The Development and Application of Crop Evaluation System Based on GRA

Ever since it was proposed, grey system theory has attracted the attention of scientific researchers and scholars. And it also has been widely used in many fields and solved a large number of practical problems in production, life, and scientific research. With the development and popularization of computer science and network technology, this traditional mathematical model can be applied more simply and efficiently to solve practical problems. Firstly, this paper, to implement steps of grey relational analysis, has made the exclusive analysis and has made the simple introduction to grey relational analysis characteristics. Then, based on grey relational theory and ASP.NET technology, the crop evaluation system is developed. Lastly, by using Excel and the crop evaluation system, the paper carries out a comprehensive evaluation about eight features of Fuji apple, which is from nine different producing areas, respectively. The experiment results show that the crop evaluation system is effective and could greatly improve the work efficiency of the researcher and expand the application scope

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05)

Liver fatty acid composition in mice with or without nonalcoholic fatty liver disease

<p>Abstract</p> <p>Background</p> <p>Nonalcoholic fatty liver disease (NAFLD) is one of the most frequent causes of abnormal liver function. Because fatty acids can damage biological membranes, fatty acid accumulation in the liver may be partially responsible for the functional and morphological changes that are observed in nonalcoholic liver disease. The aim of this study was to use gas chromatography-mass spectrometry to evaluate the fatty acid composition of an experimental mouse model of NAFLD induced by high-fat feed and CCl₄and to assess the association between liver fatty acid accumulation and NAFLD. C57BL/6J mice were given high-fat feed for six consecutive weeks to develop experimental NAFLD. Meanwhile, these mice were given subcutaneous injections of a 40% CCl₄-vegetable oil mixture twice per week.</p> <p>Results</p> <p>A pathological examination found that NAFLD had developed in the C57BL/6J mice. High-fat feed and CCl₄led to significant increases in C14:0, C16:0, C18:0 and C20:3 (P < 0.01), and decreases in C15:0, C18:1, C18:2 and C18:3 (P < 0.01) in the mouse liver. The treatment also led to an increase in SFA and decreases in other fatty acids (UFA, PUFA and MUFA). An increase in the ratio of product/precursor n-6 (C20:4/C18:2) and n-3 ([C20:5+C22:6]/C18:3) and a decrease in the ratio of n-6/n-3 (C20:4/[C20:5+C22:6]) were also observed.</p> <p>Conclusion</p> <p>These data are consistent with the hypothesis that fatty acids are deranged in mice with non-alcoholic fatty liver injury induced by high-fat feed and CCl₄, which may be involved in its pathogenesis and/or progression via an unclear mechanism.</p

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outliers are asymmetric and concentrated in specific channels. To address this issue, we propose the Outlier Suppression+ framework. First, we introduce channel-wise shifting and scaling operations to eliminate asymmetric presentation and scale down problematic channels. We demonstrate that these operations can be seamlessly migrated into subsequent modules while maintaining equivalence. Second, we quantitatively analyze the optimal values for shifting and scaling, taking into account both the asymmetric property and quantization errors of weights in the next layer. Our lightweight framework can incur minimal performance degradation under static and standard post-training quantization settings. Comprehensive results across various tasks and models reveal that our approach achieves near-floating-point performance on both small models, such as BERT, and large language models (LLMs) including OPTs, BLOOM, and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state of the art for 4-bit BERT

Improving the Robustness of Summarization Systems with Dual Augmentation

A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. To create semantic-consistent substitutes, we propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models. Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation. Specifically, the first brittleness factor we found is the poor understanding of infrequent words in the input. Correspondingly, we feed the encoder with more diverse cases created by SummAttacker in the input space. The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states. Hence, we construct adversarial decoder input and devise manifold softmixing operation in hidden space to introduce more diversity. Experimental results on Gigaword and CNN/DM datasets demonstrate that our approach achieves significant improvements over strong baselines and exhibits higher robustness on noisy, attacked, and clean datasets.Comment: 10 pages, 6 figures, ACL 2023 main coferenc

A high-resolution map of reactive nitrogen inputs to China

To feed an increasingly affluent population, reactive nitrogen (Nr) inputs to China’s lands and waters have substantially increased over the past century. Today, China’s Nr emissions account for over one third of global total emissions, leading to serious environmental pollution and health damages. Quantifying the spatial variability of Nr inputs is crucial for the identification of intervention points to mitigate Nr pollution, which, however, is not well known. Here, we present a database describing Nr inputs to China for the year 2017 with a 1 km × 1 km resolution, considering land use and Nr sources, compiled by using the CHANS model. Results show that the North China Plain, the Sichuan Basin and the Middle-Lower Yangtze River Plain are hotspots of Nr inputs, where per hectare Nr input is an order of magnitude higher than that in other regions. Cropland and surface water bodies receive much higher Nr inputs than other land use types. This unique database will provide basic data for research on environmental health and global change modelling