Match Performance of Soccer Teams in The Chinese Super League: Effects of Situational and Environmental Factors

To investigate The effects of situational factors (match location, strength of team andopponent) and environmental factors (relative air humidity, temperature and air quality index)on The technical and physical match performance of Chinese Soccer Super League teams (CSL).The generalized mixed modelling was employed to determine The effects by using The data of all 240matches in The season 2015 collected by Amisco Pro®. Increase in The rank difference would increaseThe number of goal-scoring related, passing and organizing related actions to a small-to-moderateextent (Effect size [ES]: 0.37–0.99). Match location had small positive effects on goal-scoring related,passing and organizing related variables (ES: 0.27–0.51), while a small negative effect on yellow card(ES=−0.35). Increment in relative air humidity and air quality index would only bring trivial orsmall effects on all The technical performance (ES:−0.06–0.23). Increase in humidity would decreaseThe physical performance at a small magnitude (ES:−0.55–−0.38). Teams achieved The highestnumber in The physical performance-related parameters at The temperature between 11.6 and 15.1◦C.In The CSL, situational variables had major effects on The technical performance but trivial effectson The physical performance, on The contrary, environmental factors affected mainly The physicalperformance but had only trivial or small impact on The technical performance

I2^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation

Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous similar positive instances in the case of limited action categories. In this work, we tackle the aforementioned problems by introducing a general Inter- and Intra-modal Mutual Distillation (I$^2$MD) framework. In I$^2$MD, we first re-formulate the cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process. Different from existing distillation solutions that transfer the knowledge of a pre-trained and fixed teacher to the student, in CMD, the knowledge is continuously updated and bidirectionally distilled between modalities during pre-training. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the Intra-modal Mutual Distillation (IMD) strategy, In IMD, the Dynamic Neighbors Aggregation (DNA) mechanism is first introduced, where an additional cluster-level discrimination branch is instantiated in each modality. It adaptively aggregates highly-correlated neighboring features, forming local cluster-level contrasting. Mutual distillation is then performed between the two branches for cross-level knowledge exchange. Extensive experiments on three datasets show that our approach sets a series of new records.Comment: submitted to IJCV. arXiv admin note: substantial text overlap with arXiv:2208.1244

Masked Motion Predictors are Strong 3D Action Representation Learners

In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. In this work, we show that instead of following the prevalent pretext task to perform masked self-component reconstruction in human joints, explicit contextual motion modeling is key to the success of learning effective feature representation for 3D action recognition. Formally, we propose the Masked Motion Prediction (MAMP) framework. To be specific, the proposed MAMP takes as input the masked spatio-temporal skeleton sequence and predicts the corresponding temporal motion of the masked human joints. Considering the high temporal redundancy of the skeleton sequence, in our MAMP, the motion information also acts as an empirical semantic richness prior that guide the masking process, promoting better attention to semantically rich temporal regions. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed MAMP pre-training substantially improves the performance of the adopted vanilla transformer, achieving state-of-the-art results without bells and whistles. The source code of our MAMP is available at https://github.com/maoyunyao/MAMP.Comment: To appear in ICCV 202

From Covert Hiding to Visual Editing: Robust Generative Video Steganography

Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process. Although existing traditional video steganography methods display a certain level of security and embedding capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we introduce an end-to-end robust generative video steganography network (RoGVS), which achieves visual editing by modifying semantic feature of videos to embed secret message. We employ face-swapping scenario to showcase the visual editing effects. We first design a secret message embedding module to adaptively hide secret message into the semantic feature of videos. Extensive experiments display that the proposed RoGVS method applied to facial video datasets demonstrate its superiority over existing video and image steganography techniques in terms of both robustness and capacity.Comment: Under Revie

Effect of leaf phenology and morphology on the coordination between stomatal and minor vein densities

Leaf phenology (evergreen vs. deciduous) and morphology (simple vs. compound) are known to be related to water use strategies in tree species and critical adaptation to certain climatic conditions. However, the effect of these two traits and their interactions on the coordination between minor vein density (MVD) and stomatal density (SD) remains unclear. In this study, we examined the leaves of 108 tree species from plots in a primary subtropical forest in southern China, including tree species with different leaf morphologies and phenologies. We assessed nine leaf water-related functional traits for all species, including MVD, SD, leaf area (LA), minor vein thickness (MVT), and stomatal length (SL). The results showed no significant differences in mean LA and SD between either functional group (simple vs. compound and evergreen vs. deciduous). However, deciduous trees displayed a significantly higher mean MVD compared to evergreen trees. Similarly, compound-leaved trees have a higher (marginally significant) MVD than simple-leaved trees. Furthermore, we found that leaf morphology and phenology have significantly interactive effects on SL, and the compound-leafed deciduous trees exhibited the largest average SL among the four groups. There were significant correlations between the MVD and SD in all different tree groups; however, the slopes and interceptions differed within both morphology and phenology. Our results indicate that MVD, rather than SD, may be the more flexible structure for supporting the coordination between leaf water supply and demand in different leaf morphologies and phenologies. The results of the present study provide mechanistic understandings of the functional advantages of different leaf types, which may involve species fitness in community assembly and divergent responses to climate changes

High Reversibility of Lattice Oxygen Redox in Na-ion and Li-ion Batteries Quantified by Direct Bulk Probes of both Anionic and Cationic Redox Reactions

The reversibility and cyclability of anionic redox in battery electrodes hold the key to its practical employments. Here, through mapping of resonant inelastic X-ray scattering (mRIXS), we have independently quantified the evolving redox states of both cations and anions in Na2/3Mg1/3Mn2/3O2. The bulk-Mn redox emerges from initial discharge and is quantified by inverse-partial fluorescence yield (iPFY) from Mn-L mRIXS. Bulk and surface Mn activities likely lead to the voltage fade. O-K super-partial fluorescence yield (sPFY) analysis of mRIXS shows 79% lattice oxygen-redox reversibility during initial cycle, with 87% capacity sustained after 100 cycles. In Li1.17Ni0.21Co0.08Mn0.54O2, lattice-oxygen redox is 76% initial-cycle reversible but with only 44% capacity retention after 500 cycles. These results unambiguously show the high reversibility of lattice-oxygen redox in both Li-ion and Na-ion systems. The contrast between Na2/3Mg1/3Mn2/3O2 and Li1.17Ni0.21Co0.08Mn0.54O2 systems suggests the importance of distinguishing lattice-oxygen redox from other oxygen activities for clarifying its intrinsic properties.Comment: 33 pages, 8 Figures. Plus 14 pages of Supplementary Materials with 12 Figure

Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery

ObjectiveThe aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery.Materials and methodsWe evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37–86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan–Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion.ResultThe rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70).ConclusionAn MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer

Key technologies of intelligent mining robot

Coal mining machine is the core equipment of completely automated working face, and the research and development of intelligent coal mining robot is crucial for achieving the intellectualization of fully mechanized working face. This paper comprehensively analyzes the current research status of sensing detection, position and attitude control, speed control, cutting trajectory planning, and tracking control in the coal mining machine roboticization process, and proposes five key technologies that must be solved in the development of intelligent shearer robots, including sensing and detection, pose control, velocity control, cutting trajectory planning and tracking control. Aiming at the problem of intelligent perception, this paper proposes the construction thought of a coal mining robot intelligent perception system, as well as the architecture of a coal mining robot intelligent per-ception system. The architecture of the intelligent perception system for coal mining robots is outlined, enabling a comprehensive sensing of running state, posture, environment, and so on, thereby ensuring the safe and reliable operation of intelligent coal mining robots. In terms of the position and attitude control problem of intelligent coal mining robots, the intelligent PID position and attitude control thought is proposed, along with an improved genetic algorithm-based PID pose control method, enabling precise pose control for the coal mining robot. As to the problem of velocity control, the thought of cutting load measurement based on the fusion of “force-electricity” heterogeneous data is proposed. Additionally, a neural network-based algorithm for cutting load measurement is presented, achieving an accurate load measurement. Furthermore, a traction and cutting speed adaptive control approach is proposed, including an artificial intelligence-based decision-making method for traction and cutting speed and a sliding mode control method for traction and cutting speed with disturbance rejection. This approach enables a precise and adaptive speed control for the coal mining robot. Regarding the problem of cutting trajectory planning and tracking control, the precise cutting trajectory planning thought is proposed, incorporating geological data and historical cutting data into a cutting trajectory planning model. The precise cutting trajectory tracking control thought is proposed, and an intelligent interpolation algorithm-based cutting trajectory tracking control method is given, achieving a high-precision trajectory planning and accurate tracking control for the coal mining robot. Considering the “position-attitude-velocity” collaborative control problem, the intelligent optimization idea of "position-attitude-velocity" collaborative control parameters is proposed, which utilizes an improved particle swarm optimization method based on multi-system constraints to optimize the coordinated control parameters, resulting in intelligent and efficient operation of the coal mining robot. The in-depth investigation of these five key technologies for intelligent coal mining robot provides some valuable insights for accelerating the development of high-performance, efficient, and reliable intelligent coal mining robot