Few-shot Domain Adaptation for IMU Denoising

Different application scenarios will cause IMU to exhibit different error characteristics which will cause trouble to robot application. However, most data processing methods need to be designed for specific scenario. To solve this problem, we propose a few-shot domain adaptation method. In this work, a domain adaptation framework is considered for denoising the IMU, a reconstitution loss is designed to improve domain adaptability. In addition, in order to further improve the adaptability in the case of limited data, a few-shot training strategy is adopted. In the experiment, we quantify our method on two datasets (EuRoC and TUM-VI) and two real robots (car and quadruped robot) with three different precision IMUs. According to the experimental results, the adaptability of our framework is verified by t-SNE. In orientation results, our proposed method shows the great denoising performance

Effects of Coronal Magnetic Field Configuration on Particle Acceleration and Release during the Ground Level Enhancement Events in Solar Cycle 24

Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, by a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic field configuration near active regions on particle acceleration and release. Although the active regions both located near the western limb, temporal variations of SEP intensities and energy spectra measured in-situ display different behaviors at early stages. By combining a potential field model, we find the CME in GLE 71 originated below the streamer belt, while in GLE 72 near the edge of the streamer belt. We reconstruct the CME shock fronts with an ellipsoid model based on nearly simultaneous coronagraph images from multi-viewpoints, and further derive the 3D shock geometry at the GLE onset. The highest-energy particles are primarily accelerated in the shock-streamer interaction regions, i.e., likely at the nose of the shock in GLE 71 and the eastern flank in GLE 72, due to quasi-perpendicular shock geometry and confinement of closed fields. Subsequently, they are released to the field lines connecting to near-Earth spacecraft when the shocks move through the streamer cusp region. This suggests that magnetic structures in the corona, especially shock-streamer interactions, may have played an important role in the acceleration and release of the highest-energy particles in the two events.Comment: Accepted for publication in Ap

Quantum chemical calculation study on the thermal decomposition of electrolyte during lithium-ion battery thermal runaway

Understanding the behavior of lithium-ion battery electrolytes during thermal runaway is essential for designing safer batteries. However, current reports on electrolyte decomposition behaviors often focus on reactions with electrode materials. Herein we use quantum chemical calculations to develop a model for the thermal decomposition mechanism of electrolytes under both electrolyte and ambient atmosphere conditions. The thermal stability is found to be associated with the dielectric constants of electrolyte constituents. Within the electrolyte, the solvation effects between molecules increase electrolyte stability, making thermal decomposition a more difficult process. Furthermore, Li+ is observed to facilitate electrolyte thermal decomposition, as the energy required for the thermal decomposition reactions of molecules decreases when they are bonded with Li+. It is hoped that this study will offer a theoretical basis for understanding the complex reactions occurring during thermal runaway events

Causal association of blood cell traits with inflammatory bowel diseases: a Mendelian randomization study

BackgroundObservational studies have found associations between blood cell traits and inflammatory bowel diseases (IBDs), whereas the causality and dose-effect relationships are still undetermined.MethodsTwo-sample Mendelian randomization (MR) analyses using linear regression approaches, as well as Bayesian model averaging (MR-BMA), were conducted to identify and prioritize the causal blood cell traits for Crohn’s disease (CD) and ulcerative colitis (UC). An observational study was also performed using restricted cubic spline (RCS) to explore the relationship between important blood cell traits and IBDs.ResultsOur uvMR analysis using the random effects inverse variance weighted (IVW) method identified eosinophil (EOS) as a causal factor for UC (OR = 1.36; 95% CI: 1.13, 1.63). Our MR-BMA analysis further prioritized that high level of lymphocyte (LYM) decreased CD risk (MIP = 0.307; θ^MACE = −0.059; PP = 0.189; θ^λ = −0.173), whereas high level of EOS increased UC risk (MIP = 0.824; θ^MACE = 0.198; PP = 0.627; θ^λ = 0.239). Furthermore, the observational study clearly depicts the nonlinear relationship between important blood cell traits and the risk of IBDs.ConclusionUsing MR approaches, several blood cell traits were identified as risk factors of CD and UC, which could be used as potential targets for the management of IBDs. Stratified genome-wide association studies (GWASs) based on the concentration of traits would be helpful owing to the nonlinear relationships between blood cell traits and IBDs, as demonstrated in our clinical observational study. Together, these findings could shed light on the clinical strategies applied to the management of CD and UC

“Island-bridge”-structured nanofluidic membranes for high-performance aqueous energy conversion and storage

The attainment of carbon neutrality requires the development of aqueous energy conversion and storage devices. However, these devices exhibit limited performance due to the permeability–selectivity trade-off of permselective membranes as core components. Herein, we report the application of a synergistic approach utilizing two-dimensional nanoribbons-entangled nanosheets to rationally balance the permeability and selectivity in permselective membranes. The nanoribbons and nanosheets can be self-assembled into a nanofluidic membrane with a distinctive “island-bridge” configuration, where the nanosheets serve as isolated islands offering adequate ionic selectivity owing to their high surface charge density, meanwhile bridge-like nanoribbons with low surface charge density but high aspect ratio remarkably enhance the membrane’s permeability and water stability, as verified by molecular simulations and experimental investigations. Using this approach, we developed a high-performance graphene oxide (GO) nanosheet/GO nanoribbon (GONR) nanofluidic membrane and achieved an ultrahigh power density of 18.1 W m–2 in a natural seawater|river water osmotic power generator, along with a high Coulombic efficiency and an extended lifespan in zinc metal batteries. The validity of our island-bridge structural design is also demonstrated for other nanosheet/nanoribbon composite membranes, providing a promising path for developing reliable aqueous energy conversion and storage devices

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io