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Results for “scale detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Vertex evoked potentials in a rating-scale detection task: Relation to signal probability

Vertex evoked potentials were recorded from human subjects performing in an auditory detection task with rating scale responses. Three values of a priori probability of signal presentation were tested. The amplitudes of the N1 and P3 components of the vertex potential associated with correct detections of the signal were found to be systematically related to the strictness of the response criterion and independent of variations in a priori signal probability. No similar evoked potential components were found associated with signal absent judgements (misses and correct rejections) regardless of the confidence level of the judgement or signal probability. These results strongly support the contention that the form of the vertex evoked response is closely correlated with the subject's psychophysical decision regarding the presence or absence of a threshold level signal.

Squires, K. C.↗
NASA NTRS2023

Automated Global-Scale Detection and Characterization of Anthropogenic Activity using Multi-Source Satellite-Based Remote Sensing Imagery

Satellite-based remote sensing imagery is an effective means for detecting objects and structures in support of many applications. However, detecting the spatial and temporal bounds of a specific activity in satellite imagery is inherently more complex and research in this area is nascent. One reason for this is that describing an activity implies defining both spatial and temporal bounds and while activity is inherently continuous in nature, the geospatial (imagery) time series for any particular swath of ground provided by satellite imagery is relatively sparse and discrete in comparison. The IARPA Space-Based Machine Automated Recognition Technique (SMART)1 program is the first large-scale research program to target advancing the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global, multispectral satellite imagery. The program has two primary research objectives: 1) the “harmonization” of multiple imagery sources and 2) automated reasoning at scale to detect, characterize, and monitor activities of interest. This paper provides details on the goals, dataset, metrics, and lessons learned of the IARPA SMART program. By releasing the annotated dataset, the program aims to foster additional research in this area by the community at large.

Hirsh R Goldberg↗
NASA NTRS1975

Vertex evoked potentials in a rating-scale detection task - Relation to signal probability

Results of vertex-evoked potential studies conducted to determine how decision confidence level and decision probability interact to determine P3 amplitude for both signal-present and signal-absent decisions. They support the contention that the form of the vertex-evoked response is closely correlated with the subject's psychophysical response regarding the presence or absence of a threshold-level signal.

Squires, K. C.↗
DOE OSTI2024

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Bjorner, Marianne B.↗
DOE OSTI2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Can tonne-scale direct detection experiments discover nuclear dark matter?

Models of nuclear dark matter propose that the dark sector contains large composite states consisting of dark nucleons in analogy to Standard Model nuclei. We examine the direct detection phenomenology of a particular class of nuclear dark matter model at the current generation of tonne-scale liquid noble experiments, in particular DEAP-3600 and XENON1T. In our chosen nuclear dark matter scenario distinctive features arise in the recoil energy spectra due to the non-point-like nature of the composite dark matter state. We calculate the number of events required to distinguish these spectra from those of a standard point-like WIMP state with a decaying exponential recoil spectrum. In the most favourable regions of nuclear dark matter parameter space, we find that a few tens of events are needed to distinguish nuclear dark matter from WIMPs at the 3 σ level in a single experiment. Given the total exposure time of DEAP-3600 and XENON1T we find that at best a 2 σ distinction is possible by these experiments individually, while 3 σ sensitivity is reached for a range of parameters by the combination of the two experiments. We show that future upgrades of these experiments have potential to distinguish a large range of nuclear dark matter models from that of a WIMP at greater than 3 σ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Toward large-scale building detection with convolutional neural network using remote sensing images

Establishing up-to-date nationwide building maps is essential to understand urban dynamics, such as estimating population and urban planning and many other applications. However, an efficient and effective solution is yet to be developed. In this paper, for the first time we evaluate three state-of-the-art CNNs for detecting buildings across entire United States using aerial images. The three CNN architectures, fully convolutional neural network, conditional random field as recurrent neural network, and SegNet, support semantic pixel-wise labeling and focus on capturing textural information at multi-scale. We use 1-meter resolution NAIP images as the test data set, and compare the detection results across the three methods. In addition, we propose to combine signed distance function labels with SegNet, which is the preferred CNN architecture identified by our extensive evaluations. The results are further improved in terms of precision, recall rate and the number of building detected. On average, model inference on test images is less than one minute for an area of size $\sim56$ $km^2$. With these promising results and the time required to process images, the framework offers great potential toward country scale building mapping with remote sensing imagery.

Yang, Lexie↗
DOE OSTI2021

Detecting multi‐scale riverine topographic variability and its influence on Chinook salmon habitat selection

Abstract Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

Duffin, Jenna↗
NASA NTRS2017

Impact-Facilitated Hydrothermal Alteration in the Rim of Endeavour Crater, Mars

Endeavour crater, a Noachian-aged, 22 km diameter impact structure on Meridiani Planum, Mars, has been investigated by the Mars Exploration Rover Opportunuity for over 2000 sols (Mars days). The rocks of the western rim region (oldest to youngest) are: (i) the pre-impact Matijevic fm.; (ii) rim-forming Shoemaker fm. polymict impact breccias; (iii) Grasberg fm., fine-grained sediments draping the lower slopes; and (iv) Burns fm., sulfate-rich sandstones that onlap the Grasberg fm. The rim is segmented and transected by radial fracture zones. Evidence for fluid-mediated alteration includes m-scale detections of phyllosilicates from orbit, and cm-scale variations in rock/soil composition/mineralogy documented by the Opportunity instrument suite. The m-scale phyllosilicate detections include Fe(3+)-Mg and aluminous smectites that occur in patches in the Matijevic and Shoemaker fms. Rock compositions do not reveal substantial differences for smectite-bearing compared to smectite-free rocks. Interpretation: large-scale hydrothermal alteration powered by impact-deposited heat acting on limited water supplies engendered mineralogic transfomations under low water/rock, near-isochemical conditions. The cm-scale alterations, localized in fracture zones, occurred at higher water/rock as evidenced by enhanced Si and Al contents through leaching of more soluble elements, and deposition of Mg, Ni and Mn sulphates and halogen salts in soils. Visible/near infrared reflectance of narrow curvilinear red zones indicate higher nanophase ferric oxide contents and possibly hydration compared to surrounding outcrops. Broad fracture zones on the rim have reflectance features consistent with development of ferric oxide minerals. Interpretation: water fluxing through the fractures in a hydrothermal system resulting from the impact engendered alteration and leaching under high water/rock conditions. Late, localized alteration is documented by Ca-sulfate-rich veins that are not confined to fracture zones; some cross-cut the Grasberg fm. Interpretation: late fluid mobilization of soluble elements, likely in a later alteration event.

Mittlefehldt, D. W.↗
DOE OSTI2022

Towards scaling community detection on distributed-memory heterogeneous systems

Distributed multi-GPU systems pose significant challenges and opportunities for efficient execution of parallel applications. Graph algorithms are generally characterized by irregular memory accesses, low computation to communication ratios, and load balancing problems that are especially hard to address on multi-GPU systems. Graph community detection is an important problem in the emerging domain of graph analytics with numerous applications. In this paper, we present our ongoing work on distributed-memory multi-GPU implementation for graph community detection. Our work parallelizes the widely used (albeit serial) Louvain method on distributed multi-GPU platforms. Supported by an extensive set of experiments on a multi-GPU enabled supercomputer (OLCF Summit) and a single compute node (Nvidia DGX-2®), we demonstrate competitive performance to existing distributed-memory CPU-based implementation, and up to 6.5 better results than Nvidia RAPIDS® CUGRAPH. To the best of our knowledge, this work represents the first effort for community detection on distributed multi-GPU systems. Our approach and related findings can be extended to numerous other iterative graph algorithms on multi-GPU systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs." We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times 6$ km 2 area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. Here, we show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this article, we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Delaunay density diagnostic

The Delaunay density diagnostic helps detect and assess geometric scales within unstructured numerical data sets. The algorithm was first introduced in the paper "Data-driven geometric scale detection via Delaunay interpolation" by Andrew Gillette and Eugene Kur, 2022. More details can be found in that paper, as well as the included README file.

Gillette, AndrewK↗