Interactive Data Analysis of Multi-Run Performance Data
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Computer science↗
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Exploratory Data Analysis of Long-Term Meteorological Data at a DOE Site for Nuclear Safety
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Yu, Xiao-Ying↗
Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis
Analyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. .
97 MATHEMATICS AND COMPUTING↗
Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets
This paper presents an exploration into the development and validation of data analysis approaches for Phasor Measurement Units (PMUs) using open-source datasets and tools. Various methods for event detection, event classification, frequency response, and oscillation analysis were tested. We leverage the capabilities of Archive Walker (AW), the Frequency Response Analysis Tool (FRAT), and the Oscillation Baselining and Analysis Tool (OBAT), all open-source tools, for efficient processing and analysis of synchrophasor data. The open-source Transmission Signature Library (TSL) dataset was employed as a dataset for a comprehensive evaluation to assess the performance and reliability of the proposed methods.
PMU, event analysis, oscillation, Frequency Respon↗
Spectral Methods in Time-dependent Data Analysis
We aim to create a new model for time-dependent data analysis, named dynamical learning, that integrates data-driven manifold learning techniques with operator-theoretic methods from dynamical systems theory. This approach has the potential to deliver more efficient methods for analyzing time-dependent data, such as video streams, by naturally separating out the temporal and spatial features of the data. We aim to apply the newly developed methods to video surveillance data related to Sandia mission applications, and particularly focus on the problems of image segmentation and object tracking. This project ended early due to the departure of the PI from Sandia about 18 months into the project. Therefore, this document reports on partial progress towards the initial goals of the project. In addition, this document reports on part of the work conducted during the project; see the Appendix for a summary of all the work conducted during the 18 months.
97 MATHEMATICS AND COMPUTING↗
Data analysis for synchrotron microcrystal native-SAD phasing
De novo structural evaluation of native biomolecules from single-wavelength anomalous diffraction (SAD) is a challenge because of the weakness of the anomalous scattering. For microcrystals with sizes less than 10 μm, native-SAD phasing at synchrotron microdiffraction beamlines is even more challenging due to difficulties in sample manipulation, diffraction data collection and data analysis. Native-SAD analysis from microcrystals by using X-ray free electron lasers has been demonstrated but has required use of thousands of thousands of microcrystals to achieve the necessary accuracy. Here we show that by exploitation of anomalous microdiffraction signals at a synchrotron beamline, by the use of polyimide wellmounts, and by an iterative crystal and frame rejection method, optimized data may be acquired and assembled which allows microcrystal native-SAD phasing from as few as about 1200 crystals. Finally, beyond microcrystals, our data analysis strategy may work equally well for microdiffraction data from larger crystals.
36 MATERIALS SCIENCE↗
XAFS studies on battery materials: Data analysis supported by a chemometric approach
The large amount of data collected during a typical operando X-ray absorption fine structure (XAFS) experiment and the interest to thoroughly investigate and comprehend a redox process occurring in a Li-ion battery need an advanced data analysis approach. In this context, multivariate curve resolution (MCR) has gained attention as chemometric technique that allows the identification of the number of the species and their existence range without any a priori information on the studied system. Herein the call for an advanced data treatment is answered by a joint XAFS-MCR approach, able to unveil the local structural dynamics of the selected species, providing a quick and efficient data interpretation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Small Angle Scattering Data Analysis Assisted by Machine Learning Methods
Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
High Resolution Data Analysis: Plans and Prospects [Book Chapter]
Herein, a report on the progress on the high resolution data analysis of the ADMX experimental results is presented. In this paper, tools are developed and tested on a blind injection mimicking a Maxwellian like signal in the frequency domain. This blind injection will be used as a test bed which can be later implemented on all the high resolution data. The high resolution data is stored in the Fermilab server. In this analysis a PostgreSQL query was made to ensure the blind injection is in the middle of the frequency spectrum and 19 such files were found. The time series data is read using a c++ program. An apodization function is applied on the time series data and zero filled to reduce the frequency spacing in order to achieve a better interpolation. A FFTW header is used to compute the Fourier transform of the time series data. A Savitzky–Golay filter is applied on the unnormalized power which then can be used to remove the spectral shape. Each frequency spectrum has a bandwidth of 50 kHz.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Columnar data analysis with ATLAS analysis formats
Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.
97 MATHEMATICS AND COMPUTING↗
Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.
60 APPLIED LIFE SCIENCES↗
INL’s Human Reliability Analysis Data Collection Based on the SHEEP Framework
This presentation introduces INL's human reliability analysis data collection studies.
99 GENERAL AND MISCELLANEOUS↗
Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm
The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.
D estefani, André↗
Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis
In this paper we present a novel approach for identifying fraudulent papers from their titles and abstracts. The premise of the approach is that there are holes in the presentation of the approach and findings of fraudulent research papers. As an abstract is intended to highlight key features of the approach as well as important conclusions the authors seek to determine if the assumed existence of holes can be identified from analysis of abstracts alone. The data set considered is derived from papers sharing a single author with labels determined based on a formal linguistic analysis of the complete documents. To detect these logical and literary holes we utilize techniques from topological data analysis which summarizes data based on the presence of multi-dimensional, topological holes. We find that, in fact, topological features derived through a combination of techniques in natural language processing and time-series analysis allow for superior detection of the fraudulent papers than the natural language processing tools alone. Thus we conclude that the connections and holes present in the abstracts of research cons contributes to an ability to infer the scientific validity of the corresponding work.
Tymochko, Sarah J.↗
Data Analysis for the Born Qualified Grand LDRD Project
This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processes for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.
42 ENGINEERING↗
Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning
SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.
Raut, Bhupendra Ashokrao [Argonne National Laborat↗
ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data
The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.
97 MATHEMATICS AND COMPUTING↗
Uncertainty in Experimental Data Analysis [Slides]
The talk was presented virtually to the Institute of Fundamental Technological Research, Polish Academy of Sciences in Warsaw, Poland, December 21, 2020. Astronomical observations, unusual medical cases, physics experiments too costly to repeat, natural events like earthquakes, hurricanes – all of them are impossible to repeat yet produce important scientific information not achievable in other way. This data should not be treated as qualitative, anecdotal evidence only. It should be analyzed in a mathematically rigorous way to produce quantitative experimental data. Analysis method for one-of-a-kind event data differs from analysis of a repeated experiment data. For a repeated experiments the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single true experimental value. Measurement uncertainty depends on the measurement system only. Modern digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one-of-a-kind experiment are analyzed, only the measurement uncertainty can be reported.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗