Prompt-based Distribution Alignment for Unsupervised Domain Adaptation

Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (UDA) problem is still not well explored. Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA. However, a major challenge for directly deploying such models on downstream UDA tasks is prompt engineering, which requires aligning the domain knowledge of source and target domains, since the performance of UDA is severely influenced by a good domain-invariant representation. We further propose a Prompt-based Distribution Alignment (PDA) method to incorporate the domain knowledge into prompt learning. Specifically, PDA employs a two-branch prompt-tuning paradigm, namely base branch and alignment branch. The base branch focuses on integrating class-related representation into prompts, ensuring discrimination among different classes. To further minimize domain discrepancy, for the alignment branch, we construct feature banks for both the source and target domains and propose image-guided feature tuning (IFT) to make the input attend to feature banks, which effectively integrates self-enhanced and cross-domain features into the model. In this way, these two branches can be mutually promoted to enhance the adaptation of VLMs for UDA. We conduct extensive experiments on three benchmarks to demonstrate that our proposed PDA achieves state-of-the-art performance. The code is available at https://github.com/BaiShuanghao/Prompt-based-Distribution-Alignment.Comment: 13pages,6figure

The Intervention Effect Assessment on Social Support Condition of the First Settlers in Dan jiangkou Reservoir Area, China

Objective: To assess the effect of psychological intervention on social support condition of the first settlers in Dan jiangkou reservoir area. Methods: Using the Social Support Rating Scale (SSRS) to measure the social support condition of the first batch of immigrants before and after the intervention, and then compare it with the immigrants who were not intervened. Results: Compared with the immigrants who were not intervened, the immigrants who received intervention have a higher score on the availability of social support (P<0.05).Conclusion: Psychological intervention can improve the social support condition for immigrants, especially in enhancing the availability of social support

Biological and genomic analysis of a symbiotic nitrogen fixation defective mutant in Medicago truncatula

Medicago truncatula has been selected as one of the model legume species for gene functional studies. To elucidate the functions of the very large number of genes present in plant genomes, genetic mutant resources are very useful and necessary tools. Fast Neutron (FN) mutagenesis is effective in inducing deletion mutations in genomes of diverse species. Through this method, we have generated a large mutant resource in M. truncatula. This mutant resources have been used to screen for different mutant using a forward genetics methods. We have isolated and identified a large amount of symbiotic nitrogen fixation (SNF) deficiency mutants. Here, we describe the detail procedures that are being used to characterize symbiotic mutants in M. truncatula. In recent years, whole genome sequencing has been used to speed up and scale up the deletion identification in the mutant. Using this method, we have successfully isolated a SNF defective mutant FN007 and identified that it has a large segment deletion on chromosome 3. The causal deletion in the mutant was confirmed by tail PCR amplication and sequencing. Our results illustrate the utility of whole genome sequencing analysis in the characterization of FN induced deletion mutants for gene discovery and functional studies in the M. truncatula. It is expected to improve our understanding of molecular mechanisms underlying symbiotic nitrogen fixation in legume plants to a great extent

Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate

Root traits such as root angle and hair length influence resource acquisition particularly for immobile nutrients like phosphorus (P). Here, we attempted to modify root angle in rice by disrupting the OsAUX1 auxin influx transporter gene in an effort to improve rice P acquisition efficiency. We show by X-ray microCT imaging that root angle is altered in the osaux1 mutant, causing preferential foraging in the top soil where P normally accumulates, yet surprisingly, P acquisition efficiency does not improve. Through closer investigation, we reveal that OsAUX1 also promotes root hair elongation in response to P limitation. Reporter studies reveal that auxin response increases in the root hair zone in low P environments. We demonstrate that OsAUX1 functions to mobilize auxin from the root apex to the differentiation zone where this signal promotes hair elongation when roots encounter low external P. We conclude that auxin and OsAUX1 play key roles in promoting root foraging for P in rice

State of Panax ginseng Research: A Global Analysis

This article aims to understand the global and longitudinal trends of research on Panax ginseng. We used bibliometrics to analyze 3974 papers collected from the Web of ScienceTM Core Collection database during 1959–2016. The number of publications showed a steady growth before 2000 and exponentially increased in stage III (2000–2016, about 86% of the papers were published). Research on P. ginseng was conducted in 64 countries, mainly in Asia; in particular, 41% and 28% of the publications were from South Korea and China, respectively. The institutions from South Korea and China had high publication output and close cooperation and provided the majority of financial support. All top 10 authors and four of the top 20 journals in terms of number of publications originated from South Korea. The leading research subjects were pharmacology (39%), plant science (26%), and integrative complementary medicine (19%). The hotspot of P. ginseng research transformed from basic science to application, and multidisciplinary sciences will play a substantial role in the future. This study provides a comprehensive analysis to elucidate the global distribution, collaboration patterns, and research trends in the P. ginseng domain

High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim to refine the local features to be more discriminative and relevant to classification. Thus, we propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. TSECS learns high-level semantic features for image-to-class similarity measurement. Based on the high-level features, we design a cross-domain self-training strategy to leverage the few labeled samples in source domain to build the classifier in target domain. In addition, we minimize the KL divergence of the high-level feature distributions between source and target domains to shorten the distance of the samples between the two domains. Extensive experiments on DomainNet show that the proposed method significantly outperforms SOTA methods in FS-UDA by a large margin (i.e., ∼ 10%)

High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim to refine the local features to be more discriminative and relevant to classification. Thus, we propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. TSECS learns high-level semantic features for image-to-class similarity measurement. Based on the high-level features, we design a cross-domain self-training strategy to leverage the few labeled samples in source domain to build the classifier in target domain. In addition, we minimize the KL divergence of the high-level feature distributions between source and target domains to shorten the distance of the samples between the two domains. Extensive experiments on DomainNet show that the proposed method significantly outperforms SOTA methods in FS-UDA by a large margin (i.e., ~10%)