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Abstract Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) can tag neutrons via their capture on gadolinium or hydrogen, which release$$\gamma $$ $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the first results of the XENONnT NV when operated with demineralized water only, before the insertion of gadolinium. Its efficiency for detecting neutrons is$$({82\pm 1}){\%}$$ $\left(82\pm 1\right)\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of$$({53\pm 3}){\%}$$ $\left(53\pm 3\right)\%$for the tagging of WIMP-like neutron signals, inside a tagging time window of$${250}~{\upmu }\hbox {s}$$ $250\mu \text{s}$between TPC and NV, leading to a livetime loss of$${1.6}{\%}$$ $1.6\%$during the first science run of XENONnT. 

We report measurements of production cross sections for ${\rho }^{+}$, ${\rho }^0$, $\omega$, $K^{*+}$, $K^{*0}$, $\varphi$, $\eta$, $K_S^0$, $f_0\left(980\right)$, $D^{+}$, $D^0$, $D_s^{+}$, $D^{*+}$, $D^{*0}$, and $D_s^{*+}$in $e^{+}{e}^{-}$collisions at a center-of-mass energy near 10.58 GeV. The data were recorded by the Belle experiment, consisting of $571{\mathrm{fb}}^{-1}$at 10.58 GeV and $74{\mathrm{fb}}^{-1}$at 10.52 GeV. Production cross sections are extracted as a function of the fractional hadron momentum $x_p$. The measurements are compared to Monte Carlo generator predictions with various fragmentation settings, including those that have increased fragmentation into vector mesons over pseudoscalar mesons. The cross sections measured for light hadrons are consistent with no additional increase of vector over pseudoscalar mesons. The charmed-meson cross sections are compared to earlier measurements—when available—including older Belle results, which they supersede. They are in agreement before application of an improved initial-state radiation correction procedure that causes slight changes in their $x_p$shapes. Published by the American Physical Society2025 

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. 

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics model dependent. We derive 90% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within $10–20\mathrm{MeV}/c^2$and electron absorption of axionlike particles and dark photons for $m_{\chi }$below $0.03\mathrm{keV}/c^2$. Published by the American Physical Society2025