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In this work, we introduce SEESys, the first system to provide online pose error estimation for Simultaneous Localization and Mapping (SLAM). Unlike prior offline error estimation approaches, the SEESys framework efficiently collects real-time system features and delivers accurate pose error magnitude estimates with low latency. This enables real-time quality-of-service information for downstream applications. To achieve this goal, we develop a SLAM system run-time status monitor (RTS monitor) that performs feature collection with minimal overhead, along with a multi-modality attention-based Deep SLAM Error Estimator (DeepSEE) for error estimation. We train and evaluate SEESys using both public SLAM benchmarks and a diverse set of synthetic datasets, achieving an RMSE of 0.235 cm of pose error estimation, which is 15.8% lower than the baseline. Additionally, we conduct a case study showcasing SEESys in a real-world scenario, where it is applied to a real-time audio error advisory system for human operators of a SLAM-enabled device. The results demonstrate that SEESys provides error estimates with an average end-to-end latency of 37.3 ms, and the audio error advisory reduces pose tracking error by 25%. 

Possessing a unique combination of properties that are traditionally contradictory in other natural or synthetical materials, Ga-based liquid metals (LMs) exhibit low mechanical stiffness and flowability like a liquid, with good electrical and thermal conductivity like metal, as well as good biocompatibility and room-temperature phase transformation. These remarkable properties have paved the way for the development of novel reconfigurable or stretchable electronics and devices. Despite these outstanding properties, the easy oxidation, high surface tension, and low rheological viscosity of LMs have presented formidable challenges in high-resolution patterning. To address this challenge, various surface modifications or additives have been employed to tailor the oxidation state, viscosity, and patterning capability of LMs. One effective approach for LM patterning is breaking down LMs into microparticles known as liquid metal particles (LMPs). This facilitates LM patterning using conventional techniques such as stencil, screening, or inkjet printing. Judiciously formulated photo-curable LMP inks or the introduction of an adhesive seed layer combined with a modified lift-off process further provide the micrometer-level LM patterns. Incorporating porous and adhesive substrates in LM-based electronics allows direct interfacing with the skin for robust and long-term monitoring of physiological signals. Combined with self-healing polymers in the form of substrates or composites, LM-based electronics can provide mechanical-robust devices to heal after damage for working in harsh environments. This review provides the latest advances in LM-based composites, fabrication methods, and their novel and unique applications in stretchable or reconfigurable sensors and resulting integrated systems. It is believed that the advancements in LM-based material preparation and high-resolution techniques have opened up opportunities for customized designs of LM-based stretchable sensors, as well as multifunctional, reconfigurable, highly integrated, and even standalone systems. 

We present details on a new measurement of the muon magnetic anomaly, $a_{\mu }=(g_{\mu }-2)/2$. The result is based on positive muon data taken at Fermilab’s Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1\mathrm{GeV}/c$polarized muons stored in a 7.1-m-radius storage ring with a 1.45 T uniform magnetic field. The value of $a_{\mu }$is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using nuclear magnetic resonance. The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_{\mu }=116592057\left(25\right)\times {10}^{-11}$(0.21 ppm). This is the world’s most precise measurement of this quantity and represents a factor of 2.2 improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_{\mu }\left(\mathrm{FNAL}\right)=116592055\left(24\right)\times {10}^{-11}$(0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_{\mu }\left(\exp \right)=116592059\left(22\right)\times {10}^{-11}$(0.19 ppm). Published by the American Physical Society2024 

This Letter reports the first measurement of the oscillation amplitude and frequency of reactor antineutrinos at Daya Bay via neutron capture on hydrogen using 1958 days of data. With over 3.6 million signal candidates, an optimized candidate selection, improved treatment of backgrounds and efficiencies, refined energy calibration, and an energy response model for the capture-on-hydrogen sensitive region, the relative ${\overline{\nu }}_e$rates and energy spectra variation among the near and far detectors gives ${\sin }^{2}2{\theta }_{13}=0.0759_{-0.0049}^{+0.0050}$and $\mathrm{\Delta }{m}_{32}^2=\left(2.72_{-0.15}^{+0.14}\right)\times {10}^{-3}{\mathrm{eV}}^2$assuming the normal neutrino mass ordering, and $\mathrm{\Delta }{m}_{32}^2=(-2.83_{-0.14}^{+0.15})\times {10}^{-3}{\mathrm{eV}}^2$for the inverted neutrino mass ordering. This estimate of ${\sin }^{2}2{\theta }_{13}$is consistent with and essentially independent from the one obtained using the capture-on-gadolinium sample at Daya Bay. The combination of these two results yields ${\sin }^{2}2{\theta }_{13}=0.0833\pm 0.0022$, which represents an 8% relative improvement in precision regarding the Daya Bay full 3158-day capture-on-gadolinium result. Published by the American Physical Society2024 

We present a new measurement of the positive muon magnetic anomaly, 𝑎𝜇≡(𝑔𝜇−2)/2, from the Fermilab Muon 𝑔−2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, 𝜔𝑝, and of the anomalous precession frequency corrected for beam dynamics effects, 𝜔𝑎. From the ratio 𝜔𝑎/𝜔𝑝, together with precisely determined external parameters, we determine 𝑎𝜇=116 592 057⁢(25)×10−11 (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain 𝑎𝜇⁡(FNAL)=116 592 055⁢(24)×10−11 (0.20 ppm). The new experimental world average is 𝑎𝜇⁡(exp)=116 592 059⁢(22)×10−11 (0.19 ppm), which represents a factor of 2 improvement in precision.