Robustness of s-wave Pairing in Electron-Overdoped A1−yFe2−xSe2\text{A}_{1-y}\text{Fe}_{2-x}\text{Se}_2

Using self consistent mean field and functional renormalization group approaches we show that s-wave pairing symmetry is robust in the heavily electron-doped iron chalcogenides $(\text{K, Cs}) \text{Fe}_{2-x}\text{Se}_2$. This is because in these materials the leading antiferromagnetic (AFM) exchange coupling is between next-nearest-neighbor (NNN) sites while the nearest neighbor (NN) magnetic exchange coupling is ferromagnetic (FM). This is different from the iron pnictides, where the NN magnetic exchange coupling is AFM and leads to strong competition between s-wave and d-wave pairing in the electron overdoped region. Our finding of a robust s-wave pairing in $(\text{K, Cs}) \text{Fe}_{2-x}\text{Se}_2$ differs from the d-wave pairing result obtained by other theories where non-local bare interaction terms and the NNN $J_2$ term are underestimated. Detecting the pairing symmetry in $(\text{K, Cs}) \text{Fe}_{2-x}\text{Se}_2$ may hence provide important insights regarding the mechanism of superconducting pairing in iron based superconductors.Comment: 10 pages, 16 figure

Explaining 750 GeV diphoton excess from top/bottom partner cascade decay in two-Higgs-doublet model extension

In this paper, we interpret the 750 GeV diphoton excess in the Zee-Babu extension of the two-Higgs-doublet model by introducing a top partner ($T$)/bottom partner ($B$). In the alignment limit, the 750 GeV resonance is identified as the heavy CP-even Higgs boson ($H$), which can be sizably produced via the QCD process $pp \to T\bar{T}$ or $pp \to B\bar{B}$ followed by the decay $T\to Ht$ or $B \to Hb$. The diphoton decay rate of $H$ is greatly enhanced by the charged singlet scalars predicted in the Zee-Babu extension and the total width of $H$ can be as large as 7 GeV. Under the current LHC constraints, we scan the parameter space and find that such an extension can account for the observed diphoton excess.Comment: 19 pages, 4 figures; some discussions and references adde

Video Captioning via Hierarchical Reinforcement Learning

Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.Comment: CVPR 2018, with supplementary materia