Vision-based navigation has become increasingly important in a variety of space
applications for enhancing autonomy and dependability. Future missions, such as active …

To harness a heterogeneous memory hierarchy, it is advantageous to integrate application
knowledge in guiding frequent memory move, ie, replicating or migrating virtual memory …

In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given
nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve …

Heterogeneous memory (HM, also known as hybrid memory) has become popular in
emerging parallel architectures due to its programming flexibility and energy efficiency …

The problem of tag completion is to learn the missing tags of an image. In this paper, we
propose to learn a tag scoring vector for each image by local linear learning. A local linear …

In this paper we study the problem of learning from multiple modal data for purpose of
document classification. In this problem, each document is composed two different modals of …

Future space missions require reliable architectures with higher performance and lower
power consumption. Exploring new architectures worthy of undergoing the expensive and …

Sparse matrix-vector multiplication (SpMV) plays a pivotal role in large-scale scientific
computing. Despite the increasing use of low-power multicore digital signal processors …

The sparse matrix-vector multiplication (SpMV) is widely used in large-scale scientific
computing and engineering. However, optimizing SpMV for high-performance digital signal …

In this paper, we study the problem of using contextual data points of a data point for its
classification problem. We propose to represent a data point as the sparse linear …