Conversational information access is an emerging research area. Currently, human evaluation is used for end-to-end system evaluation, which is both very time and resource intensive at scale, and thus becomes a bottleneck of progress. As an alternative, we propose automated evaluation by means of simulating users. Our user simulator aims to generate responses that a real human would give by considering both individual preferences and the general flow of interaction with the system. We evaluate our simulation approach on an item recommendation task by comparing three existing conversational recommender systems. We show that preference modeling and task-specific interaction models both contribute to more realistic simulations, and can help achieve high correlation between automatic evaluation measures and manual human assessments. CCS CONCEPTS• Information systems → Users and interactive retrieval; Recommender systems; • Human-centered computing → HCI design and evaluation methods;

Asymmetric total synthesis of TAN-1085 via Pd-catalyzed atroposelective C−H olefination is described. This synthesis features the gram-scale construction of axially chiral biaryls in an enantiopure form employing the readily available Ltert-leucine as the chiral transient auxiliary. The synthetic approach might provide a unified strategy for the total synthesis of natural products containing trans-9,10-dihydrophenanthrene-9,10-diol motifs.

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In permanent-magnet synchronous machine (PMSM) applications, traditional deadbeat predictive current control (DPCC) utilizes the PMSM model to evaluate the expected voltage vector and applies it to the inverter through space vector pulse width modulation (SVPWM). Once the expected voltage vector is inaccurate, the torque ripple and speed fluctuation are amplified. There are two main factors that cause the inaccurate voltage vector, namely model parameter mismatch, and current measurement error. To enhance the robustness of DPCC, first, this paper proposes an accurate PMSM voltage model with nonperiodic and periodic disturbance models. Second, this paper proposes a novel current and disturbance observer (NCDO) which is able to predict future stator currents and disturbances caused by model parameter mismatch and current measurement error simultaneously. Finally, the scheme of the proposed DPCC with NCDO is presented to enhance the robustness. This paper presents a comparative study of two types of algorithms, namely traditional DPCC and the proposed DPCC with NCDO. The theoretical verification, simulation results, and experimental results are demonstrated to verify the effectiveness of the proposed DPCC with NCDO.INDEX TERMS Permanent-magnet synchronous machine (PMSM), deadbeat predictive current control (DPCC), iterative learning control (ILC), sliding-mode control (SMC).

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