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The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies either adopt a two-step paradigm, which optimizes knowledge selection and response generation separately and may overlook the inherent correlation between these two tasks, or leverage conditional variational method to jointly optimize knowledge selection and response generation by employing an inference network. In this paper, we present an end-to-end learning framework, termed Sequential Posterior Inference (SPI), capable of se- lecting knowledge and generating dialogues by approximately sampling from the posterior distribution. Unlike other methods, SPI does not require the inference network or assume a simple geometry of the posterior distribution. This straightforward and intuitive inference procedure of SPI directly queries the response generation model, allowing for accurate knowledge selection and generation of faithful responses. In addition to modeling contributions, our experimental results on two common dialogue datasets (Wizard of Wikipedia and Holl-E) demonstrate that SPI outperforms previous strong baselines according to both automatic and human evaluation metrics. 

We use the conjugate angle of radial action (θ_R), the best representation of the orbital phase, to explore the “midplane,” “north branch,” “south branch,” and “Monoceros area” disk structures that were previously revealed in the LAMOST K giants. The former three substructures, identified by their 3D kinematical distributions, have been shown to be projections of the phase space spiral (resulting from nonequilibrium phase mixing). In this work, we find that all of these substructures associated with the phase spiral show high aggregation in conjugate angle phase space, indicating that the clumping in conjugate angle space is a feature of ongoing, incomplete phase mixing. We do not find theZ–V_Zphase spiral located in the “Monoceros area,” but we do find a very highly concentrated substructure in the quadrant of conjugate angle space with the orbital phase from the apocenter to the guiding radius. The existence of the clump in conjugate angle space provides a complementary way to connect the “Monoceros area” with the direct response to a perturbation from a significant gravitationally interactive event. Using test particle simulations, we show that these features are analogous to disturbances caused by the impact of the last passage of the Sagittarius dwarf spheroidal galaxy.

 

The observation of X-rays during quiescence from transiently accreting neutron stars provides unique clues about the nature of dense matter. This, however, requires extensive modeling of the crusts and matching the results to observations. The pycnonuclear fusion reaction rates implemented in these models are theoretically calculated by extending phenomenological expressions and have large uncertainties spanning many orders of magnitude. We present the first sensitivity studies of these pycnonuclear fusion reactions in realistic network calculations. We also couple the reaction network with the thermal evolution codedStarto further study their impact on the neutron star cooling curves in quiescence. Varying the pycnonuclear fusion reaction rates alters the depth at which nuclear heat is deposited although the total heating remains constant. The enhancement of the pycnonuclear fusion reaction rates leads to an overall shallower deposition of nuclear heat. The impurity factors are also altered depending on the type of ashes deposited on the crust. These total changes correspond to a variation of up to 9 eV in the modeled cooling curves. While this is not sufficient to explain the shallow heat source, it is comparable to the observational uncertainties and can still be important for modeling the neutron star crust.

 

Social chatbots are designed to build emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversation between a hypothetical chatbot and user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to post-observation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, thought these effects were small. Importantly, transparency appeared to have a larger effect in increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users. 

Train-induced vibrations act as potential powerful high-frequency source for imaging subsurface with higher resolution than typical ambient noise interferometry. In this study, we present results of seismic interferometry applied on three days of railroad traffic data recorded by an array of seismographs along a railway in Dublin, Ireland. Our virtual shot gathers show significant surface and body wave energy that could be used for seismic interferometry. Reflection sections obtained with our interferometry approaches applied on selected time windows of train-induced vibrations is consistent with nearby borehole data and an active seismic profile. The consistency of the results given by these approaches confirms that train-generated vibrations represent a valuable source of signal for high-resolution subsurface imaging. Furthermore, our results show spurious arrivals that are due to the train geometry and also the cross-correlation approach that needs consideration for body wave interferometry studies.