Advantages of Unfair Quantum Ground-State Sampling

The debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field by Kadowaki and Nishimori close to two decades ago. Recent technological breakthroughs in the field, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. Here, we examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating ideal stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a commercially available quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than classical thermal samplers. We demonstrate that i) quantum annealers in general sample the ground-state manifolds of spin glasses very differently than thermal optimizers, ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution over ground-states, and iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms.Comment: 13 pages, 11 figure

Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment

New Threats to Privacy-preserving Text Representations

The users’ privacy concerns mandate data publishers to protect privacy by anonymizing the data before sharing it with data consumers. Thus, the ultimate goal of privacy-preserving representation learning is to protect user privacy while ensuring the utility, e.g., the accuracy of the published data, for future tasks and usages. Privacy-preserving embeddings are usually functions that are encoded to low-dimensional vectors to protect privacy while preserving important semantic information about an input text. We demonstrate that these embeddings still leak private information, even though the low dimensional embeddings encode generic semantics. We develop two classes of attacks, i.e., adversarial classification attack and adversarial generation attack, to study the threats for these embeddings. In particular, the threats are (1) these embeddings may reveal sensitive attributes letting alone if they explicitly exist in the input text, and (2) the embedding vectors can be partially recovered via generation models. Besides, our experimental results show that our approach can produce higher-performing adversary models than other adversary baselines

A Detailed Procedure for Using Copulas to Classify E-Business Data

Decision support systems are widely implemented to effectively utilize the tremendous amount of data generated by information systems throughout an organization. In one common implementation, the goal is to correctly classify a customer so that appropriate action can take place. This may take the form of a customized purchase incentive given to increase the probability that a transaction is completed, while enhancing profitability. Intelligent agents employing neural network technology that function as Bayesian classifiers are one approach used here. Another approach that has been around for decades, called copulas, to our knowledge has yet to be utilized for classification in e-business applications. Copulas are functions that can describe the dependence among random variables. The very fact that copulas directly address co-dependence among variables may make them especially attractive in e-business applications where large numbers of correlated attributes may be present that could negatively affect the performance of other methods. In this paper, the basics of Bayesian decision making and posterior probabilities are reviewed. A detailed procedure for using copulas as Bayesian classifiers for e-business data is presented. The emphasis in describing the method is placed upon practitioner understanding to facilitate replication in real situations while maintaining technical rigor to ease computerized implementation

Viscous dark fluid

The unified dark energy and dark matter model within the framework of a model of a continuous medium with bulk viscosity (dark fluid) is considered. It is supposed that the bulk viscosity coefficient is an arbitrary function of the Hubble parameter. The choice of this function is carried out under the requirement to satisfy the observational data from recombination ($z\approx 1000$) till present time.Comment: 4 pages, 1 figure, added reference

Numerical Simulation of Low-Pressure Explosive Combustion in Compartment Fires

A filtered progress variable approach is adopted for large eddy simulations (LES) of turbulent deflagrations. The deflagration model is coupled with a non-premixed combustion model, either an equilibrium-chemistry, mixture-fraction based model, or an eddy dissipation model. The coupling interface uses a LES-resolved flame index formulation and provides partially-premixed combustion (PPC) modeling capability. The PPC sub-model is implemented into the Fire Dynamic Simulator (FDS) developed by the National Institute of Standards and Technology, which is then applied to the study of explosive combustion in confined fuel vapor clouds. Current limitations of the PPC model are identified first in two separate series of simulations: 1) a series of simulation corresponding to laminar flame propagation across homogeneous mixtures in open or closed tunnel-like configurations; and 2) a grid refinement study corresponding to laminar flame propagation across a vertically-stratified layer. An experimental database previously developed by FM Global Research, featuring controlled ignition followed by explosive combustion in an enclosure filled with vertically-stratified mixtures of propane in air, is used as a test configuration for model validation. Sealed and vented configurations are both considered, with and without obstacles in the chamber. These pressurized combustion cases present a particular challenge to the bulk pressure algorithm in FDS, which has robustness, accuracy and stability issues, in particular in vented configurations. Two modified bulk pressure models are proposed and evaluated by comparison between measured and simulated pressure data in the Factory Mutual Global (FMG) test configuration. The first model is based on a modified bulk pressure algorithm and uses a simplified expression for pressure valid in a vented compartment under quasi-steady conditions. The second model is based on solving an ordinary differential equation for bulk pressure (including a relaxation term proposed to stabilize possible Helmholtz oscillations) and modified vent flow velocity boundary conditions that are made bulk-pressure-sensitive. Comparisons with experiments are encouraging and demonstrate the potential of the new modeling capability for simulations of low pressure explosions in stratified fuel vapor clouds

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A working knowledge of research – both how it is done, and how it can be used – is important for everyone involved in direct patient care and the planning & delivery of eye programmes