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—Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with " Big Data " analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the(More)
—In the backbone of large-scale networks, traffic flows experience abrupt unusual changes which can result in congestion, and limit the extent to which end-user quality of service requirements are met. Diagnosing such traffic volume anomalies is a crucial task towards engineering the traffic in the network. This is challenging however, since the available(More)
—In the backbone of large-scale networks, origin-to-destination (OD) traffic flows experience abrupt unusual changes known as traffic volume anomalies, which can result in congestion and limit the extent to which end-user quality of service requirements are met. As a means of maintaining seamless end-user experience in dynamic environments, as well as for(More)
Given the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, the goal of this paper is to establish deterministic conditions under which exact recovery of the low-rank and sparse components becomes possible. This fundamental identifiability issue arises with traffic anomaly detection in backbone(More)
—Given a limited number of entries from the superposi-tion of a low-rank matrix plus the product of a known compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for decentralized(More)
maximizing this system performance measure, we then optimize an AMC scheme which directly satisfies a prescribed packet loss rate constraint at the data-link layer. The results indicate that utilizing cooperative ARQ as a retransmission strategy, noticeably enhances the spectral efficiency compared with the system that employs AMC alone at the physical(More)
Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed(More)
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with 'Big Data' analytics. However, increasingly noisy, heterogeneous , and incomplete datasets as well as the need for real-time processing pose major challenges towards achieving this goal. In this context, the fresh look(More)
Mapping origin-destination (OD) network traffic is pivotal for network management and proactive security tasks. However, lack of sufficient flow-level measurements as well as potential anomalies pose major challenges towards this goal. Leveraging the spatiotemporal correlation of nominal traffic, and the sparse nature of anomalies, this paper brings forth a(More)
—In a cooperative relay network, a relay (R) node may facilitate data transmission to the destination (D) node when the latter node cannot correctly decode the source (S) node data. This paper considers such a system model and presents a cross-layer approach to jointly design adaptive modulation and coding (AMC) at the physical layer and the truncated(More)