The silicate model and carbon rich model of CoRoT-7b, Kepler-9d and Kepler-10b

Possible bulk compositions of the super-Earth exoplanets, CoRoT-7b, Kepler-9d, and Kepler-10b are investigated by applying a commonly used silicate and a non-standard carbon model. Their internal structures are deduced using the suitable equation of state of the materials. The degeneracy problems of their compositions can be partly overcome, based on the fact that all three planets are extremely close to their host stars. By analyzing the numerical results, we conclude: 1) The iron core of CoRoT-7b is not more than 27% of its total mass within 1 $\sigma$ mass-radius error bars, so an Earth-like composition is less likely, but its carbon rich model can be compatible with an Earth-like core/mantle mass fraction; 2) Kepler-10b is more likely with a Mercury-like composition, its old age implies that its high iron content may be a result of strong solar wind or giant impact; 3) the transiting-only super-Earth Kepler-9d is also discussed. Combining its possible composition with the formation theory, we can place some constraints on its mass and bulk composition.Comment: 20 pages, 8figures, accepted for publication in RAA. arXiv admin note: text overlap with arXiv:0707.289

Deflationary Expansion : an Overshooting Perspective to the Recent Business Cycle in China

Deflationary expansion has puzzled economists both in and outside China. We study this business cycles phenomenon within a model of discrete time dynamics. We find that deflationary expansion could be possible if driven by an overshooting in investing and if the state of the economy maintains high rate of growth. This expression is consistent with the recent variables. The high steady state of growth could be explained by the current institutional environment of China.Deflationary Expansion, China, Existence and Stability Conditions of Equilibrium, Business Fluctuations, monetary policy, Central Banking, Supply of Money and Credit

End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on data-driven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptom-disease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats state-of-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.Comment: 8 pages, 5 figues, AAA