122644283
Implementation of a modified Marder-Weitzner method for solving nonlinear eigenvalue problems
Our goal is to propose four versions of modified Marder-Weitzner methods and to present the implementation of the new-type methods with incremental unknowns for solving nonlinear eigenvalue problems. By combining with compact schemes and modified Marder-Weitzner methods, six schemes well suited for the calculation of unstable solutions are obtained. We illustrate the efficiency of the new algorithms by using numerical computations and by comparing them with existing methods for some two-dimensional problems.
15938722
An Implicit Finite Difference Scheme with Preconditioning for Convection Dominated Diffusion Equation
An implicit finite difference scheme for solving time-dependent convection dominated diffusion equations in two space variables is presented. A one-sided difference approximation is used for the convection terms and a second-order central difference approximationfor the diffusion term. The implicit scheme is consistent,unconditionally stable and its $L^2$ error estimation is optimal. By preconditioning technique of incremental unknowns, the implicit scheme is an efficient one.
8929565
Integrating the QMR Method with First Principles Material Science Application Code
First principles methods are used to aid the material designers and metallurgists in the investigation and design of new materials. These methods suffer from a divergent O(N{sup 3}) scaling which restricts the problem sizes that can be addressed. It will be demonstrated that the O(N{sup 3}) scaling problem can be overcome by employing real-space multiple scattering techniques to calculate the scattering properties, combined with the QMR algorithm. By incorporating the QMR method into our real-space multiple scattering code, we observed O(N) scaling for the large problem sizes of interest. This advancement will provide researchers with the necessary tools to treat large systems.
40027798
Multiple Ranker Method in Document Retrieval
In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new queries. But in practice, user queries vary in large diversity, which makes the single ranker learned from a close set of data not representative. The single ranker cannot be guaranteed with the best ranking result for every single query, and this becomes the bottleneck of traditional learning to rank approaches. To address this problem, we propose a multi-ranker approach. We train multiple diverse rankers which can cover diverse categories of queries, instead of an isolated one, and take an ensemble of these rankers for final prediction. We verify the proposed multipleranker approach over real-world datasets. The experimental results indicate that the proposed approach can outperform existing ‘learning to rank’ methods significantly.
62021419
IA-32 execution layer: a two-phase dynamic translator designed to support IA-32 applications on Itanium/spl reg/-based systems
IA-32 execution layer (IA-32 EL) is a new technology that executes IA-32 applications on Intel Itanium processor family systems. Currently, support for IA-32 applications on Itanium-based platforms is achieved using hardware circuitry on the Itanium processors. This capability will be enhanced with IA-32 EL - software that will ship with Itanium-based operating systems and will convert IA-32 instructions into Itanium instructions via dynamic translation. In this paper, we describe aspects of the IA-32 execution layer technology, including the general two-phase translation architecture and the usage of a single translator for multiple operating systems. The paper provides details of some of the technical challenges such as precise exception, emulation of FP, MMX, and Intel streaming SIMD extension instructions, and misalignment handling. Finally, the paper presents some performance results.
642679
Representing Discovered Patterns Using Attributed Hypergraph
One of the fundamental problems in knowledge discovery in databases and other applications of AI is how to represent knowledge and patterns. Existing representation schemes have various shortcomings. In this paper, we propose a new knowledge representation scheme using attributed hypergraph (AHG), which is simple yet general enough to directly encode different order patterns discovered from large databases. In AHG, both the qualitative and quantitative relations are represented as attributed hyperedges. Such representation is lucid and transparent for visualization. Besides, patterns in AHG are easy to understand. In the discussion, some basic manipulations of AHG for data mining tasks are briefly addressed. The paper ends with examples of pattern representation using AHG.
18554673
Network processing performability evaluation on heterogeneous reliability multicore processors using SRN model
Future network systems and embedded infrastructure devices in ubiquitous environments will need to consume low power and process large amounts of network packet traffic. In order to meet necessary high processing efficiency requirements, future processors will have many heterogeneous cores with reduced reliability due to low voltage, small transistor sizes, semiconductor wearout, and environmental factors such as noise and interference. It will be necessary for multi-core network infrastructure software to mitigate transient hardware faults to maintain acceptable system reliability. Applications such as packet processing can benefit from the reliability versus performance tradeoff. We propose a model based on Stochastic Reward Nets to evaluate the performance vs. reliability tradeoff of unreliable embedded multi-core network processors, and apply this model to a multi-core packet processing application.