GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose

3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible results. To address this issue, we propose a novel solution, called GATOR, that contains an encoder of Graph-Aware Transformer (GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore these multiple relations. Specifically, GAT combines a GCN and a graph-aware self-attention in parallel to capture physical and hidden joint-joint relations. Furthermore, MDR models joint-vertex and vertex-vertex interactions to explore joint and vertex relations. Based on the clustering characteristics of vertex offset fields, MDR regresses the vertices by composing the predicted base motions. Extensive experiments show that GATOR achieves state-of-the-art performance on two challenging benchmarks.Comment: Accepted by ICASSP 202

Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video

Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based methods generally recover human mesh by estimating the complex pose and shape parameters from coupled image features, whose high complexity and low representation ability often result in inconsistent pose motion and limited shape patterns. To alleviate this issue, we introduce 3D pose as the intermediary and propose a Pose and Mesh Co-Evolution network (PMCE) that decouples this task into two parts: 1) video-based 3D human pose estimation and 2) mesh vertices regression from the estimated 3D pose and temporal image feature. Specifically, we propose a two-stream encoder that estimates mid-frame 3D pose and extracts a temporal image feature from the input image sequence. In addition, we design a co-evolution decoder that performs pose and mesh interactions with the image-guided Adaptive Layer Normalization (AdaLN) to make pose and mesh fit the human body shape. Extensive experiments demonstrate that the proposed PMCE outperforms previous state-of-the-art methods in terms of both per-frame accuracy and temporal consistency on three benchmark datasets: 3DPW, Human3.6M, and MPI-INF-3DHP. Our code is available at https://github.com/kasvii/PMCE.Comment: Accepted by ICCV 2023. Project page: https://kasvii.github.io/PMC

Edge-guided Representation Learning for Underwater Object Detection

Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet's sustainable development. The main challenges of this task arise from low-contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. We observe that the edges of underwater objects are highly unique and can be distinguished from low-contrast or mimicry environments based on their edges. Motivated by this observation, we propose an Edge-guided Representation Learning Network, termed ERL-Net, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, we introduce an edge-guided attention module to model the explicit boundary information, which generates more discriminative features. Secondly, a feature aggregation module is proposed to aggregate the multi-scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognizing underwater objects. Finally, we propose a wide and asymmetric receptive field block to enable features to have a wider receptive field, allowing the model to focus on more small object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task

Tin-graphene tubes as anodes for lithium-ion batteries with high volumetric and gravimetric energy densities.

Limited by the size of microelectronics, as well as the space of electrical vehicles, there are tremendous demands for lithium-ion batteries with high volumetric energy densities. Current lithium-ion batteries, however, adopt graphite-based anodes with low tap density and gravimetric capacity, resulting in poor volumetric performance metric. Here, by encapsulating nanoparticles of metallic tin in mechanically robust graphene tubes, we show tin anodes with high volumetric and gravimetric capacities, high rate performance, and long cycling life. Pairing with a commercial cathode material LiNi0.6Mn0.2Co0.2O2, full cells exhibit a gravimetric and volumetric energy density of 590 W h Kg-1 and 1,252 W h L-1, respectively, the latter of which doubles that of the cell based on graphite anodes. This work provides an effective route towards lithium-ion batteries with high energy density for a broad range of applications

High-quality mesoporous graphene particles as high-energy and fast-charging anodes for lithium-ion batteries.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g-1 at 0.2 C or 440 mA h g-1 at 60 C with a mass loading of 1 mg cm-2), and excellent cycling stability (e.g., >99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm-2). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm-2 at 0.9 mA cm-2), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance

Edge-Mediated Skyrmion Chain and Its Collective Dynamics in a Confined Geometry

The emergence of a topologically nontrivial vortex-like magnetic structure, the magnetic skyrmion, has launched new concepts for memory devices. There, extensive studies have theoretically demonstrated the ability to encode information bits by using a chain of skyrmions in one-dimensional nanostripes. Here, we report the first experimental observation of the skyrmion chain in FeGe nanostripes by using high resolution Lorentz transmission electron microscopy. Under an applied field normal to the nanostripes plane, we observe that the helical ground states with distorted edge spins would evolves into individual skyrmions, which assemble in the form of chain at low field and move collectively into the center of nanostripes at elevated field. Such skyrmion chain survives even as the width of nanostripe is much larger than the single skyrmion size. These discovery demonstrates new way of skyrmion formation through the edge effect, and might, in the long term, shed light on the applications.Comment: 7 pages, 3 figure

Intrinsically Elastic Organic Semiconductors (IEOSs)

Elastic semiconductors are becoming more and more important to the development of flexible wearable electronic devices, which can be prepared by structural engineering design, blending, and the intrinsic elastification of organic semiconductors (intrinsically elastic organic semiconductor, IEOS). Compared with the elastic semiconductors prepared by structural engineering and blending, the IEOS prepared by organic synthesis has attracted numerous attentions for its solution processability and highly tunable chemical structures. For IEOSs, reasonable designs of synthetic routes and methods are the basis for realizing good mechanical and electrical properties. This brief review begins with a concise introduction of elastic semiconductors, then follows with several synthetic methods of IEOSs, and concludes the characteristics of each method, which provides guidance for the synthesis of IEOSs in the future. Furthermore, the properties of IEOSs are involved from the aspects of electrical, mechanical properties, and the applications of the IEOSs in elastic electronic devices. Finally, the challenge and an outlook which IEOSs are facing are presented in conclusion

Predicting Groundwater PFOA Exposure Risks with Bayesian Networks: Empirical Impact of Data Preprocessing on Model Performance

The plethora of data on PFASs in environmental samples collected in response to growing concern about these chemicals could enable the training of machine-learning models for predicting exposure risks. However, differences in sampling and analysis methods across data sets must be reconciled through data preprocessing, and little information is available about how such manipulations affect the resulting models. This study evaluates how data preprocessing influences machine-learned Bayesian network models of PFOA in groundwater. We link 19 years of PFOA measurements from Minnesota, USA, to publicly available information about potential PFOA sources and factors that may influence their environmental fate. Nine different preprocessing methods were tested, and the resulting data sets were used to train models to predict the probability of PFOA ≥ 35 ppt, the 2017 Minnesota health advisory level. Different preprocessing approaches produced varying model structures with significantly different accuracies. Nonetheless, models showed similar relationships between predictor variables and PFOA exposure risks, and all models were relatively accurate, distinguishing wells at high risk from those at low risk for 82.0% to 89.0% of test data samples. There was a trade-off between data quality and model performance since a stricter data screening strategy decreased the sample size for model training

Agricultural Greenhouse Gas Emissions in a Data-Scarce Region Using a Scenario-Based Modeling Approach: A Case Study in Southeastern USA

Climate change may impact agricultural greenhouse gas emissions (GHGs) and yields under higher temperatures, higher atmospheric CO2 concentrations, and variable precipitations. This calls for adaptation strategies to optimize agricultural productions with minimal GHGs. This study aimed to identify these optimum agricultural managements in response to current and projected climatic scenarios for the Choctawhatchee Basin in Southeastern USA, an experimentally unexplored data-scarce region lacking validation data. This scenario-based modeling study analyzed a total of 1344 scenarios consisting of four major crops, eight managements (varying tillage, manuring, and residue), and forty climatic combinations under current as wells as two representative concentration pathways with process-based Denitrification and Decomposition (DNDC) model. The results indicated that the region’s GHGs and yields were most affected by higher temperatures (≥+3 °C) and extreme precipitation changes (≥±40%), while high atmospheric CO2 concentrations exerted positive fertilization effects. The manure-related and higher residue incorporation scenarios were found to be better options in varying climates with minimal present global warming potentials (GWP) of 0.23 k to −29.1 k MT equivalent CO2. As such, the study presented climate change impacts and potential mitigation options in the study region while presenting a framework to design GHG mitigation in similar data-scarce regions

Influence of Oblique Sputtering on Stripe Magnetic Domain Structure and Magnetic Anisotropy of CoFeB Thin Films

Magnetic anisotropy is one of the most important fundamental properties of magnetic thin film. The strength of magnetic anisotropy determines the ferromagnetic resonance frequency of magnetic films in the high-frequency applications. Because of the directionality of conventional static magnetic anisotropy in magnetic film, the high-frequency device usually shows an obvious directionality. When the microwave magnetic fi eld deviates from the perpendicular direction of magnetic anisotropy, the devices cannot reveal their best performance. The magnetic film with a stripe magnetic domain structure displays an in-plane rotatable magnetic anisotropy, which can be an important strategy to solve the problem of magnetic fi eld orientation dependent performance in high-frequency device. Therefore, the magnetic domain, the magnetic anisotropy, and the high-frequency behaviors for magnetic fi lms with a stripe magnetic domain structure have received extensive attention. Previously, most of the studies focused on the stripe magnetic domain structure of polycrystalline thin films. However, less attention was paid on amorphous magnetic thin films. Since the amorphous magnetic films have no long-range ordered crystal structure, no magnetocrystalline anisotropy, no grain boundary defects resistance hindering the domain wall displacement, they usually show excellent soft magnetic properties and have been widely applied in high-frequency devices. CoFeB alloy is one of the most important amorphous magnetic materials and has been extensively applied in various spintronic devices. In this work, amorphous CoFeB magnetic thin films were prepared by using a method of oblique sputtering technique at room temperature. The influences of oblique sputtering on the stripe magnetic domain structure, the in-plane static magnetic anisotropy, the in-plane rotational magnetic anisotropy, and the perpendicular magnetic anisotropy of the amorphous CoFeB films were studied by scanning probe microscope, vibrating sample magnetometer, ferromagnetic resonance. It is found that the method of oblique sputtering could effectively reduce the critical thickness for the appearance of stripe magnetic domain in amorphous CoFeB films. For a non-oblique sputtered CoFeB film, the critical thickness for the appearance of the stripe magnetic domain is above 240 nm. In contrast, after been subjected to the oblique sputtering, the critical thickness becomes below 240 nm. The different magnetic characterizations indicate that for the growth of CoFeB films with stripe magnetic domain structure, the oblique sputtering could not only enhance the strength of in-plane static magnetic anisotropy, but also improve the in-plane rotational magnetic anisotropy and the perpendicular magnetic anisotropy. All of the magnetic anisotropies are increased with the angle of oblique sputtering. The observation results of XRD and TEM prove that the prepared CoFeB thin films tend to amorphous structure. The characterization of SEM observation indicates that although the amorphous CoFeB films do not possess long-range ordered crystalline structure, they still could form a kind of columnar structure. The slanted columnar structure of CoFeB films could significantly increase the perpendicular magnetic anisotropy, thus lead to the appearance of stripe magnetic domain structure