The Present Situation and Future Prospect of Online Fitness in the Post-Epidemic Era

After the COVID-19 epidemic broke out around the world, large-scale home isolations have restricted the activities of ordinary residents. Therefore, online activities have become more frequent and online fitness have ushered in a new round of large-scale rise. The concept of national fitness has gradually rooted in the hearts of the people. The 14th five-year Plan of the people\u27s Republic of China for National Economic and Social Development and the outline of long-term goals for 2035 further make it clear that it is necessary to create new advantages in the digital economy and promote the digital transformation of the industry. Under the background of the epidemic, the online fitness industry promotes the digital transformation of China\u27s sports industry and provides new ideas and directions for it. Combined with the current social background, this study was to expound the development status of online fitness in the post-epidemic era and put forward prospects and suggestions for the future development of online fitness. This study took the global online fitness market as the research object and mainly used the literature method and data analysis method with two main purposes: (1) Investigating the current status of online fitness development and relevant national policies on national fitness and the promotion of industrial digital transformation through online literature platforms such as CNKI and Wanfang platform and online news platforms such as Xinhuanet, and serve as the research background of this article . (2) Collecting relevant second-hand data from the global online fitness market through online database platforms such as the Sports Information Network and the China Economic and Social Big Data Research Platform and analyzing relevant data on the online fitness market before and after the outbreak of the new crown epidemic. The findings showed the necessity of digital transformation in the sports industry. The vigorous development of emerging digital industries such as artificial intelligence, big data, and cloud computing has brought human society into a new era of digitalization. In the context of digitalization, the digital transformation of all walks of life has become an inevitable trend for the survival and development of the industry. In 2020, the global digital economy will reach US$32.6 trillion, a nominal year-on-year increase of 3.0$6.04 billion. In 2021, the scale of the global online fitness industry reached US$10.71 billion, an increase of 77.33%. Although the epidemic has restricted residents’ outing activities, more and more people have begun to choose online sports, including the use of smart wearable devices, online app guidance and recording, etc., which has brought online fitness models. In the context of the epidemic, the online fitness model has greatly promoted the implementation of the national fitness policy and is also an important path for the digital transformation of the sports industry. It is suggested that the digital transformation of the sports industry is an important direction for the future development of the sports industry. In the context of technological development and support and the new crown pneumonia epidemic, online fitness, an emerging fitness model, has emerged and has become an important fitness model, and at the same time has promoted the digital transformation of the sports industry. Online fitness should focus on the development of personalized customization of fitness courses and programs to meet the individual needs of users; strengthen the association with social platforms to increase user stickiness; dig deep into user data, identify user pain points, and then take advantage of their products make improvements with services

Abdominal multi-organ segmentation in CT using Swinunter

Abdominal multi-organ segmentation in computed tomography (CT) is crucial for many clinical applications including disease detection and treatment planning. Deep learning methods have shown unprecedented performance in this perspective. However, it is still quite challenging to accurately segment different organs utilizing a single network due to the vague boundaries of organs, the complex background, and the substantially different organ size scales. In this work we used make transformer-based model for training. It was found through previous years' competitions that basically all of the top 5 methods used CNN-based methods, which is likely due to the lack of data volume that prevents transformer-based methods from taking full advantage. The thousands of samples in this competition may enable the transformer-based model to have more excellent results. The results on the public validation set also show that the transformer-based model can achieve an acceptable result and inference time.Comment: 8pages. arXiv admin note: text overlap with arXiv:2201.01266 by other author

Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation

This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy. We observe that, on standard medical image segmentation benchmarks, the loss gradient norm-based metrics of individual training examples applied in image classification fail to identify the important samples. To address this issue, we propose a data pruning method by taking into consideration the training dynamics on target regions using Dynamic Average Dice (DAD) score. To the best of our knowledge, we are among the first to address the data importance in dense labeling tasks in the field of medical image analysis, making the following contributions: (1) investigating the underlying causes with rigorous empirical analysis, and (2) determining effective data pruning approach in dense labeling problems. Our solution can be used as a strong yet simple baseline to select important examples for medical image segmentation with combined data sources.Comment: Accepted by ICML workshops 202

Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation

We aim at incorporating explicit shape information into current 3D organ segmentation models. Different from previous works, we formulate shape learning as an in-painting task, which is named Masked Label Mask Modeling (MLM). Through MLM, learnable mask tokens are fed into transformer blocks to complete the label mask of organ. To transfer MLM shape knowledge to target, we further propose a novel shape-aware self-distillation with both in-painting reconstruction loss and pseudo loss. Extensive experiments on five public organ segmentation datasets show consistent improvements over prior arts with at least 1.2 points gain in the Dice score, demonstrating the effectiveness of our method in challenging unsupervised domain adaptation scenarios including: (1) In-domain organ segmentation; (2) Unseen domain segmentation and (3) Unseen organ segmentation. We hope this work will advance shape analysis and geometric learning in medical imaging

Unconventional and conventional quantum criticalities in CeRh0.58_{0.58}Ir0.42_{0.42}In5_5

An appropriate description of the state of matter that appears as a second order phase transition is tuned toward zero temperature, {\it viz.} quantum-critical point (QCP), poses fundamental and still not fully answered questions. Experiments are needed both to test basic conclusions and to guide further refinement of theoretical models. Here, charge and entropy transport properties as well as AC specific heat of the heavy-fermion compound CeRh$_{0.58}$Ir$_{0.42}$In$_5$, measured as a function of pressure, reveal two qualitatively different QCPs in a {\it single} material driven by a {\it single} non-symmetry-breaking tuning parameter. A discontinuous sign-change jump in thermopower suggests an unconventional QCP at $p_{c1}$ accompanied by an abrupt Fermi-surface reconstruction that is followed by a conventional spin-density-wave critical point at $p_{c2}$ across which the Fermi surface evolves smoothly to a heavy Fermi-liquid state. These experiments are consistent with some theoretical predictions, including the sequence of critical points and the temperature dependence of the thermopower in their vicinity.Comment: 21+3 pages, 4+2 figures. Change the title, figures et a

Strong plasmonic confinement and optical force in phosphorene pairs

The plasmonic responses in the spatially separated phosphorene (single-layer black phosphorus) pairs are investigated, mainly containing the field enhancement, light confinement, and optical force. It is found that the strong anisotropic dispersion of black phosphorus gives rise to the direction-dependent symmetric and anti-symmetric plasmonic modes. Our results demonstrate that the symmetrical modes possess stronger field enhancement, higher light confinement, and larger optical force than the anti-symmetric modes in the nanoscale structures. Especially, the light confinement ratio and optical force for the symmetric mode along the armchair direction of black phosphorus can reach as high as >90% and >3000 pN/mW, respectively. These results may open a new door for the light manipulation at nanoscale and the design of black phosphorus based photonic devices

The observation of quantum fluctuations in a kagome Heisenberg antiferromagnet

The search for the experimental evidence of quantum spin liquid (QSL) states is critical but extremely challenging, as the quenched interaction randomness introduced by structural imperfection is usually inevitable in real materials. YCu$_3$(OH)$_{6.5}$Br$_{2.5}$ (YCOB) is a spin-1/2 kagome Heisenberg antiferromagnet (KHA) with strong coupling of $\langle J_1\rangle\sim$ 51 K but without conventional magnetic freezing down to 50 mK $\sim$ 0.001$\langle J_1\rangle$. Here, we report a Br nuclear magnetic resonance (NMR) study of the local spin susceptibility and dynamics on the single crystal of YCOB. The temperature dependence of NMR main-line shifts and broadening can be well understood within the frame of the KHA model with randomly distributed hexagons of alternate exchanges, compatible with the formation of a randomness-induced QSL state at low temperatures. The in-plane spin fluctuations as measured by the spin-lattice relaxation rates ($1/T_1$) exhibit a weak temperature dependence down to $T$ $\sim$ 0.03$\langle J_1\rangle$. Our results demonstrate that the majority of spins remain highly fluctuating at low temperatures despite the quenched disorder in YCOB.Comment: NMR work on YCu3(OH)6.5Br2.5, accepted in Communications Physic

A New Race (X12) of Soybean Cyst Nematode in China

The soybean cyst nematode (SCN), Heterodera glycines, is a serious economic threat to soybean-producing regions worldwide. A new SCN population (called race X12) was detected in Shanxi province, China. Race X12 could reproduce on all the indicator lines of both race and Heterodera glycines (HG) type tests. The average number of females on Lee68 (susceptible control) was 171.40 with the lowest Female Index (FI) 61.31 on PI88788 and the highest FI 117.32 on Pickett in the race test. The average number of females on Lee68 was 323.17 with the lowest FI 44.18 on PI88788 and the highest FI 97.83 on PI548316 in the HG type test. ZDD2315 and ZDD24656 are elite resistant germplasms in China. ZDD2315 is highly resistant to race 4, the strongest infection race in the 16 races with FI 1.51 while being highly sensitive to race X12 with FI 64.32. ZDD24656, a variety derived from PI437654 and ZDD2315, is highly resistant to race 1 and race 2. ZDD24656 is highly sensitive to race X12 with FI 99.12. Morphological and molecular studies of J2 and cysts confirmed the population as the SCN H. glycines. This is a new SCN race with stronger virulence than that of race 4 and is a potential threat to soybean production in China