Neural Programming by Example

Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to-end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs.Comment: 7 pages, Association for the Advancement of Artificial Intelligence (AAAI

Lorentz violation from gamma-ray bursts

The constancy of light speed is a basic assumption in Einstein's special relativity, and consequently the Lorentz invariance is a fundamental symmetry of space-time in modern physics. However, it is speculated that the speed of light becomes energy-dependent due to the Lorentz invariance violation~(LV) in various new physics theories. We analyse the data of the energetic photons from the gamma-ray bursts (GRBs) by the Fermi Gamma-Ray Space Telescope, and find more events to support the energy dependence in the light speed with both linear and quadratic form corrections. We provide two scenarios to understand all the new-released Pass~8 data of bright GRBs by the Fermi-LAT Collaboration, with predictions from such scenarios being testable by future detected GRBs.Comment: 7 latex pages, 2 figures, final version for journal publicatio