15937058
Implementing lattice Boltzmann computation on graphics hardware
The Lattice Boltzmann Model (LBM) is a physically-based approach that simulates the microscopic movement of fluid particles by simple, identical, and local rules. We accelerate the computation of the LBM on general-purpose graphics hardware, by grouping particle packets into 2D textures and mapping the Boltzmann equations completely to the rasterization and frame buffer operations. We apply stitching and packing to further improve the performance. In addition, we propose techniques, namely range scaling and range separation, that systematically transform variables into the range required by the graphics hardware and thus prevent overflow. Our approach can be extended to acceleration of the computation of any cellular automata model.
1153571
Simulating fire with texture splats
We propose the use of textured splats as the basic display primitives for an open surface fire model. The high-detail textures help to achieve a smooth boundary of the fire and gain the small-scale turbulence appearance. We utilize the Lattice Boltzmann Model (LBM) to simulate physically-based equations describing the fire evolution and its interaction with the environment (e.g., obstacles, wind and temperature). The property of fuel and non-burning objects are defined on the lattice of the computation domain. A temperature field is also incorporated to model the generation of smoke from the fire due to incomplete combustion. The linear and local characteristics of the LBM enable us to accelerate the computation with graphics hardware to reach real-time simulation speed, while the texture splat primitives enable interactive rendering frame rates.
7023282
Empty space skipping and occlusion clipping for texture-based volume rendering
We propose methods to accelerate texture-based volume rendering by skipping invisible voxels. We partition the volume into sub-volumes, each containing voxels with similar properties. Sub-volumes composed of only voxels mapped to empty by the transfer function are skipped. To render the adaptively partitioned sub-volumes in visibility order, we reorganize them into an orthogonal BSP tree. We also present an algorithm that computes incrementally the intersection of the volume with the slicing planes, which avoids the overhead of the intersection and texture coordinates computation introduced by the partitioning. Rendering with empty space skipping is 2 to 5 times faster than without it. To skip occluded voxels, we introduce the concept of orthogonal opacity map, that simplifies the transformation between the volume coordinates and the opacity map coordinates, which is intensively used for occlusion detection. The map is updated efficiently by the GPU. The sub-volumes are then culled and clipped against the opacity map. We also present a method that adaptively adjusts the optimal number of the opacity map updates. With occlusion clipping, about 60% of non-empty voxels can be skipped and an additional 80% speedup on average is gained for iso-surface-like rendering.
9473484
Bootstrapping for Named Entity Tagging Using Concept-based Seeds
A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the parsing-based NE rules. Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types.
18531635
A generalized model for network survivability
The high expectation of network to be available and perform at all time has created growing concerns between network operators and engineers all over the globe. Network should be available to users whenever they want to use them. In the wake of an attack, network should be also able to recover as soon as possible. A survivable network is one that can provide services in the presence of an attack or failure and be able to recover fully in a timely manner. Designing a model for network survivability basically involves the understanding of three key issues, i.e, how the system will function in the wake of failures, what will be the impact of failures on the user, and how to overcome these failures. To achieve these goals, this paper proposes a quantitative approach to evaluate the network survivability in terms of using a generalized model; we consider a more complex system with several faults to study the survivability of a network using Markov chain. In presenting a better survivability performance, some numerical comparisons for the network availability, failure frequency and survivability is also developed.
10437441
Tolerating some redundancy significantly speeds up clustering of large protein databases
MOTIVATION
Sequence clustering replaces groups of similar sequences in a database with single representatives. Clustering large protein databases like the NCBI Non-Redundant database (NR) using even the best currently available clustering algorithms is very time-consuming and only practical at relatively high sequence identity thresholds. Our previous program, CD-HI, clustered NR at 90% identity in approximately 1 h and at 75% identity in approximately 1 day on a 1 GHz Linux PC (Li et al., Bioinformatics, 17, 282, 2001); however even faster clustering speed is needed because the size of protein databases are rapidly growing and many applications desire a lower attainable thresholds.


RESULTS
For our previous algorithm (CD-HI), we have employed short-word filters to speed up the clustering. In this paper, we show that tolerating some redundancy makes for more efficient use of these short-word filters and increases the program's speed 100 times. Our new program implements this technique and clusters NR at 70% identity within 2 h, and at 50% identity in approximately 5 days. Although some redundancy is present after clustering, our new program's results only differ from our previous program's by less than 0.4%.
16138674
Mesh editing with an embedded network of curves
We propose a new topological data structure for representing a set of polygonal curves embedded in a meshed surface. In this embedding, the vertices of the curve do not necessarily correspond to the vertices of the surface. The partition of the surface yielded by the intersecting curves is efficiently represented as a "cut-graph". The cut-graph stores combinatorial information of the network of curves. In our approach, the combinatorial form of information is systematically preferred to geometrical information since it improves both robustness and efficiency. Thanks to the topological data structure and algorithms, the cut-graph can be sketched through iterations of designing and erasing curves on the mesh surface in a "nondestructive" way, i.e. without modifying the mesh until the cutting operation is committed. We also demonstrate several prototype curve design tools inspired by 2D vector and bitmap graphics paradigms. We show how to sketch the cut-graph and how these tools can be mixedly used.
6293846
Cryptographic Key Generation from Biometric Data Using Lattice Mapping
Crypto-biometric systems are recently emerging as an effective process of key management to address the security weakness of conventional key release systems using pass-codes, tokens or pattern recognition based biometrics. This paper presents a lattice mapping based fuzzy commitment method for cryptographic key generation from biometric data. The proposed method not only outputs high entropy keys, but also conceals the original biometric data such that it is impossible to recover the biometric data even when the stored information in the system is open to an attacker. Simulated results have demonstrated that its authentication accuracy is comparable to the well-known k-nearest neighbour classification