arXiv subjects

Huiyuan Zhang

Publications and source records attributed to Huiyuan Zhang.

6 recordsLinked to original sources

arXiv2026

Hessian sparsity-constrained self-supervised network for near-infrared single-photon single-pixel imaging

Near-infrared (NIR) imaging has emerged as an important technology for night vision, remote sensing, and biological imaging, yet conventional array-detector-based systems are often limited by insufficient sensitivity, high cost, and substantial dark noise. Single-pixel imaging (SPI) offers an attractive alternative, enabling single-photon-level NIR imaging by using a cost-effective single-element detector. Nevertheless, SPI remains restricted by photon noise, leading to degraded imaging quality and limited frame rate under extremely low photon flux conditions. Here, we present a Hessian sparsity-constrained self-supervised network (HS3N) for single-photon NIR SPI, which can suppress noise and enable high-fidelity and real-time imaging under ultra-low illumination conditions. The HS3N integrates the physical forward model of SPI with an untrained neural network regularized by both sparsity priors and Hessian-based structural constraints, enabling effective noise suppression while preserving structural fidelity and continuity. Both simulated and experimental results demonstrate that HS3N enables high-fidelity reconstructions under ultra-low NIR photon levels down to ~0.01 photons per pixel. Furthermore, we demonstrate its dynamic capability by monitoring the dynamic evolution and detachment of infrared-absorbing droplets, at a frame rate of ~20 Hz under ~0.19 photons per pixel, highlighting its potential for high-sensitivity infrared inspection. The proposed reconstruction framework paves the way for practical NIR imaging in extreme low light conditions, which can be extended to visible, mid-infrared or terahertz imaging, offering broad potential for photon-efficient sensing across a wide spectral range.

physics.optics↗

arXiv2026

Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD

Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing methods typically decode text directly into executable code (e.g., bpy) without explicitly modeling assembly hierarchy or geometric constraints, which enlarges the search space, accumulates local errors, and often causes cascading failures in complex assemblies. To address this issue, we propose a hierarchical and geometry-aware graph as an intermediate representation. The graph models multi-level parts and components as nodes and encodes explicit geometric constraints as edges. Instead of mapping text directly to code, our framework first predicts structure and constraints, then conditions action sequencing and code generation, thereby improving geometric fidelity and constraint satisfaction. We further introduce a structure-aware progressive curriculum learning strategy that constructs graded tasks through controlled structural edits, explores the model's capability boundary, and synthesizes boundary examples for iterative training. In addition, we build a 12K dataset with instructions, decomposition graphs, action sequences, and bpy code, together with graph- and constraint-oriented evaluation metrics. Extensive experiments show that our method consistently outperforms existing approaches in both geometric fidelity and accurate satisfaction of geometric constraints.

cs.AI↗

arXiv2025

Order-Level Attention Similarity Across Language Models: A Latent Commonality

In this paper, we explore an important yet previously neglected question: Do context aggregation patterns across Language Models (LMs) share commonalities? While some works have investigated context aggregation or attention weights in LMs, they typically focus on individual models or attention heads, lacking a systematic analysis across multiple LMs to explore their commonalities. In contrast, we focus on the commonalities among LMs, which can deepen our understanding of LMs and even facilitate cross-model knowledge transfer. In this work, we introduce the Order-Level Attention (OLA) derived from the order-wise decomposition of Attention Rollout and reveal that the OLA at the same order across LMs exhibits significant similarities. Furthermore, we discover an implicit mapping between OLA and syntactic knowledge. Based on these two findings, we propose the Transferable OLA Adapter (TOA), a training-free cross-LM adapter transfer method. Specifically, we treat the OLA as a unified syntactic feature representation and train an adapter that takes OLA as input. Due to the similarities in OLA across LMs, the adapter generalizes to unseen LMs without requiring any parameter updates. Extensive experiments demonstrate that TOA's cross-LM generalization effectively enhances the performance of unseen LMs. Code is available at https://github.com/jinglin-liang/OLAS.

cs.CL↗

arXiv2025

The electronic structures, magnetic transition and Fermi surface instability of room-temperature altermagnet KV$_{2}$Se$_{2}$O

Altermagnetism has recently emerged as a distinct and fundamental class of magnetic order. Exploring its interplay with quantum phenomena such as unconventional superconductivity, density-wave instabilities, and many-body effects represents a compelling frontier. In this work, we theoretically confirm the presence of high-temperature metallic altermagnetism in KV$_2$Se$_2$O. We demonstrate that the anomalous metal-insulator-metal transition arises from a Lifshitz transition associated with Fermi surface reconstruction. The previously reported spin-density wave gap is found to lie below the Fermi level in our study and is now recognized to be attributed to the V-shaped density of states, originating from orbital-selective and sublattice-resolved half-metal-like behavior on a specific V atom. Furthermore, we identify the instability from the nesting of spin-momentum-locked two-dimensional Fermi surfaces, which induces the SDW state. These findings position KV$_2$Se$_2$O as a promising platform for investigating the interplay among altermagnetism, unconventional superconductivity, and density-wave order.

cond-mat.str-el↗

arXiv2025

DFT+DMFT investigation of the magnetic phase transition in the itinerant ferromagnet Fe$_{3}$GaTe$_{2}$

Finding and designing ferromagnets that operate above room temperature is crucial in advancing high-performance spintronic devices. The pioneering van der Waals (vdW) ferromagnet Fe$_{3}$GaTe$_{2}$ has extended the way for spintronic applications by achieving a record-high Curie temperature among its analogues. However, the physical mechanism of increasing Curie temperature in this material still needs to be explored. Here, we systematically investigate the electronic structures and magnetic properties of Fe$_{3}$GaTe$_{2}$ as a function of temperature using strongly correlated calculations, reconciling the dual nature of $d$-electrons with both localization and itinerant characters. Significantly, our study reveals the emergence of quasi-particle flat bands driven by many-body interactions, which enhance magnetic stability through a positive feedback mechanism. Furthermore, our results demonstrate the hybridization of these flat bands at low temperatures, indicating the possible presence of heavy fermion behavior in this system. Our findings suggest that tunable flat bands near the Fermi level may serve as a key factor in realizing materials with high magnetic transition temperatures and strong magnetic anisotropy. This research provides a promising pathway for exploring next-generation spintronic devices utilizing vdW flat band systems.

cond-mat.mtrl-sci↗

arXiv2016

Least Cost Influence Maximization Across Multiple Social Networks

Recently in Online Social Networks (OSNs), the Least Cost Influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.

cs.SI↗