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Horizontal and Vertical Scalability of Machine Learning Methods
No full textThe main stages of Machine Learning Pipelines are considered in the paper, such as: train data collection and storage, training and scoring. The effect of the Big Data phenomenon on each of the stages is discussed. Different approaches to efficient organization of computation are on each of the stage are evaluated. In the first part of the paper we introduce the notion of horizontal and vertical scalability together with corresponding cons and pros. We consider some limitations of scaling, such as Amdahl's law. In the second part of the paper we consider scalability of data storage routines. First we discuss relational databases and scalability limitations related to ACID guarantees, which such database satisfy. Then we consider horizontally scalable non-relational databases, so called NoSQL databases. We formulate CAP-theorem as a fundamental limitation of horizontally scalable databases. The third part of the paper is dedicated to scalability of computation based on the MapReduce programming model. We discuss some implementations of this programming model, such as Hadoop and Spark together with some basic principles which they are based on. In the fourth part of the article we consider various approaches towards scaling of Machine Learning methods. We give the general statement of Machine Learning problem. Then we show how MapReduce programming model can be applied for horizontal scaling of Machine Learning methods on the example of Bayessian pattern recognition procedure. On the example of Deep Neural Networks we discuss Machine Learning methods which are not horizontally scalable. Then we consider some approaches towards vertical scaling of such methods based on GPU’s and the TensorFlow programming model.
Горизонтальне та вертикальне масштабування методів машинного навчання
Get PDFThe main stages of Machine Learning Pipelines are considered in the paper, such as: train data collection and storage, training and scoring. The effect of the Big Data phenomenon on each of the stages is discussed. Different approaches to efficient organization of computation are on each of the stage are evaluated. In the first part of the paper we introduce the notion of horizontal and vertical scalability together with corresponding cons and pros. We consider some limitations of scaling, such as Amdahl's law. In the second part of the paper we consider scalability of data storage routines. First we discuss relational databases and scalability limitations related to ACID guarantees, which such database satisfy. Then we consider horizontally scalable non-relational databases, so called NoSQL databases. We formulate CAP-theorem as a fundamental limitation of horizontally scalable databases. The third part of the paper is dedicated to scalability of computation based on the MapReduce programming model. We discuss some implementations of this programming model, such as Hadoop and Spark together with some basic principles which they are based on. In the fourth part of the article we consider various approaches towards scaling of Machine Learning methods. We give the general statement of Machine Learning problem. Then we show how MapReduce programming model can be applied for horizontal scaling of Machine Learning methods on the example of Bayessian pattern recognition procedure. On the example of Deep Neural Networks we discuss Machine Learning methods which are not horizontally scalable. Then we consider some approaches towards vertical scaling of such methods based on GPU’s and the TensorFlow programming model. В работе рассматриваются основные этапы решения задачи обучения распознаванию образов, а именно: обработка и хранение обучающих данных, обучение распознаванию и распознавание. Обговаривается влияние феномена больших данных на каждый из этих этапов. Сравниваются различные подходы к эффективной организации вычислений на различных этапах. Первый раздел статьи посвящен определению понятия масштабирования, вводятся понятия горизонтального и вертикального масштабирования, обсуждаются их преимущества и недостатки. Рассматриваются некоторые ограничения при масштабировании на примере закона Амдала. Второй раздел статьи посвящен масштабированию хранилищ обучающих данных. Обговаривается подходы к масштабированию реляционных баз данных, и ограничения связанные с гарантиями ACID, которым удовлетворяют такие базы данных. Отдельно рассматриваются горизонтально масштабируемые нереляционные т. н. NoSQL базы данных. Приводится формулировка CAP-теоремы, как одного из фундаментальных ограничений при горизонтальном масштабировании таких баз данных. Третий раздел работы посвящен горизонтальному масштабированию вычислений на основе модели программирования MapReduce. Рассматриваются различные реализации этой модели программирования, такие как Hadoop и Spark, их строение и основные принципы работы. В четвертом разделе рассматриваются различные подходы к масштабированию методов машинного обучения. Приводится общая постановка задачи машинного обучения. На примере Байесовской процедуры обучения показывается, как модель программирования MapReduce применима для горизонтального масштабирования методов машинного обучения. Далее на основе глубоких нейронных сетей обговариваются методы обучения, не подлежащие горизонтальному масштабированию. Рассматриваются подходы к масштабированию таких методов при помощи графических процессоров (GPU) и модели программирования Tensor Flow. В роботі розглядаються основні етапи розв’язку задач машинного навчання (з учителем) розпізнаванню образів, а саме: управління навчальними виборками, навчання, розпізнавання. Обговорюється вплив феномену великих даних (BigData) на кожен з етапів, а також методи ефективної організації обчислень на кожному з етапів при розв’язанні зазначених задач.
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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