A train backbone network consists of a sequence of nodes arranged in a linear topology. A key step that enables communication in such a network is that of topology discovery, or train inauguration, whereby nodes learn in a distributed fashion the phy
sical topology of the backbone network. While the current standard for train inauguration assumes wired links between adjacent backbone nodes, this work investigates the more challenging scenario in which the nodes communicate wirelessly. The key motivations for this desired switch from wired topology discovery to wireless one are the flexibility and capability for expansion and upgrading of a wireless backbone. The implementation of topology discovery over wireless channels is made difficult by the broadcast nature of the wireless medium, and by fading and interference. A novel topology discovery protocol is proposed that overcomes these issues and requires relatively minor changes to the wired standard. The protocol is shown via analysis and numerical results to be robust to the impairments caused by the wireless channel including interference from other trains.
In the sake of connecting the charge-density-wave (CDW) of TaSe$_2$ and single-emph{textbf{q}} CDW-type distortion of TaTe$_2$, we present an overall electronic phase diagram of 1emph{T}-TaSe$_{2-x}$Te$_x$ ($0 leq x leq 2$). In the experimentally pre
pared single crystals, the CDW is completely suppressed as $0.5 < x < 1.5$, while superconductivity emerges as $0.2 < x < 1.2$. Theoretically, similar to 1emph{T}-TaSe$_2$ and 1emph{T}-TaTe$_2$, the hypothetic 1emph{T}-TaSeTe with ordered Se/Ta/Te stacking shows instability in the phonon dispersion, indicating the presence of CDW in the ideally ordered sample. The contradictory between experimental and theoretical results suggests that the CDW is suppressed by disorder in 1emph{T}-TaSe$_{2-x}$Te$_x$. The formation and suppression of CDW are found to be independent with Fermi surface nesting based on the generated electron susceptibility calculations. The calculation of phonon linewidth suggests the strong textbf{emph{q}}-dependent electron-phonon coupling induced period-lattice-distortion (PLD) should be related to our observation: The doping can largely distort the TaX$_6$ (X = Se, Te) octahedra, which are disorderly distributed. The resulted puckered Ta-Ta layers are not compatible with the two-dimensional PLD. Therefore, CDW is suppressed in 1emph{T}-TaSe$_{2-x}$Te$_x$. Our results offer an indirect evidence that PLD, which can be influenced by strong disorder, is the origin of CDW in the system.
A classic measure of ecological stability describes the tendency of a community to return to equilibrium after small perturbation. While many advances show how the network structure of these communities severely constrains such tendencies, few if any
of these advances address one of the most fundamental properties of network structure: heterogeneity among nodes with different numbers of links. Here we systematically explore this property of degree heterogeneity and find that its effects on stability systematically vary with different types of interspecific interactions. Degree heterogeneity is always destabilizing in ecological networks with both competitive and mutualistic interactions while its effects on networks of predator-prey interactions such as food webs depend on prey contiguity, i.e., the extent to which the species consume an unbroken sequence of prey in community niche space. Increasing degree heterogeneity stabilizes food webs except those with the most contiguity. These findings help explain previously unexplained observations that food webs are highly but not completely contiguous and, more broadly, deepens our understanding of the stability of complex ecological networks with important implications for other types of dynamical systems.
A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed
latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.
The Sky Brightness Monitor (SBM) is an important instrument to measure the brightness level for the sky condition, which is a critical parameter for judging a site for solar coronal observations. In this paper we present an automatic method for the p
rocessing of SBM data in large quantity, which can separate the regions of the Sun and the nearby sky as well as recognize the regions of the supporting arms in the field of view. These processes are implemented on the data acquired by more than one SBM instruments during our site survey project in western China. An analysis applying the result from our processes has been done for the assessment of the scattered-light levels by the instrument. Those results are considerably significant for further investigations and studies, notably to derive a series of the other important atmospheric parameters such as extinctions, aerosol content and precipitable water vapor content for candidate sites. Our processes also provide a possible way for full-disk solar telescopes to track the Sun without an extra guiding system.
Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are risk-averse with respect to gains and risk-seeking with respect to losses, a pheno
menon called loss aversion. Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of them have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the loss aversion phenomenon, an essence in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the effect of loss aversion. Moreover, we introduce three risk-adjusted metrics inspired by prospect theory to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading, where traders are allowed to watch and follow the trading activities of others, by predicting potential winners statistically based on their historical trading behavior rather than their trading performance at any given point in time.
As a fundamental structural transition in complex networks, core percolation is related to a wide range of important problems. Yet, previous theoretical studies of core percolation have been focusing on the classical ErdH{o}s-Renyi random networks wi
th Poisson degree distribution, which are quite unlike many real-world networks with scale-free or fat-tailed degree distributions. Here we show that core percolation can be analytically studied for complex networks with arbitrary degree distributions. We derive the condition for core percolation and find that purely scale-free networks have no core for any degree exponents. We show that for undirected networks if core percolation occurs then it is always continuous while for directed networks it becomes discontinuous when the in- and out-degree distributions are different. We also apply our theory to real-world directed networks and find, surprisingly, that they often have much larger core sizes as compared to random models. These findings would help us better understand the interesting interplay between the structural and dynamical properties of complex networks.
We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the
networks degree distribution. We rigorously prove that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.
