High spin baryon in hot strongly coupled plasma

We consider a strings-junction holographic model of probe baryon in the finite-temperature supersymmetric Yang-Mills dual of the AdS-Schwarzschild black hole background. In particular, we investigate the screening length for high spin baryon composed of rotating N_c heavy quarks. To rotate quarks by finite force, we put hard infrared cutoff in the bulk and give quarks finite mass. We find that N_c microscopic strings are embedded reasonably in the bulk geometry when they have finite angular velocity \omega, similar to the meson case. By defining the screening length as the critical separation of quarks, we compute the \omega dependence of the baryon screening length numerically and obtain a reasonable result which shows that baryons with high spin dissociate more easily. Finally, we discuss the relation between J and E^2 for baryons.Comment: 18 pages, 19 figures, version to appear in JHE

On Tree-Based Neural Sentence Modeling

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.Comment: To Appear at EMNLP 201