Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs

We study a general framework for broadcast gossip algorithms which use companion variables to solve the average consensus problem. Each node maintains an initial state and a companion variable. Iterative updates are performed asynchronously whereby one random node broadcasts its current state and companion variable and all other nodes receiving the broadcast update their state and companion variable. We provide conditions under which this scheme is guaranteed to converge to a consensus solution, where all nodes have the same limiting values, on any strongly connected directed graph. Under stronger conditions, which are reasonable when the underlying communication graph is undirected, we guarantee that the consensus value is equal to the average, both in expectation and in the mean-squared sense. Our analysis uses tools from non-negative matrix theory and perturbation theory. The perturbation results rely on a parameter being sufficiently small. We characterize the allowable upper bound as well as the optimal setting for the perturbation parameter as a function of the network topology, and this allows us to characterize the worst-case rate of convergence. Simulations illustrate that, in comparison to existing broadcast gossip algorithms, the approaches proposed in this paper have the advantage that they simultaneously can be guaranteed to converge to the average consensus and they converge in a small number of broadcasts.Comment: 30 pages, submitte

MicroRNAs and long non-coding RNAs in cartilage homeostasis and osteoarthritis

During the last decade, osteoarthritis (OA) has become one of the most prevalent musculoskeletal diseases worldwide. OA is characterized by progressive loss of articular cartilage, abnormal remodeling of subchondral bone, hyperplasia of synovial cells, and growth of osteophytes, which lead to chronic pain and disability. The pathological mechanisms underlying OA initiation and progression are still poorly understood. Non-coding RNAs (ncRNAs) constitute a large portion of the transcriptome that do not encode proteins but function in numerous biological processes. Cumulating evidence has revealed a strong association between the changes in expression levels of ncRNA and the disease progression of OA. Moreover, loss- and gain-of-function studies utilizing transgenic animal models have demonstrated that ncRNAs exert vital functions in regulating cartilage homeostasis, degeneration, and regeneration, and changes in ncRNA expression can promote or decelerate the progression of OA through distinct molecular mechanisms. Recent studies highlighted the potential of ncRNAs to serve as diagnostic biomarkers, prognostic indicators, and therapeutic targets for OA. MiRNAs and lncRNAs are two major classes of ncRNAs that have been the most widely studied in cartilage tissues. In this review, we focused on miRNAs and lncRNAs and provided a comprehensive understanding of their functional roles as well as molecular mechanisms in cartilage homeostasis and OA pathogenesis

The Ninth Visual Object Tracking VOT2021 Challenge Results

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CC-MUSIC: An Optimization Estimator for Mutual Coupling Correction of L-Shaped Nonuniform Array with Single Snapshot

For the case of the single snapshot, the integrated SNR gain could not be obtained without the multiple snapshots, which degrades the mutual coupling correction performance under the lower SNR case. In this paper, a Convex Chain MUSIC (CC-MUSIC) algorithm is proposed for the mutual coupling correction of the L-shaped nonuniform array with single snapshot. It is an online self-calibration algorithm and does not require the prior knowledge of the correction matrix initialization and the calibration source with the known position. An optimization for the approximation between the no mutual coupling covariance matrix without the interpolated transformation and the covariance matrix with the mutual coupling and the interpolated transformation is derived. A global optimization problem is formed for the mutual coupling correction and the spatial spectrum estimation. Furthermore, the nonconvex optimization problem of this global optimization is transformed as a chain of the convex optimization, which is basically an alternating optimization routine. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the multisources with the single snapshot

An OPMA for Robust Mutual Coupling Coefficients Estimation of URA with Single Snapshot in MIMO HF Sky-Wave Radar

Due to the fluctuation of the signal-to-noise ratio (SNR) and the single snapshot case in the MIMO HF sky-wave radar system, the accuracy of the online estimation of the mutual coupling coefficients matrix of the uniform rectangle array (URA) might be degraded by the classical approach, especially in the case of low SNR. In this paper, an Online Particle Mean-Shift Approach (OPMA) is proposed, which is to get a relatively more effective estimation of the mutual coupling coefficients matrix with the low SNR. Firstly, the spatial smoothing technique combined with the MUSIC algorithm of URA is introduced for the DOA estimation of the multiple targets in the case of single snapshot which are taken as coherent sources. Then, based on the idea of the particle filter, the online particles with a moderate computational complexity are used to generate some different estimation results. Finally, the mean-shift algorithm is applied to get a more robust estimate of the equivalent mutual coupling coefficients matrix. The simulation results demonstrate the validity of the proposed approach in terms of the success probability, the statistics of bias, and the variance. The proposed approach is more robust and more accurate than the other two approaches

Convergence of Gossip Algorithms for Consensus in Wireless Sensor Networks with Intermittent Links and Mobile Nodes

We study the convergence of pairwise gossip algorithms and broadcast gossip algorithms for consensus with intermittent links and mobile nodes. By nonnegative matrix theory and ergodicity coefficient theory, we prove gossip algorithms surely converge as long as the graph is partitionally weakly connected which, in comparison with existing analysis, is the weakest condition and can be satisfied for most networks. In addition we characterize the supremum for the mean squared error of convergence as a function associated with the initial states and the number of nodes. Furthermore, on the condition that the graph is partitionally strongly connected, the rate of convergence is proved to be exponential and governed by the second largest eigenvalue of expected coefficient matrix. For partitionally strongly connected digraphs, simulation results illustrate that gossip algorithms actually converge, and broadcast gossip algorithms can converge faster than pairwise gossip algorithms at the cost of larger error of convergence

Joint beam and resource allocation in 5G mmWave small cell systems

Resource allocation in mmWave small cell is a key issue of the fifth generation (5G) systems. To solve this problem, we first present a hybrid beamforming structure for base station of small cell, which consists of orthogonal frequency division multiplexing (OFDM), identity matrix and switched-beam. Next, we formulate the joint allocation of the beam and transmitted power on the sub-carriers into a mixed integer non-linear programming problem (MINLP). Due to the non-convexity of this problem, we decompose it into two subproblems which are the beam selection and the power allocation. For the beam selection, we propose an algorithm based on cooperative games. For the power allocation, we present an optimal and sub-optimal solution based on Lagrange duality and non-cooperative games respectively. Simulation results show that the beam allocation algorithm proposed in this paper can effectively improve the sum rate of the system, and the optimal power allocation solution is far superior to the sub-optimal one in sum rate

Time-triggered federated learning over wireless networks

Abstract The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead