Covariance Eigenvector Sparsity for Compression and Denoising

Sparsity in the eigenvectors of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present in many practical compression schemes such as transform codecs, are designed to capitalize on this form of sparsity and achieve improved reconstruction performance compared to existing sparsity-agnostic codecs. Using training data that may be noisy a novel sparsity-aware linear DR scheme is developed to fully exploit sparsity in the covariance eigenvectors and form noise-resilient estimates of the principal covariance eigenbasis. Sparsity is effected via norm-one regularization, and the associated minimization problems are solved using computationally efficient coordinate descent iterations. The resulting eigenspace estimator is shown capable of identifying a subset of the unknown support of the eigenspace basis vectors even when the observation noise covariance matrix is unknown, as long as the noise power is sufficiently low. It is proved that the sparsity-aware estimator is asymptotically normal, and the probability to correctly identify the signal subspace basis support approaches one, as the number of training data grows large. Simulations using synthetic data and images, corroborate that the proposed algorithms achieve improved reconstruction quality relative to alternatives.Comment: IEEE Transcations on Signal Processing, 2012 (to appear

An Improved Approximate Consensus Algorithm in the Presence of Mobile Faults

This paper explores the problem of reaching approximate consensus in synchronous point-to-point networks, where each pair of nodes is able to communicate with each other directly and reliably. We consider the mobile Byzantine fault model proposed by Garay '94 -- in the model, an omniscient adversary can corrupt up to $f$ nodes in each round, and at the beginning of each round, faults may "move" in the system (i.e., different sets of nodes may become faulty in different rounds). Recent work by Bonomi et al. '16 proposed a simple iterative approximate consensus algorithm which requires at least $4f+1$ nodes. This paper proposes a novel technique of using "confession" (a mechanism to allow others to ignore past behavior) and a variant of reliable broadcast to improve the fault-tolerance level. In particular, we present an approximate consensus algorithm that requires only $\lceil 7f/2\rceil + 1$ nodes, an $\lfloor f/2 \rfloor$ improvement over the state-of-the-art algorithms. Moreover, we also show that the proposed algorithm is optimal within a family of round-based algorithms

Successful combined surgical approach in a rare case of retrotracheal goitre in a patient with anatomical impediments

Diving goitres can descend the cervical region expanding directly into the thoracic cavity. In most cases, diving goitres extend into the anterosuperior compartment, but they may also extend behind the trachea. We herein present a case of a male patient with retrotracheal goitre and history of left thyroid lobectomy and median sternotomy for thoracic aortic aneurysm repair with graft placement. After detailed preoperative evaluation, the patient underwent surgical resection of the mass through a combined approach; the existing cervical incision and a right posterolateral mini-thoracotomy. The postoperative course of the patient was uncomplicated. One year after surgery, the patient is asymptomatic and disease-free. (Folia Morphol 2018; 77, 1: 166–169

Distributed Power Profile Tracking for Heterogeneous Charging of Electric Vehicles

Coordinated charging of plug-in electric vehicles (PEVs) can effectively mitigate the negative effects imposed on the power distribution grid by uncoordinated charging. Simultaneously, coordinated charging algorithms can accommodate the PEV user's needs in terms of desired state-of-charge and charging time. In this paper, the problem of tracking an arbitrary power profile by coordinated charging of PEVs is formulated as a discrete scheduling process, while accounting for the heterogeneity in charging rates and restricting the charging to only the maximum rated power. Then, a novel distributed algorithm is proposed to coordinate the PEV charging and eliminate the need for a central aggregator. It is guaranteed to track, and not exceed, the power profile imposed by the utility, while maximizing the user convenience. A formal optimality analysis is provided to show that the algorithm is asymptotically optimal in case of Homogeneous charging, while it has a very small optimality gap for the heterogeneous case. Numerical simulations considering realistic charging scenarios with different penetration levels and tracking of a valley-filing profile are presented to validate the proposed charging algorithm

Iterative Approximate Consensus in the presence of Byzantine Link Failures

This paper explores the problem of reaching approximate consensus in synchronous point-to-point networks, where each directed link of the underlying communication graph represents a communication channel between a pair of nodes. We adopt the transient Byzantine link failure model [15, 16], where an omniscient adversary controls a subset of the directed communication links, but the nodes are assumed to be fault-free. Recent work has addressed the problem of reaching approximate consen- sus in incomplete graphs with Byzantine nodes using a restricted class of iterative algorithms that maintain only a small amount of memory across iterations [22, 21, 23, 12]. However, to the best of our knowledge, we are the first to consider approximate consensus in the presence of Byzan- tine links. We extend our past work that provided exact characterization of graphs in which the iterative approximate consensus problem in the presence of Byzantine node failures is solvable [22, 21]. In particular, we prove a tight necessary and sufficient condition on the underlying com- munication graph for the existence of iterative approximate consensus algorithms under transient Byzantine link model. The condition answers (part of) the open problem stated in [16].Comment: arXiv admin note: text overlap with arXiv:1202.609

The Role of Endoscopic Ultrasound in the Diagnosis and Management of Primary Gastric Lymphoma

Endoscopic ultrasound (EUS) is considered a valuable diagnostic tool during the workup of malignant gastric lesions, including primary gastric lymphomas (PGL). Although endoscopy combined with multiple biopsies remains essential in the establishment of PGL diagnosis, EUS utilization in locoregional disease staging has been well documented in the literature. Data also support the possible role of EUS in prediction of response to first-line treatment, that is, Helicobacter pylori eradication. However, its application in the posttreatment setting remains problematic, since concordance rates between endosonography and histology findings during follow-up seem to vary substantially. The aim of the present review is to summarize all available data regarding the role of EUS in the management of PGL