Analysis of Body Weight and Feed Intake Curves in Selection Lines for Residual Feed Intake in Pigs

A selection experiment for reducing residual feed intake (RFI= feed consumed over and above expected requirements for production and maintenance) in Yorkshire pigs consists of a line selected for lower RFI (LRFI) and a random control line (CTRL). Using 64 LRFI and 87 CTRL boars from generation 5 of the selection experiment, cubic polynomial random regression with heterogeneous residual variance for daily feed intake (DFI) and with homogeneous residual variance for bi-weekly body weight (BW) were identified as the best linear mixed models to describe feed intake and body weight curves. Based on the Gompertz model, significant differences in the decay parameter for DFI and in mature body weight and the inflection point for BW were observed between the lines. In conclusion, selection for lower RFI has resulted in a lower feed intake curve toward maturity, lower mature body weight, and earlier inflection points for growth

Principal Clusters Analysis: Analyzing Web Navigation Using a Multivariate Technique

We present a new statistical approach, called principal clusters analysis, for analyzing millions of user navigations among Web documents. This technique can identify distinct clusters of related information on a given topic. In addition, it can determine which information items within a cluster are useful starting points to explore the topic of the cluster, as well as key documents within the cluster to explore the topic in greater detail. This technique should prove promising in addressing information overload and other knowledge management issues

A Review of Forest Resources and Forest Biodiversity Evaluation System in China

China is a country rich in diverse forest ecosystems due to the large span of the country, complex topography, and multiple climate regimes. In this paper, the basic information of forest resources in China was briefly introduced and the current state in the measurements of forest biodiversity and the establishment of forest biodiversity index systems in related studies were reviewed. The results showed that a lot of studies on forest biodiversity have been conducted mostly at landscape or stand level in China and the commonly used biodiversity indicators were identified and compared. Several comprehensive forest biodiversity index systems were proposed. However, there are still some problems during the construction of forest biodiversity assessment system. Due to the late establishment of biodiversity monitoring system in China, the availability of data that could be included in a forest biodiversity index system is limited, which hurdles the precise assessment of forest biodiversity. It is suggested to develop long-term monitoring stations and keep data recording consistently. Concerns should also be given to the construction of the framework of the forest biodiversity index system and the determination of the indicators’ weight. The results will provide reference for the establishment of national or regional forest biodiversity evaluation indicator systems in China

An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network

We propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea. The new algorithm brings more robustness and simplicity than classic Expectation Maximization (EM) algorithm. It also improves the accuracy and only take 1 iteration for learning. We theoretically proof that this new algorithm is guarantee to converge regardless the parameters initialisation. We compare our GMM expansion method with classic probability layers in neural network leads to demonstrably better capability to overcome data uncertainty and inverse problem. Finally, we test GMM based generator which shows a potential to build further application that able to utilized distribution random sampling for stochastic variation as well as variation control

Effect of temperature on elastic constants, generalized stacking fault energy and dislocation cores in MgO and CaO

AbstractTemperature effect on the elastic constants and anisotropy of MgO and CaO are performed via first-principles approach combing the quasistatic approximation to elasticity and the quasiharmonic phonon approximation to thermal expansion. Generalized stacking fault energy curves at different temperature are also computed due to the importance for dislocation properties. The core structures of 1/2〈110〉{110} dislocations in MgO and CaO at different temperature are investigated within the improved Peierls–Nabarro dislocation theory using Foreman's method. It is found that the core width of dislocation increases with the increasing of temperature

Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient

Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation. In this work, we propose a conditioning mechanism utilizing Gaussian mixture models (GMMs) as feature conditioning to guide the denoising process. Based on set theory, we provide a comprehensive theoretical analysis that shows that conditional latent distribution based on features and classes is significantly different, so that conditional latent distribution on features produces fewer defect generations than conditioning on classes. Two diffusion models conditioned on the Gaussian mixture model are trained separately for comparison. Experiments support our findings. A novel gradient function called the negative Gaussian mixture gradient (NGMG) is proposed and applied in diffusion model training with an additional classifier. Training stability has improved. We also theoretically prove that NGMG shares the same benefit as the Earth Mover distance (Wasserstein) as a more sensible cost function when learning distributions supported by low-dimensional manifolds