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    We use superconducting rings with asymmetric link-up of current leads for experimental investigation of winding number change at magnetic field corresponding to the half of the flux quantum inside the ring. According to the conventional theory, the critical current of such rings should change by jump due to this change. Experimental data obtained at measurements of aluminum rings agree with theoretical prediction in magnetic flux region close to integer numbers of the flux quantum and disagree in the region close to the half of the one, where a smooth change is observed instead of the jump. First measurements of tantalum ring give a hope for the jump. Investigation of this problem may have both fundamental and practical importance.
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    The exceptional conducting nature of graphene makes it a viable candidate for enhancing the effectiveness of photocatalytic and biomedical nanomaterials. Herein, the immobilization of monodispersed silicon dioxide (SiO2) nanoparticles on multiple graphene layers is demonstrated for intercalation of graphene nanoplatelets (GNPs). Interestingly, the loading of graphene nanoplatelets with SiO2 nanoparticles enhances the photocatalytic efficiency from 46% to 99%. For biomedical applications, it is found that 75% of Gram positive and 50% of Gram negative bacteria have been killed, hence bacterial proliferation is significantly restricted. Further, the cytotoxicity study reveals that the synthesised nanocomposites are non-toxic for both normal (HCEC) and cancerous (MCF-7, HEp-2) cell lines which signifies their potential as carriers for drug delivery. The prepared nanocomposites with controlled amount of carbon in the form of graphene can be employed for photocatalysis based waste water remediation, biomedicine and nano drug delivery
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    We study a stochastic model of urban growth generating spatial distributions of population densities at an intermediate mesoscopic scale. The model is based on the antagonist interplay between the two opposite abstract processes of aggregation (preferential attachment) and diffusion (urban sprawl). Introducing indicators to quantify precisely urban form, the model is first statistically validated and intensively explored to understand its complex behavior across the parameter space. We then compute real morphological measures on local areas of size 50km covering all European Union, and show that the model can reproduce most of existing urban morphologies in Europe. It implies that the morphological dimension of urban growth processes at this scale are sufficiently captured by the two abstract processes of aggregation and diffusion.
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    A quite general device analysis method that allows the direct evaluation of optical and recombination losses in crystalline silicon (c-Si)-based solar cells has been developed. By applying this technique, the optical and physical limiting factors of the state-of-the-art solar cells with ~20% efficiencies have been revealed. In the established method, the carrier loss mechanisms are characterized from the external quantum efficiency (EQE) analysis with very low computational cost. In particular, the EQE analyses of textured c-Si solar cells are implemented by employing the experimental reflectance spectra obtained directly from the actual devices while using flat optical models without any fitting parameters. We find that the developed method provides almost perfect fitting to EQE spectra reported for various textured c-Si solar cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell with a MoOx layer, and an n-type passivated emitter with rear locally diffused (PERL) solar cell. The modeling of the recombination loss further allows the extraction of the minority carrier diffusion length and surface recombination velocity from the EQE analysis. Based on the EQE analysis results, the carrier loss mechanisms in different types of c-Si solar cells are discussed.
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    In the real world, many complex systems interact with other systems. In addition, the intra- or inter-systems for the spread of information about infectious diseases and the transmission of infectious diseases are often not random, but with direction. Hence, in this paper, we build epidemic model based on an interconnected directed network, which can be considered as the generalization of undirected networks and bipartite networks. By using the mean-field approach, we establish the Susceptible-Infectious-Susceptible model on this network. We theoretically analyze the model, and obtain the basic reproduction number, which is also the generalization of the critical number corresponding to undirected or bipartite networks. And we prove the global stability of disease-free and endemic equilibria via the basic reproduction number as a forward bifurcation parameter. We also give a condition for epidemic prevalence only on a single subnetwork. Furthermore, we carry out numerical simulations, and find that the independence between each node's in- and out-degrees greatly reduce the impact of the network's topological structure on disease spread.
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    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.
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    In this paper, we propose a methodology that bears close resemblance to the Fourier analysis of the first harmonic to study networks subjected to pendular behavior. In this context, pendular behavior is characterized by the phenomenon of people's dislocation from their homes to work in the morning and people's dislocation in the opposite direction in the afternoon. Pendular behavior is a relevant phenomenon that takes place in public transport networks because it may reduce the overall efficiency of the system as a result of the asymmetric utilization of the system in different directions. We apply this methodology to the bus transport system of Brasilia, which is a city that has commercial and residential activities in distinct boroughs. We show that this methodology can be used to characterize the pendular behavior of this system, identifying the most critical nodes and times of the day when this system is in more severe demanded.
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    Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topological measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study on motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.
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    The complex nature of organizational culture challenges our ability to infers its underlying dynamics from observational studies. Recent computational studies have adopted a distinct different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work - (a) omittance of an individual's strive for achieving cognitive coherence, (b) limited integration of important contextual factors - by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of organizational culture, yet be composed of individuals with reduced levels of coherence, (ii) the components of social conformity - peer-pressure and social rank - are influential at different aggregation levels.
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    This paper is devoted to assess the presence of some regularities in the magnitudes of the earthquakes in Italy between January $24^{th}$, 2016 and January $24^{th}$, 2017, and to propose an earthquakes cost indicator. The considered data includes the catastrophic events in Amatrice and in Marche region. To our purpose, we implement two typologies of rank-size analysis: the classical Zipf-Mandelbrot law and the so-called universal law proposed by Cerqueti and Ausloos (2016). The proposed generic measure of the economic impact of earthquakes moves from the assumption of the existence of a cause-effect relation between earthquakes magnitudes and economic costs. At this aim, we hypothesize that such a relation can be formalized in a functional way to show how infrastructure resistance affects the cost. Results allow us to clarify the impact of an earthquake on the social context and might serve for strengthen the struggle against the dramatic outcomes of such natural phenomena.
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    Self Organizing Networks (SONs) are considered as vital deployments towards upcoming dense cellular networks. From a mobile carrier point of view, continuous coverage optimization is critical for better user perceptions. The majority of SON contributions introduce novel algorithms that optimize specific performance metrics. However, they require extensive processing delays and advanced knowledge of network statistics that may not be available. In this work, a progressive Autonomous Coverage Optimization (ACO) method combined with adaptive cell dimensioning is proposed. The proposed method emphasizes the fact that the effective cell coverage is a variant on actual user distributions. ACO algorithm builds a generic Space-Time virtual coverage map per cell to detect coverage holes in addition to limited or extended coverage conditions. Progressive levels of optimization are followed to timely resolve coverage issues with maintaining optimization stability. Proposed ACO is verified under both simulations and practical deployment in a pilot cluster for a worldwide mobile carrier. Key Performance Indicators show that proposed ACO method significantly enhances system coverage and performance.
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    This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

Recent comments

Travis Scholten Aug 23 2017 14:30 UTC

Wanted to let you know this paper has been updated with new technical results. In particular, we have provided a new section which gives a generalization of [local asymptotic normality](https://en.wikipedia.org/wiki/Local_asymptotic_normality) that is applicable to models with convex constraints (wh

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Māris Ozols Aug 03 2017 09:34 UTC

If I'm not mistaken, what you describe here is equivalent to the [QR decomposition][1]. The matrices $R_{ij}$ that act non-trivially only in a two-dimensional subspace are known as [Givens rotations][2]. The fact that any $n \times n$ unitary can be decomposed as a sequence of Givens rotations is ex

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gae Jul 26 2017 21:19 UTC

For those interested in the literature on teleportation simulation of quantum channels, a detailed and *comprehensive* review is provided in Supplementary Note 8 of https://images.nature.com/original/nature-assets/ncomms/2017/170426/ncomms15043/extref/ncomms15043-s1.pdf
The note describes well the t

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Maciej Malinowski Jul 26 2017 15:56 UTC

In what sense is the ground state for large detuning ordered and antiferromagnetic? I understand that there is symmetry breaking, but other than that, what is the fundamental difference between ground states for large negative and large positive detunings? It seems to be they both exhibit some order

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Stefano Pirandola Jul 26 2017 15:28 UTC

The performance of the memory assisted MDI-QKD with "quasi-EPR" sources is remarkable. It improves the key rate by 5 orders of magnitude over the PLOB bound at about 600 km (take a look at Figure 4).

Māris Ozols Jul 26 2017 11:07 UTC

Conway's list still has four other $1000 problems left:

https://oeis.org/A248380/a248380.pdf

SHUAI ZHANG Jul 26 2017 00:20 UTC

I am still working on improving this survey. If you have any suggestions, questions or find any mistakes, please do not hesitate to contact me: shuai.zhang@student.unsw.edu.au.

Alvaro M. Alhambra Jul 24 2017 16:10 UTC

This paper has just been updated and we thought it would be a good
idea to advertise it here. It was originally submitted a year ago, and
it has now been essentially rewritten, with two new authors added.

We have fixed some of the original results and now we:
-Show how some fundamental theorem

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Steve Flammia Jul 21 2017 13:43 UTC

Actually, there is even earlier work that shows this result. In [arXiv:1109.6887][1], Magesan, Gambetta, and Emerson showed that for any Pauli channel the diamond distance to the identity is equal to the trace distance between the associated Choi states. They prefer to phrase their results in terms

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