Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential equations are solved to most efficiently represent the solution. Particularly in astrophysics and cosmology such simulations now can capture spatial scales ten orders of magnitude apart and more. The irregular locations and extensions of the refined regions in the SAMR scheme and the fact that different resolution levels partially overlap, poses a challenge for GPU-based direct volume rendering methods. kD-trees have proven to be advantageous to subdivide the data domain into non-overlapping blocks of equally sized cells, optimal for the texture units of current graphics hardware, but previous GPU-supported raycasting approaches for SAMR data using this data structure required a separate rendering pass for each node, preventing the application of many advanced lighting schemes that require simultaneous access to more than one block of cells. In this paper we present the first single-pass GPU-raycasting algorithm for SAMR data that is based on a kD-tree. The tree is efficiently encoded by a set of 3D-textures, which allows to adaptively sample complete rays entirely on the GPU without any CPU interaction. We discuss two different data storage strategies to access the grid data on the GPU and apply them to several datasets to prove the benefits of the proposed method.
All previous approaches for single-pass multi-resolution GPU-raycasting were based on regular data structures, such as octrees or other partition strategies using regularly shaped nodes @cite_8 @cite_22 @cite_10 @cite_19 . In principle also AMR data structures can be partitioned in blocks of cells from the same resolution level using octrees. However, the resulting tree is usually inefficient, in particular if higher order interpolation is desired, because of the large number of resulting nodes @cite_16 . In contrast kD-trees allow to minimize the number of nodes by adaptively choosing the position of the spatial subdivision planes and have been successfully applied to CPU-, and GPU-based volume rendering of AMR data @cite_17 @cite_23 @cite_15 @cite_1 . In this paper we present the first single-pass GPU-raycasting approach for AMR data based on a kD-partition of the data domain.
@cite_22 @cite_8 @cite_1 @cite_19 @cite_23 @cite_15 @cite_16 @cite_10 @cite_17
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We present a framework for interactively visualizing volumetric Adaptive Mesh Renement (AMR) data. For this purpose we employ complex data structures to map the entire AMR dataset to graphics memory. This allows to apply hardware accelerated visualization algorithms previously only operating on uniform cartesian grids. For mapping the data to graphics memory we consider two approaches, a space-efcient, texture-based octree and a fast method based on page table address translation. We demonstrate the utility of our approach by extending an existing GPU raycasting implementation to render AMR data. As a r st step to vector eld visualization techniques we successfully integrated our framework into a commercial CFD postprocessing tool and visualized scalar properties of the vector eld. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. We present an adaptive out-of-core technique for rendering massive scalar volumes employing single-pass GPU ray casting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless ray casting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-gigavoxel datasets on a desktop PC.