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Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical(More)
Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similar-ity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an(More)
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors(More)
On-chip network is becoming critical to the scalability of future many-core architectures. Recently, nanophotonics has been proposed for on-chip networks because of its low latency and high bandwidth. However, nanophotonics has relatively high static power consumption, which can lead to inefficient architectures. In this work, we propose FlexiShare —(More)
The mechanisms of human mutant superoxide dismutase-1 (mSOD1) toxicity to motor neurons (MNs) are unresolved. We show that MNs in G93A-mSOD1 transgenic mice undergo slow degeneration lacking similarity to apoptosis structurally and biochemically. It is characterized by somal and mitochondrial swelling and formation of DNA single-strand breaks prior to(More)
—Learning-to-rank for information retrieval has gained increasing interest in recent years. Inspired by the success of sparse models, we consider the problem of sparse learning-to-rank, where the learned ranking models are constrained to be with only a few non-zero coefficients. We begin by formulating the sparse learning-to-rank problem as a convex(More)
—This paper studies a constrained optical signal-to-noise ratio (OSNR) optimization problem in optical networks from the perspective of system performance. A system optimization problem is formulated with the objective of achieving an OSNR target for each channel while satisfying the total power constraint. In order to establish existence of a unique(More)
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of motor neurons (MNs) that causes paralysis. Some forms of ALS are inherited, caused by mutations in the superoxide dismutase-1 (SOD1) gene. The mechanisms of human mutant SOD1 (mSOD1) toxicity to MNs are unresolved. Mitochondria in MNs might be key sites for ALS pathogenesis, but(More)
There are many ongoing investigations to improve the oral bioavailability of peptide and protein formulations. Bioadhesive polysaccharide chitosan nanoparticles (CS-NPs) would seem to further enhance intestinal absorption of them. In this study, Insulin-loaded CS-NPs were prepared by ionotropic gelation of CS with tripolyphosphate anions. Its particle size(More)
In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while(More)