Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension

In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference. However, in cross-lingual machine reading comprehension (MRC), it is difficult to perform a deep level of assistance to enhance cross-lingual transfer because of the variation of answer span positions in different languages. In this paper, we propose X-STA, a new approach for cross-lingual MRC. Specifically, we leverage an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. A Gradient-Disentangled Knowledge Sharing technique is proposed as an improved cross-attention block. In addition, we force the model to learn semantic alignments from multiple granularities and calibrate the model outputs with teacher guidance to enhance cross-lingual transferability. Experiments on three multi-lingual MRC datasets show the effectiveness of our method, outperforming state-of-the-art approaches.Comment: emnlp 202

Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing

Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative Text-to-image generation. Yet, for domain-specific scenarios, tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers, which modify objects or object properties in images by manipulating feature components in attention layers during the generation process. However, little is known about what semantic meanings these attention layers have learned and which parts of the attention maps contribute to the success of image editing. In this paper, we conduct an in-depth probing analysis and demonstrate that cross-attention maps in Stable Diffusion often contain object attribution information that can result in editing failures. In contrast, self-attention maps play a crucial role in preserving the geometric and shape details of the source image during the transformation to the target image. Our analysis offers valuable insights into understanding cross and self-attention maps in diffusion models. Moreover, based on our findings, we simplify popular image editing methods and propose a more straightforward yet more stable and efficient tuning-free procedure that only modifies self-attention maps of the specified attention layers during the denoising process. Experimental results show that our simplified method consistently surpasses the performance of popular approaches on multiple datasets

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order to produce satisfactory results for real-world applications. We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores. Our results demonstrate that learning from visual AI feedback promises the potential to improve the quality of generated prompts and images significantly. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service in the cloud.Comment: emnlp 202

Phosphorus Solubilizing and Releasing Bacteria Screening from the Rhizosphere in a Natural Wetland

Inorganic phosphorus (P)-solubilizing bacteria (IPSB) and organic P-mineralizing bacteria (OPMB) were isolated from bacteria that were first extracted from the rhizosphere soil of a natural wetland and then grown on either tricalcium phosphate or lecithin medium. The solubilizing of inorganic P was the major contribution to P availability, since the isolated bacteria released much more available P from inorganic tricalcium phosphate than lecithin. IPSB No. 5 had the highest P release rate, that is, 0.53 mg·L−1·h−1 in 96 h, and R10′s release rate was 0.52 mg·L−1·h−1 in 10 days. The bacteria were identified as Pseudomonas sp. and Pseudomonas knackmussii, respectively. R10 released as much as 125.88 mg·L−1 dissolved P from tricalcium phosphate medium, while R4 released the most dissolved P from organic P medium among the isolates, with a concentration of 1.88 mg·L−1 and a releasing rate of 0.0078 mg·L−1·h−1 in ten days. P releasing increased with a pH decrease only when it was from inorganic P, not organic lecithin, and there was no significant correlation between the culture pH and P solubilizing. High-throughput sequencing analysis revealed that the dominant phylum in the studied wetland rhizosphere consisted of Acidobacteria, Proteobacteria, Bacteroidetes and Chloroflexi, accounting for 34.9%, 34.2%, 8.8% and 4.8%, respectively

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration

Hormonal Regulation and Transcriptomic Insights into Flower Development in <i>Hydrangea paniculata</i> ‘Vanilla Strawberry’

Understanding the molecular mechanisms that regulate flower growth, development, and opening is of paramount importance, yet these processes remain less explored at the genetic level. Flower development in Hydrangea paniculata ‘Vanilla Strawberry’ is finely tuned through hormonal signals, yet the genetic underpinnings are not well defined. This study addresses the gap by examining the influence of gibberellic acid (GA3), salicylic acid (SA), and ethylene (ETH) on the flowering traits and underlying molecular responses. Treatment with 100 mg/L SA significantly improved chlorophyll content and bolstered the accumulation of soluble sugars and proteins, advancing the flowering onset by 6 days and lengthening the flowering period by 11 days. Concurrently, this treatment enhanced inflorescence dimensions, increasing length, width, and petal area by 22.76%, 26.74%, and 27.45%, respectively. Contrastingly, 100 mg/L GA3 expanded inflorescence size but postponed flowering initiation and decreased inflorescence count. Higher concentrations of SA and GA3, as well as any concentration of ETH, resulted in delayed flowering and inferior inflorescence attributes. A physiological analysis over 50 days revealed that these regulators variably affected sugar and protein levels and modified antioxidant enzyme activities. An RNA-seq analysis during floral development highlighted significant transcriptomic reprogramming, with SA treatment downregulating Myb transcription factors, implicating them in the modulation of flowering timing and stress adaptation. These findings illuminate the complex interplay between hormonal treatments, gene expression, and flowering phenotypes in Hydrangea paniculata, offering valuable perspectives for ornamental horticulture optimization

Multi-omics analyses demonstrate the modulating role of gut microbiota on the associations of unbalanced dietary intake with gastrointestinal symptoms in children with autism spectrum disorder

ABSTRACTOur previous work revealed that unbalanced dietary intake was an important independent factor associated with constipation and gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Growing evidence has shown the alterations in the gut microbiota and gut microbiota-derived metabolites in ASD. However, how the altered microbiota might affect the associations between unbalanced diets and GI symptoms in ASD remains unknown. We analyzed microbiome and metabolomics data in 90 ASD and 90 typically developing (TD) children based on 16S rRNA and untargeted metabolomics, together with dietary intake and GI symptoms assessment. We found that there existed 11 altered gut microbiota (FDR-corrected P-value <0.05) and 397 altered metabolites (P-value <0.05) in children with ASD compared with TD children. Among the 11 altered microbiota, the Turicibacter, Coprococcus 1, and Lachnospiraceae FCS020 group were positively correlated with constipation (FDR-corrected P-value <0.25). The Eggerthellaceae was positively correlated with total GI symptoms (FDR-corrected P-value <0.25). More importantly, three increased microbiota including Turicibacter, Coprococcus 1, and Eggerthellaceae positively modulated the associations of unbalanced dietary intake with constipation and total GI symptoms, and the decreased Clostridium sp. BR31 negatively modulated their associations in ASD children (P-value <0.05). Together, the altered microbiota strengthens the relationship between unbalanced dietary intake and GI symptoms. Among the altered metabolites, ten metabolites derived from microbiota (Turicibacter, Coprococcus 1, Eggerthellaceae, and Clostridium sp. BR31) were screened out, enriched in eight metabolic pathways, and were identified to correlate with constipation and total GI symptoms in ASD children (FDR-corrected P-value <0.25). These metabolomics findings further support the modulating role of gut microbiota on the associations of unbalanced dietary intake with GI symptoms. Collectively, our research provides insights into the relationship between diet, the gut microbiota, and GI symptoms in children with ASD