Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

Ruminal microbiota and muscle metabolome characteristics of Tibetan plateau yaks fed different dietary protein levels

IntroductionThe dietary protein level plays a crucial role in maintaining the equilibrium of rumen microbiota in yaks. To explore the association between dietary protein levels, rumen microbiota, and muscle metabolites, we examined the rumen microbiome and muscle metabolome characteristics in yaks subjected to varying dietary protein levels.MethodsIn this study, 36 yaks were randomly assigned to three groups (n = 12 per group): low dietary protein group (LP, 12% protein concentration), medium dietary protein group (MP, 14% protein concentration), and high dietary protein group (HP, 16% protein concentration).Results16S rDNA sequencing revealed that the HP group exhibited the highest Chao1 and Observed_species indices, while the LP group demonstrated the lowest. Shannon and Simpson indices were significantly elevated in the MP group relative to the LP group (P < 0.05). At the genus level, the relative abundance of Christensenellaceae_R-7_group in the HP group was notably greater than that in the LP and MP groups (P < 0.05). Conversely, the relative abundance of Rikenellaceae_RC9_gut_group displayed an increasing tendency with escalating feed protein levels. Muscle metabolism analysis revealed that the content of the metabolite Uric acid was significantly higher in the LP group compared to the MP group (P < 0.05). The content of the metabolite L-(+)-Arabinose was significantly increased in the MP group compared to the HP group (P < 0.05), while the content of D-(-)-Glutamine and L-arginine was significantly reduced in the LP group (P < 0.05). The levels of metabolites 13-HPODE, Decanoylcarnitine, Lauric acid, L-(+)-Arabinose, and Uric acid were significantly elevated in the LP group relative to the HP group (P < 0.05). Furthermore, our observations disclosed correlations between rumen microbes and muscle metabolites. The relative abundance of NK4A214_group was negatively correlated with Orlistat concentration; the relative abundance of Christensenellaceae_R-7_group was positively correlated with D-(-)-Glutamine and L-arginine concentrations.DiscussionOur findings offer a foundation for comprehending the rumen microbiome of yaks subjected to different dietary protein levels and the intimately associated metabolic pathways of the yak muscle metabolome. Elucidating the rumen microbiome and muscle metabolome of yaks may facilitate the determination of dietary protein levels

Gaussian Kernel Fuzzy C-Means Algorithm for Service Resource Allocation

With respect to the cluster problem of the evaluation information of mass customers in service management, a cluster algorithm of new Gaussian kernel FCM (fuzzy C-means) is proposed based on the idea of FCM. First, the paper defines a Euclidean distance formula between two data points and makes them cluster adaptively based on the distance classification approach and nearest neighbors in deleting relative data. Second, the defects of the FCM algorithm are analyzed, and a solution algorithm is designed based on the dual goals of obtaining a short distance between whole classes and long distances between different classes. Finally, an example is given to illustrate the results compared with the existing FCM algorithm

Uncovering the Driving Factors of Carbon Emissions in an Investment Allocation Model of China’s High-Carbon and Low-Carbon Energy

In the view of long-term comprehensive development, the concept of low-carbon economy has long been a concern. In this paper, we build a pure energy-economic system and explore the exact influencing factors in the investment allocation of high-carbon and low-carbon energy with the purpose of mitigating carbon dioxide in the atmosphere. The dynamic analysis shows that the model that we built is applicable for the current market situation and the way we adjust the investments of high-carbon and low-carbon energy are conductive to carbon abatement in the atmosphere. On the basis of the stability analysis and numerical simulation, some strategies are given to decrease the carbon dioxide in the atmosphere. The results show that the social consumption and public consumption behavior are the most important factors responsible for the variation in the atmospheric carbon dioxide. The cleanliness of high carbon presents an obvious mitigating effect on carbon in the atmosphere and the effect of marginal profit of high-carbon energy is the weakest. In addition, enhancing marginal profit, return on investment and investment share of low-carbon energy are beneficial to reduce carbon dioxide in the atmosphere, while a return on investment of high-carbon energy increasing is the detriment of the carbon dioxide in the atmosphere. Finally, we provide carbon mitigation effort by considering both economic development and carbon abatement for policymakers to achieve a desirable emission-reduction effect