arXiv subjects

Jie Hua

Publications and source records attributed to Jie Hua.

8 recordsLinked to original sources

arXiv2026

Firstborn Advantage in the Ivory Tower: Mass Science, Expanding Scholarly Families, and the Reshaping of Academic Stratification

This paper investigates the mechanisms underlying scientific stratification in the era of transition from elite to mass science. Existing scholarship has largely examined scientific stratification through the Matthew effect framework at the individual, institutional, and lineage levels, but this theoretical lens has grown limited in today's academic landscape, where mass, team-based, and lab-centered research has become the dominant mode of knowledge production. As scientists increasingly share institutional and lineage backgrounds, considerable variation within these units remains unexplained. We propose a new framework that integrates concepts and methodological tools from demography into the social study of science. Drawing on the parallel between biological families and scholarly lineages as fundamental units of reproduction, we adapt the concept of birth order to examine how the sequence of doctoral students within a lineage shapes their career trajectories. Using data on more than one million U.S. doctoral graduates, our analysis shows that, much like in biological families, later students systematically perform worse than earlier ones across multiple dimensions of academic achievement, both short and long term. Examining the underlying mechanisms, we find that later students receive less cognitive stimulation from mature scholars and instead more from peers, and specialize in narrower intellectual domains as senior siblings occupy adjacent territories. These factors constrain their intellectual development as independent scholars. By introducing a demographic framework into the study of science, this paper offers a new perspective on scientific stratification and demonstrates how demographic concepts can be fruitfully extended to analyze broader social and epistemic systems.

cs.CY↗

arXiv2026

Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics

Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-cell multimodal assays can now simultaneously measure various molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual cells, providing a comprehensive molecular readout. A significant analytical challenge is integrating single-cell measurements across different modalities. Various methods have been developed to address this challenge, but there has been no systematic evaluation of these techniques with different preprocessing strategies. This study examines a general pipeline for single-cell data analysis, which includes normalization, data integration, and dimensionality reduction. The performance of different algorithm combinations often depends on the dataset sizes and characteristics. We evaluate six datasets across diverse modalities, tissues, and organisms using three metrics: Silhouette Coefficient Score, Adjusted Rand Index, and Calinski-Harabasz Index. Our experiments involve combinations of seven normalization methods, four dimensional reduction methods, and five integration methods. The results show that Seurat and Harmony excel in data integration, with Harmony being more time-efficient, especially for large datasets. UMAP is the most compatible dimensionality reduction method with the integration techniques, and the choice of normalization method varies depending on the integration method used.

q-bio.QM↗

arXiv2026

Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies

As dermatological conditions become increasingly common and the availability of dermatologists remains limited, there is a growing need for intelligent tools to support both patients and clinicians in the timely and accurate diagnosis of skin diseases. In this project, we developed a deep learning based model for the classification and diagnosis of skin conditions. By leveraging pretraining on publicly available skin disease image datasets, our model effectively extracted visual features and accurately classified various dermatological cases. Throughout the project, we refined the model architecture, optimized data preprocessing workflows, and applied targeted data augmentation techniques to improve overall performance. The final model, based on the Swin Transformer, achieved a prediction accuracy of 87.71 percent across eight skin lesion classes on the ISIC2019 dataset. These results demonstrate the model's potential as a diagnostic support tool for clinicians and a self assessment aid for patients.

cs.CV↗

arXiv2025

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment

Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challenge. Existing methods for transfer learning face fundamental limitations in this setting: knowledge distillation suffers from transfer fidelity degradation in the large-data regime, and static user or item embeddings lack the expressiveness to capture contextualized user-item interactions. We propose the Foundation-Expert paradigm, where a central FM generates target-aware embeddings which are ingested by lightweight surface-specific expert models. Target-aware embeddings are representations that dynamically capture a user's interest in a specific item conditioned on their full interaction history. Unlike knowledge distillation, which transfers FM knowledge as soft labels, our approach provides these embeddings as input features to each expert model, enabling direct interaction with surface-specific representations. This paradigm achieves transfer ratios of 0.64--1.0 from FM to experts, substantially exceeding existing methods. Fully deployed at Meta serving tens of billions of daily requests since 2025, it delivers 0.050% statistically significant online topline metric improvement and 0.359% cumulative gains across multiple surfaces.

cs.IR↗

arXiv2023

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding of analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors against the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

stat.AP↗

arXiv2021

Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the "smartness" of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: "What kinds of behaviors do humans expect from their IoT devices?"

cs.HC↗

arXiv2021

Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications

Pervasive computing applications commonly involve user's personal smartphones collecting data to influence application behavior. Applications are often backed by models that learn from the user's experiences to provide personalized and responsive behavior. While models are often pre-trained on massive datasets, federated learning has gained attention for its ability to train globally shared models on users' private data without requiring the users to share their data directly. However, federated learning requires devices to collaborate via a central server, under the assumption that all users desire to learn the same model. We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences. However, instead of learning in isolation, these models opportunistically incorporate the learned experiences of other devices they encounter opportunistically. In this paper, we explore the feasibility and limits of such an approach, culminating in a framework that supports encounter-based pairwise collaborative learning. The use of our opportunistic encounter-based learning amplifies the performance of personalized learning while resisting overfitting to encountered data.

cs.LG↗

arXiv2020

rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two real-world data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.

cs.CY↗