arXiv subjects

Siyuan Yin

Publications and source records attributed to Siyuan Yin.

5 recordsLinked to original sources

arXiv2026

Quantum Dot Colloidosomes as Triggerable Microlasers

Quantum dot (QD) supraparticle lasers are promising platforms for microscale light sources and photonic labeling, yet their optical properties are set during assembly. Here we introduce QD colloidosomes, liquid-core/solid-shell supraparticles that combine whispering-gallery-mode optical feedback with stimulus-triggered structural collapse. Intact QD colloidosomes show cavity-defined lasing with fluence thresholds of ~2.8 mJ/cm2 and linewidths of 2.2-3.7 nm, demonstrating efficient light trapping without a solid core. We identify shell continuity as a critical determinant for whispering-gallery feedback and lasing by exploring a morphological continuum of suprastructures comprising solid supraparticles, colloidosomes, and microporous hollow shells. Intact colloidosomes support narrow cavity modes, whereas porous shells remain broadband even at high pump fluence. We show that colloidosomes can be driven to rupture and release payload through well-defined pathways, including meniscus-driven capillary failure, uniform heating, and localized near-infrared activation, thereby reconfiguring their optical response from cavity-defined lasing to broadband emission. These results establish QD colloidosomes as reconfigurable microlasers that couple optical-state switching and triggered release within a single self-assembled platform.

physics.optics↗

arXiv2026

FlexRC: A Flexible Multi-Point Model Order Reduction Method for Many-Port RC Networks

Efficient model order reduction for many-port resistor-capacitor (RC) networks is essential in post-layout circuit simulation. Existing high-accuracy elimination-based methods have certain limitations, such as fixed frequency points, large reduced-order models, or high reduction cost. This paper proposes FlexRC, a flexible multi-point model order reduction method for many-port RC networks. FlexRC starts from the same elimination step as previous methods, and then constructs a nonorthogonal projection basis by a modified block rational Arnoldi process to generate a sparse banded reduced model. FlexRC features three adjustable components: user-specified frequency points, a tolerance-controlled port-reduction technique for the internal subsystem, and an optional sparsity-control strategy. We discuss passivity under port-reduction perturbations, analyze moment matching, and provide a conservative error estimate for port reduction. Numerical experiments on industrial RC examples and IBM power-grid examples demonstrate the effectiveness of FlexRC in terms of reduction time and transient simulation time.

eess.SY↗

arXiv2025

SMP-RCR: A Sparse Multipoint Moment Matching Method for RC Reduction

In post--layout circuit simulation, efficient model order reduction (MOR) for many--port resistor--capacitor (RC) circuits remains a crucial issue. The current mainstream MOR methods for such circuits include high--order moment matching methods and elimination methods. High-order moment matching methods--characterized by high accuracy, such as PRIMA and TurboMOR--tend to generate large dense reduced-order systems when the number of ports is large, which impairs the efficiency of MOR. Another common type of MOR method for many--port circuits is based on Gaussian elimination, with the SIP method as a representative. The main limitation of this method lies in the inadequate matching of high--order moments. In this paper, we propose a sparse multipoint moment matching method and present comprehensive theoretical analysis results regarding the multi--frequency high--order moment matching property. Meanwhile, to enhance the algorithm's efficiency, sparse control and deflation techniques are introduced to further optimize the algorithm. Numerical experiments demonstrated that, compared to SIP, the accuracy is improved by more than two orders of magnitude at high frequency points without adding many extra linear components. Compared to TurboMOR methods, our method achieves a speed improvement of more than twice while maintaining the same level of precision.

eess.SY↗

arXiv2025

A graph neural network based on feature network for identifying influential nodes

Identifying influential nodes in complex networks is of great importance, and has many applications in practice. For example, finding influential nodes in e-commerce network can provide merchants with customers with strong purchase intent; identifying influential nodes in computer information system can help locating the components that cause the system break down and identifying influential nodes in these networks can accelerate the flow of information in networks. Thus, a lot of efforts have been made on the problem of indentifying influential nodes. However, previous efforts either consider only one aspect of the network structure, or using global centralities with high time consuming as node features to identify influential nodes, and the existing methods do not consider the relationships between different centralities. To solve these problems, we propose a Graph Convolutional Network Framework based on Feature Network, abbreviated as FNGCN (graph convolutional network is abbreviated as GCN in the following text). Further, to exclude noises and reduce redundency, FNGCN utilizes feature network to represent the complicated relationships among the local centralities, based on which the most suitable local centralities are determined. By taking a shallow GCN and a deep GCN into the FNGCN framework, two FNGCNs are developed. With ground truth obtained from the widely used Susceptible Infected Recovered (SIR) model, the two FNGCNs are compared with the state-of-art methods on several real-world networks. Experimental results show that the two FNGCNs can identify the influential nodes more accurately than the compared methods, indicating that the proposed framework is effective in identifying influential nodes in complex networks.

cs.SI↗

arXiv2025

Single-shot volumetric fluorescence imaging with neural fields

Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. In this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the 3D scene within a compact PSF without depth ambiguity. Additionally, we propose a reconstruction algorithm based on the neural fields technique that provides improved reconstruction quality compared to classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by approximately 20 times and captures a 100 mm$^3$ cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

physics.optics↗