Convolutional Neural Networks over Tree Structures for Programming Language Processing

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.Comment: Accepted at AAAI-1

The P-wave Λ\Lambda-type bottom baryon states via the QCD sum rules

Our study focuses on the P-wave bottom baryon states with the spin-parity $J^P=\frac{1}{2}^-$, $\frac{3}{2}^-$. We introduce an explicit P-wave between the two light quarks in the interpolating currents to investigate the $\Lambda_b$ and $\Xi_b$ states within the framework of the full QCD sum rules. The predicted masses show that the $\Xi_b(6087)$ and $\Xi_b(6095/6100)$ could to be the P-wave bottom-strange baryon states with the spin-parity $J^P=\frac{1}{2}^-$ and $\frac{3}{2}^-$, respectively, meanwhile, the $\Lambda_b(5912)$ and $\Lambda_b(5920)$ could be the P-wave bottom baryon states with the spin-parity $J^P=\frac{1}{2}^-$ and $\frac{3}{2}^-$, respectively.Comment: 17 pages, 12 figure

Analysis of the D-wave Σ\Sigma-type charmed baryon states with the QCD sum rules

We construct the $\Sigma$-type currents to investigate the D-wave charmed baryon states with the QCD sum rules systematically. The predicted masses $M=3.35^{+0.13}_{-0.18}\,\rm{GeV}$ ($3.33^{+0.13}_{-0.16}\,\rm{GeV}$), $3.34^{+0.14}_{-0.18}\,\rm{GeV}$ ($3.35^{+0.13}_{-0.16}\,\rm{GeV}$) and $3.35^{+0.12}_{-0.13}\,\rm{GeV}$ ($3.35^{+0.12}_{-0.14}\,\rm{GeV}$) for the $\Omega_c(0,2,{\frac{1}{2}}^+)$, $\Omega_c(0,2,{\frac{3}{2}}^+)$ and $\Omega_c(0,2,{\frac{5}{2}}^+)$ states are in excellent agreement with the experimental data 3327.1\pm1.2 \mbox{ MeV} from the LHCb collaboration, and support assigning the $\Omega_c(3327)$ to be the $\Sigma$-type D-wave $\Omega_c$ state with the spin-parity $J^P={\frac{1}{2}}^+$, ${\frac{3}{2}}^+$ or ${\frac{5}{2}}^+$.Comment: 26 pages, 13 figure

Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.Comment: 15 figures, accepted by IEEE Transactions on Communication