Local Tomography of Large Networks under the Low-Observability Regime

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of $partial$ observations, where only a small fraction of the agents can be feasibly observed. The goal is to infer the underlying subnetwork of interactions and we refer to this problem as $local$ $tomography$. In order to study the large-scale setting, we adopt a proper stochastic formulation where the unobserved part of the network is modeled as an Erd\"{o}s-R\'enyi random graph, while the observable subnetwork is left arbitrary. The main result of this work is establishing that, under this setting, local tomography is actually possible with high probability, provided that certain conditions on the network model are met (such as stability and symmetry of the network combination matrix). Remarkably, such conclusion is established under the $low$-$observability$ $regime$, where the cardinality of the observable subnetwork is fixed, while the size of the overall network scales to infinity.Comment: To appear in IEEE Transactions on Information Theor

Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime

This work examines the close interplay between cooperation and adaptation for distributed detection schemes over fully decentralized networks. The combined attributes of cooperation and adaptation are necessary to enable networks of detectors to continually learn from streaming data and to continually track drifts in the state of nature when deciding in favor of one hypothesis or another. The results in the paper establish a fundamental scaling law for the steady-state probabilities of miss-detection and false-alarm in the slow adaptation regime, when the agents interact with each other according to distributed strategies that employ small constant step-sizes. The latter are critical to enable continuous adaptation and learning. The work establishes three key results. First, it is shown that the output of the collaborative process at each agent has a steady-state distribution. Second, it is shown that this distribution is asymptotically Gaussian in the slow adaptation regime of small step-sizes. And third, by carrying out a detailed large deviations analysis, closed-form expressions are derived for the decaying rates of the false-alarm and miss-detection probabilities. Interesting insights are gained. In particular, it is verified that as the step-size $\mu$ decreases, the error probabilities are driven to zero exponentially fast as functions of $1/\mu$, and that the error exponents increase linearly in the number of agents. It is also verified that the scaling laws governing errors of detection and errors of estimation over networks behave very differently, with the former having an exponential decay proportional to $1/\mu$, while the latter scales linearly with decay proportional to $\mu$. It is shown that the cooperative strategy allows each agent to reach the same detection performance, in terms of detection error exponents, of a centralized stochastic-gradient solution.Comment: The paper will appear in IEEE Trans. Inf. Theor

Kaposi's sarcoma-associated herpesvirus oncoprotein K13 protects against B cell receptor induced growth arrest and apoptosis through NF-κB activation

Kaposi's sarcoma-associated herpesvirus (KSHV) has been linked to the development of Kaposi's sarcoma, primary effusion lymphoma and multicentric Castleman's disease (MCD). We have characterized the role of KSHV-encoded viral FLICE inhibitory protein K13 in the modulation of anti-IgM induced growth arrest and apoptosis in B cells. We demonstrate that K13 protects WEHI 231, an immature B cell line, against anti-IgM induced growth arrest and apoptosis. The protective effect of K13 was associated with the activation of the NF-κB pathway and was deficient in its mutant, K13-58AAA, and a structural homolog, vFLIP E8, which lack NF-κB activity. K13 upregulated the expression of NF-κB subunit RelB and blocked the anti-IgM induced decline in c-Myc and rise in p27(Kip1) that have been associated with growth arrest and apoptosis. K13 also upregulated the expression of Mcl-1, an anti-apoptotic member of the Bcl2 family. Finally, K13 protected the mature B cell line Ramos against anti-IgM induced apoptosis through NF-κB activation. Inhibition of anti-IgM induced apoptosis by K13 may contribute to the development of KSHV-associated lymphoproliferative disorders

Effect of Temperature & Concentration on Dissolution Potentials of Sodium Chloride, Bromide & Iodide

344-34

Precipitation Potentials of Sodium, Potassium & Ammonium Chlorides, Bromides & Iodides

582-58

Dissolution Potentials of Nitrates of Sodium, Potassium & Ammonium

160-16

Multi-fidelity surrogate-based optimal design of road vehicle suspension systems

Ride comfort is a relevant performance for road vehicles. The suspension system can filter vibration caused by the uneven road to improve ride comfort. Optimization of the road vehicle suspension system has been extensively studied. As detailed models require significant computational effort, it becomes increasingly important to develop an efficient optimization framework. In this work, a multi-fidelity surrogate-based optimization framework based on the Approximate Normal Constraint method and Extended Kernel Regression surrogate modeling method is proposed and applied. An analytical model and a multi-body model of the suspension system are used as the low-fidelity and high-fidelity models, respectively. Compared with other well-known methods, the proposed method can provide good accuracy and high efficiency. In addition, the proposed method is applied to different types of vehicle suspension optimization problems and shows good robustness and efficiency