Canadian Prostitution Law

2016 ◽  
Author(s):  
Lauren Jones
Keyword(s):  
Descriptive Analysis ◽  
Relevant Literature ◽  
Crime Reporting ◽  
Online Review ◽  
Data Sets ◽  
Data Set ◽  
Sex Trade ◽  
Research Directions ◽  
History Of ◽  
Uniform Crime Reporting

This chapter reviews the history of prostitution law in Canada. It begins with a review of relevant literature on the history and policy of the sex trade in Canada, along with current laws and their enforcement. It then discusses two sources of data available for use in prostitution research in Canada: the Uniform Crime Reporting Survey, a data set that tracks crime and arrest information, and the Erotic Review (TER), a data set drawn from an online review website for sex professionals. These data sets are employed in descriptive analysis of the state of prostitution markets in Canada. The chapter also considers the challenges brought against Canadian prostitution law and concludes by suggesting potential research directions.

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 Modeling Insulin-Glucose Dynamics during Insulin Induced Hypoglycemia. Evaluation of Glucose Counterregulation

1999 ◽  
Vol 1 (4) ◽  
pp. 313-323 ◽  
Author(s):  
Boris P. Kovatchev ◽  
Leon S. Farhy ◽  
Daniel J. Cox ◽  
Martin Straume ◽  
Vladimir I. Yankov ◽  
...  
Keyword(s):  
Temporal Pattern ◽  
Type I Diabetes ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Type I ◽  
Euglycemic Hyperinsulinemic Clamp ◽  
Data Set ◽  
Utilization Coefficient ◽  
History Of ◽  
Glucose Dynamics

A dynamical network model of insulin-glucose interactions in subjects with Type I Diabetes was developed and applied to data sets for 40 subjects. Each data set contained the amount of dextrose + insulin infused and blood glucose (BG) determinations, sampled every 5 minutes during a one-hour standardized euglycemic hyperinsulinemic clamp and a subsequent one-hour BG reduction to moderate hypoglycemic levels. The model approximated the temporal pattern of BG and on that basis predicted the counterregulatory response of each subject. The nonlinear fits explained more than 95% of the variance of subjects' BG fluctuations, with a median coefficient of determination 97.7%. For all subjects the model-predicted counterregulatory responses correlated with measured plasma epinephrine concentrations. The observed nadirs of BG during the tests correlated negatively with the model-predicted insulin utilization coefficient (r = -0.51,p< 0.001) and counterregulation rates (r= -0.63,p< 0.001). Subjects with a history of multiple severe hypoglycemic episodes demonstrated slower onset of counterregulation compared to subjects with no such history (p< 0.03).

Investigation into the development and historical roots of collection development by referenced publication year spectroscopy

2020 ◽  
Vol 39 (4) ◽  
pp. 97-103
Author(s):  
Basharat Ahmad Malik ◽  
Ashiya Ahmadi
Keyword(s):  
Research Work ◽  
Research Field ◽  
Library Science ◽  
Data Sets ◽  
Collection Development ◽  
Data Set ◽  
Content Type ◽  
Abstract Author ◽  
Time Period ◽  
History Of

Purpose The purpose of this study is the application of a recently developed quantitative method named Referenced Publication Year Spectroscopy (RPYS) in the spectrum of Collection Development. RPYS portrays peak years to be recognized in citations in a research field that guarantees to assist in the identification of significant contributions and groundbreaking revelations in a research field. Design/methodology/approach Preliminary data of the study has been extracted from Web of Science (WoS) by using two phrases “collection development” and “collection building” to search in terms of the topic (comprising four parts: title, abstract, author keywords and KeyWords Plus). The search was restricted to the time period 1974-2017, which formulated a data set of 1,682 documents covering 29,017 cited references. The program CRExplorer (www.crexplorer.net) was used for the extraction of cited references from the data sets downloaded from WoS. Further analysis was performed manually using MS-Excel 2016. Findings The present study identified seminal works, which contributed to a high extent to the evolution and development of collection development. The analysis of all cited references using the RPYS method showed nine peaks, which present historical roots of collection development and revealed that the basic idea of this very subfield of library science dates centuries back. Moreover, the results of the investigation on most effective documents (in the form of peaks) revealed that the field of collection development significantly influenced by the works of authors such as Gabriel Naudé, Gabriel Peignot, Giulio Petzholdt, P L Gross, E M Gross, Richard Trueswell, Allen Kent, Ross Atkinson, etc. Practical implications The analysis of works cited in publications helps to ascertain important intellectual contributions related to a particular domain of knowledge. It not only helps in extracting the most important works but also it helps to reconstruct the history of a specific research field by examining the specific role of the cited references. Therefore, the results of the study could be useful for researchers, practitioners, scholars and more specifically bibliophiles, bibliographers and librarians to gain a better understanding of seminal works in the spectrum of collection development. Originality/value To the best of authors’ knowledge, the present research work is unique and novel in the spectrum of collection development, which explored and examined the pivotal works in the field by using the RPYS method.

scholarly journals DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data

2008 ◽  
Vol 7 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Niklas Elmqvist ◽  
John Stasko ◽  
Philippas Tsigas
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Set ◽  
Data Dependencies ◽  
Expert Review ◽  
History Of ◽  
Multidimensional Data Sets ◽  
High Degree

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

scholarly journals Transcriptomics provides a robust framework for the relationships of the major clades of cladobranch sea slugs (Mollusca, Gastropoda, Heterobranchia), but fails to resolve the position of the enigmatic genus Embletonia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dario Karmeinski ◽  
Karen Meusemann ◽  
Jessica A. Goodheart ◽  
Michael Schroedl ◽  
Alexander Martynov ◽  
...  
Keyword(s):  
Phylogenetic Relationships ◽  
Evolutionary History ◽  
Phylogenetic Signal ◽  
Set Covering ◽  
Phylogenetic Position ◽  
Data Sets ◽  
Transcriptome Data ◽  
Data Set ◽  
Sea Slugs ◽  
History Of

Abstract Background The soft-bodied cladobranch sea slugs represent roughly half of the biodiversity of marine nudibranch molluscs on the planet. Despite their global distribution from shallow waters to the deep sea, from tropical into polar seas, and their important role in marine ecosystems and for humans (as targets for drug discovery), the evolutionary history of cladobranch sea slugs is not yet fully understood. Results To enlarge the current knowledge on the phylogenetic relationships, we generated new transcriptome data for 19 species of cladobranch sea slugs and two additional outgroup taxa (Berthella plumula and Polycera quadrilineata). We complemented our taxon sampling with previously published transcriptome data, resulting in a final data set covering 56 species from all but one accepted cladobranch superfamilies. We assembled all transcriptomes using six different assemblers, selecting those assemblies that provided the largest amount of potentially phylogenetically informative sites. Quality-driven compilation of data sets resulted in four different supermatrices: two with full coverage of genes per species (446 and 335 single-copy protein-coding genes, respectively) and two with a less stringent coverage (667 genes with 98.9% partition coverage and 1767 genes with 86% partition coverage, respectively). We used these supermatrices to infer statistically robust maximum-likelihood trees. All analyses, irrespective of the data set, indicate maximal statistical support for all major splits and phylogenetic relationships at the family level. Besides the questionable position of Noumeaella rubrofasciata, rendering the Facelinidae as polyphyletic, the only notable discordance between the inferred trees is the position of Embletonia pulchra. Extensive testing using Four-cluster Likelihood Mapping, Approximately Unbiased tests, and Quartet Scores revealed that its position is not due to any informative phylogenetic signal, but caused by confounding signal. Conclusions Our data matrices and the inferred trees can serve as a solid foundation for future work on the taxonomy and evolutionary history of Cladobranchia. The placement of E. pulchra, however, proves challenging, even with large data sets and various optimization strategies. Moreover, quartet mapping results show that confounding signal present in the data is sufficient to explain the inferred position of E. pulchra, again leaving its phylogenetic position as an enigma.

Influences of weather and moonlight on activity patterns of small mammals: a biogeographical perspective

2001 ◽  
Vol 79 (6) ◽  
pp. 966-972 ◽  
Author(s):  
Michael K Stokes ◽  
Norman A Slade ◽  
Susan M Blair
Keyword(s):  
Activity Patterns ◽  
Time Of Day ◽  
Data Sets ◽  
Prairie Voles ◽  
Data Set ◽  
History Of ◽  
Cotton Rats ◽  
Northern Edge ◽  
Term Data ◽  
Probability Of Capture

We analyzed 15 years of trapping data on prairie voles (Microtus ochrogaster) and cotton rats (Sigmodon hispidus) to elucidate behavioural responses to weather by season and time of day. Use of such a long-term data set is rare and ameliorates many of the problems with short-term data sets typically used for such analysis. The trapping was conducted in the east-central part of Kansas (U.S.A.), near the southern edge of the distribution of prairie voles and the northern edge of the distribution of cotton rats. These distributions provide the framework for differing hypotheses as to responsiveness of individuals of the two species to weather phenomena as indicated by the probability of capture. Probability of capture was statistically significantly affected by weather, most frequently by precipitation and temperature. Effects varied with season and between species, and were generally consistent with hypotheses based on the northern (boreal and temperate) history of prairie voles and southern (subtropical and temperate) history of cotton rats and with predation-avoidance hypotheses. Variation in the probabilities of capture of cotton rats was more associated with weather, especially in the colder seasons, than was variation in the probabilities of capture of prairie voles. In summer, capture rates of prairie voles were more susceptible to weather than were those of cotton rats.

Application of Machine Learning in Data Analytics on COVID-19 First Wave in India

2022 ◽  
pp. 41-56
Author(s):  
Jeya Mala Dharmalingam ◽  
Pradeep Reynold A.
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Great Benefit ◽  
Current Status ◽  
Data Sets ◽  
Data Set ◽  
Analytical Results ◽  
Patient Status ◽  
History Of

As there are several data sets available, this chapter gives insight on which regions of India have been heavily impacted during the first wave of COVID-19 and the classification of patient status using an ML-based data analytics algorithm. The chapter provides a greater insight on the background work and the reports generated based on the analytical results gathered from the data set. In this pandemic situation, such reports will be a great benefit to assess the history of occurrence and the current status of the COVID-19 situation in India.

Survey of Clustering

2013 ◽  
Vol 3 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Raymond Greenlaw ◽  
Sanpawat Kantabutra
Keyword(s):  
Parallel Computation ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Clustering Methods ◽  
Top Down ◽  
Research Directions ◽  
History Of ◽  
Representative Points ◽  
Parallel Clustering ◽  
Extensive List

This article is a survey into clustering applications and algorithms. A number of important well-known clustering methods are discussed. The authors present a brief history of the development of the field of clustering, discuss various types of clustering, and mention some of the current research directions in the field of clustering. More specifically, top-down and bottom-up hierarchical clustering are described. Additionally, K-Means and K-Medians clustering algorithms are also shown. The concept of representative points is introduced and the technique of discovering them is presented. Immense data sets in clustering often necessitate parallel computation. The authors discuss issues involving parallel clustering as well. Clustering deals with a large number of experimental results. The authors provide references to these works throughout the article. A table for comparing various clustering methods is given in the end. The authors give a summary and an extensive list of references, including some of the latest works in the field, to conclude the article.

Introduction to Clustering

2010 ◽  
pp. 224-254
Author(s):  
Raymond Greenlaw ◽  
Sanpawat Kantabutra
Keyword(s):  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Clustering Methods ◽  
Research Directions ◽  
History Of ◽  
Representative Points ◽  
Parallel Clustering ◽  
Extensive List

This chapter provides the reader with an introduction to clustering algorithms and applications. A number of important well-known clustering methods are surveyed. The authors present a brief history of the development of the field of clustering, discuss various types of clustering, and mention some of the current research directions in the field of clustering. Algorithms are described for top-down and bottom-up hierarchical clustering, as are algorithms for K-Means clustering and for K-Medians clustering. The technique of representative points is also presented. Given the large data sets involved with clustering, the need to apply parallel computing to clustering arises, so they discuss issues related to parallel clustering as well. Throughout the chapter references are provided to works that contain a large number of experimental results. A comparison of the various clustering methods is given in tabular format. They conclude the chapter with a summary and an extensive list of references.

scholarly journals Windowing artefacts likely account for recent claimed detection of oscillating cosmic scale factor

2020 ◽  
Vol 498 (4) ◽  
pp. 5512-5516
Author(s):  
Sasha R Brownsberger ◽  
Christopher W Stubbs ◽  
Daniel M Scolnic
Keyword(s):  
Type Ia Supernovae ◽  
Data Sets ◽  
Analysis Method ◽  
Data Set ◽  
Statistical Fluctuations ◽  
Data Analyses ◽  
Type Ia ◽  
History Of ◽  
Ia Supernovae ◽  
The Universe

ABSTRACT Using the Pantheon data set of Type Ia supernovae, a recent publication (R20 in this work) reports a  2σ detection of oscillations in the expansion history of the Universe. The study conducted by R20 is wholly worthwhile. However, we demonstrate that there is a $\gt 10{{\ \rm per\ cent}}$ chance of statistical fluctuations in the Pantheon data producing a false oscillatory signal larger than the oscillatory signal that R20 report. Their results are a less than 2σ detection. Applying the R20 methodology to simulated Pantheon data, we determine that these oscillations could arise due to analysis artefacts. The uneven spacing of Type Ia supernovae in redshift space and the complicated analysis method of R20 impose a structured throughput function. When analysed with the R20 prescription, about $11{{\ \rm per\ cent}}$ of artificial ΛCDM data sets produce a stronger oscillatory signal than the actual Pantheon data. Our results underscore the importance of understanding the false ‘signals’ that can be introduced by complicated data analyses.

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