Reconstruction of pairwise interactions using energy-based models*

Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics. Closely connected is the problem of inverse statistical mechanics, where the goal is to infer the parameters of such models given observed data. An open problem in this field is the question of how to train these models in the case where the data contain additional higher-order interactions that are not present in the pairwise model. In this work, we propose an approach based on energy-based models and pseudolikelihood maximization to address these complications: we show that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions. We show these improvements to hold consistently when compared to a standard approach using only the pairwise model and to an approach using only a neural network. This is in line with the general idea that simple interpretable models and complex black-box models are not necessarily a dichotomy: interpolating these two classes of models can allow to keep some advantages of both.

An SIR rumor pairwise model with spreading age

In this paper, we extend the classical rumor model with non-Markovian recovery process in a complex network. We follow the ideas from R\"{o}st to analyze the pairwise model; then, the hyperbolic system can be transformed into integro-differential equations. For the rumor model, the reproduction number is obtained. Next, we use numerical simulation to verify the accuracy of the result. In the end, we focus on how the three different distributions of recovery time with spreading age influence on rumor model. The result illustrates the significant effect of different distribution functions on the process of rumor spreading.

Biomolecular-Level Event Detection: A New Representation of Generating Short Sentence and Sample Selection Strategy

Background: Biomolecular-level event extraction is one of the most important branches of information extraction. With the rapid growth of biomedical literature, it is difficult for researchers to manually obtain information of interest, e.g. unknown information of threatening human disease or some biological processes. Therefore, researchers are interested in automatically acquiring information of biomolecular-level events. However, the annotated biomolecular-level event corpus is limited and highly imbalanced, which affects the performance of the classification algorithms and can even lead to over-fitting. associations while known disease-lncRNA associations are required only. Method: In this paper, a new approach using the Pairwise model and convolutional neural network for biomolecular-level event extraction is introduced. The method can identify more accurate positive instances from unlabeled data to enlarge the labeled data. First, unlabeled samples are categorized using the Pairwise model. Then, the shortest dependency path with additional information is generated. Furthermore, two input forms with a new representation of the convolutional neural network model, which are dependency word sequence and dependency relation sequence are presented. Finally, with the sample selection strategy, the expanded labeled samples from unlabeled domain corpus incrementally enlarge the training data to improve the performance of the classifier. </P><P> Result & Conclusion: Our proposed method achieved better performance than other excellent systems. This is due to our new representation of generated short sentence and proposed sample selection strategy, which greatly improved the accuracy of classification. The extensive experimental results indicate that the new method can effectively inculcate unlabeled data to improve the performance of classifier for biomolecular-level events extraction.</P>