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This paper derives upper and lower bounds on the secrecy capacity-memory tradeoff of a wiretap erasure broadcast channel (BC) with K-w weak receivers and K-s strong receivers, where weak receivers and strong receivers have the sam...
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This paper derives upper and lower bounds on the secrecy capacity-memory tradeoff of a wiretap erasure broadcast channel (BC) with K-w weak receivers and K-s strong receivers, where weak receivers and strong receivers have the same erasure probabilities and cache sizes, respectively. The lower bounds are achieved by the schemes that meticulously combine joint cache-channel coding with wiretap coding and key-aided one-time pads. The presented upper bound holds more generally for arbitrary degraded BCs and arbitrary cache sizes. When only weak receivers have cache memories, upper and lower bounds coincide for small and large cache memories, thus providing the exact secrecy capacity-memory tradeoff for this setup. The derived bounds further allow us to conclude that the secrecy capacity is positive even when the eavesdropper is stronger than all the legitimate receivers with cache memories. Moreover, they show that the secrecy capacity-memory tradeoff can be significantly smaller than its non-secure counterpart, but it grows much faster when cache memories are small. This paper also presents a lower bound on the global secrecy capacity-memory tradeoff where one is allowed to optimize the cache assignment subject to a total cache budget. It is close to the best known lower bound without secrecy constraint. For small total cache budget, the global secrecy capacity-memory tradeoff is achieved by assigning all the available cache memory uniformly over all the receivers if the eavesdropper is stronger than all the legitimate receivers, and it is achieved by assigning the cache memory uniformly only over the weak receivers if the eavesdropper is weaker than the strong receivers.
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The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic ...
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The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counterpart based on vanishing boundary conditions. In this article, we design a first experiment based on the PNFT in which information is encoded in the invariant nonlinearmain spectrum. To this end, we propose a method to construct a set of periodic waveforms each having the same fixed period, by employing the exact inverse PNFT algorithm developed in Part I. We demonstrate feasibility of the transmission scheme in experiment in good agreement with simulations and obtain a bit-error ratio of 10(-3) over a distance of 2000 km. It is shown that the transmission reach is significantly longer than expected from a naive estimate based on group velocity dispersion and cyclic prefix length, which is explained through a dominating solitonic component in the transmitted waveform. Our constellation design can be generalized to an arbitrary number of nonlinear degrees of freedom.
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The nonlinear Fourier transform (NFT) decomposes waveforms propagating through optical fiber into nonlinear degrees of freedom, which are preserved during transmission. By encoding information on the nonlinear spectrum, a transmis...
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The nonlinear Fourier transform (NFT) decomposes waveforms propagating through optical fiber into nonlinear degrees of freedom, which are preserved during transmission. By encoding information on the nonlinear spectrum, a transmission scheme inherently compatible with the nonlinear fiber is obtained. Despite potential advantages, the periodic NFT (PNFT) has been studied less compared to its counterpart based on vanishing boundary conditions, due to the mathematical complexity of the inverse transform. In this article we extract the theory of the algebro-geometric integration method underlying the inverse PNFT from the literature, and tailor it to the communication problem. We provide a complete algorithm to compute the inverse PNFT. As an application, we employ the algorithm to design a novel modulation scheme called nonlinear frequency amplitude modulation, where four different nonlinear frequencies are modulated independently. Finally we provide two further modulation schemes that may be considered in future research. The algorithm is further applied in Part II of this article to the design of a PNFT-based communication experiment.
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We experimentally demonstrate dual-polarization nonlinear frequency division multiplexing (NFDM) using the continuous spectrum in 1680 km of normal dispersion fiber, at the net data rate of 25.6 Gb/s. NFDM exhibits a gain of 0.4 d...
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We experimentally demonstrate dual-polarization nonlinear frequency division multiplexing (NFDM) using the continuous spectrum in 1680 km of normal dispersion fiber, at the net data rate of 25.6 Gb/s. NFDM exhibits a gain of 0.4 dB in Q-factor and 1 dB in total launch power when compared with burst mode OFDM. DGD penalties are shown to be negligible in NFDM transmission.
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We derive a steering inequality based on a fine-grained uncertainty relation to capture Einstein-Podolsky-Rosen steering for bipartite systems. Our steering inequality improves over previous ones since it can experimentally detect...
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We derive a steering inequality based on a fine-grained uncertainty relation to capture Einstein-Podolsky-Rosen steering for bipartite systems. Our steering inequality improves over previous ones since it can experimentally detect all steerable two-qubit Werner state with only two measurement settings on each side. According to our inequality, pure entangled states are maximally steerable. Moreover, by slightly changing the setting, we can express the amount of violation of our inequality as a function of their violation of the Clauser-Horne-Shimony-Holt inequality. Finally, after deriving a monogamy relation we prove that the amount of violation of our steering inequality is, up to a constant factor, a lower bound on the key rate of a one-sided device-independent quantum key distribution protocol secure against individual attacks.
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Majorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been particularly successful to tackle inverse problems and ...
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Majorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been particularly successful to tackle inverse problems and statistical machine learning problems where the regularization term is a sparsity-promoting concave function. However, due to non-convexity, the solution found by MM depends on its initialization. Uniform initialization is the most natural and often employed strategy as it boils down to penalizing all coefficients equally in the first MM iteration. Yet, this arbitrary choice can lead to unsatisfactory results in severely underdetermined inverse problems such as source imaging with magneto-and electro-encephalography (M/EEG). The framework of hierarchical Bayesian modeling (HBM) is an alternative approach to encode sparsity. This work shows that for certain hierarchical models, a simple alternating scheme to compute fully Bayesian maximum a posteriori (MAP) estimates leads to the exact same sequence of updates as a standard MM strategy (see the adaptive lasso). With this parallel outlined, we show how to improve upon these MM techniques by probing the multimodal posterior density using Markov Chain Monte-Carlo (MCMC) techniques. Firstly, we show that these samples can provide well-informed initializations that help MM schemes to reach better local minima. Secondly, we demonstrate how it can reveal the different modes of the posterior distribution in order to explore and quantify the inherent uncertainty and ambiguity of such ill-posed inference procedure. In the context of M/EEG, each mode corresponds to a plausible configuration of neural sources, which is crucial for data interpretation, especially in clinical contexts. Results on both simulations and real datasets show how the number or the type of sensors affect the uncertainties on the estimates.
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Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought ...
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Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi-automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.51, which corresponds to the usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized. (C) 2018 Elsevier B.V. All rights reserved.
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Steering, a quantum property stronger than entanglement but weaker than non-locality in the quantum correlation hierarchy, is a key resource for one-sided device-independent quantum key distribution applications, in which only one...
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Steering, a quantum property stronger than entanglement but weaker than non-locality in the quantum correlation hierarchy, is a key resource for one-sided device-independent quantum key distribution applications, in which only one of the communicating parties is trusted. A fine-grained steering inequality was introduced in (2014 Phys. Rev. A 90 050305), enabling for the first time the detection of steering in all steerable two-qubit Werner states using only two measurement settings. Here, we numerically and experimentally investigate this inequality for generalized Werner states and successfully detect steerability in a wide range of two-photon polarization-entangled Bell local states generated by a parametric down-conversion source.
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Designing software architectures and optimizing them based on extra-functional properties (EFPs) require to identify appropriate design decisions and to apply them on valid architectural elements. Software designers have to check ...
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Designing software architectures and optimizing them based on extra-functional properties (EFPs) require to identify appropriate design decisions and to apply them on valid architectural elements. Software designers have to check whether the resulting architecture fulfills the requirements and how it positively improves (possibly conflicting) EFPs. In practice, they apply well-known solutions such as design patterns manually. This is time-consuming, error-prone, and possibly sub-optimal. Well-established approaches automate the search of the design space for an optimal solution. They are based model-driven engineering techniques that formalized design decisions as model transformations and architectural elements as components. Using multi-objective optimizations techniques, they explore the design space by randomly selecting a set of components and applying to them variation operators that include a fixed set of predefined design decisions. In this work, we claim that the design space exploration requires to reason on both architectural components as well as model transformations. More specifically, we focus on possible instantiations of model transformations materialized as the application of model transformation alternatives on a set of architectural components. This approach was prototyped in RAMSES, a model transformation and code generation framework. Experimental results show the capability of our approach (i) to combine evolutionary algorithms and model transformation techniques to explore efficiently a set of architectural alternatives with conflicting EFPs, (ii) to instantiate, and select transformation instances that generate architectures satisfying stringent structural constraints, and (iii) to explore design spaces by chaining more than one transformation. In particular, we evaluated our approach on EFPs, architectures, and design alternatives inspired from the railway industry by chaining model transformations dedicated to implement safety design patterns and software components allocation on a multi-processor hardware platform.
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Magnetic tunnel junction (MTJ) with spin transfer torque (STT) switching method features fast speed, low power, great scalability and high compatibility with conventional CMOS process. Nevertheless, its magnetic and electrical pro...
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Magnetic tunnel junction (MTJ) with spin transfer torque (STT) switching method features fast speed, low power, great scalability and high compatibility with conventional CMOS process. Nevertheless, its magnetic and electrical properties can be easily influenced by operation temperature and self-heating effect, which further results in performance degradation and reliability issues of MTJ based memories and logic circuits. This paper investigates the behaviors of MTJ under different temperatures and further proposes a model in consideration of temperature impact on performance of MTJ, which can be used to optimize the design of STT-MRAM in terms of dynamic operations and temperature tolerance. (C) 2015 Elsevier Ltd. All rights reserved.
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