to reflect recent progress and next steps (Section 4). Of particular note are the experiences of our teams on early-access systems for Frontier. (3) The E4S is described further. E4S is now updated via quarterly releases. E4S is the primary integration and delivery vehicle for ECP ST capabilities (Section 2.1.1). (4) The ECP ST software development kit (SDK) effort further refined its groupings (Section 2.1.2). The ECP ST focus area represents the key bridge between exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to approximately 70 software products (Section 2.1.3) in six technical areas (Table 1). Since publishing the previous revision of the CAR, the team has continued to evolve the product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The non-Riemannian nature of perceptual color space

Significance For over 100 y, the scientific community has adhered to a paradigm, introduced by Riemann and furthered by Helmholtz and Schrodinger, where perceptual color space is a three-dimensional Riemannian space. This implies that the distance between two colors is the length of the shortest path that connects them. We show that a Riemannian metric overestimates the perception of large color differences because large color differences are perceived as less than the sum of small differences. This effect, called diminishing returns, cannot exist in a Riemannian geometry. Consequently, we need to adapt how we model color differences, as the current standard, Δ E , recognized by the International Commission for Weights and Measures, does not account for diminishing returns in color difference perception.

Bujack, Roxana↗

DOE OSTI2021

Research on Remote and Hybrid Scientific Work: A Literature Review

This literature review is divided into two parts. The first part looks at new developments and emerging research specifically related to remote work since the start of the COVID-19 pandemic. This encompasses research and reporting on the impacts of COVID-19 on the workplace, more speculative writing on the possible future of remote and hybrid work, and impacts of remote work during COVID-19 for diversity, equity, and inclusion. The second part focuses on more fundamental research done on remote collaboration and remote work tools prior to COVID-19, which addresses in more detail how different types, aspects, or stages of work can best be supported using virtual collaboration tools. In both sections, we review literature that directly focuses on remote scientific collaboration, which is somewhat limited, as well as the broader literature on remote and hybrid work, which is relevant to a wide variety of workplaces, including scientific ones.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗