arXiv subjects

Yurui Huang

Publications and source records attributed to Yurui Huang.

8 recordsLinked to original sources

arXiv2026

SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts

Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significant challenges in the effective fusion of heterogeneous multi-source data. Existing multimodal methods often suffer from "modality laziness" due to disparate convergence speeds, which hinders the exploitation of complementary information. This is also one reason why most existing SL prediction models cannot perform well on both pan-cancer and single-cancer SL pair prediction. In this study, we propose SynLeaF, a dual-stage multimodal fusion framework for SL prediction across pan- and single-cancer contexts. The framework employs a VAE-based cross-encoder with a product of experts mechanism to fuse four omics data types (gene expression, mutation, methylation, and CNV), while simultaneously utilizing a relational graph convolutional network to capture structured gene representations from biomedical knowledge graphs. To mitigate modality laziness, SynLeaF introduces a dual-stage training mechanism employing featurelevel knowledge distillation with adaptive uni-modal teacher and ensemble strategies. In extensive experiments across eight specific cancer types and a pancancer dataset, SynLeaF achieves superior performance in 17 out of 19 scenarios. Ablation studies and gradient analyses further validate the critical contributions of the proposed fusion and distillation mechanisms to model robustness and generalization. To facilitate community use, a web server is available at https://synleaf.bioinformatics-lilab.cn.

q-bio.GN↗

arXiv2024

The Innovative Distinctiveness of Prizewinners and their Networks

Science prizes purportedly reward innovation and explorations of new phenomena. Yet, in practice prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, knowledge of how prizewinning relates to innovation is nascent even as prizes proliferate widely. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched non-prizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic's historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched non-prizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners' papers become more innovative than their matched peers, a difference that widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to non-prizewinners, prizewinners' collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The implications of the findings for the efficacy of reward systems and innovation in science are discussed.

cs.DL↗

arXiv2024

The Solution for Temporal Sound Localisation Task of ICCV 1st Perception Test Challenge 2023

In this paper, we propose a solution for improving the quality of temporal sound localization. We employ a multimodal fusion approach to combine visual and audio features. High-quality visual features are extracted using a state-of-the-art self-supervised pre-training network, resulting in efficient video feature representations. At the same time, audio features serve as complementary information to help the model better localize the start and end of sounds. The fused features are trained in a multi-scale Transformer for training. In the final test dataset, we achieved a mean average precision (mAP) of 0.33, obtaining the second-best performance in this track.

cs.SD↗

arXiv2024

The Solution for the CVPR 2023 1st foundation model challenge-Track2

In this paper, we propose a solution for cross-modal transportation retrieval. Due to the cross-domain problem of traffic images, we divide the problem into two sub-tasks of pedestrian retrieval and vehicle retrieval through a simple strategy. In pedestrian retrieval tasks, we use IRRA as the base model and specifically design an Attribute Classification to mine the knowledge implied by attribute labels. More importantly, We use the strategy of Inclusion Relation Matching to make the image-text pairs with inclusion relation have similar representation in the feature space. For the vehicle retrieval task, we use BLIP as the base model. Since aligning the color attributes of vehicles is challenging, we introduce attribute-based object detection techniques to add color patch blocks to vehicle images for color data augmentation. This serves as strong prior information, helping the model perform the image-text alignment. At the same time, we incorporate labeled attributes into the image-text alignment loss to learn fine-grained alignment and prevent similar images and texts from being incorrectly separated. Our approach ranked first in the final B-board test with a score of 70.9.

cs.CV↗

arXiv2024

Talent hat, cross-border mobility, and career development in China

This study aims to investigate the influence of cross-border recruitment program in China, which confers scientists with a 'talent hat' including a startup package comprising significant bonuses, pay, and funding, on their future performance and career development. By curating a unique dataset from China's 10-year talent recruitment program, we employed multiple matching designs to quantify the effects of the cross-border recruitment with 'talent hat' on early career STEM scholars. Our findings indicate that the cross-border talents perform better than their comparable contenders who move without talent hats and those who do not move, given equivalent scientific performance before relocation. Moreover, we observed that scholars in experimental fields derive greater benefits from the talent program than those in non-experimental fields. Finally, we investigated how the changes in scientific environment of scientists affect their future performance. We found that talents who reassembled their collaboration network with new collaborators in new institutions after job replacement experienced significant improvements in their academic performance. However, shifting research directions entails risks, which results in a subsequent decrease of future productivity and citation impact following the relocation. This study has significant implications for young scientists, research institutions, and governments concerning cultivating cross-border talents.

cs.DL↗

arXiv2024

Decoding University Hierarchy and Prestige in China through Domestic Ph.D. Hiring Network

The academic job market for fresh Ph.D. students to pursue postdoctoral and junior faculty positions plays a crucial role in shaping the future orientations, developments, and status of the global academic system. In this work, we focus on the domestic Ph.D. hiring network among universities in China by exploring the doctoral education and academic employment of nearly 28,000 scientists across all Ph.D.-granting Chinese universities over three decades. We employ the minimum violation rankings algorithm to decode the rankings for universities based on the Ph.D. hiring network, which offers a deep understanding of the structure and dynamics within the network. Our results uncover a consistent, highly structured hierarchy within this hiring network, indicating the imbalances wherein a limited number of universities serve as the main sources of fresh Ph.D. across diverse disciplines. Furthermore, over time, it has become increasingly challenging for Chinese Ph.D. graduates to secure positions at institutions more prestigious than their alma maters. This study quantitatively captures the evolving structure of talent circulation in the domestic environment, providing valuable insights to enhance the organization, diversity, and talent distribution in China's academic enterprise.

cs.DL↗

arXiv2024

SciConNav: Knowledge navigation through contextual learning of extensive scientific research trajectories

New knowledge builds upon existing foundations, which means an interdependent relationship exists between knowledge, manifested in the historical development of the scientific system for hundreds of years. By leveraging natural language processing techniques, this study introduces the Scientific Concept Navigator (SciConNav), an embedding-based navigation model to infer the "knowledge pathway" from the research trajectories of millions of scholars. We validate that the learned representations effectively delineate disciplinary boundaries and capture the intricate relationships between diverse concepts. The utility of the inferred navigation space is showcased through multiple applications. Firstly, we demonstrated the multi-step analogy inferences within the knowledge space and the interconnectivity between concepts in different disciplines. Secondly, we formulated the attribute dimensions of knowledge across domains, observing the distributional shifts in the arrangement of 19 disciplines along these conceptual dimensions, including "Theoretical" to "Applied", and "Chemical" to "Biomedical', highlighting the evolution of functional attributes within knowledge domains. Lastly, by analyzing the high-dimensional knowledge network structure, we found that knowledge connects with shorter global pathways, and interdisciplinary knowledge plays a critical role in the accessibility of the global knowledge network. Our framework offers a novel approach to mining knowledge inheritance pathways in extensive scientific literature, which is of great significance for understanding scientific progression patterns, tailoring scientific learning trajectories, and accelerating scientific progress.

cs.IR↗

arXiv2024

Quantifying the hierarchical scales of scientists'mobility

Human behaviors, including scientific activities, are shaped by the hierarchical divisions of geography. As a result, researchers' mobility patterns vary across regions, influencing several aspects of the scientific community. These aspects encompass career trajectories, knowledge transfer, international collaborations, talent circulation, innovation diffusion, resource distribution, and policy development. However, our understanding of the relationship between the hierarchical regional scale and scientific movements is limited. This study aims to understand the subtle role of the geographical scales on scientists' mobility patterns across cities, countries, and continents. To this end, we analyzed 2.03 million scientists from 1960 to 2021, spanning institutions, cities, countries, and continents. We built a model based on hierarchical regions with different administrative levels and assessed the tendency for mobility from one region to another and the attractiveness of each region. Our findings reveal distinct nested hierarchies of regional scales and the dynamic of scientists' relocation patterns. This study sheds light on the complex dynamics of scientists' mobility and offers insights into how geographical scale and administrative divisions influence career decisions.

physics.soc-ph↗