Spin-Charge Separation in the t−Jt-J Model: Magnetic and Transport Anomalies

A real spin-charge separation scheme is found based on a saddle-point state of the $t-J$ model. In the one-dimensional (1D) case, such a saddle-point reproduces the correct asymptotic correlations at the strong-coupling fixed-point of the model. In the two-dimensional (2D) case, the transverse gauge field confining spinon and holon is shown to be gapped at {\em finite doping} so that a spin-charge deconfinement is obtained for its first time in 2D. The gap in the gauge fluctuation disappears at half-filling limit, where a long-range antiferromagnetic order is recovered at zero temperature and spinons become confined. The most interesting features of spin dynamics and transport are exhibited at finite doping where exotic {\em residual} couplings between spin and charge degrees of freedom lead to systematic anomalies with regard to a Fermi-liquid system. In spin dynamics, a commensurate antiferromagnetic fluctuation with a small, doping-dependent energy scale is found, which is characterized in momentum space by a Gaussian peak at ($\pi/a$, $\pi/a$) with a doping-dependent width ($\propto \sqrt{\delta}$, $\delta$ is the doping concentration). This commensurate magnetic fluctuation contributes a non-Korringa behavior for the NMR spin-lattice relaxation rate. There also exits a characteristic temperature scale below which a pseudogap behavior appears in the spin dynamics. Furthermore, an incommensurate magnetic fluctuation is also obtained at a {\em finite} energy regime. In transport, a strong short-range phase interference leads to an effective holon Lagrangian which can give rise to a series of interesting phenomena including linear-$T$ resistivity and $T^2$ Hall-angle. We discuss the striking similarities of these theoretical features with those found in the high-$T_c$ cuprates and give aComment: 70 pages, RevTex, hard copies of 7 figures available upon request; minor revisions in the text and references have been made; To be published in July 1 issue of Phys. Rev. B52, (1995

A Latent Variable Model for Plant Stress Phenotyping Using Deep Learning

With a growing population and a changing climate, increasing crop yields in a diversity of environmental conditions is becoming increasingly important. Studying genome-by-environment (GxE) effects is a critical path for such improvements, and high-throughput plant phenotyping is necessary for carrying out such experiments at scale. Image-based phenotyping techniques offer a scalable, non-destructive way of quantifying plants' responses to their environment - however, these techniques can be cumbersome and subjective. Each image dataset is unique, and requires either a hand-crafted image processing pipeline or a large annotated training set, which can be expensive and time-consuming. Additionally, researchers must select what feature is to be used to quantify changes due to the treatment, such as biomass, colour, the number of organs, or some other visual indication of the individual's response to its environment. This dissertation explores image-based plant phenotyping, beginning with a discussion of image processing tools. Deep learning is introduced, with a survey of popular deep learning tasks applicable to plant phenotyping. Deep Plant Phenomics, a novel software platform for deep learning research in plant phenotyping, is introduced. A model of the Arabidopsis thaliana rosette is introduced and it is demonstrated that the use of synthetic data has the potential mediate some of the issues common to plant image datasets in deep learning. Finally, Latent Space Phenotyping (LSP) is introduced. LSP is a novel paradigm for quantifying response to treatment in plants which requires no hand-engineered pipelines or annotation of training data. The ability of LSP to detect arbitrary visual responses to treatment is demonstrated through five case studies involving both real as well as synthetic data. These case studies show that the method replicates two previously identified candidate loci for drought tolerance in an interspecific cross of Setaria, as well as demonstrating the flowering-time dependent drought response of Brassica napus L. The flexibility of the previously described synthetic A. thaliana model facilitates follow-up discussion where the behaviour of LSP is studied in additional experiments. The techniques described in this dissertation lay the groundwork for future developments in image-based plant phenotyping, particularly in the use of deep learning, simulation, and latent variable models

Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana

The use of plant models in deep learning: an application to leaf counting in rosette plants

Abstract Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task

MOESM1 of The use of plant models in deep learning: an application to leaf counting in rosette plants

Additional file 1.  The complete code of the Arabidopsis thaliana rosette model L-system