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This study demonstrates that in targeted non-viral gene transfer, the vector architecture plays as significant a role as its amino acid sequence.
The objective of this study was to evaluate the effect of vector architecture on DNA condensation, particle stability, and gene transfer efficiency.
Several years of optimization on the cache-based super-scalar architecture has made it more difficult to port the current version of the 3D particle-in-cell code GTC to the NEC SX-6 vector architecture.
To accommodate such workloads, we propose the Distributed Vector Architecture (DIVA), that uses multiple vector-capable processor/memory nodes in a distributed shared-memory configuration, while maintaining the simple vector programming model.
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The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures.
The portability of the program is good, it runs regularly on several common single- and multiprocessor platforms, both scalar and vector architectures included.
Moreover, whereas Reid-Miller and Blelloch targeted their algorithm for implementation on a vector multiprocessor architecture, we develop our algorithm for implementation on the symmetric multiprocessor architecture (SMP).
This paper presents an implementation of a novel architectural transformation technique for mapping a word bit wide algorithm to byte vector serial architecture.
A sort of cascaded support vector machines architecture is capable of improving the classification accuracy based on AdaBoost boosting algorithm, namely, AdaboostSVM.
Second, the approach is demonstrated on the example of using Vector Symbolic Architecture for solving a problem of finding common substrings.
We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples.
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