Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g. to trust versus not to trust a trustee). According to the evolutionary game theory, the antisocial strategies (e.g. not to trust) evolve such that the investor and trustee end up with lower payoffs than those that they would get with the prosocial strategies (e.g. to trust). A generalisation of the TG to a multiplayer (i.e. more than two players) TG was recently proposed. However, its outcomes hinge upon two assumptions that various real situations may substantially deviate from: (i) investors are forced to trust trustees and (ii) investors can turn into trustees by imitation and vice versa. We propose an asymmetric multiplayer TG that allows investors not to trust and prohibits the imitation between players of different roles; instead, investors learn from other investors and the same for trustees. We show that the evolutionary game dynamics of the proposed TG qualitatively depends on the nonlinearity of the payoff function and the amount of incentives collected from and distributed to players through an institution. We also show that incentives given to trustees can be useful and sufficient to cost-effectively promote trust and trustworthiness among self-interested players.

We present a visualization tool aimed specifically at the development and optimization of photon map denoising methods. Our tool allows the rapid testing of hypotheses and algorithms through the use of parallel coordinates, domain‐specific scripting, colour mapping and point plots. Interaction is carried out by brushing, adjusting parameters and focus‐plus‐context and yields interactive visual feedback and debugging information. We demonstrate the use of the tool to explore high‐dimensional photon map data, facilitating the discovery of novel parameter spaces which can be used to dissociate complex caustic illumination. We then show how these new parametrizations may be used to improve upon pre‐existing noise removal methods in the context of the photon relaxation framework.

Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images.

Abstract Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In‐Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray‐tracing‐based visualization of volumetric data. We demonstrate that the proposed In‐Kernel compaction outperforms the standard out‐of‐kernel Thrust parallel‐scan method for performing stream compaction in this real‐world application. For the data visualization, we also propose a novel multi‐kernel ray‐tracing pipeline for increased thread coherency and show that it outperforms a conventional single‐kernel approach.

The aim of this work is to identify the depth cues that provide intuitive depth-ordering when used to visualize abstract data. In particular we focus on the depth cues that are effective on a high-dynamic-range (HDR) display: contrast and brightness. In an experiment participants were shown a visualization of the volume layers at different depths with a single isolated monocular cue as the only indication of depth. The observers were asked to identify which slice of the volume appears to be closer. The results show that brightness, contrast and relative size are the most effective monocular depth cues for providing an intuitive depth ordering.

This article presents a new approach for Fourier-based modeling of bone anatomies for compression and smoothing. By treating the bone surface as a level set of a 3D scalar field, we can model topologically complex models such as bones. In particular, we experiment with five different alternatives, and prove that 3D scalar field which allows monotonous continuity around the boundary can be a good choice for volumetric description of the surface. This allows avoiding Gibbs phenomena which previous volume-based methods have suffered from, returning better results in compression and smoothing than other scalar fields. We demonstrate the efficacy of the proposed method by showing results with various bone data.

Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restart by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.

This article addresses 2-dimensional layout of high-dimensional biomedical datasets, which is useful for browsing them efficiently We employ the isomap technique, which is based on classical MDS (multi-dimensional scaling) but seeks to preserve the intrinsic geometry of the data, as captured in the geodesic manifold distances between all pairs of data points while classical approaches can see just the Euclidean structure. According to first two of isomap's coordinates, the high-dimensional data points are arranged in a plane. Experimental results with images of marine creatures' shapes and 3D bone renderings are presented.

This article addresses visualization of deformation or shape differences between bones while conventional visualization techniques are often about a single bone such as its 3D reconstruction. Given a pair of bones with a set of corresponding anatomical landmarks, we compute displacement vectors describing the deformation from one bone to the other at the landmark points on one of the bones. Out of these prescribed ones, a displacement vector at each vertex on the bone surface is derived using a multi-level approximation technique of scattered data. Based on the value of inner product between a displacement vector and a surface normal at each vertex, color is mapped. Considering error-prone estimation of the landmark location in real applications, approximations at different levels are realized instead of exact interpolation as usually done in elastic image registration. Experimental results on a pair of femoral bones are presented.

We present a method for improving robustness in feature-based tracking of human motion. Motion flows of features estimated by a standard tracker are modified to be coherent with neighboring ones. This coherence constraint is computed based on a smooth approximation to initial motion flows computed by the tracker. With these tracking results, we demonstrate motion segmentation of different body parts in an image sequence.

This article describes an approach based on hierarchical radial basis functions for the reconstruction and modification of motion/image data, which are formulated as problems of scattered data interpolation. Assuming little about the data and choosing highly scalable/well adjustable basis functions, the approach can handle both the reconstruction and the manipulation in a unified and widely applicable manner: standard approaches are often applicable, but have limited scope due to their constrained assumptions about the number or location of the data points. Reconstruction of captured motion data and warping of face images are demonstrated.

This article describes a method of constructing parametric models out of captured motion and skeleton data. Casting the problem as scattered data interpolation, our work is based on a multi-step approximation for the interpolation function with motion data compressed by principal component analysis. This leads to smaller storage and faster computation than those of previous approaches based on classical methods of exact interpolation. As a result, motion models can be constructed out of a rich set of example data, but can be used for real-time applications. We demonstrate a motion model controllable by attributes including those invariant for each individual, such as age, gender, height and weight. A parametric skeleton model is also constructed and demonstrated.