arXiv subjects

Yihan Yu

Publications and source records attributed to Yihan Yu.

5 recordsLinked to original sources

arXiv2026

Computer-Aided Tagging on Wikimedia Commons: Designing for Human-AI Collaboration in Open Knowledge Work

This study investigates Wikimedia Commons contributors' lived experiences with the Computer-Aided Tagging (CAT) tool, an AI-assisted image tagging system designed to improve Commons' discoverability, searchability, accessibility, and multilingual support. Using a qualitative analysis of 595 CAT-related community comments from 11 wiki pages and 16 in-depth interviews, we identify seven key issues that contributed to CAT's mixed reception and eventual deactivation. We also offer community-informed suggestions for improving the tool. We reflect on the implications for designing human-AI collaboration on Commons and for developing AI-assisted tools that support open knowledge work. This work contributes to HCI and CSCW research by extending the understanding of human-AI collaboration beyond Anglophone, text-centric, corporate platforms.

cs.HC↗

arXiv2025

Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Masking

Counterfactual causal inference faces significant challenges when extended to multi-category, multi-valued treatments, where complex cross-effects between heterogeneous interventions are difficult to model. Existing methodologies remain constrained to binary or single-type treatments and suffer from restrictive assumptions, limited scalability, and inadequate evaluation frameworks for complex intervention scenarios. We present XTNet, a novel network architecture for multi-category, multi-valued treatment effect estimation. Our approach introduces a cross-effect estimation module with dynamic masking mechanisms to capture treatment interactions without restrictive structural assumptions. The architecture employs a decomposition strategy separating basic effects from cross-treatment interactions, enabling efficient modeling of combinatorial treatment spaces. We also propose MCMV-AUCC, a suitable evaluation metric that accounts for treatment costs and interaction effects. Extensive experiments on synthetic and real-world datasets demonstrate that XTNet consistently outperforms state-of-the-art baselines in both ranking accuracy and effect estimation quality. The results of the real-world A/B test further confirm its effectiveness.

cs.LG↗

arXiv2025

Tuning the Quantum Mpemba Effect in Isolated System by Initial State Engineering

We investigate the quantum Mpemba effect (QME) in isolated, non-integrable quantum systems, where relaxation dynamics depend on structure of the initial states. By analyzing the distribution of initial states across symmetrical subspaces, we identify a tunable mechanism that influences the emergence of QME, showing faster relaxation from certain out-of-equilibrium states. Additionally, we propose an experimentally realizable quantum circuit, which requires no complex controls on quantum simulator platforms and serves to verify our theoretical predictions. These results establish symmetry-resolved state engineering as a practical tool for manipulating non-equilibrium quantum dynamics.

quant-ph↗

arXiv2023

UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement

Underwater image enhancement (UIE) is a meaningful but challenging task, and many learning-based UIE methods have been proposed in recent years. Although much progress has been made, these methods still exist two issues: (1) There exists a significant region-wise quality difference in a single underwater image due to the underwater imaging process, especially in regions with different scene depths. However, existing methods neglect this internal characteristic of underwater images, resulting in inferior performance; (2) Due to the uniqueness of the acquisition approach, underwater image acquisition tools usually capture multiple images in the same or similar scenes. Thus, the underwater images to be enhanced in practical usage are highly correlated. However, when processing a single image, existing methods do not consider the rich external information provided by the related images. There is still room for improvement in their performance. Motivated by these two aspects, we propose a novel internal-external representation learning (UIERL) network to better perform UIE tasks with internal and external information, simultaneously. In the internal representation learning stage, a new depth-based region feature guidance network is designed, including a region segmentation based on scene depth to sense regions with different quality levels, followed by a region-wise space encoder module. With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement. In the external representation learning stage, we first propose an external information extraction network to mine the rich external information in the related images. Then, internal and external features interact with each other via the proposed external-assist-internal module and internal-assist-e

cs.CV↗

arXiv2019

Global Reactions to the Cambridge Analytica Scandal: An Inter-Language Social Media Study

Currently, there is a limited understanding of how data privacy concerns vary across the world. The Cambridge Analytica scandal triggered a wide-ranging discussion on social media about user data collection and use practices. We conducted an inter-language study of this online conversation to compare how people speaking different languages react to data privacy breaches. We collected tweets about the scandal written in Spanish and English between April and July 2018. We used the Meaning Extraction Method in both datasets to identify their main topics. They reveal a similar emphasis on Zuckerberg's hearing in the US Congress and the scandal's impact on political issues. However, our analysis also shows that while English speakers tend to attribute responsibilities to companies, Spanish speakers are more likely to connect them to people. These findings show the potential of inter-language comparisons of social media data to deepen the understanding of cultural differences in data privacy perspectives.

cs.IR↗