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A prerequisite for social coordination is bidirectional communication between teammates, each playing two roles simultaneously: as receptive listeners and expressive speakers. For robots working with humans in complex situations with multiple goals that differ in importance, failure to fulfill the expectation of either role could undermine group performance due to misalignment of values between humans and robots. Specifically, a robot needs to serve as an effective listener to infer human users’ intents from instructions and feedback and as an expressive speaker to explain its decision processes to users. Here, we investigate how to foster effective bidirectional human-robot communications in the context of value alignment—collaborative robots and users form an aligned understanding of the importance of possible task goals. We propose an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, our XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, the system simulates human mental dynamics and predicts optimal explanations using graphical models. We conducted psychological experiments to examine the core components of the proposed computational framework. Our results show that real-time human-robot mutual understanding in complex cooperative tasks is achievable with a learning model based on bidirectional communication. We believe that this interaction framework can shed light on bidirectional value alignment in communicative XAI systems and, more broadly, in future human-machine teaming systems. 

Since its publication, the authors of Wang et al. (2021) have brought to our attention an error in their article. A grant awarded by the National Science Foundation (grant no. MCB 1817985) to author Elizabeth Vierling was omitted from the Acknowledgements section. The correct Acknowledgements section is shown below. Acknowledgements We thank Suiwen Hou (Lanzhou University) and Zhaojun Ding (Shandong University) for providing the seeds used in this study. We thank Xiaoping Gou (Lanzhou University) and Ravishankar Palanivelu (University of Arizona) for critically reading the manuscript and for suggestions regarding the article. This work was supported by grants from National Natural Science Foundation of China (31870298) to SX, the US Department of Agriculture (USDA-CSREES-NRI-001030) and the National Science Foundation (MCB 1817985) to EV, and the Youth 1000-Talent Program of China (A279021801) to LY. 

In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher’s instruction mechanism, can infer the teacher’s intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn’t been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher’s cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers. 

This paper details the first application of a software tagging algorithm to reduce radon-induced backgrounds in liquid noble element time projection chambers, such as XENON1T and XENONnT. The convection velocity field in XENON1T was mapped out using ${}^{222}\mathrm{Rn}$and ${}^{218}\mathrm{Po}$events, and the rms convection speed was measured to be $0.30\pm 0.01\mathrm{cm}/\mathrm{s}$. Given this velocity field, ${}^{214}\mathrm{Pb}$background events can be tagged when they are followed by ${}^{214}\mathrm{Bi}$and ${}^{214}\mathrm{Po}$decays, or preceded by ${}^{218}\mathrm{Po}$decays. This was achieved by evolving a point cloud in the direction of a measured convection velocity field, and searching for ${}^{214}\mathrm{Bi}$and ${}^{214}\mathrm{Po}$decays or ${}^{218}\mathrm{Po}$decays within a volume defined by the point cloud. In XENON1T, this tagging system achieved a ${}^{214}\mathrm{Pb}$background reduction of ${6.2}_{-0.9}^{+0.4}\%$with an exposure loss of $1.8\pm 0.2\%$, despite the timescales of convection being smaller than the relevant decay times. We show that the performance can be improved in XENONnT, and that the performance of such a software-tagging approach can be expected to be further improved in a diffusion-limited scenario. Finally, a similar method might be useful to tag the cosmogenic ${}^{137}\mathrm{Xe}$background, which is relevant to the search for neutrinoless double-beta decay. Published by the American Physical Society2024 

In this work, we expand on the XENON1T nuclear recoil searches to study the individual signals of dark matter interactions from operators up to dimension eight in a chiral effective field theory (ChEFT) and a model of inelastic dark matter (iDM). We analyze data from two science runs of the XENON1T detector totaling $1\mathrm{t}\times \mathrm{yr}$exposure. For these analyses, we extended the region of interest from $[4.9,40.9]{\mathrm{keV}}_{\mathrm{NR}}$to $[4.9,54.4]{\mathrm{keV}}_{\mathrm{NR}}$to enhance our sensitivity for signals that peak at nonzero energies. We show that the data are consistent with the background-only hypothesis, with a small background overfluctuation observed peaking between 20 and $50{\mathrm{keV}}_{\mathrm{NR}}$, resulting in a maximum local discovery significance of $1.7\sigma$for the $\mathrm{Vector}\otimes {\text{Vector}}_{\text{strange}}$ChEFT channel for a dark matter particle of $70\mathrm{GeV}/c^2$and $1.8\sigma$for an iDM particle of $50\mathrm{GeV}/c^2$with a mass splitting of $100\mathrm{keV}/c^2$. For each model, we report 90% confidence level upper limits. We also report upper limits on three benchmark models of dark matter interaction using ChEFT where we investigate the effect of isospin-breaking interactions. We observe rate-driven cancellations in regions of the isospin-breaking couplings, leading to up to 6 orders of magnitude weaker upper limits with respect to the isospin-conserving case. Published by the American Physical Society2024 

We report a search for a heavy neutral lepton (HNL) that mixes predominantly with ${\nu }_{\tau }$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy $e^{+}{e}^{-}$collider. The data sample was collected at and just below the center-of-mass energies of the $\mathrm{\Upsilon }\left(4S\right)$and $\mathrm{\Upsilon }\left(5S\right)$resonances and has an integrated luminosity of $915{\mathrm{fb}}^{-1}$, corresponding to $(836\pm 12)\times {10}^6$ $e^{+}{e}^{-}\to {\tau }^{+}{\tau }^{-}$events. We search for production of the HNL (denoted $N$) in the decay ${\tau }^{-}\to {\pi }^{-}N$followed by its decay via $N\to {\mu }^{+}{\mu }^{-}{\nu }_{\tau }$. The search focuses on the parameter-space region in which the HNL is long-lived, so that the ${\mu }^{+}{\mu }^{-}$originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the $\tau$neutrino as a function of the HNL mass. Published by the American Physical Society2024 

Abstract The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run. 

Abstract The precision in reconstructing events detected in a dual-phase time projection chamber depends on an homogeneous and well understood electric field within the liquid target. In the XENONnT TPC the field homogeneity is achieved through a double-array field cage, consisting of two nested arrays of field shaping rings connected by an easily accessible resistor chain. Rather than being connected to the gate electrode, the topmost field shaping ring is independently biased, adding a degree of freedom to tune the electric field during operation. Two-dimensional finite element simulations were used to optimize the field cage, as well as its operation. Simulation results were compared to$${}^{83\textrm{m}}\hbox {Kr }$$ ${}^{83\text{m}}\text{Kr}$calibration data. This comparison indicates an accumulation of charge on the panels of the TPC which is constant over time, as no evolution of the reconstructed position distribution of events is observed. The simulated electric field was then used to correct the charge signal for the field dependence of the charge yield. This correction resolves the inconsistent measurement of the drift electron lifetime when using different calibrations sources and different field cage tuning voltages.