Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases.

The goal of diagnosing the coronavirus disease 2019 (COVID-19) from suspected pneumonia cases, that is, recognizing COVID-19 from chest X-ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep-learning-based methods. (2) Many public COVID-19 data sets contain only a few images without fine-grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID-19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID-19. To address these issues, we propose a novel framework called Unsupervised Meta-Learning with Self-Knowledge Distillation to address the problem of differentiating COVID-19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self-knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods. Moreover, we construct a new COVID-19 pneumonia data set based on text mining, consisting of 2696 COVID-19 images (347 X-ray + 2349 CT), 10,155 images (9661 X-ray + 494 CT) about other types of pneumonia, and the fine-grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

A Novel Parallel Control Method for Optimal Consensus of Nonlinear Multiagent Systems.

This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.

Nano-mineral assemblages in mercury- and silver-contaminated soils: records of sequestration, transformation, and release of mercury- and silver-bearing nanoparticles.

Mercury-bearing nano-mineral assemblages (Hg-NMAs) are chemically and mineralogically heterogeneous, micrometer-sized aggregates of nanoparticles (NPs) found in contaminated soils and sediments. Although these NMAs control sequestration and release of Hg that is a global contaminant, our understanding is limited with respect to the conditions of different types of Hg-NMAs, the diversity of its minerals, the size distribution of its NPs and whether mineral replacement and alteration reactions in these NMAs result in the release of Hg-bearing NPs. For this purpose, Hg-NMAs in four sediment samples from the Guanajuato Mining District (GMD) in Mexico, a region that was polluted by Hg and silver (Ag) due to historical mining involving Hg amalgamation, are characterized at the micro- and nanoscale. Microscale examinations with SEM show that the majority of Hg-NMAs occurs in mineral surface coatings (MSC) and fillings in fractures within quartz grains and are enriched in Hg and sulfur (S) relative to Ag, and in S and selenium (Se) relative to chloride (Cl). Examinations at the nanoscale show that Hg-NMAs contain (a) residuals of the patio process such as amalgam phases and elemental Ag; (b) associations of Hg- and Ag-sulfide NPs with pyrite and marcasite; (c) associations of Hg- and Ag-sulfide NPs with goethite and clay minerals along the rims of the MSC. The latter minerals replaced the Fe-Si-rich matrix at high-water rock ratios most likely due to an increase in porosity during flooding of the Pastita River. Consequently, the rims are depleted in Hg-Ag-sulfide NPs relative to the unaltered Fe-Si-rich matrices indicating that changes in the physiochemical conditions of soils and sediments in the GMD can result in the release of Hg-Ag-bearing NPs. In this context, this study discusses whether release and dissolution of Hg-Ag-bearing NPs contribute to the recently observed elevated gaseous elemental Hg concentrations in the soil, interstitial air and ambient air, and to the fate and effects of Hg in local aquatic environments.

Homogeneous Adsorption of Multiple Potassiation Products of Red Phosphorus Anode toward Stable Potassium Storage.

Potassium ion batteries (PIBs) are a viable alternative to lithium-ion batteries for energy storage. Red phosphorus (RP) has attracted a great deal of interest as an anode for PIBs owing to its cheapness, ideal electrode potential, and high theoretical specific capacity. However, the direct preparation of phosphorus-carbon composites usually results in exposure of the RP to the exterior of the carbon layer, which can lead to the deactivation of the active material and the production of "dead phosphorus". Here, the advantage of the π-π bond conjugated structure and high catalytic activity of metal phthalocyanine (MPc) is used to prepare MPc@RP/C composites as a highly stable anode for PIBs. It is shown that the introduction of MPc greatly improves the uneven distribution of the carbon layer on RP, and thus improves the initial Coulombic efficiency (ICE) of PIBs (the ICE of FePc@RP/C is 75.5% relative to 62.9% of RP/C). The addition of MPc promotes the growth of solid electrolyte interphase with high mechanical strength, improving the cycle stability of PIBs (the discharge-specific capacity of FePc@RP/C is 411.9 mAh g-1 after 100 cycles at 0.05 A g-1). Besides, density functional theory theoretical calculations show that MPc exhibits homogeneous adsorption energies for multiple potassiation products, thereby improving the electrochemical reactivity of RP. The use of organic molecules with high electrocatalytic activity provides a universal approach for designing superior high-capacity, large-volume expansion anodes for PIBs.

Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces.

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the "6S" goals of parallel driving.

Exceptionally low mercury concentrations and fluxes from the 2021 and 2022 eruptions of Fagradalsfjall volcano, Iceland.

Mercury (Hg) is naturally released by volcanoes and geothermal systems, but the global flux from these natural sources is highly uncertain due to a lack of direct measurements and uncertainties with upscaling Hg/SO2 mass ratios to estimate Hg fluxes. The 2021 and 2022 eruptions of Fagradalsfjall volcano, southwest Iceland, provided an opportunity to measure Hg concentrations and fluxes from a hotspot/rift system using modern analytical techniques. We measured gaseous Hg and SO2 concentrations in the volcanic plume by near-source drone-based sampling and simultaneous downwind ground-based sampling. Mean Hg/SO2 was an order of magnitude higher at the downwind locations relative to near-source data. This was attributed to the elevated local background Hg at ground level (4.0 ng m-3) likely due to emissions from outgassing lava fields. The background-corrected plume Hg/SO2 mass ratio (5.6 × 10-8) therefore appeared conservative from the near-source to several hundred meters distant, which has important implications for the upscaling of volcanic Hg fluxes based on SO2 measurements. Using this ratio and the total SO2 flux from both eruptions, we estimate the total mass of gaseous Hg released from the 2021 and 2022 Fagradalsfjall eruptions was 46 ± 33 kg, equivalent to a flux of 0.23 ± 0.17 kg d-1. This is the lowest Hg flux estimate in the literature for active open-conduit volcanoes, which range from 0.6 to 12 kg d-1 for other hotspot/rift volcanoes, and 0.5-110 kg d-1 for arc volcanoes. Our results suggest that Icelandic volcanic systems are fed from an especially Hg-poor mantle. Furthermore, we demonstrate that the aerial near-source plume Hg measurement is feasible with a drone-based active sampling configuration that captures all gaseous and particulate Hg species, and recommend this as the preferred method for quantifying volcanic Hg emissions going forward.

Large mercury release from the Greenland Ice Sheet invalidated.

The major input of mercury (Hg) to the Arctic is normally ascribed to long-range transport of anthropogenic Hg emissions. Recently, alarming concentrations of Hg in meltwater from the Greenland Ice Sheet (GrIS) were reported with bedrock as the proposed source. Reported Hg concentrations were 100 to 1000 times higher than in known freshwater systems of Greenland, calling for independent validation of the extraordinary concentrations and conclusions. Here, we present measurements of Hg at 21 glacial outlets in West Greenland showing that extreme Hg concentrations cannot be reproduced. In contrast, we find that meltwater from below the GrIS is very low in Hg, has minor implications for the global Hg budget, and pose only a very limited risk for local communities and the natural environment of Greenland.

SASAN: Shape-Adaptive Set Abstraction Network for Point-Voxel 3D Object Detection.

Point-voxel 3D object detectors have achieved impressive performance in complex traffic scenes. However, they utilize the 3D sparse convolution (spconv) layers with fixed receptive fields, such as voxel-based detectors, and inherit the fixed sphere radius from point-based methods for generating the features of keypoints, which make them weak in adaptively modeling various geometrical deformations and sizes of real objects. To tackle this issue, we propose a shape-adaptive set abstraction network (SASAN) for point-voxel 3D object detection. First, the proposal and offset generation module is adopted to learn the coordinates and confidences of 3D proposals and shape-adaptive offsets of the certain number of offset points for each voxel. Meanwhile, an extra offset supervision task is employed to guide the learning of shifting values of offset points, aiming at motivating the predicted offsets to preferably adapt to the various shapes of objects. Then, the shape-adaptive set abstraction module is proposed to extract multiscale keypoints features by grouping the neighboring offset points' features, as well as features learned from adjacent raw points and the 2-D bird-view map. Finally, the region of interest (RoI)-grid proposal refinement module is used to aggregate the keypoints features for further proposal refinement and confidence prediction. Extensive experiments on the competitive KITTI 3D detection benchmark demonstrate that the proposed SASAN gains superior performance as compared with state-of-the-art methods.