24 papers · ranked by Valyu relevance
Cenyang Wu, Runhao Lin, Qinhan Yu, Liang Zhou
We present a new visualization method for contour ensembles through probabilistic modeling. We aim to improve the coherence between different visual representations, such as contour boxplots and density plots for a 2D scalar field ensemble. We model each ensemble member with a probabilistic representation in the latent…
Mingdong Zhang, Li Chen, Quan Li, Xiaoru Yuan + 1 more
Ensemble visualization has been intensively studied in recent years; it has been applied to various domains, such as meteorology and ocean-atmosphere research , and various analysis tasks, such as uncertainty and parameter analyses . In contrast to multivariate and spatiotemporal data, ensemble data introduce a new…
Seth Johnson, Daniel Orban, Hakizumwami Birali Runesha, Lingyu Meng + 4 more
'Bethany Juhnke' 'Arthur Erdman' 'Francesca Samsel' 'Daniel F. Keefe'] We present Bento Box, a virtual reality data visualization technique and bimanual 3D user interface for exploratory analysis of 4D data ensembles. Bento Box helps scientists and engineers make detailed comparative judgments about multiple…
James. A. Beauchamp, Obaid U Khurram, Julius P.A. Dewald, Charles J. Heckman + 1 more
Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the…
B. Schneider, Dominik Jäckle, Florian Stoffel, Alexandra Diehl + 2 more
Given a set of known categories (classes), Classification is defined as the process of identifying to which category a new observation belongs. In the context of machine learning, classification is performed on the basis of a training set that contains observations whose categories are known. A key challenge in…
Sebastian Spänig, Alexander Michel, Dominik Heider
Background Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers leverage artificial…
Leslie Solorzano, Stephanie Robertson, Johan Hartman, Mattias Rantalainen
Accurate detection of invasive breast cancer (IC) can provide decision support to pathologists as well as improve downstream computational analyses, where detection of IC is a first step. Tissue containing IC is characterized by the presence of specific morphological features, which can be learned by convolutional…
Louis Meuret, Julien Rey, Samuela Pasquali
Analyzing RNA structural ensembles, whether derived from molecular dynamics, enhanced sampling techniques, or experimental data, poses significant challenges due to the intrinsic flexibility and diverse conformational landscapes of RNA. We present ARNy Plotter, a web-based platform designed for comprehensive analysis…
Alexander Kumpf, Josef Stumpfegger, P. Hartl, Rüdiger Westermann
In many scientific fields like meteorology and computational fluid dynamics, numerical ensemble simulations are carried out with varying magnitudes of initial condition uncertainty, and by introducing uncertainty in the representation of certain physical processes. In such an ensemble, each simulation predicts possible…
Subhashis Hazarika, Ayan Biswas, Soumya Dutta, Han-Wei Shen
Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these…
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
In the following, we define five design goals (G1–G5) built on top of the knowledge generation model for VA proposed by Sacha et al. . This original model has two core pillars: the computer (Fig. 2, left) and the human (Fig. 2, right). On the computer side, the VA system comprises data, visualization(s), and model(s).…
Lace M. Padilla, Ian T. Ruginski, Sarah H. Creem-Regehr
Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same…
Rong Ma, Eric D. Sun, James Zou
Dimension reduction and data visualization aim to project a high-dimensional dataset to a low-dimensional space while capturing the intrinsic structures in the data. It is an indispensable part of modern data science, and many dimensional reduction and visualization algorithms have been developed. However, different…
Viktor Vad, Douglas Cedrim, Wolfgang Busch, Peter Filzmoser + 1 more
'Ivan Viola'] Background In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often…
Yimin Zheng, Zhihang Zheng, André F. Rendeiro, Edwin Cheung
Contemporary data visualization is challenged by the growing complexity and size of datasets, often comprising numerous interrelated features. Traditional visualization methods struggle to capture these complex relationships fully or are specialized to a domain requiring familiarity with multiple visualization tools.…
Amanda Bleichrodt, Lydia Bourouiba, Gerardo Chowell, Eric Lofgren + 3 more
'J. Michael Reed' 'Sadie J. Ryan' 'Nina H. Fefferman'] When we think of model ensembling or ensemble modeling, there are many possibilities that come to mind in different disciplines. For example, one might think of a set of descriptions of a phenomenon in the world, perhaps a time series or a snapshot of multivariate…
Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann + 7 more
Chemical reaction optimization (RO) is an iterative process that results in large and high-dimensional datasets. Current tools only allow for limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using…
Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot…
Ronald A. Rensink
Traditionally, vision science and information/data visualization have interacted by using knowledge of human vision to help design effective displays. It is argued here, however, that this interaction can also go in the opposite direction: the investigation of successful visualizations can lead to the discovery of…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. van der Hooft
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets…
Simon Warchol, Grace Guo, Johannes Knittel, Dan Freeman + 4 more
Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial…
Sergio Pablo-García, Raúl Pérez-Soto, Albert Sabadell-Rendón, Diego Garay-Ruiz + 2 more
In the study of chemical reactions, visualizing reaction networks is pivotal for identifying crucial compounds and reactions. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity.…
Sergey V. Kovalchuk, Aleksey V. Krikunov, Konstantin V. Knyazkov, Alexander V. Boukhanovsky
'Alexander V. Boukhanovsky'] Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty…