Out-of-domain GAN inversion via Invertibility Decomposition for Photo-Realistic Human Face Manipulation

The fidelity of Generative Adversarial Networks (GAN) inversion is impeded by Out-Of-Domain (OOD) areas (e.g., background, accessories) in the image. Detecting the OOD areas beyond the generation ability of the pre-trained model and blending these regions with the input image can enhance fidelity. The "invertibility mask" figures out these OOD areas, and existing methods predict the mask with the reconstruction error. However, the estimated mask is usually inaccurate due to the influence of the reconstruction error in the In-Domain (ID) area. In this paper, we propose a novel framework that enhances the fidelity of human face inversion by designing a new module to decompose the input images to ID and OOD partitions with invertibility masks. Unlike previous works, our invertibility detector is simultaneously learned with a spatial alignment module. We iteratively align the generated features to the input geometry and reduce the reconstruction error in the ID regions. Thus, the OOD areas are more distinguishable and can be precisely predicted. Then, we improve the fidelity of our results by blending the OOD areas from the input image with the ID GAN inversion results. Our method produces photo-realistic results for real-world human face image inversion and manipulation. Extensive experiments demonstrate our method's superiority over existing methods in the quality of GAN inversion and attribute manipulation

Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge

Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition. The demand for rPPG tasks has expanded from demonstrating good performance on intra-dataset testing to cross-dataset testing (i.e., domain generalization). However, most existing methods have overlooked the prior knowledge of rPPG, resulting in poor generalization ability. In this paper, we propose a novel framework that simultaneously utilizes explicit and implicit prior knowledge in the rPPG task. Specifically, we systematically analyze the causes of noise sources (e.g., different camera, lighting, skin types, and movement) across different domains and incorporate these prior knowledge into the network. Additionally, we leverage a two-branch network to disentangle the physiological feature distribution from noises through implicit label correlation. Our extensive experiments demonstrate that the proposed method not only outperforms state-of-the-art methods on RGB cross-dataset evaluation but also generalizes well from RGB datasets to NIR datasets. The code is available at https://github.com/keke-nice/Greip

Addressing Variable Dependency in GNN-based SAT Solving

Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions concurrently. We show that for a group of symmetric SAT problems, the concurrent prediction is guaranteed to produce a wrong answer because it neglects the dependency among Boolean variables in SAT problems. % We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. The experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets

LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching

The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.Comment: The first two authors contributed equally to this work. Our code will be available at: https://github.com/EnVision-Research/LucidDreame

Ribosomal protein S3 mediates drug resistance of proteasome inhibitor: potential therapeutic application in multiple myeloma

Multiple myeloma (MM) remains incurable due to drug resistance. Ribosomal protein S3 (RPS3) has been identified as a non-Rel subunit of NF-κB. However, the detailed biological roles of RPS3 remain unclear. Here, we report for the first time that RPS3 is necessary for MM survival and drug resistance. RPS3 was highly expressed in MM, and knockout of RPS3 in MM inhibited cell growth and induced cell apoptosis both in vitro and in vivo. Overexpression of RPS3 mediated the proteasome inhibitor resistance of MM and shortened the survival of MM tumor-bearing animals. Moreover, our present study found an interaction between RPS3 and the thyroid hormone receptor interactor 13 (TRIP13), an oncogene related to MM tumorigenesis and drug resistance. We demonstrated that the phosphorylation of RPS3 was mediated by TRIP13 via PKCδ, which played an important role in activating the canonical NF-κB signaling and inducing cell survival and drug resistance in MM. Notably, the inhibition of NF-κB signaling by the small-molecule inhibitor targeting TRIP13, DCZ0415, was capable of triggering synergistic cytotoxicity when combined with bortezomib in drug-resistant MM. This study identifies RPS3 as a novel biomarker and therapeutic target in MM

高棅的詩學理論及其影響與流變

Other than Han and Wei’s poetry, High Tang’s poetry contributes most to the formation and development of the poetic style of Ming Dynasty. Based on the achievement of the predecessors, this dissertation attempts to find out the formatting and developing process of the consciousness of High Tang in Ming poetry and reveal the characteristics of this prevailing consciousness and its theoretical significance in the poetics’ history. In this dissertation, taking Canglang’s Remarks on Poetry written by Yan Yu as the starting point of the consciousness of High Tang in Ming Dynasty, the values of this consciousness in early Ming period is extracted from the poetic style of central Fujian. In addition, Gao Bing’s Tangshi Pinhui, considered as the first distinguishing symbol in both studying and disseminating this consciousness, is believed to be the foundation of poetry creations and studies in the whole Ming Dynasty. Moreover, in an effort to better outlining and understanding Gao Bing’s work in literary history, anthologies Kang Lin’s Yayin Huibian, Li Panlong’s Tang Poetry selections from Gujin Shishan, Zhong Xing and Tan Yuanchun’s Tangshi Gui, Tang Ruxun’s Tangshi Jie, Zhou Jing and Zhou Ting’s Tangshi Xuanmai Huitong Pinglin are compared and summarized, mainly in exploring on their origin, development and interrelationship, thus to reveal the paths and features of the poetics development in Gao Bing’s era and onwards. Chapter 1 comes with literature review and research purpose, significance and methodology of this dissertation. Chapter 2 introduces the life of Gao Bing, the publication of Tangshi Pinhui and the Poetics background of early Ming, and explores the spread of Tang Poetry since Tang and Song Dynasty and the conception of Tang Poetry before Ming Dynasty. Chapter 3 details the introduction and analysis of the anthologies involved. The major discussion parts of this dissertation starts from Chapter 4 to Chapter 8 in which several theories raised by Gao Bing and having great impacts on Ming Dynasty are analyzed and their inheritance and development in all phases of Ming dynasty is also clarified. These theories are Fundamental elements of Poetry, the awareness of style discrimination, conception of Gushi, Orthodoxy and Variation of poetry, the superiority and inferiority of Li Bai and Du Fu, etc. A comprehensive cognition of the criticism and developing history of Tang Poetry in Ming Dynasty is thus established by comparisons of these anthologies in various perspectives

Long noncoding RNA MALAT1 regulates autophagy associated chemoresistance via miR-23b-3p sequestration in gastric cancer

Abstract Background Chemoresistance has long been recognized as a major obstacle in cancer therapy. Clarifying the underlying mechanism of chemoresistance would result in novel strategies to improve patient’s response to chemotherapeutics. Methods lncRNA expression levels in gastric cancer (GC) cells was detected by quantitative real-time PCR (qPCR). MALAT1 shRNAs and overexpression vector were transfected into GC cells to down-regulate or up-regulate MALAT1 expression. In vitro and in vivo assays were performed to investigate the functional role of MALAT1 in autophagy associated chemoresistance. Results We showed that chemoresistant GC cells had higher levels of MALAT1 and increased autophagy compared with parental cells. Silencing of MALAT1 inhibited chemo-induced autophagy, whereas MALAT1 promoted autophagy in gastric cancer cells. Knockdown of MALAT1 sensitized GC cells to chemotherapeutics. MALAT1 acts as a competing endogenous RNA for miR-23b-3p and attenuates the inhibitory effect of miR-23b-3p on ATG12, leading to chemo-induced autophagy and chemoresistance in GC cells. Conclusions Taken together, our study revealed a novel mechanism of lncRNA-regulated autophagy-related chemoresistance in GC, casting new lights on the understanding of chemoresistance

Roles of BDNF, dopamine D-3 receptors, and their interactions in the expression of morphine-induced context-specific locomotor sensitization

Drug seeking, craving, and relapse can be triggered by environmental stimuli that acquire motivational salience through repeated associations with the drug&#39;s effects. Previous studies indicated that the dopamine D-3 receptor (Drd3) might be involved in the expression of drug-conditioned responses in rats, and brain-derived neurotrophic factor (BDNF) could modulate Drd3 expression in the nucleus accumbens (NAc). However, the involvement of neural regions with Drd3 activation and the underlying interaction between BDNF and Drd3 in the expression of behavioral responses controlled by a drug-associated environment have remained poorly understood. The present study used a conditioning procedure to assess the roles of BDNF, Drd3, and their interactions in the NAc in the expression of morphine-induced context-specific locomotor sensitization. We showed that the expression of locomotor sensitization in the morphine-paired environment was accompanied by significantly increased expression of Drd3 mRNA and BDNF mRNA and protein levels. Both sensitized locomotion in morphine-paired rats and enhanced Drd3 mRNA were suppressed by intra-NAc infusion of anti-tyrosine kinase receptor B (TrkB) IgG. Furthermore, intra-NAc infusion of the Drd3-selective antagonist SB-277011A significantly decreased the expression of context-specific locomotor sensitization and upregulated BDNF mRNA. Altogether, these results suggest that BDNF/TrkB signaling and activation of Drd3 in the NAc are required for the expression of morphine-induced context-specific locomotor sensitization. (C) 2011 Elsevier B.V. and ECNP. All rights reserved.</p