Experimental Research of Body Functional Training on Promoting Physical Quality of College Students

After being introduced into China, body functional training has been widely applied to the training of high-level athletes (Wang & Liu, 2014; Cui & Qiu, 2013). However, it is still in the exploratory stage in college teaching. In this research, body functional training was designed for the physical characteristics of ordinary college students, and the physical health status of students was monitored in order to understand the promoting effect of body functional training on college students\u27 physical quality. It also provides theoretical and data support for the continuous improvement of body functional training courses. The research process was approved by the institutional review board of Zhengzhou University. Participants were 50 freshmen (non-physical education major, 17-19 years) enrolled into a college in central China. Among them, 25 students (13 female students and 12 male students) were selected as one group. The experimental group and the control group were given body functional training and normal physical exercise for 12 weeks (2 times a week, 1 hour each time), while the other physical activities of each group were the same. Physical fitness test and physical fitness item test were conducted before and after the experiment. SPSS 22.0 statistical software was used to conduct paired T-test on the data before and after the experiment, and independent sample T-test was conducted on the data before and after the experiment. In terms of physical health, there was no significant difference in body weight and forced vital capacity in the control group (P \u3e 0.05), but significant change in body weight (P \u3c 0.05) and significant increase in FVC (P \u3c 0.01) were observed in the experimental group. From the perspective of physical fitness, the results of the control group showed an improvement trend but not reached a statistical significance (P \u3e 0.05), while the flexibility of the experimental group showed a very significant change (P \u3c 0.01), the strength of lower limbs and upper limbs increased significantly (P \u3c 0.05), and the endurance improved significantly (P \u3c 0.05). The differences between the experimental group and the control group were statistically significant. Body functional training can improve the physical fitness of college students and has a positive effect on improving their physical fitness. In addition, body functional training can be used as an effective training method in college physical education. However, due to the limited of time and ability, experiments were conducted for only 12 weeks, future researchers could make more detailed studies on this training method

Data integration for urban transport planning

Urban transport planning aims at balancing conflicting challenges by promoting more efficient transport systems while reducing negative impacts. The availability of better and more reliable data has not only stimulated new planning methodologies, but also created challenges for efficient data management and data integration. The major focus of this study is to improve methodologies for representing and integrating multi-source and multi-format urban transport data. This research approaches the issue of data integration based on the classification of urban transport data both from a functional and a representational perspective. The functional perspective considers characteristics of the urban transport system and planning requirements, and categorises data into supply, demand, performance and impact. The representational perspective considers transport data in terms of their spatial and non-spatial characteristics that are important for data representation. These two perspectives correspond to institutional and methodological data integration respectively, and are the foundation of transport data integration. This research is based on the city of Wuhan in China. The methodological issues of transport data integration are based on the representational perspective. A framework for data integration has been put forward, in which spatial data are classified as point, linear and areal types, and the non-spatial data are sorted out as values and temporal attributes. This research has respectively probed the integration of point, linear and areal transport data within a GIS environment. The locations of socio-economic activities are point-type data that need to be spatially referenced. A location referencing process requires a referencing base, source address units and referencing methods. The referencing base consists of such spatial features as streets, street addresses, points of interest and publicly known zones. These referencing bases have different levels of spatial preciseness and have to be kept in a hierarchy. Source addresses in Chinese cities are usually written as one sentence, which has to be divided into address units for automatic geo-coding. As it is difficult to separate from the sentences, the address units have to be clearly identified in survey forms. Depending on the types of address units, the referencing process makes use of either semantic name matching or address matching to link source addresses to features in the referencing base. The name-based and road-based referencing schemes constitute a comprehensive location referencing framework that is applicable to Chinese cities. The relationship between two sets of linear features can be identified with spatial overlay in the case of independent representation, or with internal linkage in a dependent representation. The bus line is such a feature that runs on the street network and can be dependently referenced by streets. In the heavily bus-oriented city of Wuhan, bus lines constitute a large public transit network that is important to transport planning and management. This research has extended conventional bus line representation to a more detailed level. Each bus line has been differentiated as two directional routes that are defined separately with reference to the street network. Accordingly, individual route stops are also represented in the database. These stop sites are spatial features with geometry that are linked to street segments and bus routes by linear location referencing methods. A data model linking base street network, bus lines and routes, line and route stops, and other bus operations data has been constructed. The benefits of the detailed model have been demonstrated in several transport applications. Zonal data transitions include three types of operations, i.e. aggregation, areal interpolation and disaggregation. This study focuses on disaggregating data from larger zones to smaller zones. In the context of Wuhan, zonal data disaggregation involves the allocation of statistical data from statistical units to smaller parcels. Given the availability of land use data, a weighted approach reflecting spatial variations has been applied in the disaggregation process. Two technical processes for disaggregation have been examined. Weighted area-weighting (WAW) is an adaptation of the classic area-weighting method, and Monte Carlo simulation (MC) is a stochastic process based on a raster data model. The MC outcome is more convenient for subsequent re-aggregation, and is also directly available for micro-simulation. An important contribution arising from this zonal integration study is that two standardised disaggregation tools have been developed within a GIS environment. The research has also explored the institutional aspect of data integration. The findings of this study show that there is generally a good institutional transport structure in the city of Wuhan and that there is also a growing awareness of using information technology. Professional cooperation exists among transport organisations, but not yet at a level for data sharing. An integrated data support framework requires data sharing. In such a framework, it should be possible to know where to get data for specific transport studies, or which kind of research an institution supports

Truncated pair-wise likelihood for the Brown-Resnick process with applications to maximum temperature data

AbstractMax-stable processes are a natural extension of multivariate extreme value theory important for modeling the spatial dependence of environmental extremes. Inference for max-stable processes observed at several spatial locations is challenging due to the intractability of the full likelihood function. Composite likelihood methods avoid these difficulties by combining a number of low-dimensional likelihood objects, typically defined on pairs or triplets of spatial locations. This work develops a new truncation procedure based on ℓ1-penalty to reduce the complexity and computational cost associated with the composite likelihood function. The new method is shown to achieve a favorable trade-off between computational burden and statistical efficiency by dropping a number of noisy sub-likelihoods. The properties of the new method are illustrated through numerical simulations and an application to real extreme temperature data

Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.Comment: accepted by CVPR 202

RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization

Text-to-image customization, which aims to synthesize text-driven images for the given subjects, has recently revolutionized content creation. Existing works follow the pseudo-word paradigm, i.e., represent the given subjects as pseudo-words and then compose them with the given text. However, the inherent entangled influence scope of pseudo-words with the given text results in a dual-optimum paradox, i.e., the similarity of the given subjects and the controllability of the given text could not be optimal simultaneously. We present RealCustom that, for the first time, disentangles similarity from controllability by precisely limiting subject influence to relevant parts only, achieved by gradually narrowing real text word from its general connotation to the specific subject and using its cross-attention to distinguish relevance. Specifically, RealCustom introduces a novel "train-inference" decoupled framework: (1) during training, RealCustom learns general alignment between visual conditions to original textual conditions by a novel adaptive scoring module to adaptively modulate influence quantity; (2) during inference, a novel adaptive mask guidance strategy is proposed to iteratively update the influence scope and influence quantity of the given subjects to gradually narrow the generation of the real text word. Comprehensive experiments demonstrate the superior real-time customization ability of RealCustom in the open domain, achieving both unprecedented similarity of the given subjects and controllability of the given text for the first time. The project page is https://corleone-huang.github.io/realcustom/.Comment: Accepted by CVPR202

DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation

Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stacking generator-discriminator pairs to engage multiple adversarial training, where the text semantics used to provide generation guidance remain static across all stages. This work argues that text features at each stage should be adaptively re-composed conditioned on the status of the historical stage (i.e., historical stage's text and image features) to provide diversified and accurate semantic guidance during the coarse-to-fine generation process. We thereby propose a novel Dynamical Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under a novel single adversarial multi-stage architecture. Specifically, we design (1) Dynamic Semantic Evolution (DSE) module, which first aggregates historical image features to summarize the generative feedback, and then dynamically selects words required to be re-composed at each stage as well as re-composed them by dynamically enhancing or suppressing different granularity subspace's semantics. (2) Single Adversarial Multi-stage Architecture (SAMA), which extends the previous structure by eliminating complicated multiple adversarial training requirements and therefore allows more stages of text-image interactions, and finally facilitates the DSE module. We conduct comprehensive experiments and show that DSE-GAN achieves 7.48\% and 37.8\% relative FID improvement on two widely used benchmarks, i.e., CUB-200 and MSCOCO, respectively

Effect of recycled concrete aggregate features on adhesion properties of asphalt mortar-aggregate interface

Asphalt-aggregate interface’s adhesion properties commonly affect the damage initiation and evolution within asphalt concrete materials, related to pavement durability and quality. The scope of this research was to investigate the influence of Recycled Concrete Aggregate (RCA) features on asphalt mortar-aggregate interface adhesion. Firstly, a three-dimensional reconstruction model of RCA was carried out using X-ray CT tomography and digital image processing. In this regard, five feature indicators, namely cement mortar content, sphericity, flat and elongated ratio, angularity, and surface texture, were proposed. Based on a bilinear cohesive zone model, the interface damage behavior of asphalt mortar-RCA was investigated by using a uniaxial compression simu- lation. Finally, a GA-BP artificial neural network was conducted to predict and quantify the effect of each feature indicator of RCA on interface adhesion. The results showed that when RCA had lower cement mortar content, higher sphericity value, and smoother surface, the asphalt mortar-RCA system was less prone to interface adhesion failure. The 5-14-1 GA-BP artificial neural network proposed in this study showed very good perfor- mance in predicting the interfacial dissipation damage energy with a mean-squared error value of 3.52 × 10^-4 for testing dataset. The cement mortar content parameter exhibited a remarkable influence on the interface adhesion property, and its global contribution to the interfacial dissipation damage energy (0.3486) was more than twice that of the surface texture parameter (0.1316). In future studies, the performance characteristics of cement mortar can be further investigated, thereby proposing RCA’s performance optimization technology

Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472159, 61572227), Development Project of Jilin Province of China (Nos. 20160204022GX, 20160414009GH, 2017C033).Postprin