Learning Personalized End-to-End Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a Profile Model which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a Preference Model captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the Personalized MemN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.Comment: Accepted by AAAI 201

Human migration patterns in large scale spatial with the resume data

Researches on the human mobility have made great progress in many aspects, but the long-term and long-distance migration behavior is lack of in-depth and extensive research because of the difficult in accessing to household data. In this paper, we use the resume data to discover the human migration behavior on the large scale scope. It is found that the asymmetry in the flow structure which reflects the influence of population competition is caused by the difference of attractiveness among cities. This flow structure can be approximately described by the gravity model of spatial economics. Besides, the value of scaling exponent of distance function in the gravity model is less than the value of short-term travel behavior. It means that, compared with the short-term travel behavior, the long-term human migration behavior is less sensitive. Moreover, the scaling coefficients of each variable in the gravity model are investigated. The result shows that the economic level is a mainly factor on the migration

Functional characterization of a short peptidoglycan recognition protein from Chinese giant salamander (Andrias davidianus)

This work was supported by the National Natural Science Foundation of China (Grant no. 31302221, 31172408 and 31272666) and Jiangsu Province (Grant no. BK20171274 and BK2011418), and partially by the Opening Project of Jiangsu Key Laboratory of Biochemistry and Biotechnology of Marine Wetland (Grant no. K2016-08). QZ was supported by the “Qinglan” project of Jiangsu province of China.Peer reviewedPublisher PD

Study of the Breathing Effect of Reciprocating Compressor Under Duty Cycle Regulation (DCR) Capacity Control by Simulation and Experiment

Duty Cycle Regulation is a new method for capacity control of reciprocating compressor. Like other suction-valve-unloaded methods, the DCR method would inevitably cause the Breathing Effect. In this article, the internal flow and heat transfer in the compressor under DCR control are analyzed using CFD simulation. The geometrical model of the breathing effect has already been worked out. The numerical analysis and experimental research have been fulfilled. The flow conditions of the breathing effect during the DCR process, the temperature field in suction chamber and cylinder after some breathing effect cycles, and the capacity regulation results using DCR method are obtained. FLUENT is used to compute temperature variation after some periods of regulation. It is found that after 20 periods of regulation the suction temperature is about 32K higher than the one in normal process of compressor. Through the numerical analysis and experiment, it could be concluded that the temperature rise resulted from the breathing effect affects the suction and discharge temperature, capacity and energy consumption. Based on the results of this work, the performance of reciprocating compressor could be improved by eliminating the influence of breathing effect