H. B. Zhuo

H. B. Zhuo
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H. B. Zhuo
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Computer Science - Artificial Intelligence (4)
 
Physics - Plasma Physics (2)
 
High Energy Physics - Phenomenology (1)
 
Computer Science - Robotics (1)
 
Computer Science - Computation and Language (1)

Publications Authored By H. B. Zhuo

Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Most existing methods only concentrate on knowledge triples, ignoring logic rules which contain rich background knowledge. Although there has been some work aiming at leveraging both knowledge triples and logic rules, they ignore the transitivity and antisymmetry of logic rules. Read More

2017Feb
Affiliations: 1Dept. of Computer Science, Sun Yat-Sen University, GuangZhou, China., 2Dept. of Computer Science, Sun Yat-Sen University, GuangZhou, China., 3Dept. of Computer Science, Sun Yat-Sen University, GuangZhou, China., 4Dept. of Computer Science, Sun Yat-Sen University, GuangZhou, China.

Topic models have been widely used in discovering latent topics which are shared across documents in text mining. Vector representations, word embeddings and topic embeddings, map words and topics into a low-dimensional and dense real-value vector space, which have obtained high performance in NLP tasks. However, most of the existing models assume the result trained by one of them are perfect correct and used as prior knowledge for improving the other model. Read More

It is shown by particle-in-cell simulation that intense circularly polarized (CP) laser light can be contained in the cavity of a solid-density circular Al-plasma shell for hundreds of light-wave periods before it is dissipated by laser-plasma interaction. A right-hand CP laser pulse can propagate almost without reflection into the cavity through a highly magnetized overdense H-plasma slab filling the entrance hole. The entrapped laser light is then multiply reflected at the inner surfaces of the slab and shell plasmas, gradually losing energy to the latter. Read More

Generation of relativistic electron (RE) beams during ultraintense laser pulse interaction with plasma targets is studied by collisional particle-in-cell (PIC) simulations. Strong magnetic field with transverse scale length of several local plasma skin depths, associated with RE currents propagation in the target, is generated by filamentation instability (FI) in collisional plasmas, inducing a great enhancement of the divergence of REs compared to that of collisionless cases. Such effect is increased with laser intensity and target charge state, suggesting that the RE divergence might be improved by using low-Z materials under appropriate laser intensities in future fast ignition experiments and in other applications of laser-driven electron beams. Read More

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Read More

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Read More

In the framework of heavy quark effective theory, the leading order Isgur-Wise form factors relevant to semileptonic decays of the ground state $\bar{b}s$ meson $B_{s}$ into orbitally excited $D$-wave $\bar{c}s$ mesons, including the newly observed narrow $D^{*}_{s1}(2860)$ and $D^{*}_{s3}(2860)$ states by the LHCb Collaboration, are calculated with the QCD sum rule method. With these universal form factors, the decay rates and branching ratios are estimated. We find that the decay widths are $\Gamma(B_s\rightarrow D^{*}_{s1}\ell\bar{\nu}) =1. Read More

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. Read More