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Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

Evaluation of Cavity Collapse and Surface Crater Formation at the Norbo Underground Nuclear Test in U8c, Nevada Nuclear Security Site, and the Impact on Stability of the Ground Surface

Lawrence Livermore National Laboratory (LLNL) Containment Program performed a review of nuclear test-related data for the Norbo underground nuclear test in U8c to assist in evaluating this legacy site as a test bed for application technologies for use in On-Site Inspections (OSI) under the Comprehensive Nuclear Test Ban Treaty. This request is similar to one made for the Salut site in U8c (Pawloski, 2012b). Review of the Norbo site is complicated because the test first exhibited subsurface collapse, which was not unusual, but it then collapsed to the surface over one year later, which was unusual. Of particular interest is the stability of the ground surface above the Norbo detonation point. Proposed methods for on-site verification include radiological signatures, artifacts from nuclear testing activities, and imaging to identify alteration to the subsurface hydrogeology due to the nuclear detonation. Aviva Sussman from the Los Alamos National Laboratory (LANL) has also proposed work at this site. Both proposals require physical access at or near the ground surface of specific underground nuclear test locations at the Nevada Nuclear Security Site (NNSS), formerly the Nevada Test Site (NTS), and focus on possible activities such as visual observation, multispectral measurements, and shallow and deep geophysical surveys.

58 GEOSCIENCES↗

Blockchain based Communication Architectures with Applications to Private Security Networks

Existing communication protocols in high consequence security networks are highly centralized. While this naively makes the controls easier to physically secure, external actors require fewer resources to disrupt the system because there are fewer points in the system can be destroyed or interrupted without the entire system failing. We present a solution to this problem using a proof-of-work-based blockchain implementation built on MultiChain. We construct a test-bed network containing two types of data input: visual imagers and microwave sensor information. These data types are ubiquitous in perimeter intrusion detection security systems and allow a realistic representation of a real-world network architecture. The cameras in this system use an object detection algorithm to nd important targets in the scene. The raw data from the camera and the outputs from the detection algorithm are then placed in a transaction on the distributed ledger. Similarly, microwave data is used to detect relevant events and are placed in a transaction. These transactions are then bundled into blocks and broadcast to the rest of the network using the Bitcoin-based MultiChain protocol. We develop five tests to examine the security metrics of our network. We performed the five security metric test using different sized networks from 7 to 39 nodes to determine how the metrics scale with respect to size. We nd that when compared to a centralized architecture our implementation provides a resiliency increase that is expected from a blockchain-based protocol without slowing the system so much that a human operator would notice. Furthermore, our approach is able to detect tampering in real time. Based on these results, we theorize that security networks in general could use a blockchain-based approach in a meaningful way.

97 MATHEMATICS AND COMPUTING↗

Next-Generation Analysis and Visualization Platform for Manufacturing and Engineering Workflows (Final Report)

Advanced manufacturing and engineering are fundamental to the economic strength and national security of the United States. With the introduction of advanced modeling and simulation practices in manufacturing and engineering over the last several decades, we are producing better products, more quickly, and at decreased cost. Through the adoption of high-performance computing (HPC) enabled workflows, in small- to medium-sized manufacturers and engineering firms, which employ fewer than 500 people, is renewing United States manufacturing and engineering. Despite successes, there are significant barriers to widespread adoption of these tools. Our Phase I SBIR project, Next-Generation Analysis and Visualization Platform for Manufacturing and Engineering Workflows, aims to address several of these barriers. Our project helped us evaluate the feasibility and benefits and mitigate risks of asynchronous processing for analysis and visualization workflows. We evaluated the impact of asynchronous, non-blocking communication can have on the user experience in a complex visualization application. Besides the impact on users, we wanted to understand the impacts on developers (development application programming interfaces (APIs))and generated applications (robustness and stability). Any asynchronous platform implementation is bound to rely on multiple threads which can make the programming model extraordinarily complex and prone to bugs. If not managed carefully, the complexity and resulting instability would be an unacceptable evolution for widely used production tools like ParaView and putting the success of the Phase II project at risk.

42 ENGINEERING↗

Visual Analytics for Power Grid Contingency Analysis

Contingency analysis is the process of employing different measures to model scenarios, analyze them, and then derive the best response to remove the threats. This application paper focuses on a class of contingency analysis problems found in the power grid management system. A power grid is a geographically distributed interconnected transmission network that transmits and delivers electricity from generators to end users. The power grid contingency analysis problem is increasingly important because of both the growing size of the underlying raw data that need to be analyzed and the urgency to deliver working solutions in an aggressive timeframe. Failure to do so may bring significant financial, economic, and security impacts to all parties involved and the society at large. The paper presents a scalable visual analytics pipeline that transforms about 100 million contingency scenarios to a manageable size and form for grid operators to examine different scenarios and come up with preventive or mitigation strategies to address the problems in a predictive and timely manner. Great attention is given to the computational scalability, information scalability, visual scalability, and display scalability issues surrounding the data analytics pipeline. Most of the large-scale computation requirements of our work are conducted on a Cray XMT multi-threaded parallel computer. The paper demonstrates a number of examples using western North American power grid models and data.

contingency analysis↗

INS Nuclear Material Accounting and Control Instructional Video Materials- FY 25 Guides

This training video provides an essential guide to understanding and utilizing Tamper Indicating Devices (TIDs), with a specific focus on a common Mylar (Adhesive) variety during the application process. TIDs are a critical part of nuclear security, used to detect unauthorized access to sensitive materials or facilities. The Mylar (adhesive) TID employes a tamper-evident film integrated into the TID that visually indicates any tampering attempts. This video will explain how an example Mylar (adhesive) TID works, demonstrate proper installation techniques, and highlight how to interpret its indicators to ensure the security of nuclear materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Safety Analytics Forecasting and Evaluation Reporting (SAFER)

The SAFER Sandbox is a web application for testing various types of data visualization tools that analyze safety data across national laboratories. By enabling the user to visualize this data through visual and textual analyses, the SAFER Sandbox helps to facilitate the research and support for the National Nuclear Security Administration (NNSA) Office of Safety, Infrastructure and Operations (NA-50). Safety data across the national laboratory complex is not currently analyzed in one location. This prohibits administrators from viewing safety data trends and making large scale decisions based on a holistic view of the data. By implementing data analytics web services such as representative text summary, noun phrase extraction and graph exploration, users can view and explore safety data based on search queries. The data visualizations in the SAFER Sandbox reveal trends and point to items of concern based on safety data. The SAFER Sandbox is a testing ground for these visualizations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Laser Wakefield Acceleration: Structural and Dynamic Studies. Final Technical Report ER40954

Particle accelerators enable scientists to study the fundamental structure of the universe, but have become the largest and most expensive of scientific instruments. In this project, we advanced the science and technology of laser-plasma accelerators, which are thousands of times smaller and less expensive than their conventional counterparts. In a laser-plasma accelerator, a powerful laser pulse exerts light pressure on an ionized gas, or plasma, thereby driving an electron density wave, which resembles the wake behind a boat. Electrostatic fields within this plasma wake reach tens of billions of volts per meter, fields far stronger than ordinary non-plasma matter (such as the matter that a conventional accelerator is made of) can withstand. Under the right conditions, stray electrons from the surrounding plasma become trapped within these “wake-fields”, surf them, and acquire energy much faster than is possible in a conventional accelerator. Laser-plasma accelerators thus might herald a new generation of compact, low-cost accelerators for future particle physics, x-ray and medical research. In this project, we made two major advances in the science of laser-plasma accelerators. The first of these was to accelerate electrons beyond 1 gigaelectronvolt (1 GeV) for the first time. In experimental results reported in Nature Communications in 2013, about 1 billion electrons were captured from a tenuous plasma (about 1/100 of atmosphere density) and accelerated to 2 GeV within about one inch, while maintaining less than 5% energy spread, and spreading out less than ½ milliradian (i.e. ½ millimeter per meter of travel). Low energy spread and high beam collimation are important for applications of accelerators as coherent x-ray sources or particle colliders. This advance was made possible by exploiting unique properties of the Texas Petawatt Laser, a powerful laser at the University of Texas at Austin that produces pulses of 150 femtoseconds (1 femtosecond is 10-15 seconds) in duration and 150 Joules in energy (equivalent to the muzzle energy of a small pistol bullet). This duration was well matched to the natural electron density oscillation period of plasma of 1/100 atmospheric density, enabling efficient excitation of a plasma wake, while this energy was sufficient to drive a high-amplitude wake of the right shape to produce an energetic, collimated electron beam. Continuing research is aimed at increasing electron energy even further, increasing the number of electrons captured and accelerated, and developing applications of the compact, multi-GeV accelerator as a coherent, hard x-ray source for materials science, biomedical imaging and homeland security applications. The second major advance under this project was to develop new methods of visualizing the laser-driven plasma wake structures that underlie laser-plasma accelerators. Visualizing these structures is essential to understanding, optimizing and scaling laser-plasma accelerators. Yet prior to work under this project, computer simulations based on estimated initial conditions were the sole source of detailed knowledge of the complex, evolving internal structure of laser-driven plasma wakes. In this project we developed and demonstrated a suite of optical visualization methods based on well-known methods such as holography, streak cameras, and coherence tomography, but adapted to the ultrafast, light-speed, microscopic world of laser-driven plasma wakes. Our methods output images of laser-driven plasma structures in a single laser shot. We first reported snapshots of low-amplitude laser wakes in Nature Physics in 2006. We subsequently reported images of high-amplitude laser-driven plasma “bubbles”, which are important for producing electron beams with low energy spread, in Physical Review Letters in 2010. More recently, we have figured out how to image laser-driven structures that change shape while propagating in a single laser shot. The latter techniques, which use the methods of computerized tomography, were demonstrated on test objects – e.g. laser-driven filaments in air and glass – and reported in Optics Letters in 2013 and Nature Communications in 2014. Their output is a multi-frame movie rather than a snapshot. Continuing research is aimed at applying these tomographic methods directly to evolving laser-driven plasma accelerator structures in our laboratory, then, once perfected, to exporting them to plasma-based accelerator laboratories around the world as standard in-line metrology instruments.

43 PARTICLE ACCELERATORS↗

The Missing Satellite Problem outside of the Local Group. II. Statistical Properties of Satellites of Milky Way–like Galaxies

We present a new observation of satellite galaxies around seven Milky Way (MW)–like galaxies located outside of the Local Group (LG) using Subaru/Hyper Suprime-Cam imaging data to statistically address the missing satellite problem. We select satellite galaxy candidates using magnitude, surface brightness, Sérsic index, axial ratio, FWHM, and surface brightness fluctuation cuts, followed by visual screening of false positives such as optical ghosts of bright stars. We identify 51 secure dwarf satellite galaxies within the virial radius of nine host galaxies, two of which are drawn from the pilot observation presented in Paper I. We find that the average luminosity function of the satellite galaxies is consistent with that of the MW satellites, although the luminosity function of each host galaxy varies significantly. We observe an indication that more massive hosts tend to have a larger number of satellites. Physical properties of the satellites such as the size–luminosity relation are also consistent with the MW satellites. However, the spatial distribution is different; we find that the satellite galaxies outside of the LG show no sign of concentration or alignment, while that of the MW satellites is more concentrated around the host and exhibits a significant alignment. As we focus on relatively massive satellites with M V < –10, we do not expect that the observational incompleteness can be responsible here. This trend might represent a peculiarity of the MW satellites, and further work is needed to understand its origin.

79 ASTRONOMY AND ASTROPHYSICS↗

Software Development for Data Visualization and Analysis of PN-Diodes & MOSFET Devices

The U.S. Department of Energy has a broad mission to ensure the nation’s security by addressing ongoing environmental challenges, developing novel energy production technologies, and mitigating nuclear security concerns. These efforts benefit from developing more effective semiconductor materials and devices. The research discussed in this report aims to further the development of these new electronics by improving implementations of vertical device structures, which offer theoretically better performance compared to lateral structures. Specifically, it reviews the background and development of a new and easy-to-use program for data processing and analysis from experiments run on vertical gallium nitride power devices. Current vertical structures fall short in critical performance metrics, such as On-Resistance and Breakdown Voltage, due to poor management of the device’s electric field. Junction termination extensions, or JTEs, are a field management technique that increases a device’s resilience to expected failure modes by controlling its surface electric fields. Each JTE design requires significant experimental validation, consisting of hundreds of tested devices, each outputting an enormous set of data points. The new software includes multiple methods of filtering large sets of device testing data to identify and remove flawed devices, automate device analyses, and provide statistical breakdowns for four metrics used to define the effectiveness of the JTE and the current carrying capacity of the device. These results can then be related back to fabrication techniques and device design, and in the future, be combined with processing simulations to further improve our understanding of the JTE mechanisms.

97 MATHEMATICS AND COMPUTING↗

Hybrid methods for cybersecurity analysis :

Early 2010 saw a signi cant change in adversarial techniques aimed at network intrusion: a shift from malware delivered via email attachments toward the use of hidden, embedded hyperlinks to initiate sequences of downloads and interactions with web sites and network servers containing malicious software. Enterprise security groups were well poised and experienced in defending the former attacks, but the new types of attacks were larger in number, more challenging to detect, dynamic in nature, and required the development of new technologies and analytic capabilities. The Hybrid LDRD project was aimed at delivering new capabilities in large-scale data modeling and analysis to enterprise security operators and analysts and understanding the challenges of detection and prevention of emerging cybersecurity threats. Leveraging previous LDRD research e orts and capabilities in large-scale relational data analysis, large-scale discrete data analysis and visualization, and streaming data analysis, new modeling and analysis capabilities were quickly brought to bear on the problems in email phishing and spear phishing attacks in the Sandia enterprise security operational groups at the onset of the Hybrid project. As part of this project, a software development and deployment framework was created within the security analyst work ow tool sets to facilitate the delivery and testing of new capabilities as they became available, and machine learning algorithms were developed to address the challenge of dynamic threats. Furthermore, researchers from the Hybrid project were embedded in the security analyst groups for almost a full year, engaged in daily operational activities and routines, creating an atmosphere of trust and collaboration between the researchers and security personnel. The Hybrid project has altered the way that research ideas can be incorporated into the production environments of Sandias enterprise security groups, reducing time to deployment from months and years to hours and days for the application of new modeling and analysis capabilities to emerging threats. The development and deployment framework has been generalized into the Hybrid Framework and incor- porated into several LDRD, WFO, and DOE/CSL projects and proposals. And most importantly, the Hybrid project has provided Sandia security analysts with new, scalable, extensible analytic capabilities that have resulted in alerts not detectable using their previous work ow tool sets.

Dunlavy, Daniel M.↗

Los Alamos Shows Airport Security Technology at Work

Los Alamos scientists have advanced a Magnetic Resonance Imaging (MRI) technology that may provide a breakthrough for screening liquids at airport security. They've added low-power X-ray data to the mix, and as a result have unlocked a new detection technology. Funded in part by the Department of Homeland Security's Science and Technology Directorate, the new system is named MagRay. The goal is to quickly and accurately distinguish between liquids that visually appear identical. For example, what appears to be a bottle of white wine could potentially be nitromethane, a liquid that could be used to make an explosive. Both are clear liquids, one would be perfectly safe on a commercial aircraft, the other would be strictly prohibited. How to tell them apart quickly without error at an airport security area is the focus of Michelle Espy, Larry Schultz and their team. In this video, Espy and the MagRay team explain how the new technology works, how they've developed an easy operator interface, and what the next steps might be in transitioning this technology to the private sector.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Situ: Identifying and Explaining Suspicious Behavior in Networks

Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. Furthermore, we discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.

97 MATHEMATICS AND COMPUTING↗

Visualizing feedstock siting in biomass production: Tradeoffs between economic and water quality objectives

New domestic, renewable energy resources must be considered to increase energy security in the U.S. Ethanol production through second-generation (cellulosic) feedstocks will help the U.S. meet the legislative Renewable Fuel Standard, which mandates 36 billion gallons of renewable fuels by 2022. Yet, conversion of cropland to meet the cellulosic feedstock production goals may have unforeseen environmental consequences. Here, using Soil Water Assessment Tool (SWAT) outputs and National Agricultural Statistics Service (USDA NASS) economic data, we conducted a spatial optimization of bioenergy feedstock introduction into the Arkansas White-Red River Basin based on water quality and economic objectives, subject to constraints on total land conversion. Results displayed tradeoffs between bioenergy yield for three crops (switchgrass, sorghum and poplar) and land rent objectives. Optimal solutions tended to prioritize conversion of land in eastern AWR subbasins where yield and water quality objective improvements were greatest. A small number of subbasins contributed to basin-wide water quality improvements, whereas subbasins contributing to economic benefits were more spatially dispersed, indicating that water quality responses are more likely to constrain feedstock placement. Biomass production targets can be met vianumerous spatial arrangements, whereas marginal improvement in water quality objectives can best be achieved by selectively siting perennial feedstocks in the eastern half of the region.

09 BIOMASS FUELS↗

Studying visual search without an eye tracker: an assessment of artificial foveation

Abstract Eye tracking is a useful tool for studying human cognition, both in the laboratory and in real-world applications. However, there are cases in which eye tracking is not possible, such as in high-security environments where recording devices cannot be introduced. After facing this challenge in our own work, we sought to test the effectiveness of using artificial foveation as an alternative to eye tracking for studying visual search performance. Two groups of participants completed the same list comparison task, which was a computer-based task designed to mimic an inventory verification process that is commonly performed by international nuclear safeguards inspectors. We manipulated the way in which the items on the inventory list were ordered and color coded. For the eye tracking group, an eye tracker was used to assess the order in which participants viewed the items and the number of fixations per trial in each list condition. For the artificial foveation group, the items were covered with a blurry mask except when participants moused over them. We tracked the order in which participants viewed the items by moving their mouse and the number of items viewed per trial in each list condition. We observed the same overall pattern of performance for the various list display conditions, regardless of the method. However, participants were much slower to complete the task when using artificial foveation and had more variability in their accuracy. Our results indicate that the artificial foveation method can reveal the same pattern of differences across conditions as eye tracking, but it can also impact participants’ task performance.

47 OTHER INSTRUMENTATION↗