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The introduction of Zn improves the wettability and surface lithiophilicity of Cu foam toward Li metal, and a sublevel scaffold of Li x Zn alloy is constructed in the pores of Cu foam, regulating uniform Li deposition in the modified 3D skeleton. [Display omitted] Lithium (Li) metal anode (LMA) is one of the most promising anodes for high energy density batteries. However, its practical application is impeded by notorious dendrite growth and huge volume expansion. Although the three-dimensional (3D) host can enhance the cycling stability of LMA, further improvements are still necessary to address the key factors limiting Li plating/stripping behavior. Herein, porous copper (Cu) foam (CF) is thermally infiltrated with molten Li-rich Li-zinc (Li-Zn) binary alloy (CFLZ) with variable Li/Zn atomic ratio. In this process, the LiZn intermetallic compound phase self-assembles into a network of mixed electron/ion conductors that are distributed within the metallic Li phase matrix and this network acts as a sublevel skeleton architecture in the pores of CF, providing a more efficient and structured framework for the material. The as-prepared CFLZ composite anodes are systematically investigated to emphasize the roles of the tunable lithiophilicity and hierarchical structure of the frameworks. Meanwhile, a thin layer of Cu-Zn alloy with strong lithiophilicity covers the CF scaffold itself. The CFLZ with high Zn content facilitates uniform Li nucleation and deposition, thereby effectively suppressing Li dendrite growth and volume fluctuation. Consequently, the hierarchical and lithiophilic framework shows low Li nucleation overpotential and highly stable Coulombic efficiency (CE) for 200 cycles in conventional carbonate based electrolyte. The full cell coupled with LiFePO 4 (LFP) cathode demonstrates high cycle stability and rate performance. This work provides valuable insights into the design of advanced dendrite-free 3D LMA toward practical application. [ABSTRACT FROM AUTHOR]

The problem of lithium dendrite growth has persistently hindered the advancement of lithium metal batteries. Lithium phosphorus oxynitride (LiPON), functioning as an amorphous solid electrolyte, is extensively employed as an artificial solid electrolyte interphase (SEI) owing to its remarkable stability and mechanical strength, which is beneficial for effectively mitigating dendrite growth. Nevertheless, the significant challenge arises from the volume changes in the Li metal anode during cycling, leading to the vulnerability of LiPON due to its high rigidity, which impedes the widespread use of LiPON. To address this problem, our study introduces a lithium-boron (Li-B) alloy as the anode, featuring a 3D structure, which can be synergistic with the artificial LiPON layer during cycling, leading to a better performance. The average Coulombic efficiency (CE) of a Li || Cu half-cell reaches 95% over 120 cycles. The symmetric cells exhibit sustained operation for 950 h with a low voltage polarization of less than 20 mV under a current density of 0.5 mA/cm2 and for 410 h under 1 mA/cm2. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured. [ABSTRACT FROM AUTHOR]

The problem of lithium dendrite growth has persistently hindered the advancement of lithium metal batteries. Lithium phosphorus oxynitride (LiPON), functioning as an amorphous solid electrolyte, is extensively employed as an artificial solid electrolyte interphase (SEI) owing to its remarkable stability and mechanical strength, which is beneficial for effectively mitigating dendrite growth. Nevertheless, the significant challenge arises from the volume changes in the Li metal anode during cycling, leading to the vulnerability of LiPON due to its high rigidity, which impedes the widespread use of LiPON. To address this problem, our study introduces a lithium-boron (Li-B) alloy as the anode, featuring a 3D structure, which can be synergistic with the artificial LiPON layer during cycling, leading to a better performance. The average Coulombic efficiency (CE) of a Li || Cu half-cell reaches 95% over 120 cycles. The symmetric cells exhibit sustained operation for 950 h with a low voltage polarization of less than 20 mV under a current density of 0.5 mA/cm2 and for 410 h under 1 mA/cm2.

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Existing 3D skeleton-based action recognition approaches reach impressive performance by encoding handcrafted action features to image format and decoding by CNNs. However, such methods are limited in two ways: a) the handcrafted action features are difficult to handle challenging actions, and b) they generally require complex CNN models to improve action recognition accuracy, which usually occur heavy computational burden. To overcome these limitations, we introduce a novel AFE-CNN , which devotes to enhance the features of 3D skeleton-based actions to adapt to challenging actions. We propose feature enhance modules from key joint, bone vector, key frame and temporal perspectives, thus the AFE-CNN is more robust to camera views and body sizes variation, and significantly improve the recognition accuracy on challenging actions. Moreover, our AFE-CNN adopts a light-weight CNN model to decode images with action feature enhanced, which ensures a much lower computational burden than the state-of-the-art methods. We evaluate the AFE-CNN on three benchmark skeleton-based action datasets: NTU RGB + D, NTU RGB + D 120, and UTKinect-Action3D, with extensive experimental results demonstrate our outstanding performance of AFE-CNN. [ABSTRACT FROM AUTHOR]

The 3D structured Zn anode which is grown on the surface of carbon cloth/carbon nanofiber framework (CC-CNF) can reduce the nucleation overpotential of the electrode and suppress dendrites owing to the high conductivity and abundant nucleation sites of the 3D skeleton. [Display omitted] Zn-ion battery (ZIB) is a promising electrochemical energy storage device because of the high performance, safety, and economic benefits, etc. However, the formation of Zn dendrites at anodes is seriously depressed their cycling life, Coulombic efficiency, safety, and capacity. Inhibition of Zn dendrites is considered as an efficient solution to improving the performance of ZIB. Herein, we demonstrate a 3D structured Zn anode coated in carbon nanofiber framework with carbon cloth as substrate (Zn@CC-CNF), which has the abundant Zn nucleation sites and homogeneous electrical field distribution for uniform Zn deposition, low nucleation/deposition energy barrier, and the capacity of alleviating side reactions. Thus, the Zn@CC-CNF based symmetric cells is stability operated over 400 h, which is over 400% longer than that of bare Zn coated on carbon cloth (Zn@CC). The impressive performance of the Zn@CC-CNF anode also endows the assembled Zn-MnO 2 full cell with long-term cycling stability. Better yet, the intrinsic flexibility of the carbon substrate enables the fabrication of flexible Zn-MnO 2 cell with gratifying mechanical deformation tolerance, illustrating their great application potential in flexible energy storage. [ABSTRACT FROM AUTHOR]

[Display omitted] • A LiF-Li 3 N/LiN x O y -enriched SEI is prepared in situ using a strongly lithiophilic 3D carbon fiber/MXene/ZnS skeleton. • Highly lithophilic 3D skeletons and LiF-Li 3 N/LiN x O y -enriched SEI synergistically regulate lithium nucleation and deposition behaviour. • The mechanisms for Li nucleation and deposition are elucidated by electrochemical in situ characterization techniques. Metallic lithium (Li) is an excellent electrode material for high-energy-density batteries; however, the continuous growth of Li dendrites and the poor deposited volume consistency of Li severely influence the stability of Li anodes. Herein, a LiF-Li 3 N/LiN x O y -enriched SEI is prepared in situ using a strongly lithiophilic 3D carbon fiber/MXene/ZnS (CMZ) skeleton. Multiscale in situ and ex situ characterizations reveal the mechanisms for Li nucleation and growth on the CMZ skeleton, proving that Li grows tightly over CMZ fibers. This behaviour is attributed to the CMZ skeleton, which provides abundant atomic-level active sites for the uniform and dense nucleation of Li. Electrochemical tests confirm that SEI enriched with LiF-Li 3 N/LiN x O y can effectively improve Li deposition kinetics, ensure uniform mass transfer and rapid migration of lithium ions in the SEI, and guarantee uniform growth of Li. Consequently, a half cell with a Li-CMZ electrode delivers a high Coulombic efficiency (99.83 %), an ultralong cycling life (17,860 h), and an excellent rate performance (30 mA cm−2). Moreover, full cells consisting of a Li-CMZ anode and different cathodes exhibit superior cycling stabilities. For Li-CMZ ‖ LiFePO 4 pouch cell, 84 % capacity is maintained after 300 cycles at 1 C. [ABSTRACT FROM AUTHOR]

A new epoxy-based polymerizing/compositing synchronic process was carried out in 3D structure of a melamine sponge. The cured polymer formed a porous and wave-shaped coating on the surface of skeleton in a uniform way. The obtained composite sponge was used as a microflow support for the immobilization of phospholipase A 1 (PLA 1 , from Thermomyces lanuginosus/Fusarium oxysporum) by co-adsorption/cross-linking with polyethylenimine (PEI). The loading amount and the specific activity of immobilized PLA 1 under the optimal immobilization conditions were 45.7 mg/g support and 9.1 U/mg protein. The PLA 1 -immobilized microflow supports were placed in a columnar reactor, in which transesterification was operated in continuous circulation way. The reaction produced DHA/EPA-incorporated phosphatidylcholine, the hydrolysis side reaction could be inhibited by removal of free water in the reaction medium. Under optimized conditions the incorporation percentage of DHA/EPA and PC yield of 54.3% and 82.4% within 18 h at 55 ℃, respectively. After 10 cycles, the immobilized PLA 1 retained 71.2% of the original activity. The use of microflow support has an advantage on easier scaling up the reactor size towards much larger productivity and higher efficiency. • The novel microflow support showed advantages on easier scaling up, mass transfer and multiphase dispersion. • The immobilized PLA 1 catalyzed transesterification on microflow supports in a pump-driving columnar reactor. • Inhibition of hydrolysis optimized the reaction to produce DHA/EPA-rich phosphatidylcholine in high efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

Pose prediction is to predict future poses given a window of previous poses. In this paper, we propose a new problem that predicts poses using 3D positions of skeletal sequences.Different from the traditional pose prediction based on mocap frames, this problem is convenient to use in real applications due to its simple sensors to capture data. We also present a new framework, pseudo-image sequence evolution-based 3D pose prediction, to address this new problem. Specifically, a skeletal representation is proposed by transforming a 3D skeletal sequence into an image sequence, which can model different correlations among different joints. With this image-based skeletal representation, we model the pose prediction as the evolution of an image sequence. Moreover, a novel inference network is proposed to predict multiple future poses in a non-recursive manner using decoders with independent parameters. In contrast to the recursive sequence-to-sequence model, we can improve the computational efficiency and avoid error accumulations significantly. Extensive experiments are carried out on two benchmark datasets (e.g., G3D and FNTU). The proposed method achieves state-of-the-art performance on both datasets, which demonstrates the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In the field of body affect recognition, the majority of literature is based on experiments performed on datasets where trained actors simulate emotional reactions. These acted and unnatural expressions differ from the more challenging genuine emotions, thus leading to less valuable results. In this article, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided. The proposed work introduces three majors contributions. First, temporal local movements performed by subjects are examined frame-by-frame, unlike the current state-of-the-art in non-acted body affect recognition where only static or global body features are considered. Second, an original set of global and time-dependent features for body movement description is provided. Third, this is one of the first works to use deep learning methods in the current non-acted body affect recognition literature. Due to the novelty of the topic, only the UCLIC dataset is currently considered the benchmark for comparative tests. On the latter, the proposed method outperforms all the competitors. [ABSTRACT FROM AUTHOR]

Person re-identification (Re-ID) is a challenging and arduous task due to non-overlapping views, complex background, and uncontrollable occlusion in video surveillance. An existing method for capturing pedestrian local region information is to divide person regions into horizontal stripes, which may lead to invalid features and erroneous learning. To solve this problem, this paper proposes a 3D skeleton and a two-stream approach to person Re-ID. The first stream of the method uses the 3D skeleton for background filtering and region segmentation. The second stream uses Siamese net to extract the global descriptor. The features of the two streams are fused to preserve the integrity of the person. An optimized region matching method for metric learning is designed. Extensive comparing experiments were conducted with state-of-the-art Re-ID methods on the Market-1501, CUHK03, and DukeMTMC-reID datasets. Experimental results show that the proposed method outperforms the existing methods in recognition accuracy. [ABSTRACT FROM AUTHOR]

Although solid composite electrolytes show tremendous potential for the practical solid‐state lithium metal batteries, searching for a straightforward tactic to promote the ion conduction at electrolyte/electrode interface, especially settling lithium dendrites formation caused by the concentration gradient polarization, are still long‐standing problems. Here, the authors report a corrugated 3D nanowires‐bulk ceramic‐nanowires (NCN) skeleton reinforced composite electrolyte with regulated interfacial Li‐ion transport behavior. The special and integrated NCN skeleton endows the electrolyte with fast Li‐ion transfer and solves the Li+ concentration polarization at electrode/electrolyte interface, thereby eliminating the energy barrier originated from the redistribution of charge carriers and offering homogeneous interfacial Li‐ion flux on lithium anode. As a "double insurance", the bulk ceramic sheet in 3D framework enables the electrolyte to block the mobility of anions. The rational designed NCN composite electrolyte exhibits excellent ionic conductivity and the assembled all‐solid‐state battery possesses 90.2% capacity retention after 500 cycles. The proposed strategy affords a special insight in designing high‐performance solid composite electrolytes. [ABSTRACT FROM AUTHOR]

The practical application of the lithium anode in lithium metal batteries (LMBs) has been hindered by the uncontrollable growth of lithium dendrite and the high volumetric change during cycling. Herein, the in situ formed three-dimensional (3D) lithium–boron (Li–B) alloy is suggested as an excellent alternative to the Li metal, in which the 3D LiB skeleton can mitigate the growth of Li dendrites and volumetric change. In this study, the Li–B alloy anodes with different B contents were manufactured by high-temperature melting. It was found that the boron content had a significant effect on the electrochemical performance of the Li–B alloy. The Li–B alloy with the least B content (10 wt%, 10LiB) demonstrated the lowest overpotential of 0.0852 V after 300 h and the lowest interface resistance. However, the full cell with 15LiB as the anode displayed the best cycling performance of 115 mAh·g−1 after 100 cycles with a columbic efficiency greater than 97%. The obtained results suggest that the in situ formed three-dimensional Li–B alloy anode can be an excellent alternative to the Li anode via tuning B contents for next-generation high energy density LMBs. [ABSTRACT FROM AUTHOR]

Recent advances in terahertz (THz) absorbing materials and technology show futuristic potentials for practical applications in THz radars and telecommunications, stealth and shielding. However, the lack of versatile materials naturally working in this specific electromagnetic wave region with simultaneously featuring high absorption efficiencies, ultrabroad bandwidths, low‐costs, good stabilities, and flexibilities, is impeding the proliferation of real THz disruptive applications. Here a kind of flexible structure material, 3D nickel (Ni) skeleton, fabricated from an electroplating sintering method with irregular pore distribution makes possible the successful realization of a highly absorbing response for ultrabroadband THz waves due to the effective combination of both material and structural absorption mechanisms. 3D Ni skeletons with 90 ppi nonuniform pore‐size ranges enable >99% absorption capabilities in the frequency range of 0.5–2.0 THz independent on both the THz incidence angles and polarizations. Experimental validation of THz shielding implemented on both 100 GHz and 4.3 THz video imaging systems corroborates the highly efficient absorbing with frequency expansibility. Such capabilities are further verified on millimeter‐wave security checkers for 32–36 GHz. This prototypical demonstration lays the foundation for the next‐generation THz absorbing technology, accelerating advanced THz technologies toward practical applications. [ABSTRACT FROM AUTHOR]

Most automatic expression analysis systems attempt to recognize a conventional set of expressions such as happiness, sadness, anger, surprise and fear, etc. Although this set of expressions is the most typical of the face, it is not the most representative/relevant for what the body expressions tell us. This paper presents a novel and generic approach for the recognition of body expressions using human postures. Our method is based on the notion of neutral motion generated from a given expressive one. In a second time, we estimate a residue function, as the difference between the two associated motions, namely the expressive and the neutral motion. More precisely, this function that is inspired by studies from psychology domain, gives a “neutrality” score of a motion. Using this “neutrality score”, we propose a cost function which enables to synthesis the neutral motion from any input expressive motion. The synthesis of neutral motion process is based on two nested Principal Component Analysis providing a space where moving and selecting realistic human animations become possible. Proposed approach is evaluated on four databases with heterogeneous movements and body expressions and it achieved recognition results for body expression recognition that exceed state of the art.

Obesity is a growing health concern, mainly caused by poor dietary habits. Yet, accurately tracking the diet and food intake of individuals with obesity is challenging. Although 3D motion capture technology is becoming increasingly important in healthcare, its potential for detecting early signs of obesity has not been fully explored. In this research, we used a deep LSTM network trained with individual identity (identity-trained deep LSTM network) to analyze 3D time-series skeleton data from mouse models with diet-induced obesity. First, we analyzed the data from two different viewpoints: allocentric and egocentric. Second, we trained various deep recurrent networks (e.g., RNN, GRU, LSTM) to predict the identity. Lastly, we tested whether these models effectively encode obese-like motion representations by training a support vector classifier with the latent features from the last layer. Our experimental results indicate that the optimal performance is achieved when utilizing an identity-trained deep LSTM network in conjunction with an egocentric viewpoint. This approach suggests a new way to use deep learning to spot health risks in mouse models of obesity and should be useful for detecting early signs of obesity in humans.

To communicate with people, robots and vision-based interactive systems often need to understand human activities in advance before the activity is performed completely. This early prediction of the activities will help them take proper near future steps to fulfill a realistic interactive session with humans. However, predicting activities in advance is a very challenging task, because some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we propose a method capable of early prediction of simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We use skeletons captured from low-cost depth sensors as high-level descriptions of the human body. Using 3D skeletons, our method will be robust to the environmental factors. Our proposed model is a fully observed PGM coupled with a clustering scheme to remove the dependency of our model to the number-of-middle-states hyperparameter. We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D and obtain accuracies of 97.6% , 100% and 96.11%, respectively. These datasets cover both simple and complex activities. When only half of the clip is observed, we achieve 93.33% and 96.9% accuracy on CAD-60 and UT-Kinect datasets, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed. Several studies describe the various numerical methods to numerical modeling of vascular structures in near-reality. However, the current approaches remain too expensive in terms of storage capacity. Therefore, it is necessary to find the right balance between the relevance of information and storage space. This article adopts two sets of human retinal blood vessel data in 3D to proceed with data reduction in the first part and then via 3D fractal reconstruction, recreate them in a second part. The results show that the reduction rate obtained is between 66% and 95% as a function of the tolerance rate. Depending on the number of iterations used, the 3D blood vessel model is successful at reconstruction with an average error of 0.19 to 5.73 percent between the original picture and the reconstructed image. [ABSTRACT FROM AUTHOR]

In recent years, the fall detection system has become an important topic in the homecare system. Compared with the traditional fall detection algorithm, the method used by neural network is more robust and has higher accuracy. However neural network consumes a large amount of energy due to a huge number of computations, and needs more memory to store parameters as compared to traditional algorithms. In this paper, we propose a fall detection system in combination of the traditional algorithm with the neural network. First, we propose a skeleton information extraction algorithm, which transforms depth information into skeleton information and extracts the important joints related to fall activity. Also we have modified the skeleton-based method with seven highlight feature points. Second, we propose a highly robust deep convolution neural network architecture, which uses a pruning method to reduce parameters and calculations in the network. The low number of parameters and calculations makes the system suitable for the implementation on an embedded system. The experiment results show the high accuracy and robustness on the popular benchmark dataset NTU RGB+D. The proposed system has been implemented on NVIDIA Jetson Tx2 platform with real-time processing.

3D‐skeleton‐reinforced hybrid polymer electrolytes (HPEs) are conducive to improving the electrochemical performance and mechanical strength, but their preparation methods are usually tedious and inappropriate for practical application. The simplification of the preparation process and the further optimization of 3D skeletons are urgent to develop high‐performance solid‐state batteries. In this work, glass fiber (GF) decorated with ZIF‐67 (GF@ZIF‐67) was originally selected as 3D skeleton to construct an anion‐immobilized and fiber‐reinforced polyethylene oxide (PEO)‐based HPE (GF@ZIF@PEO). The presence of GF@ZIF‐67 3D skeleton can enhance the mechanical strength, whereas Lewis‐acidic metal sites of ZIF‐67 can immobilize free anions of the polymer matrix, accelerating ion migration and facilitating long‐term cycling stability. Consequently, the assembled LiFePO4/Li solid‐state batteries delivered a reversible capacity of 132 mAh g−1 with the capacity retention of 92 % at 1 C after 200 cycles. Our universal approach paves the way for advanced solid‐state electrolytes. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of 3D motion recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. We propose a new 3D motion recovery method using spatio-temporal reconstruction, which uses joint low-rank and sparse priors to exploit temporal correlation and an isometric constraint for spatial correlation. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the augmented Lagrangian method with a Gauss–Newton solver for the subproblem of isometric optimization. The experimental results on the CMU motion capture dataset, Edinburgh dataset, and two Kinect datasets demonstrate that the proposed approach achieves better motion recovery than the state-of-the-art methods. The proposed method is applicable to Kinect-like skeleton tracking devices and pose estimation methods that cannot provide accurate estimation of complex motions, especially in the presence of occlusion. [ABSTRACT FROM AUTHOR]

Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequences. However, this method still has some imperfections: only short-range correlations are appreciated, due to the limited receptive field of graph convolution. However, long-range dependence is essential for recognizing human action. In this work, we propose the use of a spatial-temporal relative transformer (ST-RT) to overcome these defects. Through introducing relay nodes, ST-RT avoids the transformer architecture, breaking the inherent skeleton topology in spatial and the order of skeleton sequence in temporal dimensions. Furthermore, we mine the dynamic information contained in motion at different scales. Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance. Extensive experiments evaluate the proposed methods on three benchmarks for skeleton-based action recognition: NTU RGB+D, NTU RGB+D 120 and UAV-Human. The results demonstrate that MSST-RT is on par with SOTA in terms of performance.

• A novel low-cost home-based training system dedicated to stroke survivors is introduced. • Our system is composed of two linked applications: therapist and patient applications. • The prescription is created and personalized in the therapist application. • A color-based visual feedback tool is proposed to guide the patients while training. Background and Objective: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist. Methods: A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion. Results: The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback. Conclusions: Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-stream 3D space-time convolutional neural network action recognition framework. The original depth map sequence data is set as the input in order to study the global space-time characteristics of each action category. The high correlation within the human action itself is considered in the time domain, and then the deep motion map sequence is introduced as the input to another stream of the 3D space-time convolutional network. Furthermore, the corresponding 3D skeleton sequence data is set as the third input of the whole recognition framework. Although the skeleton sequence data has the advantage of including 3D information, it is also confronted with the problems of the existence of rate change, temporal mismatch and noise. Thus, specially designed space-time features are applied to cope with these problems. The proposed methods allow the whole recognition system to fully exploit and utilize the discriminatory space-time features from different perspectives, and ultimately improve the classification accuracy of the system. Experimental results on different public 3D data sets illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

K metal battery is a kind of high-energy-density storage device with economic advantages. However, due to the dendrite growth and difficult processing characteristics, it is difficult to prepare stable K metal anode with thin thickness and fixed area capacity, which severely limits its development. In this work, a multi-functional 3D skeleton (rGCA) is synthesized by simple vacuum filtration and thermal reduction, and K metal anodes with controllable thickness and area capacity (K content) can be fabricated by changing the raw material mass and graphene layer spacing of rGCA. Moreover, the graphene sheet layer of rGCA can relax stress and relieve volume expansion; carbon nanotubes can serve as the fast transport channel of electrons, reducing internal impedance and local current density; Ag nanoparticles can induce the uniform nucleation and deposition of K + . The K metal composite anodes (rGCA-K) based on the conductive skeleton can effectively suppress dendrites and exhibit excellent electrochemical performance in symmetric and full cells. The controllable fabrication process of stable K metal anode is expected to help K metal batteries move toward the stage of commercial production., (© 2023 Wiley-VCH GmbH.)

Understanding 3D object structure from a single image is an important but challenging task in computer vision, mostly due to the lack of 3D object annotations to real images. Previous research tackled this problem by either searching for a 3D shape that best explains 2D annotations, or training purely on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Networks (3D-INN), an end-to-end trainable framework that sequentially estimates 2D keypoint heatmaps and 3D object skeletons and poses. Our system learns from both 2D-annotated real images and synthetic 3D data. This is made possible mainly by two technical innovations. First, heatmaps of 2D keypoints serve as an intermediate representation to connect real and synthetic data. 3D-INN is trained on real images to estimate 2D keypoint heatmaps from an input image; it then predicts 3D object structure from heatmaps using knowledge learned from synthetic 3D shapes. By doing so, 3D-INN benefits from the variation and abundance of synthetic 3D objects, without suffering from the domain difference between real and synthesized images, often due to imperfect rendering. Second, we propose a Projection Layer, mapping estimated 3D structure back to 2D. During training, it ensures 3D-INN to predict 3D structure whose projection is consistent with the 2D annotations to real images. Experiments show that the proposed system performs well on both 2D keypoint estimation and 3D structure recovery. We also demonstrate that the recovered 3D information has wide vision applications, such as image retrieval. [ABSTRACT FROM AUTHOR]

In this paper, we present an image classification approach to action recognition with 3D skeleton videos. First, we propose a video domain translation-scale invariant image mapping, which transforms the 3D skeleton videos to color images, namely skeleton images. Second, a multi-scale dilated convolutional neural network (CNN) is designed for the classification of the skeleton images. Our multi-scale dilated CNN model could effectively improve the frequency adaptiveness and exploit the discriminative temporal-spatial cues for the skeleton images. Even though the skeleton images are very different from natural images, we show that the fine-tuning strategy still works well. Furthermore, we propose different kinds of data augmentation strategies to improve the generalization and robustness of our method. Experimental results on popular benchmark datasets such as NTU RGB + D, UTD-MHAD, MSRC-12 and G3D demonstrate the superiority of our approach, which outperforms the state-of-the-art methods by a large margin. [ABSTRACT FROM AUTHOR]

Adding heat conducting particles to a polymer matrix to prepare thermally conductive and electrical insulation materials is an effective approach to address the safety issues arising from the accumulation of heat in the working process of electronic devices. In this work, thermally conductive and electrical insulation nano-paper, consisting of Boron Nitride nano-sheet (BNNS) and cellulose nanofiber (CNF), was prepared using an aerogel 3D skeleton template method. For comparison, BNNS/CNF nano-paper was also produced using a simple blending method. At a BNNS loading of 50 wt%, the thermal conductivity of BNNS/CNF aerogel nano-paper and blended nano-paper at 70 °C are 2.4 W/mK and 1.2 W/mK respectively, revealing an increase of 94.4%. Under similar conditions, the volume resistivity of BNNS/CNF aerogel nano-paper and blended nano-paper are 4.0 × 1014 and 4.2 × 1014 Ω·cm respectively. In view of its excellent thermal conductivity and electrical insulation performance, therefore, BNNS/CNF aerogel nano-paper holds great potential for electronic-related applications.

This paper proposes a dual-stream 3D space-time convolutional neural network action recognition framework. The original depth map sequence data is set as the input in order to study the global space-time characteristics of each action category. The high correlation within the human action itself is considered in the time domain, and then the deep motion map sequence is introduced as the input to another stream of the 3D space-time convolutional network. Furthermore, the corresponding 3D skeleton sequence data is set as the third input of the whole recognition framework. Although the skeleton sequence data has the advantage of including 3D information, it is also confronted with the problems of the existence of rate change, temporal mismatch and noise. Thus, specially designed space-time features are applied to cope with these problems. The proposed methods allow the whole recognition system to fully exploit and utilize the discriminatory space-time features from different perspectives, and ultimately improve the classification accuracy of the system. Experimental results on different public 3D data sets illustrate the effectiveness of the proposed method.

In this paper we propose a novel local feature descriptor based on energy information for human activity recognition. Instead of detecting spatio-temporal interest points, we combine the kinetic energy, gesture potential energy of 3D skeleton joints and others as a feature matrix. The semantic features are obtained by the Bag of Word (BOW) based on k-means clustering. These features conform to not only kinematics and biology of human action, but also the natural visual saliency for action recognition. During the activity recognition, we first present a temporal segmentation method based on kinetic features of human skeleton to cut the long videos into the sub-action segments. Then the sub-action units are iteratively incorporated in the meaningful groups by considering similarity of feature information. Finally, SVM based on kernel function is used to carry out human activity recognition. The experimental results show that our approach outperforms several state-of-the-art algorithms based on our proposed low dimensional features of energy information. [ABSTRACT FROM AUTHOR]

As we all known, the most promising alternative anode for the next-generation energy storage devices is the lithium metal anode. But the unpredictable dendrite growth and volume expansion during the cycles impedes its applications and commercialization. The Li-Cu-Zn three-phases alloy materials (including Li 2 Cu 3 Zn, Li-Zn and Li) have been synthesized to solve these problems. According to the density functional theory (DFT) analysis, all results indicate that the Li 2 Cu 3 Zn ternary alloy has the higher absorption energy of Li than bare Li, suggesting the superior lithiophilic. Meanwhile, the construction of Li 2 Cu 3 Zn alloy 3D skeleton is beneficial to uniform the Li+ flux and reducing the local current density. Furthermore, based on the analysis of in-situ X-ray diffraction, Li 2 Cu 3 Zn skeleton can keep stable during the cycles. The Li-Cu-Zn electrodes exhibit the stable cycling performance about more than 1200 h and pure lithium only for about 120 h (at 1 mA cm−2 and 1 mAh cm−2). And at 0.5 C, after 100 cycles, the NCM811 (LiNi 0.8 Co 0.1 Mn 0.1 O 2) ||Li-Cu-Zn cell delivers an excellent cycling stability for 92.3% capacity retention. In this work, uniform deintercalation of lithium ions through three-dimensional alloy framework structure provides a research basis for lithium-free anodes. • Introducing the 3D skeleton structure to homogenize the Li-ion flux. • The improvement of mechanical property and interfacial stability. • Forming uniformly nucleation sites to suppress the growth of lithium dendrites. • The synergies of 3D skeleton and strong Li+ affinity site. [ABSTRACT FROM AUTHOR]