Representation Learning with Fine-grained Patterns

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.Comment: EMNLP '1

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Half Metallic Bilayer Graphene

Charge neutral bilayer graphene has a gapped ground state as transport experiments demonstrate. One of the plausible such ground states is layered antiferromagnetic spin density wave (LAF) state, where the spins in top and bottom layers have same magnitude with opposite directions. We propose that lightly charged bilayer graphene in an electric field perpendicular to the graphene plane may be a half metal as a consequence of the inversion and particle-hole symmetry broken in the LAF state. We show this explicitly by using a mean field theory on a 2-layer Hubbard model for the bilayer graphene.Comment: 4+ pages, 4 figure

Metagenomic analyses reveal phylogenetic diversity of carboxypeptidase gene sequences in activated sludge of a wastewater treatment plant in Shanghai, China

Activated sludge of wastewater treatment plants carries a diverse microflora. However, up to 80–90 % of microorganisms in activated sludge cannot be cultured by current laboratory techniques, leaving an enzyme reservoir largely unexplored. In this study, we investigated carboxypeptidase diversity in activated sludge of a wastewater treatment plant in Shanghai, China, by a culture-independent metagenomic approach. Three sets of consensus degenerate hybrid oligonucleotide primers (CODEHOPs) targeting conserved domains of public carboxypeptidases have been designed to amplify carboxypeptidase gene sequences in the metagenomic DNA of activated sludge by PCR. The desired amplicons were evaluated by carboxypeptidase sequence clone libraries and phylogenetic analyses. We uncovered a significant diversity of carboxypeptidases present in the activated sludge. Deduced carboxypeptidase amino acid sequences (127–208 amino acids) were classified into three distinct clusters, α, β, and γ. Sequences belonging to clusters α and β shared 58–97 % identity to known carboxypeptidase sequences from diverse species, whereas sequences in the cluster γ were remarkably less related to public carboxypeptidase homologous in the GenBank database, strongly suggesting that novel carboxypeptidase families or microbial niches exist in the activated sludge. We also observed numerous carboxypeptidase sequences that were much closer to those from representative strains present in industrial and sewage treatment and bioremediation. Thermostable and halotolerant carboxypeptidase sequences were also detected in clusters α and β. Coexistence of various carboxypeptidases is evidence of a diverse microflora in the activated sludge, a feature suggesting a valuable gene resource to be further explored for biotechnology application