arXiv subjects

Kaiwen Tan

Publications and source records attributed to Kaiwen Tan.

5 recordsLinked to original sources

arXiv2026

Masking Intent, Sustaining Equilibrium: Risk-Aware Potential-Game-Based Service Provision in Dynamic Mobile Crowdsensing

Mobile crowdsensing (MCS) is evolving from basic data collection to dynamic service provisioning, where platforms must maintain task completion, budget feasibility, and sensing quality under uncertain worker availability. Beyond raw-data and location privacy, workers' long-term intent traces, such as task-selection tendencies and participation histories, can be exploited by an honest-but-curious platform to infer private preferences from one or multiple allocation snapshots. Worker dropouts and execution uncertainty further destabilize sensing coverage, while frequent global re-optimization increases interaction overhead and observable exposure. To address these issues, we propose \textit{iParts}, an intent-preserving and risk-aware two-stage service provisioning framework for dynamic MCS. In the offline stage, workers report perturbed intent vectors through personalized local differential privacy with memoized permanent randomized response, suppressing frequency-based intent inference while retaining decision utility. The platform then builds a redundancy-aware quality model and performs risk-aware pre-planning under budget, quality-risk, and intent-mismatch constraints. This offline problem is formulated as an exact potential game with expected social welfare as the potential function, guaranteeing constrained equilibrium existence and finite-step convergence under feasible improvement dynamics. In the online stage, quality deficits are repaired through bounded-round temporary recruitment from idle or standby workers, enabling feasibility-preserving adjustment with limited exposure. Experiments show that iParts improves welfare and task completion while reducing redundancy and communication overhead against representative benchmarks.

cs.NI↗

arXiv2025

MRGSEM-Sum: An Unsupervised Multi-document Summarization Framework based on Multi-Relational Graphs and Structural Entropy Minimization

The core challenge faced by multi-document summarization is the complexity of relationships among documents and the presence of information redundancy. Graph clustering is an effective paradigm for addressing this issue, as it models the complex relationships among documents using graph structures and reduces information redundancy through clustering, achieving significant research progress. However, existing methods often only consider single-relational graphs and require a predefined number of clusters, which hinders their ability to fully represent rich relational information and adaptively partition sentence groups to reduce redundancy. To overcome these limitations, we propose MRGSEM-Sum, an unsupervised multi-document summarization framework based on multi-relational graphs and structural entropy minimization. Specifically, we construct a multi-relational graph that integrates semantic and discourse relations between sentences, comprehensively modeling the intricate and dynamic connections among sentences across documents. We then apply a two-dimensional structural entropy minimization algorithm for clustering, automatically determining the optimal number of clusters and effectively organizing sentences into coherent groups. Finally, we introduce a position-aware compression mechanism to distill each cluster, generating concise and informative summaries. Extensive experiments on four benchmark datasets (Multi-News, DUC-2004, PubMed, and WikiSum) demonstrate that our approach consistently outperforms previous unsupervised methods and, in several cases, achieves performance comparable to supervised models and large language models. Human evaluation demonstrates that the summaries generated by MRGSEM-Sum exhibit high consistency and coverage, approaching human-level quality.

cs.CL↗

arXiv2024

A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model

Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD). However, in real-world scenarios, news about a international event often involves multiple documents in different languages, i.e., mixed-language multi-document (MLMD). Therefore, summarizing MLMD news is of great significance. However, the lack of datasets for MLMD news summarization has constrained the development of research in this area. To fill this gap, we construct a mixed-language multi-document news summarization dataset (MLMD-news), which contains four different languages and 10,992 source document cluster and target summary pairs. Additionally, we propose a graph-based extract-generate model and benchmark various methods on the MLMD-news dataset and publicly release our dataset and code\footnote[1]{https://github.com/Southnf9/MLMD-news}, aiming to advance research in summarization within MLMD scenarios.

cs.CL↗

arXiv2023

Focusing solutions of Vlasov-Poisson equation on a spherical shell

We study smooth, spherically-symmetric solutions to the Vlasov-Poisson system and relativistic Vlasov-Poisson system in the plasma physical case. In particular, we construct solutions that initially possess arbitrarily small charge densities and electric fields, but attain arbitrarily large value of these quantities and concentrate on any given spherical shell at some later time.

math.AP↗

arXiv2022

A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e.g., survival analysis or grade classification), and thus neglect the correlation between different tasks. In this study, we present a multi-modal fusion framework based on multi-task correlation learning (MultiCoFusion) for survival analysis and cancer grade classification, which combines the power of multiple modalities and multiple tasks. Specifically, a pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used to learn the representations of histopathological images and mRNA expression data respectively. Then these representations are fused by a fully connected neural network (FCNN), which is also a multi-task shared network. Finally, the results of survival analysis and cancer grade classification output simultaneously. The framework is trained by an alternate scheme. We systematically evaluate our framework using glioma datasets from The Cancer Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better representations than traditional feature extraction methods. With the help of multi-task alternating learning, even simple multi-modal concatenation can achieve better performance than other deep learning and traditional methods. Multi-task learning can improve the performance of multiple tasks not just one of them, and it is effective in both single-modal and multi-modal data.

cs.LG↗