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Current computing systems depend on adaptation mechanisms to ensure that they remain in quiescent operating regions. These regions are often defined using efficiency, fairness, and stability properties. To that end, traditional research works in scalable server architectures and protocols have focused on promoting these properties by proposing even more(More)
Object detection systems based on the deep convolutional neural network (CNN) have recently made ground-breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon(More)
WRKY transcription factors play important roles in various stress responses in diverse plant species. In cotton, this family has not been well studied, especially in relation to fiber development. Here, the genomes and transcriptomes of Gossypium raimondii and Gossypium arboreum were investigated to identify fiber development related WRKY genes. This(More)
This paper describes an algorithm for scheduling packets in real-time multimedia data streams. Common to these classes of data streams are service constraints in terms of bandwidth and delay. However, it is typical for real-time multimedia streams to tolerate bounded delay variations and, in some cases, finite losses of packets. We have therefore developed(More)
Many latent factors of variation interact to generate sensory data; for example, pose, morphology and expression in face images. In this work, we propose to learn manifold coordinates for the relevant factors of variation and to model their joint interaction. Many existing feature learning algorithms focus on a single task and extract features that are(More)
Unsupervised learning and supervised learning are key research topics in deep learning. However , as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of un-supervised learning. Inspired by the(More)
Given the growing number of elderly people and patients diagnosed with Parkinson's disease, monitoring functional activities using wearable wireless sensors can be used to promote the Quality of Life and healthier life styles. We propose a novel and practical solution using three small wearable wireless Functional Activity Monitor (FAM) sensors and a(More)
Leymus chinensis is a dominant, rhizomatous perennial C3 species in the grasslands of Songnen Plain of Northern China, and its productivity has decreased year by year. To determine how productivity of this species responds to different precipitation regimes, elevated CO2 and their interaction in future, we measured photosynthetic parameters, along with the(More)
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG(More)