The stability of perennial grasses mediates the negative impacts of long-term warming and increasing precipitation on community stability in a desert steppe.

Background: Desert steppe, as an ecotone between desert and grassland, has few species and is sensitive to climate change. Climate change alters species diversity and the stability of functional groups, which may positively or negatively affect community stability. However, the response of plant community stability in the desert steppe to experimental warming and increasing precipitation remains largely unexplored. Methods: In a factorial experiment of warming and increasing precipitation for five to seven years (ambient precipitation (P0), ambient precipitation increased by 25% and 50% (P1 and P2), ambient temperature (W0), ambient temperature increased by 2°C and 4°C (W1 and W2)), we estimated the importance value (IV) of four functional groups (perennial grasses, semi-shrubs, perennial forbs and annual herbs), species diversity and community stability. Results: Compared to W0P0, the IV of perennial grasses was reduced by 37.66% in W2P2, whereas the IV of perennial forbs increased by 48.96%. Although increasing precipitation and experimental warming significantly altered species composition, the effect on species diversity was insignificant (P > 0.05). In addition, increasing precipitation and experimental warming had a significant negative impact on community stability. The stability of perennial grasses significantly explained community stability. Conclusion: Our results suggest that the small number of species in desert steppe limits the contribution of species diversity to regulating community stability. By contrast, maintaining high stability of perennial grasses can improve community stability in the desert steppe.

Soil Moisture Content Dominates the Photosynthesis of C3 and C4 Plants in a Desert Steppe after Long-Term Warming and Increasing Precipitation.

Plant photosynthesis has a non-negligible influence on forage quality and ecosystem carbon sequestration. However, the influence of long-term warming, increasing precipitation, and their interactions on the photosynthesis of dominant species in desert steppe remains unclear, and the main factors regulating plant photosynthesis in desert steppes have remained unrevealed. Therefore, we measured the photosynthetic parameters and specific leaf area of the dominant species and calculated the water and nitrogen content of leaves and soil in a desert steppe after long-term warming and increasing precipitation (air temperature, W0, air temperature increases of 2 °C and 4 °C, W1 and W2; natural precipitation, P0, natural precipitation increases of 25% and 50%, P1 and P2). Results showed that warming and increasing precipitation significantly enhanced photosynthesis in C3 and C4 species (p < 0.05). Compared to W0P0, the net photosynthetic rate of C3 and C4 species in W2P2 increased by 159.46% and 178.88%, respectively. Redundancy analysis showed that soil water content significantly explained the photosynthesis of C3 and C4 plants (the degree of explanation was 48% and 67.7%), followed by soil-available nitrogen content (the degree of explanation was 19.6% and 5.3%). Therefore, our study found that climate change enhanced photosynthesis in C3 and C4 plants, and soil water content plays a critical role in regulating photosynthesis in desert steppes.

Description-Enhanced Label Embedding Contrastive Learning for Text Classification.

Text classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially pretrained language models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this article, we employ self-supervised learning (SSL) in model learning process and design a novel self-supervised relation of relation (R 2 ) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel () for text classification, in which text classification and R 2 classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multiaspect descriptions for label semantic learning and extend to a novel () from a label embedding perspective. One step further, since these fine-grained descriptions may introduce unexpected noise, we develop a mutual interaction module to select appropriate parts from input sentences and labels simultaneously based on contrastive learning (CL) for noise mitigation. Extensive experiments on different text classification tasks reveal that can effectively improve the classification performance and can make better use of label information and further improve the performance. As a byproduct, we have released the codes to facilitate other research.

LadRa-Net: Locally Aware Dynamic Reread Attention Net for Sentence Semantic Matching.

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as natural language inference (NLI) and paraphrase identification (PI). Much recent progress has been made in this area, especially attention-based methods and pretrained language model-based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of the attention mechanism. In order to overcome this problem and boost the performance of the attention mechanism, we propose a novel dynamic reread (DRr) attention, which can pay close attention to one small region of sentences at each step and reread the important parts for better sentence representations. Based on this attention variation, we develop a novel DRr network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in DRr attention seems insufficient for sentence semantics, and employing pretrained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to locally aware dynamic reread attention net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of byte-pair encoding (BPE) in pretrained language models and boost the performance of DRr attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research.