Automated correction for the movement of suspended particulate in microtomographic data
Not Available
Vigor, James E.↗
SEARCH · Engineering Papers
Results for “data movement”
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Reproduction drives changes in space use and habitat selection in a highly adaptable invasive mammal
Abstract For ungulates, it is not well understood how the interaction between habitat and reproduction affects movement behavior, space use, and habitat selection. We used known parturition (farrowing) data to validate First Passage Time (FPT) movement analysis to identify wild pig (Sus scrofa) parturition events from data collected with GPS collars. We examined home range and habitat selection during three physiologically distinct time periods in the reproductive cycle: late-gestation; farrowing; and neonate-care period. Sows exhibited a behavioral change 1-2 days prior to farrowing, suggesting FPT analysis can reliably identify parturition events for wide-ranging species that have a focused birthing area. Home range was smallest during the farrowing period, likely reflective of nest building, parturition, and protection of neonates. Home range size during the neonate-care period was intermediate between the late-gestation and farrowing periods, indicative of offspring care that may restrict maternal movement. Across all periods, sows avoided developed areas that have sparse canopy and ground cover and are associated with human activities. During late-gestation, sows avoided pine forests that have an open understory and less vegetative cover. During late-gestation and neonate-care, sows selected bottomland hardwood forests, habitat associated with ample food, cover, and water. During farrowing and neonate-care periods, sows selected upland hardwood forests, habitat with high quality food and cover for neonates. The physiological requirements of reproduction drive female habitat selection and spatial scale of movement patterns associated with parturition. Our study contributes to delineation of the appropriate scale at which to analyze movement data to provide insight about where individuals chose to place their home range, how much space to use, and how they use resources on the landscape to maximize reproductive success and fitness.
Zoology↗
Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere
For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.
59 BASIC BIOLOGICAL SCIENCES↗
Black-box statistical prediction of lossy compression ratios for scientific data
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection.
97 MATHEMATICS AND COMPUTING↗
The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future
The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.
ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
Adaptive cache management based on programming model information
A processing system selectively allocates space to store a group of one or more cache lines at a cache level of a cache hierarchy having a plurality of cache levels based on memory access patterns of a software application executing at the processing system. The processing system generates bit vectors indicating which cache levels are to allocate space to store groups of one or more cache lines based on the memory access patterns, which are derived from data granularity and movement information. Based on the bit vectors, the processing system provides hints to the cache hierarchy indicating the lowest cache level that can exploit the reuse potential for a particular data.
Na, Weon Taek↗
Analysis of Urban Freight Flows and Retail Goods Movement Using GPS Trajectory and Land Use Data
Not Available
Sahin, Olcay↗
Entropy-Assisted Quality Pattern Identification in Finance
Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.
Physics↗
Role of social structure in establishment of an invasive large mammal after translocation
Abstract Background Data on the movement behavior of translocated wild pigs is needed to develop appropriate response strategies for containing and eliminating new source populations following translocation events. We conducted experimental trials to compare the home range establishment and space‐use metrics, including the number of days and distance traveled before becoming range residents, for wild pigs translocated with their social group and individually. Results We found wild pigs translocated with their social group made less extensive movements away from the release location and established a stable home range ~5 days faster than those translocated individually. We also examined how habitat quality impacted the home range sizes of translocated wild pigs and found wild pigs maintained larger ranges in areas with higher proportion of low‐quality habitat. Conclusion Collectively, our findings suggest translocations of invasive wild pigs have a greater probability of establishing a viable population near the release site when habitat quality is high and when released with members of their social unit compared to individuals moved independent of their social group or to low‐quality habitat. However, all wild pigs translocated in our study made extensive movements from their release location, highlighting the potential for single translocation events of either individuals or groups to have far‐reaching consequences within a much broader landscape beyond the location where they are released. These results highlight the challenges associated with containing populations in areas where illegal introduction of wild pigs occurs, and the need for rapid response once releases are identified. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Clontz, Lindsay M.↗
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it remains notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes the first GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs' inference efficiency. Specifically, on the algorithm level, GCoD integrates a divide and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently outperforms state-of-the-art designs in terms of accelerator efficiency while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices to better understand its advantages. All codes and pre-trained models will be released upon acceptance.
You, Haoran↗
ORCHA: A performance portability system for extreme heterogeneity
Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.
Lee, Youngjun↗
Movement Models to Predict Low‐Altitude Flight of Soaring Birds Using Look‐Ahead Environmental Factors
Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (≤ 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.
17 WIND ENERGY↗
Salt Diapir‐Driven Recycling of Gas Hydrate
Abstract By harnessing both hypothetical, synthetic basin and gas hydrate (GH) system models and real‐world models of well‐studied salt diapir‐associated GH sites at Green Canyon (Gulf of Mexico) and Blake Ridge (U.S. Atlantic coast), we propose and demonstrate salt movement (and in particular, diapirism) to be a new mechanism for the recycling of marine GH. At Green Canyon, for example, we show that by considering this newly proposed diapir‐driven recycling mechanism in conjunction with previously proposed lithological control on sandy‐reservoir‐hosted hydrate at the base of the GH stability zone (BGHSZ; ∼bottom‐simulating reflector, BSR), modeled GH saturations match drilling data. Overall, salt diapir movement‐induced GH recycling provides a temperature‐driven mechanism by which GH saturations at the BGHSZ may reach >90 vol. % and by which GH volumes near and free gas volumes beneath the BGHSZ may be increased significantly through time. Interestingly, comparison of salt diapir‐driven recycling and sediment burial‐driven recycling scenarios suggests notably higher rates of recycling via diapir‐driven versus burial‐driven processes. Our results suggest that GH and associated free gas accumulations above salt diapir crests represent particularly attractive targets for unconventional and conventional hydrocarbon resource exploration and for scientific and academic drilling expeditions aimed at exploiting GH systems. Salt basins containing GH systems—including passive margin basins of the Gulf of Mexico, southeastern Brazil, and southwestern Africa—are therefore compelling localities for studying salt‐driven GH recycling and for salt diapir‐associated natural gas exploration.
58 GEOSCIENCES↗
MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions
Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation-/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the data-intensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPUs. The evaluation demonstrates MICCO outperforms other state-of-art works, achieving up to 2.25× speedup in synthesized datasets, and 1.49× speedup in real-world correlation functions.
Wang, Qihan↗
Challenges and strategies for probing the composite interface of PEM electrolyzers and fuel cells using operando AP-XPS
Left: cross-section schematic of a membrane electrode assembly, the working electrode changing state with applied potential. Center: the operando cell design that enables snapshot data acquisition during trajectory movement. Right: resulting spectra. Understanding the surface chemistry of electrocatalyst systems under operando conditions is central to revealing the electrocatalytic cell's working mechanisms. Determination of these catalytic processes on a molecular scale and the involved components is fundamental to streamlining material design for energy conversion and storage applications. X-ray photoelectron spectroscopy (XPS) is an established technique used to study the chemical and electronic states of materials. While the surface sensitivity of XPS is typically high, use of tender X-ray energies and technical advancements have allowed for the direct probing of solid–vapor and solid–liquid interfaces. However, protocols and documentation of experimental considerations for operando XPS probing of working electrolyzers and fuel cells remain scarce. Herein, we report an approach for the study of working polymer electrolyte membrane (PEM) electrolysis cells using ambient pressure X-ray photoelectron spectroscopy (AP-XPS). This approach directly probes the composite electrode surface on the membrane electrode assembly (MEA) in 100% relative humidity to establish a meaningful liquid layer for electrocatalysis. We carry out a systematic investigation from the cell constituent components to a fully assembled working operando electrolytic system and establish a method for AP-XPS study of the complex composite MEA, providing recommendations for data acquisition and component analysis.
Hamlyn, Rebecca↗
HIDRA-MAT liquid metal droplet injector for liquid metal applications in HIDRA
A liquid metal droplet injector was explicitly designed for the Hybrid Illinois Device for Research and Applications Material Analysis Test-stand (HIDRA-MAT) at the University of Illinois Urbana-Champaign to prepare liquid metal plasma-facing components (PFCs) for plasma exposure. The design goals were to create a compact, reliable, and robust design that could apply liquid metal droplets in-vacuo to a variety of samples. The injector was designed for liquid metal use, and results discussed pertain to lithium. The injector can produce lithium droplets of consistent size by utilizing programmable piston movement. Here, droplet formation data is presented for four different piston step sizes (0.125 mm, 0.25 mm, 0.5 mm, and 1 mm) at three different nozzle temperatures (185 °C, 230 °C, and 270 °C). Droplets are formed on the injector nozzle tip and a linear shift mechanism vertically translates the droplet to the substrate for application. This design prevents the droplet from inadvertently detaching off the nozzle and falling to the sample. Application of the liquid metal droplet ensures placement consistency on the substrate and helps avoid damage to components in HIDRA-MAT that should not interact with liquid metals. A description of the liquid lithium droplet creation and application results is given and provides additional insight into the cooling and oxidation of lithium droplets under vacuum conditions. A second nozzle was fabricated and demonstrated repeatability in droplet diameter creation having a variance of ±0.14 mm for droplets created from the same number of piston steps. The end-use for the injector on HIDRA-MAT is to apply liquid metals to PFCs and expose them to HIDRA's plasma with subsequent intershot in-vacuo surface analysis in HIDRA-MAT for plasma-material interaction studies.
42 ENGINEERING↗
Globus service enhancements for exascale applications and facilities
Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had not been a focus of previous Globus optimization work, which had emphasized rather the movement of many smaller (megabyte to gigabyte) files. We report here on how automated client-driven chunking can be used to accelerate both the movement of large files and the integrity checking operations that have proven to be essential for large data transfers. In conclusion, we present detailed performance studies that provide insights into the benefits of these modifications in a range of file transfer scenarios.
97 MATHEMATICS AND COMPUTING↗
Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories
Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.
24 POWER TRANSMISSION AND DISTRIBUTION↗