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102. Unifying Effects of Direct and Relational Associations for Visual Communication

Author

Schoenlein, Melissa A., Campos, Johnny, Lande, Kevin J., Lessard, Laurent, and Schloss, Karen B.

Abstract

People have expectations about how colors map to concepts in visualizations, and they are better at interpreting visualizations that match their expectations. Traditionally, studies on these expectations (inferred mappings) distinguished distinct factors relevant for visualizations of categorical vs. continuous information. Studies on categorical information focused on direct associations (e.g., mangos are associated with yellows) whereas studies on continuous information focused on relational associations (e.g., darker colors map to larger quantities; dark-is-more bias). We unite these two areas within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. Observers infer globally optimal assignments by maximizing the “merit,” or “goodness,” of each possible assignment. Previous work on assignment inference focused on visualizations of categorical information. We extend this approach to visualizations of continuous data by (a) broadening the notion of merit to include relational associations and (b) developing a method for combining multiple (sometimes conflicting) sources of merit to predict people's inferred mappings. We developed and tested our model on data from experiments in which participants interpreted colormap data visualizations, representing fictitious data about environmental concepts (sunshine, shade, wild fire, ocean water, glacial ice). We found both direct and relational associations contribute independently to inferred mappings. These results can be used to optimize visualization design to facilitate visual communication.

Published
2023
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131. Estimating Color-Concept Associations from Image Statistics.

Author

Rathore, Ragini, Leggon, Zachary, Lessard, Laurent, and Schloss, Karen B.

Subjects
COLOR vision, IMAGE color analysis, DATA visualization, SEMANTICS
Abstract

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color. [ABSTRACT FROM AUTHOR]

Published
2020
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133. PTP1B Deficiency Enables the Ability of a High-Fat Diet to Drive the Invasive Character of PTEN-Deficient Prostate Cancers

Author

Labbé, David P., primary, Uetani, Noriko, additional, Vinette, Valérie, additional, Lessard, Laurent, additional, Aubry, Isabelle, additional, Migon, Eva, additional, Sirois, Jacinthe, additional, Haigh, Jody J., additional, Bégin, Louis R., additional, Trotman, Lloyd C., additional, Paquet, Marilène, additional, and Tremblay, Michel L., additional

Published
2016
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135. Prostate cancer genetic-susceptibility locus on chromosome 20q13 is androgen receptor-regulated and amplified in metastatic tumors

Author

Labbé, David P., Nowak, Dawid G., Deblois, Geneviève, Lessard, Laurent, Giguère, Vincent, Trotman, Lloyd C., and Tremblay, Michel L.

Subjects
Gene Expression Regulation, Neoplastic, Male, Prostatic Neoplasms, Castration-Resistant, Genetic Loci, Receptors, Androgen, Cell Line, Tumor, Chromosomes, Human, Pair 20, Gene Amplification, Humans, Prostatic Neoplasms, Genetic Predisposition to Disease, Article, Genetic Association Studies
Abstract

The 20q13 chromosomal region has been previously identified as the hereditary prostate cancer genetic-susceptibility locus on chromosome 20 (HPC20). In this study, the 20q13 region was shown to be frequently co-amplified with the androgen receptor (AR) in metastatic prostate cancer. Furthermore, the AR signaling axis, which plays an essential role in the pathogenesis of prostate cancer, was demonstrated to be central to the regulation of the 20q13 common amplified region (CAR). High-resolution mapping analyses revealed hot spots of AR recruitment to response elements in the vicinity of most genes located on the 20q13 CAR. Moreover, amplification of AR significantly co-occurred with CAR amplification on 20q13 and it was confirmed that the majority of AR-bound genes on the 20q13 CAR were indeed regulated by androgens. These data reveal that amplification of the AR is tightly linked to amplification of the AR-regulated CAR region on 20q13. These results suggest that the cross-talk between gene amplification and gene transcription is an important step in the development of castration-resistant metastatic disease.These novel results are a noteworthy example of the cross-talk between gene amplification and gene transcription in the development of advanced prostate cancer.

Published
2013

136. Abstract B39: PTP1B deficiency potentiate prostate cancer invasiveness by sensitizing Pten-null tumors to high-fat diet

Author

Labbé, David P., primary, Uetani, Noriko, additional, Vinette, Valérie, additional, Aubry, Isabelle, additional, Migon, Eva, additional, Sirois, Jacinthe, additional, Haigh, Jody J., additional, Lessard, Laurent, additional, Bégin, Louis R., additional, Trotman, Lloyd C., additional, Paquet, Marilène, additional, and Tremblay, Michel L., additional

Published
2016
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148. Warm-started wavefront reconstruction for adaptive optics

Author

Lessard, Laurent, West, Matthew, MacMynowski, Douglas, Lall, Sanjay, Lessard, Laurent, West, Matthew, MacMynowski, Douglas, and Lall, Sanjay

Abstract

Future extreme adaptive optics (ExAO) systems have been suggested with up to 10^5 sensors and actuators. We analyze the computational speed of iterative reconstruction algorithms for such large systems. We compare a total of 15 different scalable methods, including multigrid, preconditioned conjugate-gradient, and several new variants of these. Simulations on a 128×128 square sensor/actuator geometry using Taylor frozen-flow dynamics are carried out using both open-loop and closed-loop measurements, and algorithms are compared on a basis of the mean squared error and floating-point multiplications required. We also investigate the use of warm starting, where the most recent estimate is used to initialize the iterative scheme. In open-loop estimation or pseudo-open-loop control, warm starting provides a significant computational speedup; almost every algorithm tested converges in one iteration. In a standard closed-loop implementation, using a single iteration per time step, most algorithms give the minimum error even in cold start, and every algorithm gives the minimum error if warm started. The best algorithm is therefore the one with the smallest computational cost per iteration, not necessarily the one with the best quasi-static performance.

Published
2008

149. Experimental validation of single-iteration multigrid wavefront reconstruction at the Palomar Observatory

Author

Lessard, Laurent, MacMynowski, Douglas G., West, Matthew, Bouchez, Antonin, Lall, Sanjay, Lessard, Laurent, MacMynowski, Douglas G., West, Matthew, Bouchez, Antonin, and Lall, Sanjay

Abstract

Single-iteration multigrid (SIMG) wavefront reconstruction schemes were implemented and validated on the adaptive optics system at the Hale 5.1 m telescope at the Palomar Observatory. Results indicate that even the simplest such method produces a performance indistinguishable from that of the standard least-squares reconstructor for both bright and dim guide stars. SIMG provides a dramatic reduction in computational cost when compared to vector-matrix multiplication and can be implemented in parallel, making it the obvious choice for reconstruction in future large-scale adaptive optics systems.

Published
2008

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