18,578 results on '"Shi, Min"'
Search Results
2. FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
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Tian, Yu, Wen, Congcong, Shi, Min, Afzal, Muhammad Muneeb, Huang, Hao, Khan, Muhammad Osama, Luo, Yan, Fang, Yi, and Wang, Mengyu
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have introduced new methodologies and datasets in medical AI. However, the fairness issue under the setting of domain transfer is almost unexplored, while it is common that clinics rely on different imaging technologies (e.g., different retinal imaging modalities) for patient diagnosis. This paper presents FairDomain, a pioneering systemic study into algorithmic fairness under domain shifts, employing state-of-the-art domain adaptation (DA) and generalization (DG) algorithms for both medical segmentation and classification tasks to understand how biases are transferred between different domains. We also introduce a novel plug-and-play fair identity attention (FIA) module that adapts to various DA and DG algorithms to improve fairness by using self-attention to adjust feature importance based on demographic attributes. Additionally, we curate the first fairness-focused dataset with two paired imaging modalities for the same patient cohort on medical segmentation and classification tasks, to rigorously assess fairness in domain-shift scenarios. Excluding the confounding impact of demographic distribution variation between source and target domains will allow clearer quantification of the performance of domain transfer models. Our extensive evaluations reveal that the proposed FIA significantly enhances both model performance accounted for fairness across all domain shift settings (i.e., DA and DG) with respect to different demographics, which outperforms existing methods on both segmentation and classification. The code and data can be accessed at https://ophai.hms.harvard.edu/datasets/harvard-fairdomain20k., Comment: ECCV 2024; Codes and datasets are available at https://github.com/Harvard-Ophthalmology-AI-Lab/FairDomain
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- 2024
3. Studying magnetic reconnection with synchrotron polarization statistics
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Zhang, Jian-Fu, Liang, Shi-Min, and Xiao, Hua-Ping
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Magnetic reconnection is a fundamental process for releasing magnetic energy in space physics and astrophysics. At present, the usual way to investigate the reconnection process is through analytical studies or first-principles numerical simulations. This paper is the first to understand the turbulent magnetic reconnection process by exploring the nature of magnetic turbulence. From the perspective of radio synchrotron polarization statistics, we study how to recover the properties of the turbulent magnetic field by considering the line of sight along different directions of the reconnection layer. We find that polarization intensity statistics can reveal the spectral properties of reconnection turbulence. This work opens up a new way of understanding turbulent magnetic reconnection., Comment: 12 pages, 7 figures, 1 table. Accepted for publication in ApJ
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- 2024
4. OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning
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Wang, Shihao, Yu, Zhiding, Jiang, Xiaohui, Lan, Shiyi, Shi, Min, Chang, Nadine, Kautz, Jan, Li, Ying, and Alvarez, Jose M.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The advances in multimodal large language models (MLLMs) have led to growing interests in LLM-based autonomous driving agents to leverage their strong reasoning capabilities. However, capitalizing on MLLMs' strong reasoning capabilities for improved planning behavior is challenging since planning requires full 3D situational awareness beyond 2D reasoning. To address this challenge, our work proposes a holistic framework for strong alignment between agent models and 3D driving tasks. Our framework starts with a novel 3D MLLM architecture that uses sparse queries to lift and compress visual representations into 3D before feeding them into an LLM. This query-based representation allows us to jointly encode dynamic objects and static map elements (e.g., traffic lanes), providing a condensed world model for perception-action alignment in 3D. We further propose OmniDrive-nuScenes, a new visual question-answering dataset challenging the true 3D situational awareness of a model with comprehensive visual question-answering (VQA) tasks, including scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making and planning. Extensive studies show the effectiveness of the proposed architecture as well as the importance of the VQA tasks for reasoning and planning in complex 3D scenes.
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- 2024
5. Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields
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Liu, Tianqi, Ye, Xinyi, Shi, Min, Huang, Zihao, Pan, Zhiyu, Peng, Zhan, and Cao, Zhiguo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D descriptors for decoding novel views. However, existing methods show limited generalization ability in challenging conditions due to inaccurate geometry, sub-optimal descriptors, and decoding strategies. We address these issues point by point. First, we find the variance-based cost volume exhibits failure patterns as the features of pixels corresponding to the same point can be inconsistent across different views due to occlusions or reflections. We introduce an Adaptive Cost Aggregation (ACA) approach to amplify the contribution of consistent pixel pairs and suppress inconsistent ones. Unlike previous methods that solely fuse 2D features into descriptors, our approach introduces a Spatial-View Aggregator (SVA) to incorporate 3D context into descriptors through spatial and inter-view interaction. When decoding the descriptors, we observe the two existing decoding strategies excel in different areas, which are complementary. A Consistency-Aware Fusion (CAF) strategy is proposed to leverage the advantages of both. We incorporate the above ACA, SVA, and CAF into a coarse-to-fine framework, termed Geometry-aware Reconstruction and Fusion-refined Rendering (GeFu). GeFu attains state-of-the-art performance across multiple datasets. Code is available at https://github.com/TQTQliu/GeFu ., Comment: Accepted by CVPR 2024. Project page: https://gefucvpr24.github.io
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- 2024
6. FairCLIP: Harnessing Fairness in Vision-Language Learning
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Luo, Yan, Shi, Min, Khan, Muhammad Osama, Afzal, Muhammad Muneeb, Huang, Hao, Yuan, Shuaihang, Tian, Yu, Song, Luo, Kouhana, Ava, Elze, Tobias, Fang, Yi, and Wang, Mengyu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap, we introduce the first fair vision-language medical dataset Harvard-FairVLMed that provides detailed demographic attributes, ground-truth labels, and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed, we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2), pre-trained on both natural and medical domains, across four different protected attributes. Our results highlight significant biases in all VL models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. In order to alleviate these biases, we propose FairCLIP, an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind, Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://ophai.hms.harvard.edu/datasets/harvard-fairvlmed10k., Comment: CVPR 2024
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- 2024
7. A New Split Algorithm for 3D Gaussian Splatting
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Feng, Qiyuan, Cao, Gengchen, Chen, Haoxiang, Mu, Tai-Jiang, Martin, Ralph R., and Hu, Shi-Min
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Computer Science - Graphics - Abstract
3D Gaussian splatting models, as a novel explicit 3D representation, have been applied in many domains recently, such as explicit geometric editing and geometry generation. Progress has been rapid. However, due to their mixed scales and cluttered shapes, 3D Gaussian splatting models can produce a blurred or needle-like effect near the surface. At the same time, 3D Gaussian splatting models tend to flatten large untextured regions, yielding a very sparse point cloud. These problems are caused by the non-uniform nature of 3D Gaussian splatting models, so in this paper, we propose a new 3D Gaussian splitting algorithm, which can produce a more uniform and surface-bounded 3D Gaussian splatting model. Our algorithm splits an $N$-dimensional Gaussian into two N-dimensional Gaussians. It ensures consistency of mathematical characteristics and similarity of appearance, allowing resulting 3D Gaussian splatting models to be more uniform and a better fit to the underlying surface, and thus more suitable for explicit editing, point cloud extraction and other tasks. Meanwhile, our 3D Gaussian splitting approach has a very simple closed-form solution, making it readily applicable to any 3D Gaussian model., Comment: 11 pages, 10 figures
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- 2024
8. 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
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Hong, Fangzhou, Tang, Jiaxiang, Cao, Ziang, Shi, Min, Wu, Tong, Chen, Zhaoxi, Yang, Shuai, Wang, Tengfei, Pan, Liang, Lin, Dahua, and Liu, Ziwei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping. The second stage utilizes 2D diffusion priors to further refine the texture of coarse 3D models from the first stage. The refinement consists of both latent and pixel space optimization for high-quality texture generation. To facilitate the training of the proposed system, we clean and caption the largest open-source 3D dataset, Objaverse, by combining the power of vision language models and large language models. Experiment results are reported qualitatively and quantitatively to show the performance of the proposed system. Our codes and models are available at https://github.com/3DTopia/3DTopia, Comment: Code available at https://github.com/3DTopia/3DTopia
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- 2024
9. Distribution Properties of the 6.7 GHz Methanol Masers and Their Surrounding Gases in the Milky Way
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Yang, Tian, Chen, Xi, Zhang, Yan-Kun, Ouyang, Xu-Jia, Song, Shi-Min, Chen, Jia-Liang, and Lu, Ying
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Astrophysics - Astrophysics of Galaxies - Abstract
An updated catalog consisting of 1092 6.7-GHz methanol maser sources was reported in this work. Additionally, the NH3 (1, 1), NH3 (2, 2), and NH3 (3, 3) transitions were observed towards 214 star forming regions using the Shanghai Tianma radio telescope (TMRT) in order to examine the differences in physical environments, such as excitation temperature and column density of molecular clouds associated with methanol masers on the Galactic scale. Statistical results reveal that the number of 6.7 GHz methanol masers in the Perseus arm is significantly lower than that in the other three main spiral arms. In addition, the Perseus arm also has the lowest gas column density among the main spiral arms traced by the NH3 observations. Both of these findings suggest that the Perseus arm has the lowest rate of high-mass star formation compared to the other three main spiral arms. We also observed a trend in which both the luminosity of 6.7 GHz methanol masers and the ammonia gas column density decreased as the galactocentric distances. This finding indicates that the density of material in the inner Milky Way is generally higher than that in the outer Milky Way. It further suggests that high-mass stars are more easily formed at the head of spiral arms. Furthermore, we found that the column density of ammonia gas is higher in the regions on the arms than that in the inter-arm regions, supporting that the former is more likely to be the birthplace of high-mass stars.
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- 2024
10. Theoretically Achieving Continuous Representation of Oriented Bounding Boxes
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Xiao, Zi-Kai, Yang, Guo-Ye, Yang, Xue, Mu, Tai-Jiang, Yan, Junchi, and Hu, Shi-min
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Considerable efforts have been devoted to Oriented Object Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved, which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically, we propose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method outperforms the peer method Gliding Vertex by 1.13% mAP50 (relative improvement 1.54%), and 2.46% mAP75 (relative improvement 5.91%), without any tricks., Comment: 17 pages, 12 tables, 8 figures. Accepted by CVPR'24. Code: https://github.com/514flowey/JDet-COBB
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- 2024
11. CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization
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Peng, Hao-Yang, Zhang, Jia-Peng, Guo, Meng-Hao, Cao, Yan-Pei, and Hu, Shi-Min
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present CharacterGen, a framework developed to efficiently generate 3D characters. CharacterGen introduces a streamlined generation pipeline along with an image-conditioned multi-view diffusion model. This model effectively calibrates input poses to a canonical form while retaining key attributes of the input image, thereby addressing the challenges posed by diverse poses. A transformer-based, generalizable sparse-view reconstruction model is the other core component of our approach, facilitating the creation of detailed 3D models from multi-view images. We also adopt a texture-back-projection strategy to produce high-quality texture maps. Additionally, we have curated a dataset of anime characters, rendered in multiple poses and views, to train and evaluate our model. Our approach has been thoroughly evaluated through quantitative and qualitative experiments, showing its proficiency in generating 3D characters with high-quality shapes and textures, ready for downstream applications such as rigging and animation.
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- 2024
12. In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking
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Gao, Peng, Li, Shi-Min, Gao, Feng, Wang, Fei, Yuan, Ru-Yue, and Fujita, Hamido
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking. However, relying solely on deep learning models to obtain better tracking results requires carefully selecting feature information that is beneficial to representing the target object and designing a reasonable template update strategy, which undoubtedly increases the difficulty of model design. Thus, recent TIR tracking methods face many challenges in complex scenarios. This paper introduces a novel Deep Bayesian Filtering (DBF) method to enhance TIR tracking in these challenging situations. DBF is distinctive in its dual-model structure: the system and observation models. The system model leverages motion data to estimate the potential positions of the target object based on two-dimensional Brownian motion, thus generating a prior probability. Following this, the observation model comes into play upon capturing the TIR image. It serves as a classifier and employs infrared information to ascertain the likelihood of these estimated positions, creating a likelihood probability. According to the guidance of the two models, the position of the target object can be determined, and the template can be dynamically updated. Experimental analysis across several benchmark datasets reveals that DBF achieves competitive performance, surpassing most existing TIR tracking methods in complex scenarios.
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- 2024
13. Polypharmacy and medication regimen complexity in transfusion-dependent thalassaemia patients: a cross- sectional study
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Chun, Geok Ying, Ng, Sharon Shi Min, Islahudin, Farida, Selvaratnam, Veena, and Mohd Tahir, Nurul Ain
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- 2024
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14. Manipulation of Na3V2(PO4)2F3 via aluminum doping to alter local electron states toward an advanced cathode for sodium-ion batteries
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Wang, Shi-Min, Li, Jin-Qi, Xu, Li, Sun, Meng-Jiao, Huang, Wen-Jin, Liu, Qing, Ren, Fu-Tong, Sun, Yong-Jiang, Duan, Ling-Yan, Ma, Hang, and Guo, Hong
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- 2024
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15. Moderate Deviations for Parameter Estimation in the Fractional Ornstein-Uhlenbeck Processes with Periodic Mean
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Jiang, Hui, Li, Shi Min, and Wang, Wei Gang
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- 2024
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16. Semantic-Aware Transformation-Invariant RoI Align
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Yang, Guo-Ye, Nakayama, George Kiyohiro, Xiao, Zi-Kai, Mu, Tai-Jiang, Huang, Xiaolei, and Hu, Shi-Min
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Great progress has been made in learning-based object detection methods in the last decade. Two-stage detectors often have higher detection accuracy than one-stage detectors, due to the use of region of interest (RoI) feature extractors which extract transformation-invariant RoI features for different RoI proposals, making refinement of bounding boxes and prediction of object categories more robust and accurate. However, previous RoI feature extractors can only extract invariant features under limited transformations. In this paper, we propose a novel RoI feature extractor, termed Semantic RoI Align (SRA), which is capable of extracting invariant RoI features under a variety of transformations for two-stage detectors. Specifically, we propose a semantic attention module to adaptively determine different sampling areas by leveraging the global and local semantic relationship within the RoI. We also propose a Dynamic Feature Sampler which dynamically samples features based on the RoI aspect ratio to enhance the efficiency of SRA, and a new position embedding, \ie Area Embedding, to provide more accurate position information for SRA through an improved sampling area representation. Experiments show that our model significantly outperforms baseline models with slight computational overhead. In addition, it shows excellent generalization ability and can be used to improve performance with various state-of-the-art backbones and detection methods.
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- 2023
17. FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
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Tian, Yu, Shi, Min, Luo, Yan, Kouhana, Ava, Elze, Tobias, and Wang, Mengyu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Fairness in artificial intelligence models has gained significantly more attention in recent years, especially in the area of medicine, as fairness in medical models is critical to people's well-being and lives. High-quality medical fairness datasets are needed to promote fairness learning research. Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians. In this paper, we propose the first fairness dataset for medical segmentation named Harvard-FairSeg with 10,000 subject samples. In addition, we propose a fair error-bound scaling approach to reweight the loss function with the upper error-bound in each identity group, using the segment anything model (SAM). We anticipate that the segmentation performance equity can be improved by explicitly tackling the hard cases with high training errors in each identity group. To facilitate fair comparisons, we utilize a novel equity-scaled segmentation performance metric to compare segmentation metrics in the context of fairness, such as the equity-scaled Dice coefficient. Through comprehensive experiments, we demonstrate that our fair error-bound scaling approach either has superior or comparable fairness performance to the state-of-the-art fairness learning models. The dataset and code are publicly accessible via https://ophai.hms.harvard.edu/datasets/harvard-fairseg10k., Comment: ICLR 2024; Codes available at https://github.com/Harvard-Ophthalmology-AI-Lab/FairSeg
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- 2023
18. Huanglian Wendan Decoction Improves Insomnia in Rats by Regulating BDNF/TrkB Signaling Pathway Through Gut Microbiota-Mediated SCFAs and Affecting Microglia Polarization
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Shi, Min, Yang, Jie, Liu, Ying, Zhao, Huan, Li, Man, Yang, Dongdong, and Xie, Quan
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- 2024
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19. The Efficacy of Ganoderma lucidum Extracts on Treating Endometrial Cancer: A Network Pharmacology Approach
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Shi, Min
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- 2024
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20. Monotone iterative technique for multi-term time fractional measure differential equations
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Gou, Haide and Shi, Min
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- 2024
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21. 3D gravity inversion using cycle-consistent generative adversarial network
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Qiao, Shu-Bo, Li, Hou-Pu, Qi, Rui, Zhang, Yu-Jie, and Xie, Shi-Min
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- 2024
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22. FAP is a prognostic marker, but not a viable therapeutic target for clinical translation in HNSCC
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Liu, Jie, Ouyang, Yeling, Xia, Zijin, Mai, Wenhao, Song, Hongrui, Zhou, Fang, Shen, Lichun, Chen, Kaiting, Li, Xiaochen, Zhuang, Shi-Min, and Liao, Jing
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- 2024
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23. DDHD2, whose mutations cause spastic paraplegia type 54, enhances lipophagy via engaging ATG8 family proteins
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Jia, Fei, Wang, Xiaoman, Fu, Yuhua, Zhao, Shi-Min, Lu, Boxun, and Wang, Chenji
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- 2024
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24. Theoretical study on bubble dynamics under hybrid-boundary and multi-bubble conditions using the unified equation
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A-Man, Zhang, Shi-Min, Li, Pu, Cui, Shuai, Li, and Yun-Long, Liu
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Physics - Fluid Dynamics - Abstract
This paper aims to use the unified bubble dynamics equation to investigate bubble behavior in complex scenarios involving hybrid free surface/wall boundaries and interactions between multiple bubbles. The effect of singularity movement on the unified equation's form is analyzed after deriving the bubble pulsation equation using a moving point source and a dipole, followed by discussions on the effect of migration compressibility-related terms on the bubble dynamics. In addition, the present study accounts for the impact of hybrid boundaries, including crossed and parallel boundaries, by introducing a finite number of mirror bubbles for the former and an infinite number of mirror bubbles for the latter. Spark bubble experiments and numerical simulation are conducted to validate the present theory. The application of the unified equation in multi-bubble interactions is exemplified by computing a spherical bubble array containing more than 100 uniformly distributed cavitation bubbles under different boundary conditions. The bubble cluster-induced pressure peak can reach nearly two times or even higher than that of an individual bubble, highlighting the damage potential caused by cavitation bubble clusters.
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- 2023
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25. FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
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Luo, Yan, Khan, Muhammad Osama, Tian, Yu, Shi, Min, Dou, Zehao, Elze, Tobias, Fang, Yi, and Wang, Mengyu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Equity in AI for healthcare is crucial due to its direct impact on human well-being. Despite advancements in 2D medical imaging fairness, the fairness of 3D models remains underexplored, hindered by the small sizes of 3D fairness datasets. Since 3D imaging surpasses 2D imaging in SOTA clinical care, it is critical to understand the fairness of these 3D models. To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes. Our investigation spans both 2D and 3D models and evaluates fairness across five architectures on three common eye diseases, revealing significant biases across race, gender, and ethnicity. To alleviate these biases, we propose a novel fair identity scaling (FIS) method that improves both overall performance and fairness, outperforming various SOTA fairness methods. Moreover, we release Harvard-FairVision, the first large-scale medical fairness dataset with 30,000 subjects featuring both 2D and 3D imaging data and six demographic identity attributes. Harvard-FairVision provides labels for three major eye disorders affecting about 380 million people worldwide, serving as a valuable resource for both 2D and 3D fairness learning. Our code and dataset are publicly accessible at \url{https://ophai.hms.harvard.edu/datasets/harvard-fairvision30k}.
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- 2023
26. When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo
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Liu, Tianqi, Ye, Xinyi, Zhao, Weiyue, Pan, Zhiyu, Shi, Min, and Cao, Zhiguo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain non-local feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-to-line search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://github.com/TQTQliu/ET-MVSNet., Comment: ICCV2023
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- 2023
27. EANet: Expert Attention Network for Online Trajectory Prediction
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Yao, Pengfei, Mao, Tianlu, Shi, Min, Sun, Jingkai, and Wang, Zhaoqi
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Robotics - Abstract
Trajectory prediction plays a crucial role in autonomous driving. Existing mainstream research and continuoual learning-based methods all require training on complete datasets, leading to poor prediction accuracy when sudden changes in scenarios occur and failing to promptly respond and update the model. Whether these methods can make a prediction in real-time and use data instances to update the model immediately(i.e., online learning settings) remains a question. The problem of gradient explosion or vanishing caused by data instance streams also needs to be addressed. Inspired by Hedge Propagation algorithm, we propose Expert Attention Network, a complete online learning framework for trajectory prediction. We introduce expert attention, which adjusts the weights of different depths of network layers, avoiding the model updated slowly due to gradient problem and enabling fast learning of new scenario's knowledge to restore prediction accuracy. Furthermore, we propose a short-term motion trend kernel function which is sensitive to scenario change, allowing the model to respond quickly. To the best of our knowledge, this work is the first attempt to address the online learning problem in trajectory prediction. The experimental results indicate that traditional methods suffer from gradient problems and that our method can quickly reduce prediction errors and reach the state-of-the-art prediction accuracy.
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- 2023
28. Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning
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Luo, Yan, Shi, Min, Tian, Yu, Elze, Tobias, and Wang, Mengyu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Glaucoma is the number one cause of irreversible blindness globally. A major challenge for accurate glaucoma detection and progression forecasting is the bottleneck of limited labeled patients with the state-of-the-art (SOTA) 3D retinal imaging data of optical coherence tomography (OCT). To address the data scarcity issue, this paper proposes two solutions. First, we develop a novel generalization-reinforced semi-supervised learning (SSL) model called pseudo supervisor to optimally utilize unlabeled data. Compared with SOTA models, the proposed pseudo supervisor optimizes the policy of predicting pseudo labels with unlabeled samples to improve empirical generalization. Our pseudo supervisor model is evaluated with two clinical tasks consisting of glaucoma detection and progression forecasting. The progression forecasting task is evaluated both unimodally and multimodally. Our pseudo supervisor model demonstrates superior performance than SOTA SSL comparison models. Moreover, our model also achieves the best results on the publicly available LAG fundus dataset. Second, we introduce the Harvard Glaucoma Detection and Progression (Harvard-GDP) Dataset, a multimodal multitask dataset that includes data from 1,000 patients with OCT imaging data, as well as labels for glaucoma detection and progression. This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available. Detailed sex and racial analysis are provided, which can be used by interested researchers for fairness learning studies. Our released dataset is benchmarked with several SOTA supervised CNN and transformer deep learning models. The dataset and code are made publicly available via \url{https://ophai.hms.harvard.edu/datasets/harvard-gdp1000}., Comment: ICCV 2023
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- 2023
29. The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
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Wang, Weiyun, Shi, Min, Li, Qingyun, Wang, Wenhai, Huang, Zhenhang, Xing, Linjie, Chen, Zhe, Li, Hao, Zhu, Xizhou, Cao, Zhiguo, Chen, Yushi, Lu, Tong, Dai, Jifeng, and Qiao, Yu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world. Using a scalable data engine that incorporates human feedback and efficient models in the loop, we create a new dataset (AS-1B) with over 1 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. It covers a wide range of 3.5 million common and rare concepts in the real world, and has 132.2 billion tokens that describe the concepts and their attributes. Leveraging this new dataset, we develop the All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding. The model is trained with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance, including region-text retrieval, region recognition, captioning, and question-answering. We hope that this project can serve as a foundation for vision-language artificial general intelligence research. Models and the dataset shall be released at https://github.com/OpenGVLab/All-Seeing, and demo can be seen at https://huggingface.co/spaces/OpenGVLab/all-seeing., Comment: Technical Report
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- 2023
30. Generating diverse clothed 3D human animations via a generative model
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Shi, Min, Feng, Wenke, Gao, Lin, and Zhu, Dengming
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- 2024
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31. Super-Tough Poly(Lactide)/Ethylene-Methyl Acrylate-Glycidyl Methacrylate Random Terpolymer Blends via Efficient Catalytic Interfacial Crosslinking of Environmentally Friendly Carboxyl-Functionalized Ionic Liquids
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Wang, Ping, Song, Jie, Liu, Jiajia, Gao, Shang, Tian, Hongyu, Xiao, Bihua, Zhou, Yiyang, Zhu, Lufang, Song, Tao, Li, Zhen, Liu, Wenxiu, Shi, Min, Feng, Shaojie, Cao, Tian, and Ding, Yunsheng
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- 2024
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32. Oncological outcomes of conversion therapy in gastric cancer patients with peritoneal metastasis: a large-scale retrospective cohort study
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Yang, Zhongyin, Lu, Sheng, Shi, Min, Yuan, Hong, Wang, Zhenqiang, Ni, Zhentian, He, Changyu, Zheng, Yanan, Zhu, Zhenglun, Liu, Wentao, Yao, Xuexin, Zhang, Jun, Li, Chen, Yan, Min, Yan, Chao, and Zhu, Zhenggang
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- 2024
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33. NG-Net: No-Grasp annotation grasp detection network for stacked scenes
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Shi, Min, Hou, Jingzhao, Li, Zhaoxin, and Zhu, Dengming
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- 2024
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34. Effects of Noise Damage on the Purinergic Signal of Cochlear Spiral Ganglion Cells in Guinea Pigs
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Shi, Min, Cao, Lei, Ding, Daxiong, Yu, Wenxing, Lv, Ping, and Yu, Ning
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- 2024
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35. Neural Video Depth Stabilizer
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Wang, Yiran, Shi, Min, Li, Jiaqi, Huang, Zihao, Cao, Zhiguo, Zhang, Jianming, Xian, Ke, and Lin, Guosheng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Video depth estimation aims to infer temporally consistent depth. Some methods achieve temporal consistency by finetuning a single-image depth model during test time using geometry and re-projection constraints, which is inefficient and not robust. An alternative approach is to learn how to enforce temporal consistency from data, but this requires well-designed models and sufficient video depth data. To address these challenges, we propose a plug-and-play framework called Neural Video Depth Stabilizer (NVDS) that stabilizes inconsistent depth estimations and can be applied to different single-image depth models without extra effort. We also introduce a large-scale dataset, Video Depth in the Wild (VDW), which consists of 14,203 videos with over two million frames, making it the largest natural-scene video depth dataset to our knowledge. We evaluate our method on the VDW dataset as well as two public benchmarks and demonstrate significant improvements in consistency, accuracy, and efficiency compared to previous approaches. Our work serves as a solid baseline and provides a data foundation for learning-based video depth models. We will release our dataset and code for future research., Comment: Accepted by ICCV2023
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- 2023
36. Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
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Luo, Yan, Tian, Yu, Shi, Min, Pasquale, Louis R., Shen, Lucy Q., Zebardast, Nazlee, Elze, Tobias, and Wang, Mengyu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}., Comment: Accepted in IEEE Transactions on Medical Imaging
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- 2023
37. Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases
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Hu, Zheng, Cai, Shi-Min, Wang, Jun, and Zhou, Tao
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Computer Science - Information Retrieval - Abstract
The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users' historical interactions are divided into positive and negative interactions, used to model the user's preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model's performance.
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- 2023
38. Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer
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Hu, Zheng, Nakagawa, Satoshi, Cai, Shi-Min, and Ren, Fuji
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Computer Science - Information Retrieval - Abstract
Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (XMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous XMR algorithms have employed techniques such as sharing bottom or incorporating inter-market similarity to optimize the performance of XMR. However, the existing approaches suffer from two crucial limitations: (1) They ignore the co-occurrences of items provided by data-rich markets. (2) They do not adequately tackle the issue of negative transfer stemming from disparities across diverse markets. In order to address these limitations, we propose a novel session-based model called Bert4XMR, which is able to model item co-occurrences across markets and mitigate negative transfer. Specifically, we employ the pre-training and fine-tuning paradigm to facilitate knowledge transfer across markets. Pre-training occurs on global markets to learn item co-occurrences, while fine-tuning happens in the target market for model customization. To mitigate potential negative transfer, we separate the item representations into market embeddings and item embeddings. Market embeddings model the bias associated with different markets, while item embeddings learn generic item representations. Extensive experiments conducted on seven real-world datasets illustrate our model's effectiveness. It outperforms the suboptimal model by an average of $4.82\%$, $4.73\%$, $7.66\%$, and $6.49\%$ across four metrics. Through the ablation study, we experimentally demonstrate that the market embedding approach helps prevent negative transfer, especially in data-scarce markets. Our implementations are available at https://github.com/laowangzi/Bert4XMR.
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- 2023
39. DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion
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Nakayama, Kiyohiro, Uy, Mikaela Angelina, Huang, Jiahui, Hu, Shi-Min, Li, Ke, and Guibas, Leonidas J
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Computer Science - Computer Vision and Pattern Recognition - Abstract
While the community of 3D point cloud generation has witnessed a big growth in recent years, there still lacks an effective way to enable intuitive user control in the generation process, hence limiting the general utility of such methods. Since an intuitive way of decomposing a shape is through its parts, we propose to tackle the task of controllable part-based point cloud generation. We introduce DiffFacto, a novel probabilistic generative model that learns the distribution of shapes with part-level control. We propose a factorization that models independent part style and part configuration distributions and presents a novel cross-diffusion network that enables us to generate coherent and plausible shapes under our proposed factorization. Experiments show that our method is able to generate novel shapes with multiple axes of control. It achieves state-of-the-art part-level generation quality and generates plausible and coherent shapes while enabling various downstream editing applications such as shape interpolation, mixing, and transformation editing. Project website: https://difffacto.github.io/
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- 2023
40. Li Shi Min, Founding the Tang Dynasty : The Strategies That Made China the Greatest Empire in Asia
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Hung, Hing Ming and Hung, Hing Ming
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- Emperors--Biography
- Abstract
Li Shi Min, Emperor Taizong of the Tang Dynasty (AD 618–907), is regarded as the greatest emperor in Chinese history. He conquered the powerful Eastern Turkic Khanate and other khanates in the north and northwest, making China the greatest empire in Asia. Under his reign China entered into a period of peace and prosperity.Li Shi Min was a man of great political and military accomplishments, narrated here with the battle stratagems and clever counsel that carried him forward. This book tells how he helped his father Li Yuan to establish the Tang Dynasty and the contributions he made to unifying China. Author Hung Hing Ming draws on China's historical records and chronicles to recount the battles to conquer the warlords and local strongmen in different parts of China, the wise policies he adopted, and the means by which he inspired officials to put forward good suggestions.His deeds, policies and constructive interactions with his ministers and generals were compiled into guides and teaching materials for successors to the Chinese throne. Much of this leadership training advice is still useful today. This book will be an asset to readers as there are few works in English that introduce these cultural motifs that color the thinking of nation so important to ours.
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- 2013
41. Analysis of coupling mechanism between roll system crossing and liner wear of hot strip mill
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Zheng, Jia-li, Huang, Hua-gui, Song, Guang-lu, Lei, Zhen-yao, Sun, Jing-na, and Xu, Shi-min
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- 2023
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42. Target organ damage in untreated hypertensive patients with primary aldosteronism
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Shi‐Min Li, Jia‐Yi Huang, Ching‐Yan Zhu, Ming‐Yen Ng, Qing‐Shan Lin, Min Wu, Ming‐Ya Liu, Run Wang, Gao‐Zhen Cao, Cong Chen, Mei‐Zhen Wu, Qing‐Wen Ren, Hung‐Fat Tse, and Kai‐Hang Yiu
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primary aldosteronism ,target organ damage ,untreated hypertension ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Abstract An increased risk of target organ damage (TOD) has been reported in patients with primary aldosteronism (PA). However, there is relatively little related research on the correlation between the degree of TOD and those with and without PA in newly diagnosed hypertensive patients. The aim of this study was to assess the association between PA and TOD among patients with newly diagnosed hypertension. Newly diagnosed hypertensive patients were consecutively recruited from January 2015 to June 2020 at the University of Hong Kong‐Shenzhen Hospital. Patients were stratified into those with and without PA. Data for left ventricular mass index (LVMI), carotid intima‐media thickness (CIMT) and plaque, and microalbuminuria were systematically collected. A total of 1044 patients with newly diagnosed hypertension were recruited, 57 (5.5%) of whom were diagnosed with PA. Patients with PA had lower blood pressure, serum lipids, body mass index, and plasma renin activity and a higher incidence of hypokalemia than those without PA. In contrast, the prevalence of left ventricular hypertrophy, increased CIMT, and microalbuminuria was higher in patients with PA than in those without PA. Multivariable regression analysis demonstrated that PA was independently associated with increased LVMI, CIMT and microalbuminuria. Among patients with newly diagnosed hypertension, those with PA had more severe TOD, including a higher LVMI, CIMT and microalbuminuria, than those without PA. These findings emphasize the need for screening TOD in newly diagnosed hypertension due to underlying PA.
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- 2024
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43. A novel protein FNDC3B-267aa encoded by circ0003692 inhibits gastric cancer metastasis via promoting proteasomal degradation of c-Myc
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Yu-Ying Liu, Yu-Ying Zhang, Ling-Yu Ran, Bo Huang, Jun-Wu Ren, Qiang Ma, Xiao-Juan Pan, Fei-Fei Yang, Ce Liang, Xiao-Lin Wang, Shi-Min Wang, Ai Ran, Hao Ning, Yan Jiang, Chang-Hong Qin, and Bin Xiao
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circ0003692 ,FNDC3B-267aa ,c-Myc ,Proteasomal degradation ,Gastric cancer ,Medicine - Abstract
Abstract Background Gastric cancer (GC) ranks fifth in global cancer incidence and third in mortality rate among all cancer types. Circular RNAs (circRNAs) have been extensively demonstrated to regulate multiple malignant biological behaviors in GC. Emerging evidence suggests that several circRNAs derived from FNDC3B play pivotal roles in cancer. However, the role of circFNDC3B in GC remains elusive. Methods We initially screened circFNDC3B with translation potential via bioinformatics algorithm prediction. Subsequently, Sanger sequencing, qRT-PCR, RNase R, RNA-FISH and nuclear-cytoplasmic fractionation assays were explored to assess the identification and localization of circ0003692, a circRNA derived from FNDC3B. qRT-PCR and ISH were performed to quantify expression of circ0003692 in human GC tissues and adjacent normal tissues. The protein-encoding ability of circ0003692 was investigated through dual-luciferase reporter assay and LC/MS. The biological behavior of circ0003692 in GC was confirmed via in vivo and in vitro experiments. Additionally, Co-IP and rescue experiments were performed to elucidate the interaction between the encoded protein and c-Myc. Results We found that circ0003692 was significantly downregulated in GC tissues. Circ0003692 had the potential to encode a novel protein FNDC3B-267aa, which was downregulated in GC cells. We verified that FNDC3B-267aa, rather than circ0003692, inhibited GC migration in vitro and in vivo. Mechanistically, FNDC3B-267aa directly interacted with c-Myc and promoted proteasomal degradation of c-Myc, resulting in the downregulation of c-Myc-Snail/Slug axis. Conclusions Our study revealed that the novel protein FNDC3B-267aa encoded by circ0003692 suppressed GC metastasis through binding to c-Myc and enhancing proteasome-mediated degradation of c-Myc. The study offers the potential applications of circ0003692 or FNDC3B-267aa as therapeutic targets for GC. Graphical abstract The mechanism of circ0003692 in suppressing metastasis of GC. FNDC3B-267aa encoded by circ0003692 interacted with c-Myc and promoted the proteasomal degradation of c-Myc, thereby down-regulated c-Myc-Snail/Slug axis and EMT pathway.
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- 2024
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44. Agrin-Lrp4 pathway in hippocampal astrocytes restrains development of temporal lobe epilepsy through adenosine signaling
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Zi-Yang Liu, Yuan-Quan Li, Die-Lin Wang, Ying Wang, Wan-Ting Qiu, Yu-Yang Qiu, He-Lin Zhang, Qiang-Long You, Shi-min Liu, Qiu-Ni Liang, Er-Jian Wu, Bing-Jie Hu, and Xiang-Dong Sun
- Subjects
Agrin ,Lrp4 ,Status epilepticus ,Epilepsy ,Astrocyte ,Adenosine ,Biotechnology ,TP248.13-248.65 ,Biology (General) ,QH301-705.5 ,Biochemistry ,QD415-436 - Abstract
Abstract Background Human patients often experience an episode of serious seizure activity, such as status epilepticus (SE), prior to the onset of temporal lobe epilepsy (TLE), suggesting that SE can trigger the development of epilepsy. Yet, the underlying mechanisms are not fully understood. The low-density lipoprotein receptor related protein (Lrp4), a receptor for proteoglycan-agrin, has been indicated to modulate seizure susceptibility. However, whether agrin-Lrp4 pathway also plays a role in the development of SE-induced TLE is not clear. Methods Lrp4 f/f mice were crossed with hGFAP-Cre and Nex-Cre mice to generate brain conditional Lrp4 knockout mice (hGFAP-Lrp4 −/− ) and pyramidal neuron specific knockout mice (Nex-Lrp4 −/− ). Lrp4 was specifically knocked down in hippocampal astrocytes by injecting AAV virus carrying hGFAP-Cre into the hippocampus. The effects of agrin-Lrp4 pathway on the development of SE-induced TLE were evaluated on the chronic seizure model generated by injecting kainic acid (KA) into the amygdala. The spontaneous recurrent seizures (SRS) in mice were video monitored. Results We found that Lrp4 deletion from the brain but not from the pyramidal neurons elevated the seizure threshold and reduced SRS numbers, with no change in the stage or duration of SRS. More importantly, knockdown of Lrp4 in the hippocampal astrocytes after SE induction decreased SRS numbers. In accord, direct injection of agrin into the lateral ventricle of control mice but not mice with Lrp4 deletion in hippocampal astrocytes also increased the SRS numbers. These results indicate a promoting effect of agrin-Lrp4 signaling in hippocampal astrocytes on the development of SE-induced TLE. Last, we observed that knockdown of Lrp4 in hippocampal astrocytes increased the extracellular adenosine levels in the hippocampus 2 weeks after SE induction. Blockade of adenosine A1 receptor in the hippocampus by DPCPX after SE induction diminished the effects of Lrp4 on the development of SE-induced TLE. Conclusion These results demonstrate a promoting role of agrin-Lrp4 signaling in hippocampal astrocytes in the development of SE-induced development of epilepsy through elevating adenosine levels. Targeting agrin-Lrp4 signaling may serve as a potential therapeutic intervention strategy to treat TLE.
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- 2024
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45. Visual attention network
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Guo, Meng-Hao, Lu, Cheng-Ze, Liu, Zheng-Ning, Cheng, Ming-Ming, and Hu, Shi-Min
- Published
- 2023
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46. Interactions between a central bubble and a surrounding bubble cluster
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Zhang, A-Man, Li, Shi-Min, Cui, Pu, Li, Shuai, and Liu, Yun-Long
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Physics - Fluid Dynamics - Abstract
The interaction of multiple bubbles is a complex physical problem. A simplified case of multiple bubbles is studied theoretically with a bubble located at the center of a circular bubble cluster. All bubbles in the cluster are equally spaced and own the same initial conditions as the central bubble. The unified theory for bubble dynamics (Zhang et al. arXiv:2301.13698) is applied to model the interaction between the central bubble and the circular bubble cluster. To account for the effect of the propagation time of pressure waves, the emission source of the wave is obtained by interpolating the physical information on the time axis. An underwater explosion experiment with two bubbles of different scales is used to validate the theoretical model. The effect of the bubble cluster with a variation in scale on the pulsation characteristics of the central bubble is studied.
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- 2023
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47. Semantic-Aware Transformation-Invariant RoI Align.
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Guo-Ye Yang, George Kiyohiro Nakayama, Zi-Kai Xiao, Tai-Jiang Mu, Xiaolei Huang, and Shi-Min Hu 0001
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- 2024
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48. Rock Mechanical Parameter Modeling of Tight Oil Reservoir: A Case Study of Certain Block
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Lu, Xiao-fang, Chen, Jing, Shi, Yong-li, Huang, Si, Shi, Min-min, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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49. High-performance cable materials for maglev trains prepared by the multiple synergistic regulation effects of the functionalized ionic liquids on EVA-based composites
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Wang, Ping, Tian, Hongyu, Liu, Wenxiu, Lu, Haibing, Liu, Jiajia, Dong, Shi, Xu, Jie, Cao, Tian, Shi, Min, Huang, Haopeng, and Zhou, Yiyang
- Published
- 2024
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50. Different Acupuncture Therapies for Postherpetic Neuralgia: An Overview of Systematic Reviews and Meta-analysis
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Xia, Yun-fan, Sun, Ruo-han, Li, Shi-min, Wang, Yi-yi, Li, Rong-rong, and Fang, Jian-qiao
- Published
- 2024
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