Visualizing the Uncertainty of Graph‐based 2D Segmentation with Min‐path Stability

This paper presents a novel approach to visualize the uncertainty in graph‐based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min‐path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live‐wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state‐of‐the‐art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.     

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.     

GPU accelerated sparse matrix‐vector multiplication and sparse matrix‐transpose vector multiplication

Many high performance computing applications require computing both sparse matrix‐vector product (SMVP) and sparse matrix‐transpose vector product (SMTVP) for better overall performance. Under such a circumstance, it is critical to maintain a similarly high throughput for these two computing patterns with the underlying sparse matrix encoded in a single storage format. The compressed sparse block (CSB) format proposed by Buluç et al. allows computing both problems on multi‐core CPUs with nearly identical throughputs. On the other hand, a direct porting of CSB to graphics processing units (GPUs), which have been recently recognized as a powerful general purpose computing platform, turns out to be inefficient. In this work, we propose a new data structure, designated as expanded CSB (eCSB), to minimize the throughput gap between SMVP and SMTVP computations on GPUs, while at the same time enable a high computing throughput. We also use a hybrid storage format to store elements in each block, which can be selected dynamically at runtime. Experimental results show that the proposed techniques implemented on a Kepler GPU delivers similar throughput on both SMVP and SMTVP and the throughput is up to 13 times faster than that of the CPU‐based CSB implementation. In addition, our eCSB procedure outperforms the previous GPU results by up to 188% and 914% in computing SMVP and SMTVP, and we validate the effectiveness of eCSB by means of wall‐clock time of bi‐conjugate gradient algorithm; our eCSB is 25% faster than Compressed Sparse Rows (CSR) and 6% faster than HYB, respectively. Copyright © 2014 John Wiley & Sons, Ltd.     

Reconstructing Animated Meshes from Time‐Varying Point Clouds

In this paper, we describe a novel approach for the reconstruction of animated meshes from a series of time‐deforming point clouds. Given a set of unordered point clouds that have been captured by a fast 3‐D scanner, our algorithm is able to compute coherent meshes which approximate the input data at arbitrary time instances. Our method is based on the computation of an implicit function in ?⁴ that approximates the time‐space surface of the time‐varying point cloud. We then use the four‐dimensional implicit function to reconstruct a polygonal model for the first time‐step. By sliding this template mesh along the time‐space surface in an as‐rigid‐as‐possible manner, we obtain reconstructions for further time‐steps which have the same connectivity as the previously extracted mesh while recovering rigid motion exactly. The resulting animated meshes allow accurate motion tracking of arbitrary points and are well suited for animation compression. We demonstrate the qualities of the proposed method by applying it to several data sets acquired by real‐time 3‐D scanners.     

Worst‐case stability and performance with mixed parametric and dynamic uncertainties

This work deals with computing the worst‐case stability and the worst‐case H_∞ performance of linear time‐invariant systems subject to mixed real‐parametric and complex‐dynamic uncertainties in a compact parameter set. Our novel algorithmic approach is tailored to the properties of the nonsmooth worst‐case functions associated with stability and performance, and this leads to a fast and reliable optimization method, which finds good lower bounds of μ. We justify our approach theoretically by proving a local convergence certificate. Because computing μ is known to be NP‐hard, our technique should be used in tandem with a classical μ upper bound to assess global optimality. Extensive testing indicates that the technique is practically attractive. Copyright © 2016 John Wiley & Sons, Ltd.     

Interactive Ray Tracing of Large Models Using Voxel Hierarchies

We propose an efficient approach for interactive visualization of massive models with CPU ray tracing. A voxel‐based hierarchical level‐of‐detail (LOD) framework is employed to minimize rendering time and required system memory. In a pre‐processing phase, a compressed out‐of‐core data structure is constructed, which contains the original primitives of the model and the LOD voxels, organized into a kd‐tree. During rendering, data is loaded asynchronously to ensure a smooth inspection of the model regardless of the available I/O bandwidth. With our technique, we are able to explore data sets consisting of hundreds of millions of triangles in real‐time on a desktop PC with a quad‐core CPU.     

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

Symbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi‐core to high‐performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open‐source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross‐platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi‐core nodes, mid‐scale distributed clusters of (multi‐core) nodes and full‐blown high‐performance computing systems, comprising large‐scale tightly connected networks of multi‐core nodes. This requires us to develop new cross‐layer programming abstractions in the form of new domain‐specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high‐performance systems comprising up to 32000 cores, as well as on ubiquitous multi‐core systems and distributed clusters. The work reported here paves the way towards full‐scale exploitation of symbolic computation by high‐performance computing systems, and we demonstrate the potential with two major case studies. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.     

A learn‐and‐construct framework for general mixed‐integer programming problems

In this paper, we propose a new framework for finding an initial feasible solution from a mixed‐integer programming (MIP) model. We call it learn‐and‐construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub‐MIPs that are solved by using a black box solver. Computational results on three well‐known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state‐of‐the‐art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.     

Multirate sampled‐data stabilization for a class of low‐order lower‐triangular nonlinear systems

This paper investigates the problem of sampled‐data controller design for a class of lower‐triangular systems in the p‐normal form (0<p<1). A multirate digital feedback control scheme is proposed to achieve the global strong stabilization of the sampled‐data closed‐loop system under some assumptions. In the design of the controller, the input‐Lyapunov matching strategy and multirate control approach are combined to obtain better stabilizing performance. Unlike the design method based on the approximate discrete‐time model, our controller is obtained from the exact discrete‐time equivalent model, which does not need to be computed completely. The approximate multirate digital controllers are proved to be effective in the practical implementation. It is shown that, compared with the emulated control scheme, our controller may provide faster decrease of Lyapunov function for each subsystem. This will lead to allow large sampling periods. An illustrative example is provided to verify the effectiveness of the proposed control scheme.     

Robust Stability of Time‐Varying Delay Systems: The Quadratic Separation Approach

In this article, we are interested in analysing the stability of systems that incorporate time‐varying delays in their dynamic. The Lyapunov‐Krasovskii approach is definitely the most popular method to address this issue and many results have proposed new functionals and enhanced techniques for deriving less conservative stability conditions. In the present work, we propose an original approach: the quadratic separation. To this end, the delay operator properties are exploited to provide delay range stability conditions. In particular, L₂‐norm of delay‐dependent operators are computed so as to reduce the conservatism of the approach. Moreover, the main result is able to assess the stability of non‐small delay systems, i.e, it can detect a stability interval for systems that are unstable without any delay. Several examples illustrate the benefit of our methodology.     

Computing Surface PolyCube‐Maps by Constrained Voxelization

We present a novel method to compute bijective PolyCube‐maps with low isometric distortion. Given a surface and its pre‐axis‐aligned shape that is not an exact PolyCube shape, the algorithm contains two steps: (i) construct a PolyCube shape to approximate the pre‐axis‐aligned shape; and (ii) generate a bijective, low isometric distortion mapping between the constructed PolyCube shape and the input surface. The PolyCube construction is formulated as a constrained optimization problem, where the objective is the number of corners in the constructed PolyCube, and the constraint is to bound the approximation error between the constructed PolyCube and the input pre‐axis‐aligned shape while ensuring topological validity. A novel erasing‐and‐filling solver is proposed to solve this challenging problem. Centeral to the algorithm for computing bijective PolyCube‐maps is a quad mesh optimization process that projects the constructed PolyCube onto the input surface with high‐quality quads. We demonstrate the efficacy of our algorithm on a data set containing 300 closed meshes. Compared to state‐of‐the‐art methods, our method achieves higher practical robustness and lower mapping distortion.     

LMI OPTIMIZATION APPROACH FOR DELAY‐DEPENDENT H∞ CONTROL OF TIME‐VARYING DELAY SYSTEMS

In this paper, the robust delay‐dependent H_∞ control for a class of uncertain systems with time‐varying delay is considered. An improved state feedback H_∞ control is proposed to minimize the H_∞‐norm bound via the LMI optimization approach. Based on the proposed result, delay‐dependent criteria are obtained without using the model transformation technique or bounded inequalities on cross product terms. The linear matrix inequality (LMI) optimization approach is used to design the robust H_∞ state feedback control. Some numerical examples are given to illustrate the effectiveness of the approach.     

Power‐aware provisioning of virtual machines for real‐time Cloud services

Reducing power consumption has been an essential requirement for Cloud resource providers not only to decrease operating costs, but also to improve the system reliability. As Cloud computing becomes emergent for the Anything as a Service (XaaS) paradigm, modern real‐time services also become available through Cloud computing. In this work, we investigate power‐aware provisioning of virtual machines for real‐time services. Our approach is (i) to model a real‐time service as a real‐time virtual machine request; and (ii) to provision virtual machines in Cloud data centers using dynamic voltage frequency scaling schemes. We propose several schemes to reduce power consumption by hard real‐time services and power‐aware profitable provisioning of soft real‐time services. Copyright © 2011 John Wiley & Sons, Ltd.     

Visualization of Particle‐based Data with Transparency and Ambient Occlusion

Particle‐based simulation techniques, like the discrete element method or molecular dynamics, are widely used in many research fields. In real‐time explorative visualization it is common to render the resulting data using opaque spherical glyphs with local lighting only. Due to massive overlaps, however, inner structures of the data are often occluded rendering visual analysis impossible. Furthermore, local lighting is not sufficient as several important features like complex shapes, holes, rifts or filaments cannot be perceived well. To address both problems we present a new technique that jointly supports transparency and ambient occlusion in a consistent illumination model. Our approach is based on the emission‐absorption model of volume rendering. We provide analytic solutions to the volume rendering integral for several density distributions within a spherical glyph. Compared to constant transparency our approach preserves the three‐dimensional impression of the glyphs much better. We approximate ambient illumination with a fast hierarchical voxel cone‐tracing approach, which builds on a new real‐time voxelization of the particle data. Our implementation achieves interactive frame rates for millions of static or dynamic particles without any preprocessing. We illustrate the merits of our method on real‐world data sets gaining several new insights.     

Improving Shift‐Reduce Phrase‐Structure Parsing with Constituent Boundary Information

Shift‐reduce parsing enjoys the property of efficiency because of the use of efficient parsing algorithms like greedy/deterministic search and beam search. In addition, shift‐reduce parsing is much simpler and easy to implement compared with other parsing algorithms. In this article, we explore constituent boundary information to improve the performance of shift‐reduce phrase‐structure parsing. In previous work, constituent boundary information has been used to speed up chart parsers successfully. However, whether it is useful for improving parsing accuracy has not been investigated. We propose two different models to capture constituent boundary information, based on which two sets of novel features are designed for a shift‐reduce parser. The first model is a boundary prediction model that uses a classifier to predict the boundaries of constituents. We use automatically parsed data to train the classifier. The second one is a Tree Likelihood Model that measures the validity of a constituent by its likelihood which is calculated on automatically parsed data. Experimental results show that our proposed method outperforms a strong baseline by 0.8% and 1.6% in F‐score on English and Chinese data, respectively, achieving the competitive parsing accuracies on Chinese (84.8%) and English (90.8%). To our knowledge, this is the first time for shift‐reduce phrase‐structure parsing to advance the state‐of‐the‐art with constituent boundary information.     

Articulated Billboards for Video‐based Rendering

We present a novel representation and rendering method for free‐viewpoint video of human characters based on multiple input video streams. The basic idea is to approximate the articulated 3D shape of the human body using a subdivision into textured billboards along the skeleton structure. Billboards are clustered to fans such that each skeleton bone contains one billboard per source camera. We call this representation articulated billboards. In the paper we describe a semi‐automatic, data‐driven algorithm to construct and render this representation, which robustly handles even challenging acquisition scenarios characterized by sparse camera positioning, inaccurate camera calibration, low video resolution, or occlusions in the scene. First, for each input view, a 2D pose estimation based on image silhouettes, motion capture data, and temporal video coherence is used to create a segmentation mask for each body part. Then, from the 2D poses and the segmentation, the actual articulated billboard model is constructed by a 3D joint optimization and compensation for camera calibration errors. The rendering method includes a novel way of blending the textural contributions of each billboard and features an adaptive seam correction to eliminate visible discontinuities between adjacent billboards textures. Our articulated billboards do not only minimize ghosting artifacts known from conventional billboard rendering, but also alleviate restrictions to the setup and sensitivities to errors of more complex 3D representations and multiview reconstruction techniques. Our results demonstrate the flexibility and the robustness of our approach with high quality free‐viewpoint video generated from broadcast footage of challenging, uncontrolled environments.     

Exaggerating Character Motions Using Sub‐Joint Hierarchy

Motion capture cannot generate cartoon‐style animation directly. We emulate the rubber‐like exaggerations common in traditional character animation as a means of converting motion capture data into cartoon‐like movement. We achieve this using trajectory‐based motion exaggeration while allowing the violation of link‐length constraints. We extend this technique to obtain smooth, rubber‐like motion by dividing the original links into shorter sub‐links and computing the positions of joints using Bézier curve interpolation and a mass‐spring simulation. This method is fast enough to be used in real time.     

Real‐Time Consensus‐Based Scene Reconstruction Using Commodity Graphics Hardware†

We present a novel use of commodity graphics hardware that effectively combines a plane‐sweeping algorithm with view synthesis for real‐time, online 3D scene acquisition and view synthesis. Using real‐time imagery from a few calibrated cameras, our method can generate new images from nearby viewpoints, estimate a dense depth map from the current viewpoint, or create a textured triangular mesh. We can do each of these without any prior geometric information or requiring any user interaction, in real time and online. The heart of our method is to use programmable Pixel Shader technology to square intensity differences between reference image pixels, and then to choose final colors (or depths) that correspond to the minimum difference, i.e. the most consistent color. In this paper we describe the method, place it in the context of related work in computer graphics and computer vision, and present some results. ACM CSS: I.3.3 Computer Graphics—Bitmap and framebuffer operations, I.4.8 Image Processing and Computer Vision—Depth cues, Stereo     

On‐the‐fly generation and rendering of infinite cities on the GPU

In this paper, we present a new approach for shape‐grammar‐based generation and rendering of huge cities in real‐time on the graphics processing unit (GPU). Traditional approaches rely on evaluating a shape grammar and storing the geometry produced as a preprocessing step. During rendering, the pregenerated data is then streamed to the GPU. By interweaving generation and rendering, we overcome the problems and limitations of streaming pregenerated data. Using our methods of visibility pruning and adaptive level of detail, we are able to dynamically generate only the geometry needed to render the current view in real‐time directly on the GPU. We also present a robust and efficient way to dynamically update a scene's derivation tree and geometry, enabling us to exploit frame‐to‐frame coherence. Our combined generation and rendering is significantly faster than all previous work. For detailed scenes, we are capable of generating geometry more rapidly than even just copying pregenerated data from main memory, enabling us to render cities with thousands of buildings at up to 100 frames per second, even with the camera moving at supersonic speed.     

Generating Color Scribble Images using Multi‐layered Monochromatic Strokes Dithering

Color scribbling is a unique form of illustration where artists use compact, overlapping, and monochromatic scribbles at microscopic scale to create astonishing colorful images at macroscopic scale. The creation process is skill‐demanded and time‐consuming, which typically involves drawing monochromatic scribbles layer‐by‐layer to depict true‐color subjects using a limited color palette delicately. In this work, we present a novel computational framework for automatic generation of color scribble images from arbitrary raster images. The core contribution of our work lies in a novel color dithering model tailor‐made for synthesizing a smooth color appearance using multiple layers of overlapped monochromatic strokes. Specifically, our system reconstructs the appearance of the input image by (i) generating layers of monochromatic scribbles based on a limited color palette derived from input image, and (ii) optimizing the drawing sequence among layers to minimize the visual color dissimilarity between dithered image and original image as well as the color banding artifacts. We demonstrate the effectiveness and robustness of our algorithm with various convincing results synthesized from a variety of input images with different stroke patterns. The experimental study further shows that our approach faithfully captures the scribble style and the color presentation at respectively microscopic and macroscopic scales, which is otherwise difficult for state‐of‐the‐art methods.