Researcher profile

Xiaoyue Liu

Xiaoyue Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate

Trust snapshot

Quick read

Trust 15 - UnverifiedVerification L1Unclaimed author
3works
0followers
4topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

3 published item(s)

preprint2026arXiv

CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI

Accurate 3D+t whole-heart mesh reconstruction from cine MRI is a clinically crucial yet technically challenging task. The difficulty of this task arises from two coupled factors: inherently sparse sampling of 3D cardiac anatomy by 2D image slices and the tight coupling between cardiac shape and motion. Current cardiac image-to-mesh approaches typically reconstruct only a subset of cardiac chambers or a single phase of the cardiac cycle. In this work, we propose CineMesh4D, a novel end-to-end 4D (3D+t) pipeline that directly reconstructs patient-specific whole-heart mesh from multi-view 2D cine MRI via cross-domain mapping. Specifically, we introduce a differentiable rendering loss that enables supervision of 3D+t whole-heart mesh from multi-view sparse contours of cine MRI. Furthermore, we develop a dual-context temporal block that fuses global and local cardiac temporal information to capture high-dimensional sequential patterns. In quantitative and qualitative evaluations, CineMesh4D outperforms existing approaches in terms of reconstruction quality and motion consistency, providing a practical pathway for personalized real-time cardiac assessment. The code will be publicly released once the manuscript is accepted.

preprint2026arXiv

Modular and Mobile Capacity Planning for Hyperconnected Supply Chain Networks

The increased volatility of markets and the pressing need for resource sustainability are driving supply chains towards more agile, distributed, and dynamic designs. Motivated by the Physical Internet initiative, we introduce the Dynamic Stochastic Modular and Mobile Capacity Planning (DSMMCP) problem, which fosters hyperconnectivity through a network-of-networks architecture with modular and mobile capacities. The problem addresses both demand and supply uncertainties by incorporating short-term leasing of modular facilities and dynamic relocation of resources. We formulate DSMMCP as a partially adaptive multi-stage stochastic program that minimizes the expected multi-period costs under uncertainty. To tackle the inherent NP-hardness, we develop an enhanced stochastic dual dynamic integer programming (SDDiP) algorithm, which integrates strengthened cut generation, a tailored alternating cut strategy, and an efficient parallelization framework, and we establish structural dominance and monotonicity properties that formalize the value of the strengthened cuts and partial adaptivity. Numerical experiments inspired by a real case study of a large U.S. construction company demonstrate that the DSMMCP framework achieves approximately 15% cost savings over static planning while improving resilience, reducing outsourcing costs, and supporting sustainability. Complementary experiments on synthetic instances confirm the effectiveness of the proposed SDDiP algorithm in terms of solution quality and runtime, as well as the scalability and robustness of the partially adaptive stochastic modeling framework across different network sizes and uncertainty levels.

preprint2020arXiv

High-performance Coherent Optical Modulators based on Thin-film Lithium Niobate Platform

The coherent transmission technology using digital signal processing and advanced modulation formats, is bringing networks closer to the theoretical capacity limit of optical fibres, the Shannon limit. The in-phase quadrature electro-optic modulator that encodes information on both the amplitude and the phase of light, is one of the underpinning devices for the coherent transmission technology. Ideally, such modulator should feature low loss, low drive voltage, large bandwidth, low chirp and compact footprint. However, these requirements have been only met on separate occasions. Here, we demonstrate integrated thin-film lithium niobate in-phase/quadrature modulators that fulfil these requirements simultaneously. The presented devices exhibit greatly improved overall performance (half-wave voltage, bandwidth and optical loss) over traditional lithium niobate counterparts, and support modulation data rate up to 320 Gbit s-1. Our devices pave new routes for future high-speed, energy-efficient, and cost-effective communication networks.