5,227 results on '"Rong, Yu"'
Search Results
2. Bipolar blobs as evidence of hidden AGN activities in the low-mass galaxies
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Yao, Yao, Wang, Enci, He, Zhicheng, Lin, Zheyu, Rong, Yu, Zhang, Hong-Xin, and Kong, Xu
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
We report the evidence of a hidden black hole (BH) in a low-mass galaxy, MaNGA 9885-9102, and provide a new method to identify active BH in low mass galaxies. This galaxy is originally selected from the MaNGA survey with distinctive bipolar H$\alpha$ blobs at the minor axis. The bipolar feature can be associated with AGN activity, while the two blobs are classified as the H II regions on the BPT diagram, making the origins confusing. The Swift UV continuum shows that the two blobs do not have UV counterparts, suggesting that the source of ionization is out of the blobs. Consistent with this, the detailed photoionization models prefer to AGN rather than star-forming origin with a significance of 5.8$\sigma$. The estimated BH mass is $M_{\rm BH}\sim$7.2$\times 10^5 M_\odot$ from the $M_{\rm BH}-\sigma_*$ relationship. This work introduces a novel method for detecting the light echo of BHs, potentially extending to intermediate mass, in low metallicity environments where the traditional BPT diagram fails., Comment: 15 pages, 11 figures, accepted in ApJL
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- 2024
3. GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars
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Sun, Keqiang, Jourabloo, Amin, Bhalodia, Riddhish, Meshry, Moustafa, Rong, Yu, Yang, Zhengyu, Nguyen-Phuoc, Thu, Haene, Christian, Xu, Jiu, Johnson, Sam, Li, Hongsheng, and Bouaziz, Sofien
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Photo-realistic and controllable 3D avatars are crucial for various applications such as virtual and mixed reality (VR/MR), telepresence, gaming, and film production. Traditional methods for avatar creation often involve time-consuming scanning and reconstruction processes for each avatar, which limits their scalability. Furthermore, these methods do not offer the flexibility to sample new identities or modify existing ones. On the other hand, by learning a strong prior from data, generative models provide a promising alternative to traditional reconstruction methods, easing the time constraints for both data capture and processing. Additionally, generative methods enable downstream applications beyond reconstruction, such as editing and stylization. Nonetheless, the research on generative 3D avatars is still in its infancy, and therefore current methods still have limitations such as creating static avatars, lacking photo-realism, having incomplete facial details, or having limited drivability. To address this, we propose a text-conditioned generative model that can generate photo-realistic facial avatars of diverse identities, with more complete details like hair, eyes and mouth interior, and which can be driven through a powerful non-parametric latent expression space. Specifically, we integrate the generative and editing capabilities of latent diffusion models with a strong prior model for avatar expression driving. Our model can generate and control high-fidelity avatars, even those out-of-distribution. We also highlight its potential for downstream applications, including avatar editing and single-shot avatar reconstruction.
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- 2024
4. Exploring the origin of cold gas and star formation in a rare population of strongly bulge-dominated early-type Galaxies
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Li, Fujia, Wang, Enci, Zhu, Ming, Peng, Yingjie, Wang, Jing, Zhang, Chuanpeng, Lin, Zesen, Rong, Yu, Zhang, Hongxin, and Kong, Xu
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Astrophysics - Astrophysics of Galaxies - Abstract
We analyze the properties of a rare population, the strongly bulge-dominated early-type galaxies (referred to as sBDEs) with significant HI gas, using the databases from the FAST All Sky HI survey (FASHI) and the Arecibo Legacy Fast ALFA (ALFALFA) survey. We select the sBDEs from the Sloan Digital Sky Survey (SDSS) and cross-match with the FASHI-ALFALFA combined HI sample, resulting in 104 HI-rich sBDEs. These sBDEs tend to have extremely high HI reservoirs, which is rare in previous studies such as ATLAS$^{3D}$. 70% of the selected sBDEs are classified as quiescent galaxies, even though they have a large HI reservoir. We study the properties of these sBDEs from five main aspects: stellar population, gas-phase metallicity, stacked HI spectra, environment, and spatially resolved MaNGA data. The majority of HI-rich sBDEs appear to show lower gas-phase metallicity and are located in significantly lower-density environments, suggesting an external origin for their HI gas. We find that star-forming sBDEs exhibit statistically higher star formation efficiency and slightly older stellar populations compared to normal star-forming galaxies, suggesting a recent star formation on Gyr-timescale. They also show narrower and more concentrated HI profiles compared to control star-forming galaxies, which may explain their higher star formation efficiency., Comment: 18 pages, 14 figures, 1 table. Accepted for publication in ApJ
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- 2024
5. Galaxy Group Ellipticity Confirms a Younger Cosmos
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Rong, Yu
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
We present an analysis of the ellipticities of galaxy groups, derived from the spatial distribution of member galaxies, revealing a notable incongruity between the observed local galaxy groups and their counterparts in the Lambda cold dark matter cosmology. Specifically, our investigation reveals a substantial disparity in the ellipticities of observed groups with masses \mbox{$10^{13.0}
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- 2024
6. Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
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Zheng, Zinan, Liu, Yang, Li, Jia, Yao, Jianhua, and Rong, Yu
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Incorporating Euclidean symmetries (e.g. rotation equivariance) as inductive biases into graph neural networks has improved their generalization ability and data efficiency in unbounded physical dynamics modeling. However, in various scientific and engineering applications, the symmetries of dynamics are frequently discrete due to the boundary conditions. Thus, existing GNNs either overlook necessary symmetry, resulting in suboptimal representation ability, or impose excessive equivariance, which fails to generalize to unobserved symmetric dynamics. In this work, we propose a general Discrete Equivariant Graph Neural Network (DEGNN) that guarantees equivariance to a given discrete point group. Specifically, we show that such discrete equivariant message passing could be constructed by transforming geometric features into permutation-invariant embeddings. Through relaxing continuous equivariant constraints, DEGNN can employ more geometric feature combinations to approximate unobserved physical object interaction functions. Two implementation approaches of DEGNN are proposed based on ranking or pooling permutation-invariant functions. We apply DEGNN to various physical dynamics, ranging from particle, molecular, crowd to vehicle dynamics. In twenty scenarios, DEGNN significantly outperforms existing state-of-the-art approaches. Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.
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- 2024
7. MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
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Chen, Zhicheng, Xiao, Xi, Xu, Ke, Zhang, Zhong, Rong, Yu, Li, Qing, Gan, Guojun, Xu, Zhiqiang, and Zhao, Peilin
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Computer Science - Machine Learning - Abstract
Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels. Unfortunately, the majority of current time series prediction models fail to simultaneously learn the correlations of multivariate time series at multi-grained levels, resulting in suboptimal performance. To address this, we propose a Multi-Grained Correlations-based Prediction (MGCP) Network, which simultaneously considers the correlations at three granularity levels to enhance prediction performance. Specifically, MGCP utilizes Adaptive Fourier Neural Operators and Graph Convolutional Networks to learn the global spatiotemporal correlations and inter-series correlations, enabling the extraction of potential features from multivariate time series at fine-grained and medium-grained levels. Additionally, MGCP employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level, ensuring high fidelity between the generated forecast results and the actual data distribution. Finally, we compare MGCP with several state-of-the-art time series prediction algorithms on real-world benchmark datasets, and our results demonstrate the generality and effectiveness of the proposed model.
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- 2024
8. Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
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Wu, Liming, Hou, Zhichao, Yuan, Jirui, Rong, Yu, and Huang, Wenbing
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task. Existing equivariant Graph Neural Network (GNN) based methods have encapsulated the symmetry of physics, \emph{e.g.}, translations, rotations, etc, leading to better generalization ability. Nevertheless, their frame-to-frame formulation of the task overlooks the non-Markov property mainly incurred by unobserved dynamics in the environment. In this paper, we reformulate dynamics simulation as a spatio-temporal prediction task, by employing the trajectory in the past period to recover the Non-Markovian interactions. We propose Equivariant Spatio-Temporal Attentive Graph Networks (ESTAG), an equivariant version of spatio-temporal GNNs, to fulfill our purpose. At its core, we design a novel Equivariant Discrete Fourier Transform (EDFT) to extract periodic patterns from the history frames, and then construct an Equivariant Spatial Module (ESM) to accomplish spatial message passing, and an Equivariant Temporal Module (ETM) with the forward attention and equivariant pooling mechanisms to aggregate temporal message. We evaluate our model on three real datasets corresponding to the molecular-, protein- and macro-level. Experimental results verify the effectiveness of ESTAG compared to typical spatio-temporal GNNs and equivariant GNNs., Comment: The paper has been published to the conference of NeurIPS 2023
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- 2024
9. Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
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Zhang, Yikun, Ye, Geyan, Yuan, Chaohao, Han, Bo, Huang, Long-Kai, Yao, Jianhua, Liu, Wei, and Rong, Yu
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Quantitative Biology - Quantitative Methods ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields, including drug discovery and materials science. Existing studies adopt a global alignment approach to learn the knowledge from different modalities. These global alignment approaches fail to capture fine-grained information, such as molecular fragments and their corresponding textual description, which is crucial for downstream tasks. Furthermore, it is incapable to model such information using a similar global alignment strategy due to data scarcity of paired local part annotated data from existing datasets. In this paper, we propose Atomas, a multi-modal molecular representation learning framework to jointly learn representations from SMILES string and text. We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels. Additionally, Atomas's end-to-end training framework incorporates the tasks of understanding and generating molecule, thereby supporting a wider range of downstream tasks. In the retrieval task, Atomas exhibits robust generalization ability and outperforms the baseline by 30.8% of recall@1 on average. In the generation task, Atomas achieves state-of-the-art results in both molecule captioning task and molecule generation task. Moreover, the visualization of the Hierarchical Adaptive Alignment model further confirms the chemical significance of our approach. Our codes can be found at https://anonymous.4open.science/r/Atomas-03C3.
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- 2024
10. Functional Protein Design with Local Domain Alignment
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Yuan, Chaohao, Li, Songyou, Ye, Geyan, Zhang, Yikun, Huang, Long-Kai, Huang, Wenbing, Liu, Wei, Yao, Jianhua, and Rong, Yu
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Quantitative Biology - Quantitative Methods ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate protein using structural and evolutionary guidance, which only provide indirect conditions concerning functions and properties. However, textual annotations of proteins, especially the annotations for protein domains, which directly describe the protein's high-level functionalities, properties, and their correlation with target amino acid sequences, remain unexplored in the context of protein design tasks. In this paper, we propose Protein-Annotation Alignment Generation (PAAG), a multi-modality protein design framework that integrates the textual annotations extracted from protein database for controllable generation in sequence space. Specifically, within a multi-level alignment module, PAAG can explicitly generate proteins containing specific domains conditioned on the corresponding domain annotations, and can even design novel proteins with flexible combinations of different kinds of annotations. Our experimental results underscore the superiority of the aligned protein representations from PAAG over 7 prediction tasks. Furthermore, PAAG demonstrates a nearly sixfold increase in generation success rate (24.7% vs 4.7% in zinc finger, and 54.3% vs 8.7% in the immunoglobulin domain) in comparison to the existing model.
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- 2024
11. Gas-rich Ultra-diffuse Galaxies Are Originated from High Specific Angular Momentum
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Rong, Yu, Hu, Huijie, He, Min, Du, Wei, Guo, Qi, Wang, Hui-Yuan, Zhang, Hong-Xin, and Mo, Houjun
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Astrophysics - Astrophysics of Galaxies - Abstract
Ultra-diffuse galaxies, characterized by comparable effective radii to the Milky Way but possessing 100-1,000 times fewer stars, offer a unique opportunity to garner novel insights into the mechanisms governing galaxy formation. Nevertheless, the existing corpus of observational and simulation studies has not yet yielded a definitive constraint or comprehensive consensus on the formation mechanisms underlying ultra-diffuse galaxies. In this study, we delve into the properties of ultra-diffuse galaxies enriched with neutral hydrogen using a semi-analytic method, with the explicit aim of constraining existing ultra-diffuse galaxy formation models. We find that the gas-rich ultra-diffuse galaxies are statistically not failed $L^{\star}$ galaxies nor dark matter deficient galaxies. In statistical terms, these ultra-diffuse galaxies exhibit comparable halo concentration, but higher baryonic mass fraction, as well as higher stellar and gas specific angular momentum, in comparison to typical dwarf galaxy counterparts. Our analysis unveils that higher gas specific angular momentum serves as the underlying factor elucidating the observed heightened baryonic mass fractions, diminished star formation efficiency, expanded stellar disk sizes, and reduced stellar densities in ultra-diffuse galaxies. Our findings make significant contributions to advancing our knowledge of ultra-diffuse galaxy formation and shed light on the intricate interplay between gas dynamics and the evolution of galaxies., Comment: comments welcome
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- 2024
12. All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
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Fang, Shuheng, Zhao, Kangfei, Rong, Yu, Li, Zhixun, and Yu, Jeffrey Xu
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Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence - Abstract
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the performance ceiling of the specific recommendation task. Motivated by this, in this paper, we propose a flexible framework dubbed heterogeneous interaction rating network (HIRE). HIRE dose not solely rely on the pre-defined interaction pattern or the manually constructed heterogeneous information network. Instead, we devise a Heterogeneous Interaction Module (HIM) to jointly model the heterogeneous interactions and directly infer the important interactions via the observed data. In the experiments, we evaluate our model under three cold-start settings on three real-world datasets. The experimental results show that HIRE outperforms other baselines by a large margin. Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model., Comment: 14 pages, 9 figures
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- 2024
13. The Baryonic Tully-Fisher Relation of HI-bearing Low Surface Brightness Galaxies Implies Their Formation Mechanism
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Hua, Zichen, Rong, Yu, and Hu, Hui-jie
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Astrophysics - Astrophysics of Galaxies - Abstract
We investigate the baryonic Tully-Fisher relation in low surface brightness galaxies selected from the Arecibo Legacy Fast ALFA survey. We find that the $\rm HI$-bearing low surface brightness galaxies still follow the baryonic Tully-Fisher relation of typical late-type galaxies, with a slope of approximately 4 in the baryonic mass versus rotational velocity diagram on the logarithmic scale, i.e., $M_{\rm{b}}\propto v_{\rm{rot}}^4$. Our findings suggest that the matter distributions in low surface brightness galaxies may resemble that of general late-type galaxies, and hint that low surface brightness galaxies may not originate from dark matter halos of low densities or stronger/weaker feedback processes, but may emerge from dark matter halos with high spin values.
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- 2024
14. Galaxy Triplets Alignment in Large-scale Filaments
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Rong, Yu, Shen, Jinzhi, and Hua, Zichen
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Leveraging the datasets of galaxy triplets and large-scale filaments obtained from the Sloan Digital Sky Survey, we scrutinize the alignment of the three sides of the triangles formed by galaxy triplets and the normal vectors of the triplet planes within observed large-scale filaments. Our statistical investigation reveals that the longest and median sides of the galaxy triplets exhibit a robust alignment with the spines of their host large-scale filaments, while the shortest sides show no or only weak alignment with the filaments. Additionally, the normal vectors of triplets tend to be perpendicular to the filaments. The alignment signal diminishes rapidly with the increasing distance from the triplet to the filament spine, and is primarily significant for triplets located within distances shorter than $0.2$~Mpc$/h$, with a confidence level exceeding $20\sigma$. Moreover, in comparison to compact galaxy triplets, the alignment signal is more conspicuous among the loose triplets. This alignment analysis contributes to the formulation of a framework depicting the clustering and relaxation of galaxies within cosmological large-scale filament regimes, providing deeper insights into the intricate interactions between galaxies and their pivotal role in shaping galaxy groups., Comment: Accepted for publication in MNRAS Letters
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- 2024
15. A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
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Han, Jiaqi, Cen, Jiacheng, Wu, Liming, Li, Zongzhao, Kong, Xiangzhe, Jiao, Rui, Yu, Ziyang, Xu, Tingyang, Wu, Fandi, Wang, Zihe, Xu, Hongteng, Wei, Zhewei, Liu, Yang, Rong, Yu, and Huang, Wenbing
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Computer Science - Machine Learning - Abstract
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections, making them ineffectively processed by current Graph Neural Networks (GNNs). To tackle this issue, researchers proposed a variety of Geometric Graph Neural Networks equipped with invariant/equivariant properties to better characterize the geometry and topology of geometric graphs. Given the current progress in this field, it is imperative to conduct a comprehensive survey of data structures, models, and applications related to geometric GNNs. In this paper, based on the necessary but concise mathematical preliminaries, we provide a unified view of existing models from the geometric message passing perspective. Additionally, we summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation. We also discuss the challenges and future potential directions of Geometric GNNs at the end of this survey.
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- 2024
16. Basic study on cryopreservation of rat calvarial osteoblasts with different cryoprotectants
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Jiang, Xu, Zhijian, Tan, Min, Cao, Rong, Yu, Xinghui, Tan, and Gong, Xin
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- 2024
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17. Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
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Li, Xuan, Zhou, Zhanke, Yao, Jiangchao, Rong, Yu, Zhang, Lu, and Han, Bo
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Computer Science - Machine Learning ,Quantitative Biology - Quantitative Methods - Abstract
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection to the traditional LRI calculation method, Ewald Summation. The Neural Atom can enhance GNNs to capture LRI by approximating the potential LRI of the molecular. We conduct extensive experiments on four long-range graph benchmarks, covering graph-level and link-level tasks on molecular graphs. We achieve up to a 27.32% and 38.27% improvement in the 2D and 3D scenarios, respectively. Empirically, our method can be equipped with an arbitrary GNN to help capture LRI. Code and datasets are publicly available in https://github.com/tmlr-group/NeuralAtom.
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- 2023
18. Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
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Chawla, Kushal, Wu, Ian, Rong, Yu, Lucas, Gale M., and Gratch, Jonathan
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future., Comment: Accepted at EMNLP 2023 (Main)
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- 2023
19. Accuracy of the mean-field theory in describing ground-state properties of light nuclei
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Rong, Yu-Ting
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Nuclear Theory - Abstract
The relativistic mean-field model, augmented with three types of center-of-mass corrections and two types of rotational corrections, is employed to investigate the ground-state properties of helium, beryllium, and carbon isotopes. The efficacy of the mean-field approach in describing the binding energies, quadrupole deformations, root-mean-square charge radii, root-mean-square matter radii, and neutron skins of these light nuclei is evaluated. By averaging the binding energies obtained from six selected effective interactions, a mass-dependent behavior of the mean-field approximation is elucidated. The findings from radii reveal that, unlike in heavy nuclei, the exchange terms of the center-of-mass correction play an indispensable role in accurately describing the radii of light nuclei. The mean-field approximation, when augmented with center-of-mass and rotational corrections, effectively reproduces the energies and radii of light nuclei. However, due to the absence of many-body correlations between valence neutrons, the mean-field approximation falls short in describing the deformations and shell evolutions of the helium and beryllium isotopic chains., Comment: 14 pages, 6 figures
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- 2023
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20. Deep Insights into Noisy Pseudo Labeling on Graph Data
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Wang, Botao, Li, Jia, Liu, Yang, Cheng, Jiashun, Rong, Yu, Wang, Wenjia, and Tsung, Fugee
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Computer Science - Machine Learning - Abstract
Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process. Several works have shown that it can improve the graph learning model performance in general. However, we notice that the incorrect labels can be fatal to the graph training process. Inappropriate PL may result in the performance degrading, especially on graph data where the noise can propagate. Surprisingly, the corresponding error is seldom theoretically analyzed in the literature. In this paper, we aim to give deep insights of PL on graph learning models. We first present the error analysis of PL strategy by showing that the error is bounded by the confidence of PL threshold and consistency of multi-view prediction. Then, we theoretically illustrate the effect of PL on convergence property. Based on the analysis, we propose a cautious pseudo labeling methodology in which we pseudo label the samples with highest confidence and multi-view consistency. Finally, extensive experiments demonstrate that the proposed strategy improves graph learning process and outperforms other PL strategies on link prediction and node classification tasks.
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- 2023
21. Closed-loop control dynamic obstacle avoidance algorithm based on a machine learning objective function
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Rong, Yu, Dou, Tianci, and Zhang, Xingchao
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- 2024
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22. scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding
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Li, Wei, Yang, Fan, Wang, Fang, Rong, Yu, Liu, Linjing, Wu, Bingzhe, Zhang, Han, and Yao, Jianhua
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- 2024
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23. Design of a Compound Reconfigurable Terahertz Antenna Based on Graphene
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Jin, Zhao, Rong, Yu, Yu, JingDong, and Wu, Fei
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- 2024
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24. SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
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Liu, Yang, Cheng, Jiashun, Zhao, Haihong, Xu, Tingyang, Zhao, Peilin, Tsung, Fugee, Li, Jia, and Rong, Yu
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of physical inductive biases: (1) Existing studies overlook the continuity of transitions among system states, opting to employ several discrete transformation layers to learn the direct mapping between two adjacent states; (2) Most models only account for first-order velocity information, despite the fact that many physical systems are governed by second-order motion laws. To incorporate these inductive biases, we propose the Second-order Equivariant Graph Neural Ordinary Differential Equation (SEGNO). Specifically, we show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization. Additionally, we prove that the discrepancy between this learned trajectory of SEGNO and the true trajectory is bounded. Extensive experiments on complex dynamical systems including molecular dynamics and motion capture demonstrate that our model yields a significant improvement over the state-of-the-art baselines.
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- 2023
25. Integrative microbiome and metabolome profiles reveal the impacts of periodontitis via oral-gut axis in first-trimester pregnant women
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Tianfan Cheng, Ping Wen, Rong Yu, Feng Zhang, Huijun Li, Xiaoyi Xu, Dan Zhao, Fang Liu, Weilan Su, Zheng Zheng, Hong Yang, Jilong Yao, and Lijian Jin
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Periodontitis ,Pregnancy ,Microbiome ,Metabolome ,Gut ,Integrative analysis ,Medicine - Abstract
Abstract Background Periodontitis results from host-microbe dysbiosis and the resultant dysregulated immunoinflammatory response. Importantly, it closely links to numerous systemic comorbidities, and perplexingly contributes to adverse pregnancy outcomes (APOs). Currently, there are limited studies on the distal consequences of periodontitis via oral-gut axis in pregnant women. This study investigated the integrative microbiome-metabolome profiles through multi-omics approaches in first-trimester pregnant women and explored the translational potentials. Methods We collected samples of subgingival plaques, saliva, sera and stool from 54 Chinese pregnant women at the first trimester, including 31 maternal periodontitis (Perio) subjects and 23 Non-Perio controls. By integrating 16S rRNA sequencing, untargeted metabolomics and clinical traits, we explored the oral-gut microbial and metabolic connection resulting from periodontitis among early pregnant women. Results We demonstrated a novel bacterial distinguisher Coprococcus from feces of periodontitis subjects in association with subgingival periodontopathogens, being different from other fecal genera in Lachnospiraceae family. The ratio of fecal Coprococcus to Lachnoclostridium could discriminate between Perio and Non-Perio groups as the ratio of subgingival Porphyromonas to Rothia did. Furthermore, there were differentially abundant fecal metabolic features pivotally enriched in periodontitis subjects like L-urobilin and kynurenic acid. We revealed a periodontitis-oriented integrative network cluster, which was centered with fecal Coprococcus and L-urobilin as well as serum triglyceride. Conclusions The current findings about the notable influence of periodontitis on fecal microbiota and metabolites in first-trimester pregnant women via oral-gut axis signify the importance and translational implications of preconceptional oral/periodontal healthcare for enhancing maternal wellbeing.
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- 2024
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26. Evaluation of the effect of death education based on the Peace of Mind Tea House: a randomized controlled trial of nursing trainees at Xiamen University, China
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Ying Duan, JianMei Huang, Rong Yu, Feng Lin, and Yang Liu
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Death education ,The Peace of Mind Tea House ,Nursing trainees ,Heart to Heart cards ,Death attitude ,Nursing ,RT1-120 - Abstract
Abstract Background There are few studies on death education models for nursing students in China. It is of great significance to construct a model of nursing students’ death education combined with clinical practice. This study aims to evaluate the effect of death education on nursing students based on the Peace of Mind Tea House. Methods The randomized controlled trial commenced from February 7 to March 18, 2021,featuring a two-month intercession at a hospital situated in Xiamen, China. The research subjects were chosen using a convenient sampling approach with nursing students from the hospital's internship program. Ninety-two participants were enrolled, with 46 in each group. Thirteen participants were lost to follow-up, corresponding to 14% of the total study population. The samples were then allocated randomly into either the intervention group or the control group. In addition to their hospital internship, the intervention group participated in six death education courses that focused on cognitive, emotional, and motor skills as well as the “Peace of Mind Tea House” program. Control participants will undergo regular internships. Before and two weeks after the course, both groups were evaluated for death anxiety, attitude towards death, and the meaning of life to assess the intervention's effectiveness. Results In the fear of death item of the Death Attitude Scale and the meaning of life section, the post-test score minus the pre-test score of the intervention group were 2.50 ± 3.90 (p = 0.011), and 8.90 ± 11.07 (p = 0.035), respectively. During the communication and sharing session of the reassurance card activity, 41 participants (95.3%) found the activity meaningful. Conclusion Our data analysis demonstrates that nursing students have accepted and acknowledged the Peace of Mind Tea House-based education on death, which positively impacted their attitudes towards deathand the meaning of life. The content of death education should be integrated with traditional culture, and a new model of death education should be constructed with the Heart to Heart cards as its core. This research presents proof of the efficacy of implementing appropriate death education for nursing students, and provides a successful intervention plan to alleviate their future death anxiety and develop a positive outlook on death. Trial registration This study was approved by the Ethical Committee of Xiamen University School of Medicine (No. XDYX202304K21)(Date:18/01/2021). Written consent to participate was obtained from all the students.
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- 2024
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27. Comparison of ocular biometric parameters between two swept-source optical coherence tomography devices and Scheimpflug tomography in patients with cataract
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Shan Ma, Cheng Li, Jing Sun, Jun Yang, Kai Wen, Xi-Teng Chen, Fang-Yu Zhao, Rong-Yu Gao, and Fang Tian
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ocular parameters ,swept-source optical coherence tomography ,cataract ,Ophthalmology ,RE1-994 - Abstract
AIM: To assess and compare the variations and agreements across different ocular biometric parameters using swept-source optical coherence tomography (SS-OCT) and Scheimpflug tomography in patients diagnosed with cataract. METHODS: This prospective case series was conducted at Tianjin Medical University Eye Hospital. In total, 212 eyes from 212 patients scheduled for phacoemulsification were included. Eyes were evaluated preoperatively using two SS-OCT devices (IOLMaster700 and CASIA2) and Scheimpflug tomography (Pentacam). Central corneal thickness (CCT), anterior chamber depth (ACD), aqueous depth (AQD), white-to-white distance (WTW), flat simulated keratometry (Kf), steep simulated keratometry (Ks), mean keratometry (Km), and total corneal keratometry (TKm) were measured. Intraclass correlation coefficient (ICC), 95% confidence intervals (CI) and limits of agreement (LoA) widths were conducted to assess differences and correlations between devices. RESULTS: All parameters, except for Ks, were significantly different. Pairwise comparison revealed no significant differences between keratometry obtained by IOLMaster 700 and Pentacam. LoA widths of all paired comparisons for Ks were >0.80 D. Except for WTW between IOLMaster 700 and CASIA2 and between CASIA2 and Pentacam, other Pearson's coefficients between devices showed a strong correlation (all r>0.95). The ICC of WTW (ICC=0.438, 95%CI 0.167-0.625) showed poor reliability. The reliability of CCT, ACD, and AQD was excellent (all ICC>0.95), whereas that of TKm was good (ICC=0.827, 95%CI 0.221-0.939). A significant linear correlation was also observed among devices. CONCLUSION: The ocular parameters derived from the use of IOLMaster700, CASIA2, and Pentacam exhibit significant discrepancies; as such, measurements from these devices should not be deemed as interchangeable.
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- 2024
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28. Silicon photocathode functionalized with osmium complex catalyst for selective catalytic conversion of CO2 to methane
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Xing-Yi Li, Ze-Lin Zhu, Fentahun Wondu Dagnaw, Jie-Rong Yu, Zhi-Xing Wu, Yi-Jing Chen, Mu-Han Zhou, Tieyu Wang, Qing-Xiao Tong, and Jing-Xin Jian
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Science - Abstract
Abstract Solar-driven CO2 reduction to yield high-value chemicals presents an appealing avenue for combating climate change, yet achieving selective production of specific products remains a significant challenge. We showcase two osmium complexes, przpOs, and trzpOs, as CO2 reduction catalysts for selective CO2-to-methane conversion. Kinetically, the przpOs and trzpOs exhibit high CO2 reduction catalytic rate constants of 0.544 and 6.41 s−1, respectively. Under AM1.5 G irradiation, the optimal Si/TiO2/trzpOs have CH4 as the main product and >90% Faradaic efficiency, reaching −14.11 mA cm−2 photocurrent density at 0.0 VRHE. Density functional theory calculations reveal that the N atoms on the bipyrazole and triazole ligands effectively stabilize the CO2-adduct intermediates, which tend to be further hydrogenated to produce CH4, leading to their ultrahigh CO2-to-CH4 selectivity. These results are comparable to cutting-edge Si-based photocathodes for CO2 reduction, revealing a vast research potential in employing molecular catalysts for the photoelectrochemical conversion of CO2 to methane.
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- 2024
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29. Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
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Han, Jiaqi, Huang, Wenbing, Rong, Yu, Xu, Tingyang, Sun, Fuchun, and Huang, Junzhou
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Computer Science - Machine Learning - Abstract
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this paper, we start by investigating refined measures upon DropEdge -- an existing simple yet effective technique to relieve over-smoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent sampler and feature-dependent sampler. Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with Mean-Edge-Number (MEN), a metric closely related to over-smoothing. For the feature-dependent sampler, we associate the edge sampling probability with the feature similarity of node pairs, and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi- supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version., Comment: IEEE Transactions on Neural Networks and Learning Systems, 2023
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- 2023
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30. Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds
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Jiang, Shuaifeng, Alkhateeb, Ahmed, Bliss, Daniel W., and Rong, Yu
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Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Radar as a remote sensing technology has been used to analyze human activity for decades. Despite all the great features such as motion sensitivity, privacy preservation, penetrability, and more, radar has limited spatial degrees of freedom compared to optical sensors and thus makes it challenging to sense crowded environments without prior information. In this paper, we develop a novel dual-sensing system, in which a vision sensor is leveraged to guide digital beamforming in a multiple-input multiple-output (MIMO) radar. Also, we develop a calibration algorithm to align the two types of sensors and show that the calibrated dual system achieves about two centimeters precision in three-dimensional space within a field of view of $75^\circ$ by $65^\circ$ and for a range of two meters. Finally, we show that the proposed approach is capable of detecting the vital signs simultaneously for a group of closely spaced subjects, sitting and standing, in a cluttered environment, which highlights a promising direction for vital signs detection in realistic environments.
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- 2023
31. Deletion of protein kinase C θ attenuates hepatic ischemia/reperfusion injury and further elucidates its mechanism in pathophysiology
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Wei Li, Meng-Yuan Shen, Ruo-Bing Liu, Jun-Yang Zhang, Rong-Yu Li, and Guo-Guang Wang
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gene knockout ,hepatic ischemia/reperfusion injury ,nrf2/ho-1 pathway pathophysiology ,protein kinase c θ ,tlr4/nf-κb/ikb α pathway ,Medicine - Abstract
Objective(s): Hepatic ischemia-reperfusion (HIR) is a severe process in pathophysiology that occurs clinically in hepatectomy, and hepatic transplantations. The present study aimed to investigate the effect of PKC θ deletion against HIR injury and elucidate its mechanism in pathophysiology.Materials and Methods: HIR injury was induced in wild-type and PKC θ deletion mice treated with or without heme. The ALT and AST levels were determined to evaluate liver function. HIR injury was observed via histological examination. Oxidative stress and inflammatory response markers, and their signaling pathways were detected.Results: The study found that PKC θ knockout decreased serum AST and ALT levels when compared to the WT mice. Furthermore, heme treatment significantly reduced the ALT and AST levels of the PKC θ deletion mice compared with the untreated PKC θ deletion mice. PKC θ deletion markedly elevated superoxide dismutase activity in the liver tissue, reduced malondialdehyde content in the tissue, and the serum TNF-α and IL-6 levels compared with the WT mice. Heme treatment was observed to elevate the activity of SOD and reduced MDA content and serum of TNF-α and IL 6 in the PKC θ deletion animals. Meanwhile, heme treatment increased HO-1 and Nrf 2 protein expression, and reduced the levels of TLR4, phosphorylated NF-κB, and IKB-α.Conclusion: These findings suggested that PKC θ deletion ameliorates HIR, and heme treatment further improves HIR, which is related to regulation of PKC θ deletion on Nrf 2/HO-1 and TLR4/NF-κB/IKB α pathway.
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- 2024
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32. Risk Assessment of Substation based on Hermite orthogonal basis Feedforward Neural Network data Fusion algorithm.
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Hui Zhang, Rong Yu, Jiangshun Yu, Wei Liu, Shan Wu, and Dandan Liu
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- 2024
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33. A 28nm Energy-Area-Efficient Row-based pipelined Training Accelerator with Mixed FXP4/FP16 for On-Device Transfer Learning.
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Wei Lu, Han-Hsiang Pei, Jheng-Rong Yu, Hung-Ming Chen, and Po-Tsang Huang
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- 2024
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34. The Impact of ChatGPT on Chinese Postgraduates’ English Learning Interest and Proficiency: An Experience of IELTS Speaking Project
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Rong, Yu-die, Xiao, Wan-yang, Zhang, Yun-feng, Xu, Xiao-shu, Casero-Ripollés, Andreu, Series Editor, Barredo Ibáñez, Daniel, Series Editor, Park, Han Woo, Series Editor, Khan, Intakhab Alam, Series Editor, Wekke, Ismail Suardi, Series Editor, Birkök, Mehmet Cüneyt, Series Editor, Striełkowski, Wadim, Series Editor, and Zhang, Quan, editor
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- 2024
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35. Integrated landscape of plasma metabolism and proteome of patients with post-traumatic deep vein thrombosis
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Zhang, Kun, Wang, Pengfei, Huang, Wei, Tang, Shi-Hao, Xue, Hanzhong, Wu, Hao, Zhang, Ying, Rong, Yu, Dong, Shan-Shan, Chen, Jia-Bin, Zou, Yan, Tian, Ding, Yang, Na, Liang, Yifan, Liu, Chungui, Li, Dongyang, Zhang, Kun, Yang, Tie-Lin, and Guo, Yan
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- 2024
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36. Unleashing the potential: a quest to understand and examine the factors enriching research and innovation productivities of South Asian universities
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Javed, Saima, Rong, Yu, Zafeer, Hafiz Muhammad Ihsan, Maqbool, Samra, and Abbasi, Babar Nawaz
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- 2024
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37. Association between preoperative anxiety states and postoperative complications in patients with esophageal cancer and COPD: a retrospective cohort study
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Rong, Yu, Hao, Yanbing, Wei, Dong, Li, Yanming, Chen, Wansheng, Wang, Li, and Li, Tian
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- 2024
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38. Increased cysteinyl-tRNA synthetase drives neuroinflammation in Alzheimer’s disease
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Qi, Xiu-Hong, Chen, Peng, Wang, Yue-Ju, Zhou, Zhe-Ping, Liu, Xue-Chun, Fang, Hui, Wang, Chen-Wei, Liu, Ji, Liu, Rong-Yu, Liu, Han-Kui, Zhang, Zhen-Xin, and Zhou, Jiang-Ning
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- 2024
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39. Design and analysis of a compatible exoskeleton rehabilitation robot system based on upper limb movement mechanism
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Ning, Yuansheng, Wang, Hongbo, Liu, Ying, Wang, Qi, Rong, Yu, and Niu, Jianye
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- 2024
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40. Designing a second-order progressive problem-based scaffold strategy to promote students’ writing performance in an SVVR environment
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Yang, Gang, Zhou, Wei, Rong, Yu-Die, Xu, Ya-Juan, Zeng, Qun-Fang, and Tu, Yun-Fang
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- 2024
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41. Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
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Bian, Tian, Jiang, Yuli, Li, Jia, Xu, Tingyang, Rong, Yu, Su, Yi, Kwok, Timothy, Meng, Helen, and Cheng, Hong
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. DSSDDI contains three modules, Drug-Drug Interaction (DDI) module, Medical Decision (MD) module and Medical Support (MS) module. The DDI module learns safer and more effective drug representations from the drug-drug interactions. To capture the potential causal relationship between DDI and medication use, the MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome to construct counterfactual links for the representation learning. Furthermore, the MS module provides drug candidates to doctors with explanations. Experiments on the chronic data collected from the Hong Kong Chronic Disease Study Project and a public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable reference for doctors in terms of safety and efficiency of clinical diagnosis, with significant improvements compared to baseline methods.
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- 2023
42. Hepatitis B virus infection as a risk factor for chronic kidney disease: a systematic review and meta-analysis
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Danjing Chen, Rong Yu, Shuo Yin, Wenxin Qiu, Jiangwang Fang, and Xian-e Peng
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Hepatitis B virus ,Chronic kidney disease ,Meta-analysis ,Risk ,Infectious and parasitic diseases ,RC109-216 - Abstract
Abstract Background Currently, several studies have observed that chronic hepatitis B virus infection is associated with the pathogenesis of kidney disease. However, the extent of the correlation between hepatitis B virus infection and the chronic kidney disease risk remains controversial. Methods In the present study, we searched all eligible literature in seven databases in English and Chinese. The random effects model was used to conduct a meta-analysis. Quality of included studies was assessed using the Newcastle-Ottawa Quality Scale. Results In this analysis, a total of 31 studies reporting the association between hepatitis B virus infection and chronic kidney disease risk were included. The results showed a significant positive association between hepatitis B virus infection and the risk of chronic kidney disease (pooled OR, 1.20; 95% CI, 1.12–1.29), which means that hepatitis B virus increases the risk of developing chronic kidney disease. Conclusion This study found that hepatitis B virus infection was associated with a significantly increased risk of chronic kidney disease. However, the current study still cannot directly determine this causal relationship. Thus, more comprehensive prospective longitudinal studies are needed in the future to provide further exploration and explanation of the association between hepatitis B virus and the risk of developing chronic kidney disease.
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- 2024
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43. Reprogramming of macrophage metabolism and inflammatory injuries in chronic kidney disease
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Jie Lyu, Yong-jun Zhu, Zi-yan Lin, Shi-qiu Zhang, and Rong Yu
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macrophages ,macrophage activation ,nephritis, interstitial ,metabolic reprogramming ,Internal medicine ,RC31-1245 - Abstract
Macrophages are important cells involved in inflammatory damage in chronic kidney disease. The characteristic alteration of macrophage function due to changes in the local microenvironment is referred to as macrophage reprogramming. In recent years, altered macrophage reprogramming affecting inflammatory tissue injury has received increasing attention. This article reviews advances in the understanding of macrophage metabolic reprogramming pathways and studies that influence macrophage polarization and impact inflammatory injury in chronic kidney disease.
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- 2024
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44. AMPK–a key factor in crosstalk between tumor cell energy metabolism and immune microenvironment?
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Na Wang, Bofang Wang, Ewetse Paul Maswikiti, Yang Yu, Kewei Song, Chenhui Ma, Xiaowen Han, Huanhuan Ma, Xiaobo Deng, Rong Yu, and Hao Chen
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 ,Cytology ,QH573-671 - Abstract
Abstract Immunotherapy has now garnered significant attention as an essential component in cancer therapy during this new era. However, due to immune tolerance, immunosuppressive environment, tumor heterogeneity, immune escape, and other factors, the efficacy of tumor immunotherapy has been limited with its application to very small population size. Energy metabolism not only affects tumor progression but also plays a crucial role in immune escape. Tumor cells are more metabolically active and need more energy and nutrients to maintain their growth, which causes the surrounding immune cells to lack glucose, oxygen, and other nutrients, with the result of decreased immune cell activity and increased immunosuppressive cells. On the other hand, immune cells need to utilize multiple metabolic pathways, for instance, cellular respiration, and oxidative phosphorylation pathways to maintain their activity and normal function. Studies have shown that there is a significant difference in the energy expenditure of immune cells in the resting and activated states. Notably, competitive uptake of glucose is the main cause of impaired T cell function. Conversely, glutamine competition often affects the activation of most immune cells and the transformation of CD4+T cells into inflammatory subtypes. Excessive metabolite lactate often impairs the function of NK cells. Furthermore, the metabolite PGE2 also often inhibits the immune response by inhibiting Th1 differentiation, B cell function, and T cell activation. Additionally, the transformation of tumor-suppressive M1 macrophages into cancer-promoting M2 macrophages is influenced by energy metabolism. Therefore, energy metabolism is a vital factor and component involved in the reconstruction of the tumor immune microenvironment. Noteworthy and vital is that not only does the metabolic program of tumor cells affect the antigen presentation and recognition of immune cells, but also the metabolic program of immune cells affects their own functions, ultimately leading to changes in tumor immune function. Metabolic intervention can not only improve the response of immune cells to tumors, but also increase the immunogenicity of tumors, thereby expanding the population who benefit from immunotherapy. Consequently, identifying metabolic crosstalk molecules that link tumor energy metabolism and immune microenvironment would be a promising anti-tumor immune strategy. AMPK (AMP-activated protein kinase) is a ubiquitous serine/threonine kinase in eukaryotes, serving as the central regulator of metabolic pathways. The sequential activation of AMPK and its associated signaling cascades profoundly impacts the dynamic alterations in tumor cell bioenergetics. By modulating energy metabolism and inflammatory responses, AMPK exerts significant influence on tumor cell development, while also playing a pivotal role in tumor immunotherapy by regulating immune cell activity and function. Furthermore, AMPK-mediated inflammatory response facilitates the recruitment of immune cells to the tumor microenvironment (TIME), thereby impeding tumorigenesis, progression, and metastasis. AMPK, as the link between cell energy homeostasis, tumor bioenergetics, and anti-tumor immunity, will have a significant impact on the treatment and management of oncology patients. That being summarized, the main objective of this review is to pinpoint the efficacy of tumor immunotherapy by regulating the energy metabolism of the tumor immune microenvironment and to provide guidance for the development of new immunotherapy strategies.
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- 2024
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45. Exploration of lymph node recurrence patterns and delineation guidelines of radiation field in middle thoracic oesophageal carcinomas after radical surgery: a real-world study
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Rongxu Du, Songqing Fan, Dan Yang, Xiaobin Wang, Xia Hou, Cheng Zeng, Dan Guo, Rongrong Tian, Leilei Jiang, Xin Dong, Rong Yu, Huiming Yu, Shuchai Zhu, Jie Li, and Anhui Shi
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Middle thoracic oesophageal carcinoma ,Postoperative lymph node recurrence ,Radiation field ,Real-world study ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Oesophageal squamous cell carcinoma is one of the most commonly diagnosed carcinomas in China, and postoperative radiotherapy plays an important role in improving the prognosis of patients. Carcinomas in different locations of the oesophagus could have different patterns of lymph node metastasis after surgery. Methods In this multicentric retrospective study, we enrolled patients with middle thoracic oesophageal squamous cell carcinomas from 3 cancer centres, and none of the patients underwent radiotherapy before or after surgery. We analysed the lymph node recurrence rates in different stations to explore the postoperative lymphatic recurrence pattern. Results From January 1st, 2014, to December 31st, 2019, 132 patients met the criteria, and were included in this study. The lymphatic recurrence rate was 62.1%. Pathological stage (P = 0.032) and lymphadenectomy method (P = 0.006) were significant predictive factors of lymph node recurrence. The recurrence rates in the supraclavicular, upper and lower paratracheal stations of lymph nodes were 32.6%, 28.8% and 16.7%, respectively, showing a high incidence. The recurrence rate of the subcarinal node station was 9.8%, while 8.3% (upper, middle and lower) thoracic para-oesophageal nodes had recurrences. Conclusions We recommend including the supraclavicular, upper and lower paratracheal stations of lymph nodes in the postoperative radiation field in middle thoracic oesophageal carcinomas. Subcarinal station is also potentially high-risk, while whether to include thoracic para-oesophageal or abdominal nodes needs careful consideration.
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- 2024
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46. Iron metabolism: backfire of cancer cell stemness and therapeutic modalities
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Rong Yu, Yinhui Hang, Hsiang-i Tsai, Dongqing Wang, and Haitao Zhu
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Cancer stem cells (CSCs) ,Ferroptosis ,Iron metabolism ,Nanotechnology ,Photodynamic diagnosis/Photodynamic therapy ,Stemness ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 ,Cytology ,QH573-671 - Abstract
Abstract Cancer stem cells (CSCs), with their ability of self-renewal, unlimited proliferation, and multi-directional differentiation, contribute to tumorigenesis, metastasis, recurrence, and resistance to conventional therapy and immunotherapy. Eliminating CSCs has long been thought to prevent tumorigenesis. Although known to negatively impact tumor prognosis, research revealed the unexpected role of iron metabolism as a key regulator of CSCs. This review explores recent advances in iron metabolism in CSCs, conventional cancer therapies targeting iron biochemistry, therapeutic resistance in these cells, and potential treatment options that could overcome them. These findings provide important insights into therapeutic modalities against intractable cancers.
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- 2024
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47. Conditional survival and annual hazard of death in older patients with esophageal cancer receiving definitive chemoradiotherapy
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Xiao Chang, Wei Deng, Rong Yu, and Weihu Wang
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Esophageal cancer ,Older ,Conditional survival ,Death hazard ,Geriatrics ,RC952-954.6 - Abstract
Abstract Background Definitive chemoradiotherapy is one of the primary treatment modalities for older patients with esophageal cancer (EC). However, the evolution of prognosis over time and the factors affected non-EC deaths remain inadequately studied. We examined the conditional survival and annual hazard of death in older patients with EC after chemoradiotherapy. Methods We collected data from patients aged 65 or older with EC registered in the Surveillance, Epidemiology, and End Results database during 2000–2019. Conditional survival was defined as the probability of survival given a specific time survived. Annual hazard of death was defined the yearly event rate. Restricted cubic spline (RCS) analysis identified the association of age at diagnosis with mortality. Results Among 3739 patients, the 3-year conditional overall survival increased annually by 7-10%. Non-EC causes accounted for 18.8% of deaths, predominantly due to cardio-cerebrovascular diseases. The hazard of death decreased from 40 to 10% in the first 6 years and then gradually increased to 20% in the tenth year. Non-EC causes surpassed EC causes in hazard starting 5 years post-treatment. RCS indicated a consistent increase in death hazard with advancing age, following a linear relationship. The overall cohort was divided into two groups: 65–74 and ≥ 75 years old, with the ≥ 75-year-old group showing poorer survival and earlier onset of non-EC deaths (HR = 1.36, 95% CI: 1.15–1.62, P
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- 2024
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48. Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
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Liu, Yang, Rong, Yu, Guo, Zhuoning, Chen, Nuo, Xu, Tingyang, Tsung, Fugee, and Li, Jia
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Computer Science - Machine Learning - Abstract
Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and then studied the effectiveness of NPIs. In this work, we focus on mobility modeling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a micro perspective prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph. we propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.
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- 2022
49. Vertical Federated Linear Contextual Bandits
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Cao, Zeyu, Liang, Zhipeng, Zhang, Shu, Li, Hangyu, Wen, Ouyang, Rong, Yu, Zhao, Peilin, and Wu, Bingzhe
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over different departments. This problem remains largely unexplored in the research community. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism(O3M) for encrypting local contextual information while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation under the vertical federated setting. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analyzed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.
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- 2022
50. Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson’s disease are correlated with gait disturbance
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Qingguo Ren, Shuai Zhao, Rong Yu, Ziliang Xu, Shuangwu Liu, Bin Zhang, Qicai Sun, Qingjun Jiang, Cuiping Zhao, and Xiangshui Meng
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Parkinson’s disease ,DTI—diffusion tensor imaging ,freeing of gait ,MRI ,network neurodegeneration ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
BackgroundLimbic structures have recently garnered increased attention in Parkinson’s disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD.MethodsBetween December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD.ResultsIn patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = −0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III (r = −0.435, p = 0.006). Topological parameters, including characteristic path length (Lp), global efficiency (Eg), normalised shortest path length (λ) and nodal local efficiency (Nle), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively.ConclusionDTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.
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- 2024
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