Decoupled Textual Embeddings for Customized Image Generation

Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently. However, existing methods usually suffer from overfitting issues and entangle the subject-unrelated information (e.g., background and pose) with the learned concept, limiting the potential to compose concept into new scenes. To address these issues, we propose the DETEX, a novel approach that learns the disentangled concept embedding for flexible customized text-to-image generation. Unlike conventional methods that learn a single concept embedding from the given images, our DETEX represents each image using multiple word embeddings during training, i.e., a learnable image-shared subject embedding and several image-specific subject-unrelated embeddings. To decouple irrelevant attributes (i.e., background and pose) from the subject embedding, we further present several attribute mappers that encode each image as several image-specific subject-unrelated embeddings. To encourage these unrelated embeddings to capture the irrelevant information, we incorporate them with corresponding attribute words and propose a joint training strategy to facilitate the disentanglement. During inference, we only use the subject embedding for image generation, while selectively using image-specific embeddings to retain image-specified attributes. Extensive experiments demonstrate that the subject embedding obtained by our method can faithfully represent the target concept, while showing superior editability compared to the state-of-the-art methods. Our code will be made published available.Comment: 16 pages, 16 figure

Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction

Few-shot Relation Extraction (FSRE) aims to extract relational facts from a sparse set of labeled corpora. Recent studies have shown promising results in FSRE by employing Pre-trained Language Models (PLMs) within the framework of supervised contrastive learning, which considers both instances and label facts. However, how to effectively harness massive instance-label pairs to encompass the learned representation with semantic richness in this learning paradigm is not fully explored. To address this gap, we introduce a novel synergistic anchored contrastive pre-training framework. This framework is motivated by the insight that the diverse viewpoints conveyed through instance-label pairs capture incomplete yet complementary intrinsic textual semantics. Specifically, our framework involves a symmetrical contrastive objective that encompasses both sentence-anchored and label-anchored contrastive losses. By combining these two losses, the model establishes a robust and uniform representation space. This space effectively captures the reciprocal alignment of feature distributions among instances and relational facts, simultaneously enhancing the maximization of mutual information across diverse perspectives within the same relation. Experimental results demonstrate that our framework achieves significant performance enhancements compared to baseline models in downstream FSRE tasks. Furthermore, our approach exhibits superior adaptability to handle the challenges of domain shift and zero-shot relation extraction. Our code is available online at https://github.com/AONE-NLP/FSRE-SaCon

Treatment of berberine alleviates diabetic nephropathy by reducing iron overload and inhibiting oxidative stress

Diabetic nephropathy (DN) has become one of the major fatal factors in diabetic patients. The aim of this study was to elucidate the function and mechanism by which berberine exerts renoprotective effects in DN. In this work, we first demonstrated that urinary iron concentration, serum ferritin and hepcidin levels were increased and total antioxidant capacity was significantly decreased in DN rats, while these changes could be partially reversed by berberine treatment. Berberine treatment also alleviated DN-induced changes in the expression of proteins involved in iron transport or iron uptake. In addition, berberine treatment also partially blocked the expression of renal fibrosis markers induced by DN, including MMP2, MMP9, TIMP3, β-arrestin-1, and TGF-β1. In conclusion, the results of this study suggest that berberine may exert renoprotective effects by ameliorating iron overload and oxidative stress and reducing D

Plasma Oxidation of H2S over Non-stoichiometric LaxMnO3 Perovskite Catalysts in a Dielectric Barrier Discharge Reactor

In this work, plasma-catalytic removal of H2S over LaxMnO3 (x = 0.90, 0.95, 1, 1.05 and 1.10) has been studied in a coaxial dielectric barrier discharge (DBD) reactor. The non-stoichiometric effect of the LaxMnO3 catalysts on the removal of H2S and sulfur balance in the plasma-catalytic process has been investigated as a function of specific energy density (SED). The integration of the plasma with the LaxMnO3 catalysts significantly enhanced the reaction performance compared to the process using plasma alone. The highest H2S removal of 96.4% and sulfur balance of 90.5% were achieved over the La0.90MnO3 catalyst, while the major products included SO2 and SO3. The missing sulfur could be ascribed to the sulfur deposited on the catalyst surfaces. The non-stoichiometric LaxMnO3 catalyst exhibited larger specific surface areas and smaller crystallite sizes compared to the LaMnO3 catalyst. The non-stoichiometric effect changed their redox properties as the decreased La/Mn ratio favored the transformation of Mn3+ to Mn4+, which contributed to the generation of oxygen vacancies on the catalyst surfaces. The XPS and H2-TPR results confirmed that the Mn-rich catalysts showed the higher relative concentration of surface adsorbed oxygen (Oads) and lower reduction temperature compared to LaMnO3 catalyst. The reaction performance of the plasma-catalytic oxidation of H2S is closely related to the relative concentration of Oads formed on the catalyst surfaces and the reducibility of the catalysts