Discrete Bins Research Articles

Oriented Ice Crystals: A Single-Scattering Property Database for Applications to Lidar and Optical Phenomenon Simulations

Abstract A database (TAMUoic2019) of the scattering, absorption, and polarization properties of horizontally oriented hexagonal plates (HOPs) and horizontally oriented hexagonal columns (HOCs) at three wavelengths (355, 532, and 1064 nm) is developed for applications to radiative transfer simulations and remote sensing implementations involving oriented ice crystals. The maximum dimension of oriented ice crystals ranges from 50 to 10 000 μm in 165 discrete size bins. The database accounts for 94 incident directions. The single-scattering properties of oriented ice crystals are computed with the physical-geometric optics method (PGOM), which is consistent with the invariant-imbedding T-matrix method for particles with size parameters larger than approximately 100–150. Note that the accuracy of PGOM increases as the size parameter increases. PGOM computes the two-dimensional phase matrix as a function of scattering polar and azimuth angles, and the phase matrix significantly varies with the incident direction. To derive the bulk optical properties of ice clouds for practical radiative transfer applications, the optical properties of individual HOPs and HOCs are averaged over the probability distribution of the tilting angle of oriented ice crystals based on the use of the TAMUoic2019 database. Simulations of lidar signals associated with ice clouds based on the bulk optical properties indicate the importance of the fraction of oriented ice crystals and the probability distribution of the tilting angle. Simulations of optical phenomena caused by oriented ice crystals demonstrate that the computed single-scattering properties of oriented ice crystals are physically rational.

Application of RADAR Imaging Analysis to SuperDARN Observations

AbstractRecently, Universal Software Radio Peripherals were installed at the McMurdo, Antartica, and Kodiak, Alaska, Super Dual Auroral Radar Network (SuperDARN) radars to replace existing synthesizer and receiver electronics. Each antenna in the radar arrays was connected to its own Universal Software Radio Peripherals input, which enables controlling the phase of the transmitted signal and sampling the received signals on each antenna. Received data are written to disk and simultaneously combined using beam forming to produce the standard SuperDARN data stream. The raw signal data from the individual antennas is retrieved from the radar sites and analyzed using an algorithm that fits a model signal based on the assumption of individual plane waves arriving from discrete angle bins in the field of view. With this analysis the target amplitudes and Doppler frequencies can be determined as a function of angle. In this paper, the theory behind the algorithm is developed, and synthetic test data are presented, as are real observations. Finally, the line‐of‐sight velocities determined from the data are used to estimate maps of the vector velocity field that produced them.

A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan

Accurate methods to track changes in lake productivity through time and space are critical to fisheries management. Chlorophyll a is the most widely studied proxy for ecosystem primary production and has been the topic of many studies. The main sources of chlorophyll a measurements are ship-based measures or multi-spectral satellite data. Autonomous underwater vehicles can survey large spatial extents approaching the scale of satellite data, but with the accuracy of ship-based water sampling methods. We use several statistical measures to compare measures of chlorophyll a collected in Lake Michigan with spatiotemporally matched satellite-derived measures of chlorophyll a from the MODIS Aqua multi-spectral sensor using NASA's OC3 and the Great Lakes Fit algorithms. Our findings show a near one to one relationship between AUV data and both satellite-derived data sets when the AUV data are coarsened to the resolution of the satellite data. A comparison of satellite-based chlorophyll a to AUV-derived chlorophyll summarized in discrete water depth bins suggested that, based on decreasing coefficients of determination, satellite estimates of chlorophyll accounted for the most variability in chlorophyll a concentrations in the upper 10 m of the water column, even though satellite sensors may detect past this depth.

Multidimensional characterization of separation processes – Part 1: Introducing kernel methods and entropy in the context of mineral processing using SEM-based image analysis

An alternative method for the particle tracking approach for scanning electron microscopy-based image analysis is introduced, using kernel density estimates instead of discrete bins. This allows for information that is more robust. Uncertainties of the data are assessed using the bootstrap resampling method. The presented methodology enables the calculation of multidimensional partition curves, which can be used for a detailed analysis of separation processes. It has been found that the statistical entropy is a helpful tool to evaluate the separation efficiency of these partition maps. The methodology was applied to a density separation process of a cassiterite-bearing skarn ore from the Hämmerlein deposit in the Erzgebirge region in Germany, which serves as a case study. A Sepro™ Falcon concentrator was utilized for the density separation.

Modelling the effect of aerosol polydispersity on unipolar charging and measurement in low-cost sensors

Low-cost ultrafine aerosol sensors are experimentally calibrated with controlled aerosol sources to provide metrics such as surface area, lung-deposited surface area, mean particle size and/or total concentration from one or more electrical current measurements. However, an aerosol with a large standard deviation in particle size provides a significantly different signal from a monodisperse aerosol with the same median particle size. In this paper, we investigate the effect of particle polydispersity on measurements in devices which employ unipolar charging. The conservation equations are solved for particle/ion charging and transport (convection, diffusion and electrical transport) in laminar, steady-state, incompressible flow. Lognormal particle size distributions are represented by over 102 coupled conservation equations for multiple size bins and discrete charge states and solved numerically for the first time. Modelling results show that integrated electrical current from a polydisperse particle distribution can be represented by a monodisperse distribution characterised by the count mean diameter (d¯; unipolar diffusion charging) or diameter of the average surface (ds¯; photoelectric charging) and total concentration, for a large range of particle distributions and operating conditions offering a convenient simplification for the interpretation of ultrafine particle measurements. The simplification reduces the number of simultaneous conservation equations required, thereby reducing computation time by up to 57 times for a polydisperse particle distribution represented by 16 discrete size bins. The method of analysis is useful to both users and developers of low-cost ultrafine particle sensors to understand the effect of particle polydispersity on measurements.

Determining grain boundary energies from triple junction geometries without discretizing the five-parameter space

Grain boundary energy anisotropy plays an important role in many science and engineering problems. This paper presents a new numerical method for determining the grain boundary energy distribution (GBED) from the microstructures of polycrystalline materials. The method assumes that triple junctions are in local equilibrium, that this equilibrium is described by the Herring equation, and can be expressed using the Hoffman-Cahn formalism of the capillarity vector. The conventional method discretizes the five-parameter space and then fits the energy for each discrete bin. The new method minimizes the difference between similar boundaries in the dataset while obeying the equilibrium equation at triple junctions. This non-parametric approach shows smaller error than the conventional method, and it can also determine the grain boundary energies from datasets that have small numbers of triple junctions if their configurations are clustered in the five-parameter space, which is not possible using the conventional method. In addition, the proposed non-parametric approach performs better when the number of triple junctions increases, which is not always true for the conventional method.

The Binary Bias: A Systematic Distortion in the Integration of Information

One of the mind’s most fundamental tasks is interpreting incoming data and weighing the value of new evidence. Across a wide variety of contexts, we show that when summarizing evidence, people exhibit a binary bias: a tendency to impose categorical distinctions on continuous data. Evidence is compressed into discrete bins, and the difference between categories forms the summary judgment. The binary bias distorts belief formation—such that when people aggregate conflicting scientific reports, they attend to valence and inaccurately weight the extremity of the evidence. The same effect occurs when people interpret popular forms of data visualization, and it cannot be explained by other statistical features of the stimuli. This effect is not confined to explicit statistical estimates; it also influences how people use data to make health, financial, and public-policy decisions. These studies (N = 1,851) support a new framework for understanding information integration across a wide variety of contexts.

Study on the Interaction between Modes of a Nanoparticle-Laden Aerosol System

Nanoparticle-laden two-phase flow systems, especially atmospheric aerosols, are usually found with several modes for particle size distribution (PSD). For the first time, a mathematical method is proposed to study the interaction of nanoparticle dynamics between modes by establishing two joint population balance equations (PBEs). The PBEs are solved using the sectional method, which divides the PSD into discrete bins. The nanoparticle-laden system involves Brownian coagulation, ventilation, and injection. The interaction between modes within a size distribution is studied quantitatively with and without injection and ventilation. The study shows that particles with smaller size are easier to be removed by background particles, but the lag time to be removed is affected by not only the total number concentration of small particles but also their sizes. Background particles play an important role in determining the evolution of small particle system, whose presence makes the secondary model absent for the small particles.

Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures

BackgroundProtein structure can be described by backbone torsion angles: rotational angles about the N-Cα bond (φ) and the Cα-C bond (ψ) or the angle between Cαi-1-Cαi-Cαi + 1 (θ) and the rotational angle about the Cαi-Cαi + 1 bond (τ). Thus, their accurate prediction is useful for structure prediction and model refinement. Early methods predicted torsion angles in a few discrete bins whereas most recent methods have focused on prediction of angles in real, continuous values. Real value prediction, however, is unable to provide the information on probabilities of predicted angles.ResultsHere, we propose to predict angles in fine grids of 5° by using deep learning neural networks. We found that this grid-based technique can yield 2–6% higher accuracy in predicting angles in the same 5° bin than existing prediction techniques compared. We further demonstrate the usefulness of predicted probabilities at given angle bins in discrimination of intrinsically disorder regions and in selection of protein models.ConclusionsThe proposed method may be useful for characterizing protein structure and disorder. The method is available at http://sparks-lab.org/server/SPIDER2/ as a part of SPIDER2 package.

Constructing Continuous Household Income Measurement on the British Election Study Internet Panel

This research note describes the procedure for constructing continuous measurements of income on the British Election Study Internet Panel using several imputation steps. As with most social surveys not focusing directly on income measurement, the British Election Study Internet Panel uses a household income question that asks respondents to place their income within discrete bins. The most common analysis practices are either to group incomes into coarser bins such as quintiles which loses information or to impute a continuous income measure based on bin midpoints, which ignores the likely underlying distribution of continuous income, which will not be uniform within each bucket. We adapt a recently developed technique to interpolate a cumulative density function from discrete categories for aggregate counts to apply to the individual level data. Using this approach will allow the incorporation of external information about mean incomes within subgroups to be further integrated into the imputation approach.

Autobin: A predictive approach towards automatic binning using data splitting

The concept of binning is known by many names: discretisation, classing, grouping and quantisation. It entails the mapping of continuous or categorical data into discrete bins. Binning is an important pre-processing step in most predictive models and considered a basic data preparation step in building a credit scorecard. Credit scorecards are mathematical models which attempt to provide a quantitative estimate of the probability that a customer will display a defined behaviour (e.g. default) with respect to their current credit position with a lender. Among the practical advantages of binning are the removal of the effects of outliers and a way to handle missing values. Many binning methods exist but they are often time consuming to actually carry out. We propose a new method, Autobin, that is based on data splitting and maximising a cross-validation form of the predicted log-likelihood. Autobin has the advantage of being nearly automatic and requires very little by way of tuning parameters. In a limited simulation study done, it was found that Autobin outperforms its competitors.

Robust DFT‐based generalised likelihood ratio test for underwater tone detection

The discrete Fourier transform (DFT) of observation sequence is usually used in the detection of tonal signal. A tone without any prior information cannot be guaranteed to lie exactly on the discrete DFT frequency bin, which will degrade the performance of the tonal detector based on DFT. In this work, the authors study the detection problem for a tone deviated from the discrete frequency bin. A generalised likelihood ratio detector utilising the information of adjacent frequency bins is designed. Theoretical analysis, computer simulations, and lake experiment show that the proposed detector is robust against the deviation and is more suitable for the tone detection in practical circumstances.

An integrated study on the sensitivity and uncertainty associated with the evaluation of stimulated reservoir volume (SRV)

Economic production from ultra-tight shale formations depends greatly on the effectiveness of the hydraulic fracturing stimulation treatment. As a critical monitoring technology for unconventional reservoir development, microseismic plays an important role in fracture location analysis and evaluation of the fracturing treatment. For microseismic analysis, particularly for fracture inversion, uncertainties are common and are associated with the collected microseismic data themselves and formation properties such as velocity analysis result. Therefore, large differences are often observed between the fitted stimulated reservoir volume (SRV) from microseismic events and the volume obtained through hydraulic fracture modeling.This paper combines the microseismic monitoring, hydraulic fracturing simulation and SRV fitting techniques to investigate the inter-relationship of data uncertainties and to provide a possible methodology for guiding the stimulation treatment during reservoir development. The hydraulic fracturing process is simulated to obtain a realistic set of microseismic events that are generated from hydraulic fracture propagation and the pore pressure increase in the matrix. In order to examine the impacts of the uncertainties on the estimated locations of the microseismic events, different levels of noise are added to the first arrival times during the inversion process. Because the microseismic event locations inverted from the first arrival times with noise are very scattered, a denoising procedure based on probability theory is applied to identify and exclude the outliers in the MS event clouds. The discrete bin, 3D Delaunay triangulation and ellipsoid fitting methods are used to compute SRV from the inverted microseismic events with noise. Comparisons are made to examine the sensitivities of the different SRV fitting methods with respect to data noise. The results show that the two discrete bin algorithms are relatively stable, while the MVEE and 3D Delaunay triangulation algorithms are not very robust and can be easily affected by the noise in the observed data.

Measuring the Transition Rates of Coalescence Events during Double Phase Separation in Microgravity.

Phase transition is a ubiquitous phenomenon in nature, science and technology. In general, the phase separation from a homogeneous phase depends on the depth of the temperature quench into the two-phase region. Earth’s gravity masks the details of phase separation phenomena, which is why experiments were performed under weightlessness. Under such conditions, the pure fluid sulphur hexafluoride (SF) near its critical point also benefits from the universality of phase separation behavior and critical slowing down of dynamics. Initially, the fluid was slightly below its critical temperature with the liquid matrix separated from the vapor phase. A 0.2 mK temperature quench further cooled down the fluid and produced a double phase separation with liquid droplets inside the vapor phase and vapor bubbles inside the liquid matrix, respectively. The liquid droplets and the vapor bubbles respective distributions were well fitted by a lognormal function. The evolution of discrete bins of different radii allowed the derivation of the transition rates for coalescence processes. Based on the largest transition rates, two main coalescence mechanisms were identified: (1) asymmetric coalescences between one small droplet of about 20 m and a wide range of larger droplets; and (2) symmetric coalescences between droplets of large and similar radii. Both mechanisms lead to a continuous decline of the fraction of small radii droplets and an increase in the fraction of the large radii droplets. Similar coalescence mechanisms were observed for vapor bubbles. However, the mean radii of liquid droplets exhibits a evolution, whereas the mean radii of the vapor bubbles exhibit a evolution.

Wideband DOA estimation for SOBFN

This study considers the problem of wideband direction of arrival (DOA) estimation for spatial optical beam-forming network (SOBFN). Since its system configuration is different from that of the traditional microwave beam-forming networks, the authors first establish the observation model of SOBFN when multiple broadband signals are received. Two kinds of DOA estimation strategies are proposed for wideband direction finding based on the established signal model. The proposed methods decompose the wideband array output into narrowband outputs in several discrete frequency bins. Then, they take advantages of the spatial sparsity of the discrete narrowband observation models to estimate the spatial power spectrum, either coherently or non-coherently. A coarse DOA estimate is obtained via peak-searching in the reconstructed spectrum. The preliminary result is then refined by a linear interpolation procedure. Profound experiments are carried out to demonstrate and compare the performance of the two strategies. Simulation results show that the proposed approaches can achieve wideband DOA estimation of high precision and super-resolution for SOBFN. Moreover, coherent process is proved to have superior performance than the non-coherent strategy.

Modeling studies on coagulation of charged particles and comparison with experiments

A generalized formulation for coagulation of charged particles is presented in this paper. The governing equations can be solved semi-implicitly for a number of discrete size and charge bins. The computationally efficient mechanism gives volume and charge conserved solution with time. A distinct way of handling electrostatic dispersion in dynamic model has been used in this work. The scheme has been tested for charged, nebulized aerosol particles by model studies and also compared with experimental observations. The model simulated results show that charge can significantly affect the coagulation dynamics if charge level is higher than the Boltzmann equilibrium charge limit for less than 1μm particles. It was found that for the bipolar charge case, there was a significant effect on the enhancement of the coagulation process. For the case of unipolar charge, electrostatic dispersion was seen to play a role in the depletion of number concentration. The model was then validated against results of six controlled experiments in which electrical low pressure impactor (ELPI) was used for measurement of charge and number size distribution. Our model results were found to be in reasonable agreement with observed values. The developed model is fast (independent of time step) and numerically stable. It can be used for several applications involving coagulation dynamics of charged aerosol particles.

LCA*: an entropy-based measure for taxonomic assignment within assembled metagenomes.

Motivation: A perennial problem in the analysis of environmental sequence information is the assignment of reads or assembled sequences, e.g. contigs or scaffolds, to discrete taxonomic bins. In the absence of reference genomes for most environmental microorganisms, the use of intrinsic nucleotide patterns and phylogenetic anchors can improve assembly-dependent binning needed for more accurate taxonomic and functional annotation in communities of microorganisms, and assist in identifying mobile genetic elements or lateral gene transfer events.Results: Here, we present a statistic called LCA* inspired by Information and Voting theories that uses the NCBI Taxonomic Database hierarchy to assign taxonomy to contigs assembled from environmental sequence information. The LCA* algorithm identifies a sufficiently strong majority on the hierarchy while minimizing entropy changes to the observed taxonomic distribution resulting in improved statistical properties. Moreover, we apply results from the order-statistic literature to formulate a likelihood-ratio hypothesis test and P-value for testing the supremacy of the assigned LCA* taxonomy. Using simulated and real-world datasets, we empirically demonstrate that voting-based methods, majority vote and LCA*, in the presence of known reference annotations, are consistently more accurate in identifying contig taxonomy than the lowest common ancestor algorithm popularized by MEGAN, and that LCA* taxonomy strikes a balance between specificity and confidence to provide an estimate appropriate to the available information in the data.Availability and Implementation: The LCA* has been implemented as a stand-alone Python library compatible with the MetaPathways pipeline; both of which are available on GitHub with installation instructions and use-cases (http://www.github.com/hallamlab/LCAStar/).Contact: shallam@mail.ubc.caSupplementary information: Supplementary data are available at Bioinformatics online.

Statistical properties of Fourier-based time-lag estimates

The study of X-ray time-lag spectra in active galactic nuclei (AGN) is currently an active research area, since it has the potential to illuminate the physics and geometry of the innermost region (i.e. close to the putative super-massive black hole) in these objects. To obtain reliable information from these studies, the statistical properties of time-lags estimated from data must be known as accurately as possible. Aims: We investigated the statistical properties of Fourier-based time-lag estimates (i.e. based on the cross-periodogram), using evenly sampled time series with no missing points. Our aim is to provide practical `guidelines' on estimating time-lags that are minimally biased (i.e. whose mean is close to their intrinsic value) and have known errors.} Methods: Our investigation is based on both analytical work and extensive numerical simulations. The latter consisted of generating artificial time series with various signal-to-noise ratios and sampling patterns/durations similar to those offered by AGN observations with present and past X-ray satellites. We also considered a range of different model time-lag spectra commonly assumed in X-ray analyses of compact accreting systems. Results: Discrete sampling, binning and finite light curve duration cause the mean of the time-lag estimates to have a smaller magnitude than their intrinsic values. Smoothing (i.e. binning over consecutive frequencies) of the cross-periodogram can add extra bias at low frequencies. The use of light curves with low signal-to-noise ratio reduces the intrinsic coherence, and can introduce a bias to the sample coherence, time-lag estimates, and their predicted error.

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays.

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.

Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments how that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.