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  1. 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. 
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  2. 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. 
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  3. 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. 
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  4. Free, publicly-accessible full text available July 1, 2024
  5. Abstract The XENONnT detector uses the latest and largest liquid xenon-based time projection chamber (TPC) operated by the XENON Collaboration, aimed at detecting Weakly Interacting Massive Particles and conducting other rare event searches.The XENONnT data acquisition (DAQ) system constitutes an upgraded and expanded version of the XENON1T DAQ system.For its operation, it relies predominantly on commercially available hardware accompanied by open-source and custom-developed software.The three constituent subsystems of the XENONnT detector, the TPC (main detector), muon veto, and the newly introduced neutron veto, are integrated into a single DAQ, and can be operated both independently and as a unified system.In total, the DAQ digitizes the signals of 698 photomultiplier tubes (PMTs), of which 253 from the top PMT array of the TPC are digitized twice, at ×10 and ×0.5 gain.The DAQ for the most part is a triggerless system, reading out and storing every signal that exceeds the digitization thresholds.Custom-developed software is used to process the acquired data, making it available within ∼30 s for live data quality monitoring and online analyses.The entire system with all the three subsystems was successfully commissioned and has been operating continuously, comfortably withstanding readout rates that exceed ∼500 MB/s during calibration.Livetime during normal operation exceeds 99% and is ∼90% during most high-rate calibrations.The combined DAQ system has collected more than 2 PB of both calibration and science data during the commissioning of XENONnT and the first science run. 
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    Free, publicly-accessible full text available July 1, 2024
  6. Free, publicly-accessible full text available July 1, 2024
  7. Abstract A low-energy electronic recoil calibration of XENON1T, a dual-phase xenon time projection chamber, with an internal $${}^{37}$$ 37 Ar source was performed. This calibration source features a 35-day half-life and provides two mono-energetic lines at 2.82 keV and 0.27 keV. The photon yield and electron yield at 2.82 keV are measured to be ( $$32.3\,\pm \,0.3$$ 32.3 ± 0.3 ) photons/keV and ( $$40.6\,\pm \,0.5$$ 40.6 ± 0.5 ) electrons/keV, respectively, in agreement with other measurements and with NEST predictions. The electron yield at 0.27 keV is also measured and it is ( $$68.0^{+6.3}_{-3.7}$$ 68 . 0 - 3.7 + 6.3 ) electrons/keV. The $${}^{37}$$ 37 Ar calibration confirms that the detector is well-understood in the energy region close to the detection threshold, with the 2.82 keV line reconstructed at ( $$2.83\,\pm \,0.02$$ 2.83 ± 0.02 ) keV, which further validates the model used to interpret the low-energy electronic recoil excess previously reported by XENON1T. The ability to efficiently remove argon with cryogenic distillation after the calibration proves that $${}^{37}$$ 37 Ar can be considered as a regular calibration source for multi-tonne xenon detectors. 
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    Free, publicly-accessible full text available June 1, 2024
  8. Free, publicly-accessible full text available June 1, 2024
  9. A bstract Charged-lepton-flavor-violation is predicted in several new physics scenarios. We update the analysis of τ lepton decays into a light charged lepton ( ℓ = e ± or μ ± ) and a vector meson ( V 0 = ρ 0 , ϕ , ω , K *0 , or $$ \overline{K} $$ K ¯ *0 ) using 980 fb − 1 of data collected with the Belle detector at the KEKB collider. No significant excess of such signal events is observed, and thus 90% credibility level upper limits are set on the τ → ℓV 0 branching fractions in the range of (1.7–4 . 3) × 10 − 8 . These limits are improved by 30% on average from the previous results. 
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    Free, publicly-accessible full text available June 1, 2024