Traditional knowledge : is the light of wisdom for conserving biodiversity and adapting to climate change

The Tibetan traditional language not only contains the worldview of the Tibetan people, but also holds significant traditional ecological knowledge that can show us alternatives to conserve biodiversity and adapt to climate chance. For indigenous peoples and local communities biodiversity is not only a matter of resource, but also a social and cultural phenomenon. And the impact of climate change on biodiversity is not only an environmental problem, but also an issue of spirit and belief.A língua tradicional tibetana não contém apenas a visão de mundo do povo tibetano como também inclui conhecimentos tradicionais ecológicos significativos que podem indicar alternativas para a conservação da biodiversidades e adaptação à mudança climática. Para os povos indígenas e comunidades locais a biodiversidade não é apenas uma questão de recursos, mas um fenômeno social e cultural. E o impacto da mudança climática sobre a biodiversidade não é apenas um problema ambiental, senão uma questão de espiritualidade e crença

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP is that one can use pre-trained denoisers when there is not sufficient data for end-to-end training. Although PnP has been recently studied extensively with great empirical success, theoretical analysis addressing even the most basic question of convergence has been insufficient. In this paper, we theoretically establish convergence of PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain Lipschitz condition on the denoisers. We then propose real spectral normalization, a technique for training deep learning-based denoisers to satisfy the proposed Lipschitz condition. Finally, we present experimental results validating the theory.Comment: Published in the International Conference on Machine Learning, 201

Safeguarded Learned Convex Optimization

Many applications require repeatedly solving a certain type of optimization problem, each time with new (but similar) data. Data-driven algorithms can "learn to optimize" (L2O) with much fewer iterations and with similar cost per iteration as general-purpose optimization algorithms. L2O algorithms are often derived from general-purpose algorithms, but with the inclusion of (possibly many) tunable parameters. Exceptional performance has been demonstrated when the parameters are optimized for a particular distribution of data. Unfortunately, it is impossible to ensure all L2O algorithms always converge to a solution. However, we present a framework that uses L2O updates together with a safeguard to guarantee convergence for convex problems with proximal and/or gradient oracles. The safeguard is simple and computationally cheap to implement, and it should be activated only when the current L2O updates would perform poorly or appear to diverge. This approach yields the numerical benefits of employing machine learning methods to create rapid L2O algorithms while still guaranteeing convergence. Our numerical examples demonstrate the efficacy of this approach for existing and new L2O schemes

Towards Constituting Mathematical Structures for Learning to Optimize

Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned model can overfit and may not generalize well to out-of-distribution test sets. In this paper, we derive the basic mathematical conditions that successful update rules commonly satisfy. Consequently, we propose a novel L2O model with a mathematics-inspired structure that is broadly applicable and generalized well to out-of-distribution problems. Numerical simulations validate our theoretical findings and demonstrate the superior empirical performance of the proposed L2O model.Comment: ICML 202

Tolerance of transgenic Arabidopsis thaliana overexpressing apple MdAGO4.1 gene to drought and salt stress

The regulatory role of apple MdAGO4.1 gene in plant drought and salt resistance is unclear. In this study, transgenic A. thaliana in which the apple MdAGO4.1 gene was over-expressed was used to analyze the regulatory effects of the MdAGO4.1 gene on plant drought and salt resistance, to verify the function of the apple MdAGO4.1 gene. The seed germination rate, seedling fresh weight and root length of transgenic Arabidopsis strains in MS medium containing different concentrations of NaCl and mannitol were better than those of the wild type. The transgenic A. thaliana seedlings were more resistant to drought than wild type under drought stress. The transgenic strains were less affected by salt stress than thewild type. Exposure to drought and salt stress reduced the relative elektrolyte leakage, malondialdehyde (MDA), superoxide anion (O2-), and hydrogen peroxide (H2O2) levels of the transgenic strain significantly compared with the levels in the wild type. The levels of proline, protective enzyme activities, and the expression of genes related to drought and salt stress resistance were significantly higher than those of the wild type. These results indicate that MdAGO4.1 overexpression improved drought and salt tolerance in transgenic Arabidopsis. This study can provide a theoretical basis for future research on stress tolerance mechanisms and breeding new varieties of fruit trees resistant to drought and salt

Genome-wide analysis of WRKY transcription factor genes in Toona sinensis: An insight into evolutionary characteristics and terpene synthesis

WRKY transcription factors (TFs), one of the largest TF families, serve critical roles in the regulation of secondary metabolite production. However, little is known about the expression pattern of WRKY genes during the germination and maturation processes of Toona sinensis buds. In the present study, the new assembly of the T. sinensis genome was used for the identification of 78 TsWRKY genes, including gene structures, phylogenetic features, chromosomal locations, conserved protein domains, cis-regulatory elements, synteny, and expression profiles. Gene duplication analysis revealed that gene tandem and segmental duplication events drove the expansion of the TsWRKYs family, with the latter playing a key role in the creation of new TsWRKY genes. The synteny and evolutionary constraint analyses of the WRKY proteins among T. sinensis and several distinct species provided more detailed evidence of gene evolution for TsWRKYs. Besides, the expression patterns and co-expression network analysis show TsWRKYs may multi-genes co-participate in regulating terpenoid biosynthesis. The findings revealed that TsWRKYs potentially play a regulatory role in secondary metabolite synthesis, forming the basis for further functional characterization of WRKY genes with the intention of improving T. sinensis