scholarly journals A quantitative analysis of environmental associations in sauropod dinosaurs

Paleobiology ◽  
2010 ◽  
Vol 36 (2) ◽  
pp. 253-282 ◽  
Author(s):  
Philip D. Mannion ◽  
Paul Upchurch
Keyword(s):  
Positive Association ◽  
Large Data ◽  
The Body ◽  
Time Range ◽  
Data Sets ◽  
Coastal Environments ◽  
Carbonate Platforms ◽  
Significant Positive Association ◽  
Data Set ◽  
Body Fossil

Both the body fossils and trackways of sauropod dinosaurs indicate that they inhabited a range of inland and coastal environments during their 160-Myr evolutionary history. Quantitative paleoecological analyses of a large data set of sauropod occurrences reveal a statistically significant positive association between non-titanosaurs and coastal environments, and between titanosaurs and inland environments. Similarly, “narrow-gauge” trackways are positively associated with coastal environments and “wide-gauge” trackways are associated with inland environments. The statistical support for these associations suggests that this is a genuine ecological signal: non-titanosaur sauropods preferred coastal environments such as carbonate platforms, whereas titanosaurs preferred inland environments such as fluvio-lacustrine systems. These results remain robust when the data set is time sliced and jackknifed in various ways. When the analyses are repeated using the more inclusive groupings of titanosauriforms and Macronaria, the signal is weakened or lost. These results reinforce the hypothesis that “wide-gauge” trackways were produced by titanosaurs. It is commonly assumed that the trackway and body fossil records will give different results, with the former providing a more reliable guide to the habitats occupied by extinct organisms because footprints are produced during life, whereas carcasses can be transported to different environments prior to burial. However, this view is challenged by our observation that separate body fossil and trackway data sets independently support the same conclusions regarding environmental preferences in sauropod dinosaurs. Similarly, analyzing localities and individuals independently results in the same environmental associations. We demonstrate that conclusions about environmental patterns among fossil taxa can be highly sensitive to an investigator's choices regarding analytical protocols. In particular, decisions regarding the taxonomic groupings used for comparison, the time range represented by the data set, and the criteria used to identify the number of localities can all have a marked effect on conclusions regarding the existence and nature of putative environmental associations. We recommend that large data sets be explored for such associations at a variety of different taxonomic and temporal scales.

The body-wave magnitude mb: an attempt to rationalize the distance-depth correction q(Δ, h)

2020 ◽  
Vol 223 (1) ◽  
pp. 270-288
Author(s):  
Nooshin Saloor ◽  
Emile A Okal
Keyword(s):  
Large Scale ◽  
Body Wave ◽  
Exact Algorithm ◽  
Large Data ◽  
The Body ◽  
P Wave ◽  
Small Scale ◽  
Data Sets ◽  
Data Set ◽  
Body Wave Magnitude

SUMMARY We explore the possible theoretical origin of the distance–depth correction q(Δ, h) introduced 75 yr ago by B. Gutenberg for the computation of the body-wave magnitude mb, and still in use today. We synthesize a large data set of seismograms using a modern model of P-wave velocity and attenuation, and process them through the exact algorithm mandated under present-day seismological practice, to build our own version, qSO, of the correction, and compare it to the original ones, q45 and q56, proposed by B. Gutenberg and C.F. Richter. While we can reproduce some of the large scale variations in their corrections, we cannot understand their small scale details. We discuss a number of possible sources of bias in the data sets used at the time, and suggest the need for a complete revision of existing mb catalogues.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

A batch-wise non-linear fitting and analysis tool for treating large X-ray diffraction data sets

2006 ◽  
Vol 39 (2) ◽  
pp. 262-266 ◽  
Author(s):  
R. J. Davies
Keyword(s):  
Diffraction Data ◽  
Operation Mode ◽  
Large Data ◽  
Scattering Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Data Set ◽  
X Ray ◽  
Linear Fitting ◽  
Non Linear

Synchrotron sources offer high-brilliance X-ray beams which are ideal for spatially and time-resolved studies. Large amounts of wide- and small-angle X-ray scattering data can now be generated rapidly, for example, during routine scanning experiments. Consequently, the analysis of the large data sets produced has become a complex and pressing issue. Even relatively simple analyses become difficult when a single data set can contain many thousands of individual diffraction patterns. This article reports on a new software application for the automated analysis of scattering intensity profiles. It is capable of batch-processing thousands of individual data files without user intervention. Diffraction data can be fitted using a combination of background functions and non-linear peak functions. To compliment the batch-wise operation mode, the software includes several specialist algorithms to ensure that the results obtained are reliable. These include peak-tracking, artefact removal, function elimination and spread-estimate fitting. Furthermore, as well as non-linear fitting, the software can calculate integrated intensities and selected orientation parameters.

Effects of genotype and lactation number on health and reproductive problems in dairy cows

1997 ◽  
Vol 1997 ◽  
pp. 143-143
Author(s):  
B.L. Nielsen ◽  
R.F. Veerkamp ◽  
J.E. Pryce ◽  
G. Simm ◽  
J.D. Oldham
Keyword(s):  
Dairy Cows ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Variation Analysis ◽  
Genetic Line ◽  
Data Set ◽  
Health Events ◽  
Use Of Data ◽  
Low Incidence

High producing dairy cows have been found to be more susceptible to disease (Jones et al., 1994; Göhn et al., 1995) raising concerns about the welfare of the modern dairy cow. Genotype and number of lactations may affect various health problems differently, and their relative importance may vary. The categorical nature and low incidence of health events necessitates large data-sets, but the use of data collected across herds may introduce unwanted variation. Analysis of a comprehensive data-set from a single herd was carried out to investigate the effects of genetic line and lactation number on the incidence of various health and reproductive problems.

scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

SKT

2021 ◽  
Vol 14 (11) ◽  
pp. 2369-2382
Author(s):  
Monica Chiosa ◽  
Thomas B. Preußer ◽  
Gustavo Alonso
Keyword(s):  
Frequency Distribution ◽  
Empirical Evaluation ◽  
Large Data ◽  
Cloud Service ◽  
Data Sets ◽  
Data Set ◽  
Single Pass ◽  
Trade Offs ◽  
Significant Performance ◽  
Spatial Architecture

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically extracted by using sketch algorithms, now widely employed to process very large data sets in manageable space and in a single pass over the data. Often, analysts need more than one parameter to characterize the stream. However, computing multiple sketches becomes expensive even when using high-end CPUs. Exploiting the increasing adoption of hardware accelerators, this paper proposes SKT , an FPGA-based accelerator that can compute several sketches along with basic statistics (average, max, min, etc.) in a single pass over the data. SKT has been designed to characterize a data set by calculating its cardinality, its second frequency moment, and its frequency distribution. The design processes data streams coming either from PCIe or TCP/IP, and it is built to fit emerging cloud service architectures, such as Microsoft's Catapult or Amazon's AQUA. The paper explores the trade-offs of designing sketch algorithms on a spatial architecture and how to combine several sketch algorithms into a single design. The empirical evaluation shows how SKT on an FPGA offers a significant performance gain over high-end, server-class CPUs.

Scalable Non-Parametric Methods for Large Data Sets

2011 ◽  
pp. 1708-1713
Author(s):  
V. Suresh Babu ◽  
P. Viswanath ◽  
Narasimha M. Murty
Keyword(s):  
Nearest Neighbor ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Parametric Methods ◽  
Clustering Method ◽  
Data Set ◽  
Computational Burden ◽  
Set Size ◽  
Non Parametric

Non-parametric methods like the nearest neighbor classifier (NNC) and the Parzen-Window based density estimation (Duda, Hart & Stork, 2000) are more general than parametric methods because they do not make any assumptions regarding the probability distribution form. Further, they show good performance in practice with large data sets. These methods, either explicitly or implicitly estimates the probability density at a given point in a feature space by counting the number of points that fall in a small region around the given point. Popular classifiers which use this approach are the NNC and its variants like the k-nearest neighbor classifier (k-NNC) (Duda, Hart & Stock, 2000). Whereas the DBSCAN is a popular density based clustering method (Han & Kamber, 2001) which uses this approach. These methods show good performance, especially with larger data sets. Asymptotic error rate of NNC is less than twice the Bayes error (Cover & Hart, 1967) and DBSCAN can find arbitrary shaped clusters along with noisy outlier detection (Ester, Kriegel & Xu, 1996). The most prominent difficulty in applying the non-parametric methods for large data sets is its computational burden. The space and classification time complexities of NNC and k-NNC are O(n) where n is the training set size. The time complexity of DBSCAN is O(n2). So, these methods are not scalable for large data sets. Some of the remedies to reduce this burden are as follows. (1) Reduce the training set size by some editing techniques in order to eliminate some of the training patterns which are redundant in some sense (Dasarathy, 1991). For example, the condensed NNC (Hart, 1968) is of this type. (2) Use only a few selected prototypes from the data set. For example, Leaders-subleaders method and l-DBSCAN method are of this type (Vijaya, Murthy & Subramanian, 2004 and Viswanath & Rajwala, 2006). These two remedies can reduce the computational burden, but this can also result in a poor performance of the method. Using enriched prototypes can improve the performance as done in (Asharaf & Murthy, 2003) where the prototypes are derived using adaptive rough fuzzy set theory and as in (Suresh Babu & Viswanath, 2007) where the prototypes are used along with their relative weights. Using a few selected prototypes can reduce the computational burden. Prototypes can be derived by employing a clustering method like the leaders method (Spath, 1980), the k-means method (Jain, Dubes, & Chen, 1987), etc., which can find a partition of the data set where each block (cluster) of the partition is represented by a prototype called leader, centroid, etc. But these prototypes can not be used to estimate the probability density, since the density information present in the data set is lost while deriving the prototypes. The chapter proposes to use a modified leader clustering method called the counted-leader method which along with deriving the leaders preserves the crucial density information in the form of a count which can be used in estimating the densities. The chapter presents a fast and efficient nearest prototype based classifier called the counted k-nearest leader classifier (ck-NLC) which is on-par with the conventional k-NNC, but is considerably faster than the k-NNC. The chapter also presents a density based clustering method called l-DBSCAN which is shown to be a faster and scalable version of DBSCAN (Viswanath & Rajwala, 2006). Formally, under some assumptions, it is shown that the number of leaders is upper-bounded by a constant which is independent of the data set size and the distribution from which the data set is drawn.

Conclusion

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Text Mining ◽  
High Throughput ◽  
Relevant Literature ◽  
Critical Role ◽  
The Body ◽  
Data Sets ◽  
Data Set ◽  
New Techniques ◽  
Scientific Text ◽  
Mining Methods

The genomics era has presented many new high throughput experimental modalities that are capable of producing large amounts of data on comprehensive sets of genes. In time there will certainly be many more new techniques that explore new avenues in biology. In any case, textual analysis will be an important aspect of the analysis. The body of the peer-reviewed scientific text represents all of our accomplishments in biology, and it plays a critical role in hypothesizing and interpreting any data set. To altogether ignore it is tantamount to reinventing the wheel with each analysis. The volume of relevant literature approaches proportions where it is all but impossible to manually search through all of it. Instead we must often rely on automated text mining methods to access the literature efficiently and effectively. The methods we present in this book provide an introduction to the avenues that one can employ to include text in a meaningful way in the analysis of these functional genomics data sets. They serve as a complement to the statistical methods such as classification and clustering that are commonly employed to analyze data sets. We are hopeful that this book will serve to encourage the reader to utilize and further develop text mining in their own analyses.

scholarly journals Learning mid-IR emission spectra of PAH populations from observations

2019 ◽  
Vol 15 (S350) ◽  
pp. 406-407
Author(s):  
Sacha Foschino ◽  
Olivier Berné ◽  
Christine Joblin
Keyword(s):  
Emission Spectra ◽  
Large Data ◽  
Blind Signal Separation ◽  
Data Sets ◽  
Data Set ◽  
Blind Signal ◽  
James Webb Space Telescope ◽  
Astrophysical Objects ◽  
Polycyclic Aromatic ◽  
Ir Emission

AbstractObservations of the mid-infrared (mid-IR, 3-15 μm) spectra of photo-dissociation regions reveal ubiquitous, broad and intense emission bands, the aromatic infrared bands (AIBs), attributed to polycyclic aromatic hydrocarbons (PAHs). Studies of the AIBs showed spectral variations (e.g. in the band positions) between different astrophysical objects, or even within single object, thanks to hyperspectral images. The James Webb Space Telescope (JWST) will allow to get further spectral and spatial details compared to former space observatories. This will come with large data sets, which will require specific tools in order to perform efficient scientific analysis.We propose in this study a method based on blind signal separation to reduce the analysis of such large data set to that of a small number of elementary spectra, spectrally representative of the data set and physically interpretable as the spectra of populations of mid-IR emitters. The robustness and fastness of the method are improved compared to former algorithms. It is tested on a ISO-SWS data set, which approaches the best the characteristics of JWST data, from which four elementary spectra are extracted, attributed to cationic, neutral PAHs, evaporating very small grains and large and ionized PAHs.

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