Fault-tolerant quantum computation demands extremely low logical error rates, yet superconducting qubit arrays are subject to radiation-induced correlated noise arising from cosmic-ray muon-generated quasiparticles. The quasiparticle density is unknown and time-varying, resulting in a mismatch between the true noise statistics and the priors assumed by standard decoders, and consequently, degraded logical performance. We formalize joint noise sensing and decoding using syndrome measurements by modeling the QP density as a latent variable, which governs correlation in physical errors and syndrome measurements. Starting from a variational expectation--maximization approach, we derive an iterative algorithm that alternates between QP density estimation and syndrome-based decoding under the updated noise model. Simulations of surface-code and bivariate bicycle quantum memory under radiation-induced correlated noise demonstrate a measurable reduction in logical error probability relative to baseline decoding with a uniform prior. Beyond improved decoding performance, the inferred QP density provides diagnostic information relevant to device characterization, shielding, and chip design. These results indicate that integrating physical noise estimation into decoding can mitigate correlated noise effects and relax effective error-rate requirements for fault-tolerant quantum computation.

In the era of big-data, the jobs submitted to the clouds exhibit complicated structures represented by graphs, where the nodes denote the sub-tasks each of which can be accommodated at a slot in a server, while the edges indicate the communication constraints among the sub-tasks. We develop a framework for efficient allocation of graph jobs in geo-distributed cloud networks (GDCNs), explicitly considering the power consumption of the datacenters (DCs). We address the following two challenges arising in graph job allocation: i) the allocation problem belongs to NP-hard nonlinear integer programming; ii) the allocation requires solving the NP-complete sub-graph isomorphism problem, which is particularly cumbersome in large-scale GDCNs. We develop a suite of efficient solutions for GDCNs of various scales. For small-scale GDCNs, we propose an analytical approach based on convex programming. For medium-scale GDCNs, we develop a distributed allocation algorithm exploiting the processing power of DCs in parallel. Afterward, we provide a novel low-complexity (decentralized) sub-graph extraction method, based on which we introduce cloud crawlers aiming to extract allocations of good potentials for large-scale GDCNs. Given these suggested strategies, we further investigate strategy selection under both fixed and adaptive DC pricing schemes, and propose an online learning algorithm for each.

The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.

Vehicular ad-hoc networks (VANETs) has recently attracted a lot of attention due to their great potentials for different applications such as collision avoidance, route finding and autonomous driving. A wide range of coverage and accessibility to the end users in VANETs make them a good target for commercial advertising. This paper addresses the problem of mobile advertising in VANETs. We consider a case where different advertisers compete for the VANET infrastructure. It is assumed that a city is partitioned into a grid of blocks and the central data center manager (CDM) sets the rental price for each block considering the geographical position and the predicted density of vehicles inside the block. The regret-based minimization method is adopted to tackle the problem in response to its dynamic nature. Regret bound of the proposed algorithm and its convergence to the best strategy are shown rigorously. Furthermore, a good potential of the proposed algorithm is revealed through simulations.

Vehicular ad-hoc networks (VANETs) have been an active area in the research community during the last decade with focus primarily on routing protocols, security aspects and safety. Recent advances in wireless communication and the inherent dynamic nature of VANETs provide excellent opportunity for advertisement dissemination. In this paper, we address the problem of dynamic advertising in VANETs. We consider a city divided into a grid, where the blocks have different vehicular densities that vary over time. Several advertising companies compete for the blocks to broadcast their advertisements in the network. The content dissemination in the network is controlled by a data management unit that receives requests from advertising companies for each block. To solve the problem of block allocation, we adapt the repeated auction scheme for the dynamic setting. Two new metrics are defined to better represent the real- world scenario and fairness in repeated auctions. We propose an algorithm which is a combination of adaptive linear prediction and nonparametric Bayesian belief update, enabling smart bidding and improving the utilities of the competing advertising companies significantly in the long- run. Through simulations, we show that the proposed algorithm achieves better performance than two baselines approaches.