RS-Corrector: Correcting the Racial Stereotypes in Latent Diffusion Models

Recent text-conditioned image generation models have demonstrated an exceptional capacity to produce diverse and creative imagery with high visual quality. However, when pre-trained on billion-sized datasets randomly collected from the Internet, where potential biased human preferences exist, these models tend to produce images with common and recurring stereotypes, particularly for certain racial groups. In this paper, we conduct an initial analysis of the publicly available Stable Diffusion model and its derivatives, highlighting the presence of racial stereotypes. These models often generate distorted or biased images for certain racial groups, emphasizing stereotypical characteristics. To address these issues, we propose a framework called "RS-Corrector", designed to establish an anti-stereotypical preference in the latent space and update the latent code for refined generated results. The correction process occurs during the inference stage without requiring fine-tuning of the original model. Extensive empirical evaluations demonstrate that the introduced \themodel effectively corrects the racial stereotypes of the well-trained Stable Diffusion model while leaving the original model unchanged.Comment: 16 pages, 15 figures, conferenc

Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field

Recently 3D-aware GAN methods with neural radiance field have developed rapidly. However, current methods model the whole image as an overall neural radiance field, which limits the partial semantic editability of synthetic results. Since NeRF renders an image pixel by pixel, it is possible to split NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field (CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image. Thus we can manipulate the synthesized semantic regions independently, while fixing the other parts unchanged. Furthermore, CNeRF is also designed to decouple shape and texture within each semantic region. Compared to state-of-the-art 3D-aware GAN methods, our approach enables fine-grained semantic region manipulation, while maintaining high-quality 3D-consistent synthesis. The ablation studies show the effectiveness of the structure and loss function used by our method. In addition real image inversion and cartoon portrait 3D editing experiments demonstrate the application potential of our method.Comment: Accepted by AAAI2023 Ora

Free-style and Fast 3D Portrait Synthesis

Efficiently generating a free-style 3D portrait with high quality and consistency is a promising yet challenging task. The portrait styles generated by most existing methods are usually restricted by their 3D generators, which are learned in specific facial datasets, such as FFHQ. To get a free-style 3D portrait, one can build a large-scale multi-style database to retrain the 3D generator, or use a off-the-shelf tool to do the style translation. However, the former is time-consuming due to data collection and training process, the latter may destroy the multi-view consistency. To tackle this problem, we propose a fast 3D portrait synthesis framework in this paper, which enable one to use text prompts to specify styles. Specifically, for a given portrait style, we first leverage two generative priors, a 3D-aware GAN generator (EG3D) and a text-guided image editor (Ip2p), to quickly construct a few-shot training set, where the inference process of Ip2p is optimized to make editing more stable. Then we replace original triplane generator of EG3D with a Image-to-Triplane (I2T) module for two purposes: 1) getting rid of the style constraints of pre-trained EG3D by fine-tuning I2T on the few-shot dataset; 2) improving training efficiency by fixing all parts of EG3D except I2T. Furthermore, we construct a multi-style and multi-identity 3D portrait database to demonstrate the scalability and generalization of our method. Experimental results show that our method is capable of synthesizing high-quality 3D portraits with specified styles in a few minutes, outperforming the state-of-the-art.Comment: project website: https://tianxiangma.github.io/FF3

Numerical simulation of flow field coupling with electric field for leakage current particulate matter sensor

With the diesel particulate filter more and more widely applied on engine, the particulate matter (PM) sensor is used to detect malfunctions of diesel particulate filter (DPF) in on board diagnostics (OBD). This paper focused on the new leakage current particulate matter sensor, which has more practical significance. The electric field and flow field were simulated by the COMSOL Multihysics, and the influence rules of sensor electrode parameters on the electric field and flow field were analyzed. The dynamic characteristics of particulate matters with different charges was studied. The simulation results showed that the average electric field strength was higher with higher electrode voltage and larger electrode spacing, which made the motion trend of charged particulate matter to electrodes more obvious. The decrease of electrode spacing or the increase of electrode length made the exhaust flow more stable, and the motion trend of charged particulate matters to electrodes is more obvious with the increase of electrode length. It was concluded that the flow of the exhaust and particulate matter was in good condition when the electrode spacing was 12.5mm, the electrode length is 12.5mm and the electrode voltage ranged between 1000V and 1500V

Impact of glucocorticoids and rapamycin on autophagy in Candida glabrata-infected macrophages from BALB/c mice

ObjectiveIn the defense against microorganisms like Candida albicans, macrophages recruit LC3(Microtubule-associated protein 1A/1B-light chain 3) to the periplasm, engaging in the elimination process through the formation of a single-membrane phagosome known as LC3-associated phagocytosis (LAP). Building on this, we propose the hypothesis that glucocorticoids may hinder macrophage phagocytosis of Candida glabrata by suppressing LAP, and rapamycin could potentially reverse this inhibitory effect.MethodsRAW264.7 cells were employed for investigating the immune response to Candida glabrata infection. Various reagents, including dexamethasone, rapamycin, and specific antibodies, were utilized in experimental setups. Assays, such as fluorescence microscopy, flow cytometry, ELISA (Enzyme-Linked Immunosorbent Assay), Western blot, and confocal microscopy, were conducted to assess phagocytosis, cytokine levels, protein expression, viability, and autophagy dynamics.ResultsGlucocorticoids significantly inhibited macrophage autophagy, impairing the cells’ ability to combat Candida glabrata. Conversely, rapamycin exhibited a dual role, initially inhibiting and subsequently promoting phagocytosis of Candida glabrata by macrophages. Glucocorticoids hinder macrophage autophagy in Candida glabrata infection by suppressing the MTOR pathway(mammalian target of rapamycin pathway), while the activation of MTOR pathway by Candida glabrata diminishes over time.ConclusionOur study elucidates the intricate interplay between glucocorticoids, rapamycin, and macrophage autophagy during Candida glabrata infection. Understanding the implications of these interactions not only sheds light on the host immune response dynamics but also unveils potential therapeutic avenues for managing fungal infections

Role of Vitamin C in Skin Diseases

Vitamin C (ascorbic acid) plays an important role in maintaining skin health and can promote the differentiation of keratinocytes and decrease melanin synthesis, leading to antioxidant protection against UV-induced photodamage. Normal skin needs high concentrations of vitamin C, which plays many roles in the skin, including the formation of the skin barrier and collagen in the dermis, the ability to counteract skin oxidation, and the modulation of cell signal pathways of cell growth and differentiation. However, vitamin C deficiency can cause or aggravate the occurrence and development of some skin diseases, such as atopic dermatitis (AD) and porphyria cutanea tarda (PCT). Levels of vitamin C in plasma are decreased in AD, and vitamin C deficiency may be one of the factors that contributes to the pathogenesis of PCT. On the other hand, high doses of vitamin C have significantly reduced cancer cell viability, as well as invasiveness, and induced apoptosis in human malignant melanoma. In this review, we will summarize the effects of vitamin C on four skin diseases (porphyria cutanea tarda, atopic dermatitis, malignant melanoma, and herpes zoster and postherpetic neuralgia) and highlight the potential of vitamin C as a therapeutic strategy to treat these diseases, emphasizing the clinical application of vitamin C as an adjuvant for drugs or physical therapy in other skin diseases

circFBXW7 attenuates malignant progression in lung adenocarcinoma by sponging miR-942-5p

Background: As a type of non-coding RNA, circular RNAs (circRNAs) are considered to be functional molecules associated with human cancers. An increasing number of circRNAs have been verified in malignant progression in a number of cancers. The circRNA, circFBXW7, has been proven to play an important role in tumor proliferation and metastasis. However, whether circFBXW7 influences progression in lung adenocarcinoma (LUAD) remains unclear. Methods: Quantitative real-time reverse transcriptase PCR (qRT-PCR) was used to verify circFBXW7 in LUAD cell lines and LUAD tissues. Kaplan-Meier analysis was then used to compare the disease-free survival (DFS) and overall survival (OS) of these LUAD patients. The biological function of circFBXW7 was examined by overexpression and knockdown of circFBXW7 using MTT assay, EdU assay, wound-healing assay, and Transwell in vitro assays. To explore the mechanism of the circFBXW7, RNA pull-down assay, dual luciferase reporter assay, and RNA immunoprecipitation (RIP) assay were employed to examine the interaction between circFBXW7 and miR-942-5p. Western blot was used to study the fundamental proteins associated with the epithelial-mesenchymal transition (EMT) pathway. In vivo studies with BALB/c nude mice subcutaneously injected with cells stably overexpressing circFBXW7 were performed to further validate the in vitro results. Results: circFBXW7 was downregulated in LUAD cell lines and tissues, and LUAD patients with lower levels had shorter DFS and OS. The in vitro study showed that circFBXW7 overexpression inhibited proliferation and migration of A549 and HCC2279 cell lines. These results were confirmed by circFBXW7 knockdown, which showed the reverse effect. The in vivo model showed that the circRNA levels influenced the tumor growth. Finally, we determined that circFBXW7 target miRNA-942-5p which regulates the EMT gene BARX2. The modulation of circFBXW7 levels produced significant changes in EMT genes in vitro and in vivo. Conclusions: Our findings showed that circFBXW7 inhibits proliferation and migration by controlling the miR-942-5p/BARX2 axis in LUAD cell lines and its levels correlates with patient survival suggesting that regulating circFBXW7 could have therapeutic value in treating LUAD patients

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/Comment: 28 pages, 16 figures, under review, work in progres