Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017

Rate of the phase transition for a charged anti-de Sitter black hole

Phase transition is a core content of black hole thermodynamics. This study adopted the Kramer's escape rate method for describing the Brownian motion of particles in an external field to investigate the intensity of the phase transition between small and large black hole states. Some existing studies mostly focused on the formal analysis of the thermodynamic phase transition of black holes, but they neglected the detailed description of the phase transition process. Our results show that the phase transition between small and large black holes for charged anti-de Sitter (AdS) black holes presents serious asymmetric features, and the overall process is dominated by the transition from a small black hole to a large black hole. This study filled a research gap of a stochastic process analysis on the issue of the first-order phase transition rate in the AdS black hole.Comment: 8 pages, 3 figures, to appear in SCIENCE CHINA Physics, Mechanics & Astronomy (http://engine.scichina.com/doi/10.1007/s11433-022-2022-6

Influencing Lenders’ Repeat Investment Intention in P2P Lending Platforms in China through Signaling

Repeated investments by lenders are critical for the survival and success of an online P2P lending platform. Lenders’ trust in a platform influences their investment decisions. This study explores the impact of trust and its antecedents on lender investment intention in online P2P lending platforms. An online survey of 358 active P2P lenders on several leading online P2P lending platforms in China was conducted. Empirical results suggest that lender trust in a platform has a strong influence upon both perceived risk and investment intention. Furthermore, the results show that top management team heterogeneity, reputation, and quality of the platform have significant effects on lender trust. Both theoretical and practical implications of these findings are discussed