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Journal articles on the topic 'Graphical representation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Clary, Renee M., and James H. Wandersee. "The evolution of non-quantitative geological graphics in texts during the formative years of geology (1788–1840)." Earth Sciences History 34, no. 1 (January 1, 2015): 59–91. http://dx.doi.org/10.17704/1944-6187-34.1.59.

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Although modern geology uses both pictorial and graphical illustrations for conveying information and data presentation, early books in the discipline did not place such a reliance on graphics. This study investigated the numbers and types of graphics in 72 texts containing geological illustrations, which were considered to be representative (excluding works with solely mineralogical or paleontological illustrations), published during the formative years of geology (1788–1840) in terms of Edward R. Tufte's principles of graphic design. The text graphics were analyzed in terms of the presence of proxy or inferred imagery, direct or keyed labeling, unnecessary embellishment, and their data density; and whether they exhibited multivariate properties, represented the small multiple format, or exhibited graphic modifications. Mixed methodology analyses revealed four stages in the evolution of geologic illustrations in the interval from 1788–1840: (1) early pictorial or proxy representations; (2) the introduction of labeled graphics, coinciding with the first geology textbooks; (3) ‘grand' or elaborate illustration; and (4) a high graphic density. Although progress was made in graphical representation during the time period studied, statistical graphics were hardly ever used.

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Fortes, Fabrício Pires. "A Distinção Gráfico-Linguístico e a Notação Musical." Philosophy of Music 74, no. 4 (December 30, 2018): 1465–92. http://dx.doi.org/10.17990/rpf/2018_74_4_1465.

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This paper examines the traditional musical notation from the viewpoint of the general problem concerning the types of visual representations. More specifically, we analyze this system in relation to the distinction between graphical and linguistic representations. We start by comparing this notation with the representational systems which are most commonly associated with such categories: on the one hand, pictorial representations as an example of a graphical representation; on the other hand, verbal writing usually associated with a linguistic representation. Then, we examine the traditional musical notation in relation to different ways of drawing the distinction graphic–linguistic, and we evaluate the applicability of such criteria to the former system. Finally, we present some general remarks about the legitimacy of this distinction both with respect to representational systems in general and to the specific case of the traditional musical notation.

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Millán-Martínez, Pere, and Pedro Valero-Mora. "Automating statistical diagrammatic representations with data characterization." Information Visualization 17, no. 4 (July 21, 2017): 316–34. http://dx.doi.org/10.1177/1473871617715326.

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The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

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T. HEALEY, PATRICK G., NIK SWOBODA, ICHIRO UMATA, and YASUHIRO KATAGIRI. "Graphical representation in graphical dialogue." International Journal of Human-Computer Studies 57, no. 4 (October 2002): 375–95. http://dx.doi.org/10.1006/ijhc.2002.1022.

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Pugh, Zachary H., and Douglas J. Gillan. "Nodes Afford Connection: A Pilot Study Examining the Design and Use of a Graphical Modeling Language." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 1024–28. http://dx.doi.org/10.1177/1071181321651150.

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External representations such as diagrams facilitate reasoning. Many diagramming systems and notations are amenable to manipulation by actual or imagined intervention (e.g., transposing terms in an equation). Such manipulation is constrained by user-enforced constraints, including rules of syntax and semantics which help preserve the representation’s validity. We argue that the concepts of affordances and signifiers can be applied to understand such representations, and we suggest the term graphical affordance to refer to rule-constrained syntactic manipulation of an external representation. Following this argument, we examine a graphical modeling language in terms of these graphical affordances, and we present a pilot study examining how participants interact with the modeling language. Preliminary results suggest that using the modeling language, as opposed to prose representation, influences user behavior in a manner aligned with the graphical affordances and signifiers of the modeling language.

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Anderson, Gail M. "The Evolution of the Cartesian Connection." Mathematics Teacher 102, no. 2 (September 2008): 107–11. http://dx.doi.org/10.5951/mt.102.2.0107.

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One of NCTM's ten standards for school mathematics is Representation: “Representations [such as diagrams, graphs, and symbols] should be treated as essential elements in supporting students' understanding of mathematical concepts and relationships; in communicating mathematical approaches, arguments, and understandings to one's self and to others; in recognizing connections among related mathematical concepts; and in applying mathematics to realistic problem situations through modeling” (NCTM 2000, p. 67). In my experience, one of the biggest issues students struggle with is the connection between equations and their graphs (referred to as the “Cartesian connection” in an interesting study by Knuth [2000]). Unfortunately, although students are becoming proficient in using algebraic and graphical representations independently, they often do not make the connection between the two representational formats (Knuth 2000; NCTM 2000; Van Dyke and White 2004). In this article, I will explore the history of the graphical representation of functions and curves, specifically, the development of the Cartesian coordinate system as the most common frame for this graphical representation.

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Anderson, Gail M. "The Evolution of the Cartesian Connection." Mathematics Teacher 102, no. 2 (September 2008): 107–11. http://dx.doi.org/10.5951/mt.102.2.0107.

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One of NCTM's ten standards for school mathematics is Representation: “Representations [such as diagrams, graphs, and symbols] should be treated as essential elements in supporting students' understanding of mathematical concepts and relationships; in communicating mathematical approaches, arguments, and understandings to one's self and to others; in recognizing connections among related mathematical concepts; and in applying mathematics to realistic problem situations through modeling” (NCTM 2000, p. 67). In my experience, one of the biggest issues students struggle with is the connection between equations and their graphs (referred to as the “Cartesian connection” in an interesting study by Knuth [2000]). Unfortunately, although students are becoming proficient in using algebraic and graphical representations independently, they often do not make the connection between the two representational formats (Knuth 2000; NCTM 2000; Van Dyke and White 2004). In this article, I will explore the history of the graphical representation of functions and curves, specifically, the development of the Cartesian coordinate system as the most common frame for this graphical representation.

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8

Randić, Milan, Jure Zupan, Alexandru T. Balaban, Dražen Vikić-Topić, and Dejan Plavšić. "Graphical Representation of Proteins†." Chemical Reviews 111, no. 2 (February 9, 2011): 790–862. http://dx.doi.org/10.1021/cr800198j.

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Barthel, Friederike Maria-Sophie, and Patrick Royston. "Graphical Representation of Interactions." Stata Journal: Promoting communications on statistics and Stata 6, no. 3 (August 2006): 348–63. http://dx.doi.org/10.1177/1536867x0600600304.

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Hapizah, Hapizah, Ely Susanti, and Puji Astuti. "TEACHER’S ABILITIES OF TRANSLATION OF SYMBOLIC REPRESENTATION TO VISUAL REPRESENTATION AND VICE VERSA: ADDITION OF INTEGERS." International Journal of Pedagogy and Teacher Education 3, no. 1 (May 3, 2019): 41. http://dx.doi.org/10.20961/ijpte.v3i1.19268.

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<p><em>Translation of representations is a process of changing </em><em>representations </em><em>from one into</em><em> </em><em>another</em><em> one</em><em>. In generally, mathematics representations consists of symbolical, </em><em>visual (</em><em>graphical</em><em>)</em><em>,</em><em> </em><em>verbal, and tabular representations. This article discusses results of research about teacher’s understanding of mathematics representations and its implementation, which is teacher’s ability of translating from symbolical representation to graphical representation and vice v</em><em>e</em><em>rsa. The sample of research were 91 mathematics teacher from some districts of South Sumatera and Bangka Belitung provinces. The data of research were collected by test related to addition of integers. The results show that teacher’s ability of translating from symbolical representation into graphical representation is very low which is only 48,4% of the sample could translate the representations correctly, meanwhile teacher’s ability of translating from graphical representation into symbolical representation</em><em> </em><em>is quite good which is 75,8% of the sample could translate the representations correctly.</em><em> The mistakes identified when the mathematics teachers translated symbolic representation to visual representation are the result of addition not presented in numbers line, no the result of addition presented at all, the order of numbers line not presented clearly, misdirection or no direction of numbers line, and no answers at all. Meanwhile the mistakes identified when the mathematics teachers translated visual representation to symbolic representation are no the result of addition presented, incorrectly order of numbers added, and no answers at all</em>.</p>

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Stotts, Daniel Brandon. "The Usefulness of Icons on the Computer Interface: Effect of Graphical Abstraction and Functional Representation on Experienced and Novice Users." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 5 (October 1998): 453–57. http://dx.doi.org/10.1177/154193129804200502.

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Icons have become a main component of interface design (Familant & Detweiler, 1993). This study examined functional representation and graphical abstraction components of icons using experienced and novice computer users. Current interface (e.g. word processing) icons were evaluated in a search and select paradigm. Experienced participants were faster and more accurate than novice participants. Functionally representative and graphically concrete icons were recognized faster and more accurately than functionally arbitrary and graphically abstract icons, respectively. Experienced participants were affected more by graphical abstraction than novice participants. Graphically concrete and functionally representative icons were recognized faster than any other type of icon in the study. These data suggest when creating icons, the icons should be made to look as much like the object of reference as possible.

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Zeng, Xuyao, Milos V. Novotny, David E. Clemmer, and Jonathan C. Trinidad. "A graphical representation of glycan heterogeneity." Glycobiology 32, no. 3 (November 22, 2021): 201–7. http://dx.doi.org/10.1093/glycob/cwab116.

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Abstract A substantial shortcoming of large-scale datasets is often the inability to easily represent and visualize key features. This problem becomes acute when considering the increasing technical ability to profile large numbers of glycopeptides and glycans in recent studies. Here, we describe a simple, concise graphical representation intended to capture the microheterogeneity associated with glycan modification at specific sites. We illustrate this method by showing visual representations of the glycans and glycopeptides from a variety of species. The graphical representation presented allows one to easily discern the compositions of all glycans, similarities and differences of modifications found in different samples and, in the case of N-linked glycans, the initial steps in the biosynthetic pathway.

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Moritz, Julia, Hauke S. Meyerhoff, Claudia Meyer-Dernbecher, and Stephan Schwan. "Representation control increases task efficiency in complex graphical representations." PLOS ONE 13, no. 4 (April 26, 2018): e0196420. http://dx.doi.org/10.1371/journal.pone.0196420.

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14

Novak, Thomas P. "MANOVAMAP: Graphical Representation of MANOVA in Marketing Research." Journal of Marketing Research 32, no. 3 (August 1995): 357–74. http://dx.doi.org/10.1177/002224379503200310.

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The author proposes a graphical representation of the multivariate analysis of variance (MANOVA). This representation, termed a “MANOVAMAP,” shows the magnitude of MANOVA model effects and their statistical significance in an easily interpreted statistical graphic. The author discusses the use and construction of MANOVAMAPs for an empirical example and compares it with both a traditional MANOVA analysis of the data and a traditional graphical analysis based on centroid plots.

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Latifa, B. R. A., E. Purwaningsih, and S. Sutopo. "Identification of students’ difficulties in understanding of vector concepts using test of understanding of vector." Journal of Physics: Conference Series 2098, no. 1 (November 1, 2021): 012018. http://dx.doi.org/10.1088/1742-6596/2098/1/012018.

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Abstract This study is aimed to identify students’ difficulties in understanding vector concepts in physics because many students think that vector concept is very difficult to understand. This research used an embedded approach research design with quantitative descriptive methods and the sampling used a random sampling technique. Total sample of 142 students from two different schools in Central Lombok district. Test of understanding of vector (TUV) used to test the understanding of students consist of 20 item questions, then followed by interview session with several students. Kruskal-Wallis non-parametric descriptive and inferential statistic was used to performed data analysis. The results of this study indicate that (i) students’ ability to understand vector concepts is still lacking and tends to be very lacking; (ii) the most difficult items for students are the unit vector graphic representation and the graphical representation of vector multiplication. The concept of vector is still considered very difficult for students, especially if the item questions use graphical representations. For further researchers, it is better to conduct a study related to what kind of learning system can support and reduce the difficulties faced by students in learning vector concepts especially on graphical representation.

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Engel, Y., and M. P. Wellman. "CUI Networks: A Graphical Representation for Conditional Utility Independence." Journal of Artificial Intelligence Research 31 (January 24, 2008): 83–112. http://dx.doi.org/10.1613/jair.2360.

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We introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well-studied and widely applicable utility independence concept. We show how conditional utility independence leads to an effective functional decomposition that can be exhibited graphically, and how local, compact data at the graph nodes can be used to calculate joint utility. We discuss aspects of elicitation, network construction, and optimization, and contrast our new representation with previous graphical preference modeling.

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17

Gudehus, G., and D. Mašín. "Graphical representation of constitutive equations." Géotechnique 59, no. 2 (March 2009): 147–51. http://dx.doi.org/10.1680/geot.2007.00155.

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18

Khodeir, Nabila. "Graphical Representation in Tutoring Systems." International Journal of Computer Science and Information Technology 9, no. 3 (June 30, 2017): 107–16. http://dx.doi.org/10.5121/ijcsit.2017.9309.

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19

Ermann, Michael, and John Samuel Victor. "Graphical representation of acoustic data." Journal of the Acoustical Society of America 124, no. 4 (October 2008): 2588. http://dx.doi.org/10.1121/1.4783211.

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Arcagni, Alberto, and Francesco Porro. "The Graphical Representation of Inequality." Revista Colombiana de Estadística 37, no. 2Spe (December 29, 2014): 419. http://dx.doi.org/10.15446/rce.v37n2spe.47947.

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MATSUBARA, Yoshihiro, and Masashi GOTO. "Graphical Representation in Survival Analysis." Japanese journal of applied statistics 18, no. 2 (1989): 85–97. http://dx.doi.org/10.5023/jappstat.18.85.

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22

Yadav, Mukesh, and D. C. Gokhroo. "Partition Identity using Graphical Representation." Journal of Computer and Mathematical Sciences 10, no. 5 (May 30, 2019): 1131–34. http://dx.doi.org/10.29055/jcms/1097.

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23

Duch, W. "Graphical representation of Salter determinants." Journal of Physics A: Mathematical and General 18, no. 17 (December 1, 1985): 3283–307. http://dx.doi.org/10.1088/0305-4470/18/17/010.

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24

Richards, T. "Graphical representation of pseudorandom sequences." Computers & Graphics 13, no. 2 (January 1989): 261–62. http://dx.doi.org/10.1016/0097-8493(89)90069-1.

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Szyszkowicz, Mieczyslaw. "Graphical representation of pseudorandom numbers." Computers & Graphics 16, no. 2 (June 1992): 237. http://dx.doi.org/10.1016/0097-8493(92)90052-w.

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Geller, James. "Propositional representation for graphical knowledge." International Journal of Man-Machine Studies 34, no. 1 (January 1991): 97–131. http://dx.doi.org/10.1016/0020-7373(91)90052-9.

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Putra, Army Al Islami Ali, Nonoh Siti Aminah, and Ahmad Marzuki. "Analysis of Students’ Multiple representation-based Problem - solving Skills." Journal of Educational Science and Technology (EST) 6, no. 1 (February 27, 2020): 99. http://dx.doi.org/10.26858/est.v6i1.11196.

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This study aims to analyze the profile of students’ problem - solving skills based on multiple representation in senior high school. Problem - solving skills in solving multiple representation are very important in learning Physics. The subjects of this study were 101 students of class XII MAN 1 Ngawi. The method used in this research was quantitative descriptive method. Indicators of the problem - solving abilities that were used included approaches, visuals, applications, and procedures. The types of representation in this research instrument were verbal, figures, graphic and mathematic. The results showed that the problem - solving skills related to the indicator of approach with the form of multiple representation questions got percentages of 36% (verbal), 42% (figural), 24% (graphical), and 43% (mathematical). The visual indicators showed the percentages of 44, 29, 38, and 0 for verbal, figural, graphical, and mathematical respectively. Then the indicators of procedures obtained 36% for the verbal, 30% for the figures, 35% for the graphics, and 0% for the mathematics. Thus, it can be concluded that problem - solving skills possessed by students are different in terms of the percentage each indicator got in the multiple representation test.

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He, Xia, Guoping Du, and Long Hong. "Graphic Deduction Based on Set." JUCS - Journal of Universal Computer Science 26, no. 10 (October 28, 2020): 1331–42. http://dx.doi.org/10.3897/jucs.2020.069.

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Based on basic concept of symbolic logic and set theory, this paper focuses on judgments and attempts to provide a new method for the study of logic. It establishes the formal language of the extension of judgment J*, and formally describes a, e, i, o judgment, and thus gives set theory representation and graphical representation that can distinguish between universal judgments and particular judgments. According to the content of non-modal deductive reasoning in formal logic, it gives weakening theorem, strengthening theorem and a number of typical graphical representation theorem (graphic theorem), where graphic deduction is carried out. Graphic deduction will be beneficial to the research of artificial intelligence, which is closely related to judgment and deduction in logic.

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Toet, Alexander, Jan BF van Erp, Erik M. Boertjes, and Stef van Buuren. "Graphical uncertainty representations for ensemble predictions." Information Visualization 18, no. 4 (October 15, 2018): 373–83. http://dx.doi.org/10.1177/1473871618807121.

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We investigated how different graphical representations convey the underlying uncertainty distribution in ensemble predictions. In ensemble predictions, a set of forecasts is produced, indicating the range of possible future states. Adopting a use case from life sciences, we asked non-expert participants to compare ensemble predictions of the growth distribution of individual children to that of the normal population. For each individual child, the historical growth data of a set of 20 of its best matching peers was adopted as the ensemble prediction of the child’s growth curve. The ensemble growth predictions were plotted in seven different graphical formats (an ensemble plot, depicting all 20 forecasts and six summary representations, depicting the peer group mean and standard deviation). These graphs were plotted on a population chart with a given mean and variance. For comparison, we included a representation showing only the initial part of the growth curve without any future predictions. For 3 months old children that were measured at four occasions since birth, participants predicted their length at the age of 2 years. They compared their prediction to either (1) the population mean or to (2) a “normal” population range (the mean ± 2(standard deviation)). Our results show that the interpretation of a given uncertainty visualization depends on its visual characteristics, on the type of estimate required and on the user’s numeracy. Numeracy correlates negatively with bias (mean response error) magnitude (i.e. people with lower numeracy show larger response bias). Compared to the summary plots that yield a substantial overestimation of probabilities, and the No-prediction representation that results in quite variable predictions, the Ensemble representation consistently shows a lower probability estimation, resulting in the smallest overall response bias. The current results suggest that an Ensemble or “spaghetti plot” representation may be the best choice for communicating the uncertainty in ensemble predictions to non-expert users.

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Guinda, Carmen Sancho. "Semiotic Shortcuts. The Graphical Abstract Strategies of Engineering Students." HERMES - Journal of Language and Communication in Business, no. 55 (August 29, 2016): 61. http://dx.doi.org/10.7146/hjlcb.v0i55.24289.

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Graphical abstracts are representative of the rising promotionalism, interdisciplinarity and changing researcher roles in the current dissemination of science and technology. Their design, moreover, amalgamates a number of transdisciplinary skills much valued in higher education, such as critical and lateral thinking, and cultural and audience awareness. In this study, I investigate a corpus of 56 samples of graphical abstracts devised by my aeronautical engineering students, to find out the ‘semiotic shortcuts’ or encoding strategies they deploy, without any previous instruction, to pack information and translate the verbal into the visual. Findings suggest that their ‘natural digital-native graphicacy’ is conservative as to the medium, format and type of representation, but versatile regarding particular meanings, although not always unambiguous or register-appropriate. Consequently, I claim the convenience of including graphicacy/visual literacy and some basic training on graphical abstract design in the English for Specific Purposes and the disciplinary English-medium curriculum.

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Akselrud, Lev, and Yuri Grin. "WinCSD: software package for crystallographic calculations (Version 4)." Journal of Applied Crystallography 47, no. 2 (March 11, 2014): 803–5. http://dx.doi.org/10.1107/s1600576714001058.

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The fourth version of the program packageWinCSDis multi-purpose computer software for crystallographic calculations using single-crystal and powder X-ray and neutron diffraction data. The software environment and the graphical user interface are built using the platform of the Microsoft .NET Framework, which grants independence from changing Windows operating systems and allows for transferring to other operating systems. Graphic applications use the three-dimensional OpenGL graphics language.WinCSDcovers the complete spectrum of crystallographic calculations, including powder diffraction pattern deconvolution, crystal structure solution and refinement in 3 + dspace, refinement of the multipole model and electron density studies from diffraction data, and graphical representation of crystallographic information.

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Frih, Abderrahim, Zakaria Chalh, and Mostafa Mrabti. "Controllability and observability of LTV systems -bond graph approach-." Asian-European Journal of Mathematics 11, no. 03 (May 3, 2018): 1850038. http://dx.doi.org/10.1142/s1793557118500389.

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In this paper, a new methodology is proposed to determinate the controllability and the observability matrices of linear time varying systems modeled by bond graph. As the bond graph model can be viewed as a state space representation and as a module (algebraic approach), the determination of controllability and observability matrices is presented with the graphical approach. The equivalence between the two approaches (Graphic, Mathematical) is proposed and a graphical methodology is pointed out directly on the bond graph representation.

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Clivaz, Stéphane. "Lesson study as a fundamental situation for the knowledge of teaching." International Journal for Lesson and Learning Studies 7, no. 3 (July 9, 2018): 172–83. http://dx.doi.org/10.1108/ijlls-03-2018-0015.

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Purpose The purpose of this paper is to elaborate on the commonality between the Theory of Didactical Situations (TDS) and lesson study to propose a model of lesson study using both the predominant graphical representation of lesson study by Lewis and the model of the didactical situation at the heart of TDS by Brousseau. Design/methodology/approach Starting by describing and adapting the predominant graphical representation of lesson study by Lewis and the model of the didactical situation at the heart of TDS by Brousseau, the paper integrates the two representations to highlight the commonalities between the students’ learning situation and the teachers’. Based on this integrated graphical representation, the key phases of lesson study are then conceptualised by the mean of the dialectic between didactical and adidactical situation. Findings The reflection about the use of the TDS graphical representation embedded in the lesson study diagram helps the reflection on the use of TDS itself to analyse lesson study. This theoretical analysis describes the process of teacher learning in lesson study and the link between their learning and the student’s. It also shows that lesson study is a good candidate for the fundamental situation of the knowledge for teaching. Originality/value The graphical conceptualisation of lesson study as a learning situation for teachers offers new insight about how teachers learn in lesson study.

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Kondaveeti, Sandeep R., Iman Izadi, Sirish L. Shah, and Tim Black. "Graphical Representation of Industrial Alarm Data." IFAC Proceedings Volumes 43, no. 13 (2010): 181–86. http://dx.doi.org/10.3182/20100831-4-fr-2021.00033.

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Libove, Charles. "Coordinated turn relations - A graphical representation." Journal of Aircraft 23, no. 9 (September 1986): 725–26. http://dx.doi.org/10.2514/3.45368.

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Guénoche, Alain. "Graphical representation of a boolean array." Computers and the Humanities 20, no. 4 (October 1986): 277–81. http://dx.doi.org/10.1007/bf02400118.

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Phillips, J. C. "A Graphical Representation of Limiting Reactant." Journal of Chemical Education 71, no. 12 (December 1994): 1048. http://dx.doi.org/10.1021/ed071p1048.

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Chen, Zhenhua. "Graphical Representation of Hückel Molecular Orbitals." Journal of Chemical Education 97, no. 2 (January 17, 2020): 448–56. http://dx.doi.org/10.1021/acs.jchemed.9b00687.

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Moseley, Merrick J. "Graphical representation of visual acuity data." Ophthalmic and Physiological Optics 17, no. 5 (September 1997): 441–42. http://dx.doi.org/10.1111/j.1475-1313.1997.tb00077.x.

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Peng, Chunlei, Xinbo Gao, Nannan Wang, and Jie Li. "Graphical Representation for Heterogeneous Face Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 2 (February 1, 2017): 301–12. http://dx.doi.org/10.1109/tpami.2016.2542816.

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Pommerenke, Ch. "On Graphical Representation and Conformal Mapping." Journal of the London Mathematical Society s2-35, no. 3 (June 1987): 481–88. http://dx.doi.org/10.1112/jlms/s2-35.3.481.

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Zanasi, R., F. Grossi, and L. Biagiotti. "Qualitative graphical representation of Nyquist plots." Systems & Control Letters 83 (September 2015): 53–60. http://dx.doi.org/10.1016/j.sysconle.2015.06.005.

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Bednorz, Adam. "Graphical representation of the excess entropy." Physica A: Statistical Mechanics and its Applications 298, no. 3-4 (September 2001): 400–418. http://dx.doi.org/10.1016/s0378-4371(01)00253-9.

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Rigbi, Zvi. "Graphical representation on logarithmic triangular coordinates." Journal of Chemical Education 62, no. 10 (October 1985): 855. http://dx.doi.org/10.1021/ed062p855.

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Höhna, Sebastian, Tracy A. Heath, Bastien Boussau, Michael J. Landis, Fredrik Ronquist, and John P. Huelsenbeck. "Probabilistic Graphical Model Representation in Phylogenetics." Systematic Biology 63, no. 5 (June 20, 2014): 753–71. http://dx.doi.org/10.1093/sysbio/syu039.

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CLARKE, A. O. "Better Graphical Representation of Earthquake Magnitude." Environmental & Engineering Geoscience xxv, no. 3 (August 1, 1988): 343–48. http://dx.doi.org/10.2113/gseegeosci.xxv.3.343.

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Han, Sang-Tae. "Graphical Representation of Partially Ranked Data." Communications for Statistical Applications and Methods 18, no. 5 (September 30, 2011): 637–44. http://dx.doi.org/10.5351/ckss.2011.18.5.637.

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Zhang, Wenchao, and K. E. Evans. "Graphical representation of anisotropic failure envelopes." Engineering Computations 6, no. 3 (March 1989): 209–16. http://dx.doi.org/10.1108/eb023776.

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Thoemmes, Felix, and Karthika Mohan. "Graphical Representation of Missing Data Problems." Structural Equation Modeling: A Multidisciplinary Journal 22, no. 4 (January 27, 2015): 631–42. http://dx.doi.org/10.1080/10705511.2014.937378.

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Abecasis, G. R., S. S. Cherny, W. O. C. Cookson, and L. R. Cardon. "GRR: graphical representation of relationship errors." Bioinformatics 17, no. 8 (August 1, 2001): 742–43. http://dx.doi.org/10.1093/bioinformatics/17.8.742.

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