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Finally, we present a new Term-Level Comparisons view (TLC) to compare and convey relative term weighting in the context of an alignment. Our visual design is guided by, used and evaluated by a domain expert specialist in German translations of Shakespeare.Computing the Voronoi diagram of a given set of points in a restricted domain (e.g. inside a 2D polygon, on a 3D surface, or within a volume) has many applications. Although existing algorithms can compute 2D and surface Voronoi diagrams in parallel on graphics hardware, computing clipped Voronoi diagrams within volumes remains a challenge. This study proposes an efficient GPU algorithm to tackle this problem. A preprocessing step discretizes the input volume into a tetrahedral mesh. Then, unlike existing approaches which use the bisecting planes of the Voronoi cells to clip the tetrahedra, we use the four planes of each tetrahedron to clip the Voronoi cells. This strategy drastically simplifies the computation, and as a result, it outperforms state-of-the-art CPU methods up to an order of magnitude.We present a technique for synthesizing realistic noise for digital photographs. It can adjust the noise level of an input photograph, either increasing or decreasing it, to match a target ISO level. Our solution learns the mappings among different ISO levels from unpaired data using generative adversarial networks. We demonstrate its effectiveness both quantitatively, using Kullback-Leibler divergence and Kolmogorov-Smirnov test, and qualitatively through a large number of examples. We also demonstrate its practical applicability by using its results to significantly improve the performance of a state-of-the-art trainable denoising method. Our technique should benefit several computer-vision applications that seek robustness to noisy scenarios.Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. selleck products During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and compare different models. Typically, this analysis is based on single-number performance measures such as accuracy. A more detailed evaluation of classifiers is possible by inspecting class errors. The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. More generally, established performance analysis systems do not allow a combined temporal and comparative analysis of class-level information. To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time. ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets. We demonstrate the usefulness of ConfusionFlow in a case study on instance selection strategies in active learning. We further assess the scalability of ConfusionFlow and present a use case in the context of neural network pruning.A commercial head-mounted display (HMD) for virtual reality (VR) presents three-dimensional imagery with a fixed focal distance. The VR HMD with a fixed focus can cause visual discomfort to an observer. In this work, we propose a novel design of a compact VR HMD supporting near-correct focus cues over a wide depth of field (from 18 cm to optical infinity). The proposed HMD consists of a low-resolution binary backlight, a liquid crystal display panel, and focus-tunable lenses. In the proposed system, the backlight locally illuminates the display panel that is floated by the focus-tunable lens at a specific distance. The illumination moment and the focus-tunable lens' focal power are synchronized to generate focal blocks at the desired distances. The distance of each focal block is determined by depth information of three-dimensional imagery to provide near-correct focus cues. We evaluate the focus cue fidelity of the proposed system considering the fill factor and resolution of the backlight. Finally, we verify the display performance with experimental results.High-dimensional labeled data widely exists in many real-world applications such as classification and clustering. One main task in analyzing such datasets is to explore class separations and class boundaries derived from machine learning models. Dimension reduction techniques are commonly applied to support analysts in exploring the underlying decision boundary structures by depicting a low-dimensional representation of the data distributions from multiple classes. However, such projection-based analyses are limited due to their lack of ability to show separations in complex non-linear decision boundary structures and can suffer from heavy distortion and low interpretability. To overcome these issues of separability and interpretability, we propose a visual analysis approach that utilizes the power of explainability from linear projections to support analysts when exploring non-linear separation structures. Our approach is to extract a set of locally linear segments that approximate the original non-linear separations. Unlike traditional projection-based analysis where the data instances are mapped to a single scatterplot, our approach supports the exploration of complex class separations through multiple local projection results. We conduct case studies on two labeled datasets to demonstrate the effectiveness of our approach.Any closed manifold of genus g can be cut open to form a topological disk and then mapped to a regular polygon with 4g sides. This construction is called the canonical polygonal schema of the manifold, and is a key ingredient for many applications in graphics and engineering, where a parameterization between two shapes with same topology is often needed. The sides of the 4g-gon define on the manifold a system of loops, which all intersect at a single point and are disjoint elsewhere. Computing a shortest system of loops of this kind is NP-hard. A computationally tractable alternative consists of computing a set of shortest loops that are not fully disjoint in polynomial time using the greedy homotopy basis algorithm proposed by Erickson and Whittlesey and then detach them in post processing via mesh refinement. Despite this operation is conceptually simple, known refinement strategies do not scale well for high genus shapes, triggering a mesh growth that may exceed the amount of memory available in modern computers, leading to failures.
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