Orbital-transverse density-wave instabilities in iron-based superconductors

Besides the conventional spin-density-wave (SDW) state, a new kind of orbital-transverse density-wave (OTDW) state is shown to exist generally in multi-orbital systems. We demonstrate that the orbital character of Fermi surface nesting plays an important role in density responses. The relationship between antiferromagnetism and structural phase transition in LaFeAsO (1111) and BaFe$_2$As$_2$ (122) compounds of iron-based superconductors may be understood in terms of the interplay between the SDW and OTDW with a five-orbital Hamiltonian. We propose that the essential difference between 1111 and 122 compounds is crucially determined by the presence of the two-dimensional $d_{xy}$-like Fermi surface around (0,0) being only in 1111 parent compounds.Comment: several parts were rewritten for clarity. 6 pages, 3 figures, 1 tabl

Emergence of Blind Areas in Information Spreading

Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. In addition, a special phenomenon, namely \emph{Information Blind Areas} where the network is separated by several information-unreachable clusters, will emerge from the spreading process. Furthermore, we also find that the size distribution of such information blind areas obeys power-law-like distribution, which has very similar exponent with that of site percolation. Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading

HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes

Bayesian optimization (BO), while proved highly effective for many black-box function optimization tasks, requires practitioners to carefully select priors that well model their functions of interest. Rather than specifying by hand, researchers have investigated transfer learning based methods to automatically learn the priors, e.g. multi-task BO (Swersky et al., 2013), few-shot BO (Wistuba and Grabocka, 2021) and HyperBO (Wang et al., 2022). However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces. In this work, we present HyperBO+: a pre-training approach for hierarchical Gaussian processes that enables the same prior to work universally for Bayesian optimization on functions with different domains. We propose a two-step pre-training method and analyze its appealing asymptotic properties and benefits to BO both theoretically and empirically. On real-world hyperparameter tuning tasks that involve multiple search spaces, we demonstrate that HyperBO+ is able to generalize to unseen search spaces and achieves lower regrets than competitive baselines.Comment: Full version of the workshop paper at 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making System